From 2713cba136c925c415656946de7c7702553a6f8b Mon Sep 17 00:00:00 2001 From: wanjia Date: Tue, 25 Feb 2025 15:23:41 +0800 Subject: [PATCH] =?UTF-8?q?=E5=A2=9E=E5=8A=A0=E7=9B=91=E6=8E=A7=E6=96=87?= =?UTF-8?q?=E4=BB=B6=E5=A4=B9=E4=B8=8B=E7=9A=84python=E8=BF=9B=E7=A8=8B?= =?UTF-8?q?=E5=92=8C=E5=89=8D=E7=AB=AF=E7=9A=84=E5=9B=BE=E8=A1=A8=E5=B1=95?= =?UTF-8?q?=E7=A4=BA?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | Bin 0 -> 18 bytes .idea/.gitignore | 56 +- .idea/automated_task_monitor.iml | 58 +- .idea/misc.xml | 12 +- .idea/modules.xml | 14 +- .idea/vcs.xml | 6 + .idea/workspace.xml | 34 +- .../__pycache__/settings.cpython-310.pyc | Bin 2645 -> 3101 bytes .../__pycache__/urls.cpython-310.pyc | Bin 1029 -> 1357 bytes automated_task_monitor/settings.py | 46 +- automated_task_monitor/urls.py | 6 +- .../process_monitor/all/2136_20250225_all.log | 23 + .../all/22424_20250225_all.log | 184 +++ .../all/22740_20250225_all.log | 184 +++ 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django.db import migrations, models + + +class Migration(migrations.Migration): + + dependencies = [ + ('monitor', '0001_initial'), + ] + + operations = [ + migrations.AlterModelOptions( + name='highcpuprocess', + options={'verbose_name': 'CPU高占用进程', 'verbose_name_plural': 'CPU高占用进程'}, + ), + migrations.AlterModelOptions( + name='highgpuprocess', + options={'verbose_name': 'GPU高占用进程', 'verbose_name_plural': 'GPU高占用进程'}, + ), + migrations.AlterModelOptions( + name='highmemoryprocess', + options={'verbose_name': '内存高占用进程', 'verbose_name_plural': '内存高占用进程'}, + ), + migrations.RemoveField( + model_name='highcpuprocess', + name='gpu_memory', + ), + migrations.RemoveField( + model_name='highcpuprocess', + name='gpu_usage', + ), + migrations.RemoveField( + model_name='highcpuprocess', + name='log_path', + ), + migrations.RemoveField( + model_name='highcpuprocess', + name='memory_usage', + ), + migrations.RemoveField( + model_name='highcpuprocess', + name='virtual_memory', + ), + migrations.RemoveField( + model_name='highgpuprocess', + name='cpu_usage', + ), + migrations.RemoveField( + model_name='highgpuprocess', + name='log_path', + ), + migrations.RemoveField( + model_name='highgpuprocess', + name='memory_usage', + ), + migrations.RemoveField( + model_name='highgpuprocess', + name='virtual_memory', + ), + migrations.RemoveField( + model_name='highmemoryprocess', + name='cpu_usage', + ), + migrations.RemoveField( + model_name='highmemoryprocess', + name='gpu_memory', + ), + migrations.RemoveField( + model_name='highmemoryprocess', + name='gpu_usage', + ), + migrations.RemoveField( + model_name='highmemoryprocess', + name='log_path', + ), + migrations.AddField( + model_name='highcpuprocess', + name='log_file', + field=models.CharField(blank=True, max_length=255, null=True, verbose_name='日志文件路径'), + ), + migrations.AddField( + model_name='highgpuprocess', + name='log_file', + field=models.CharField(blank=True, max_length=255, null=True, verbose_name='日志文件路径'), + ), + migrations.AddField( + model_name='highmemoryprocess', + name='log_file', + field=models.CharField(blank=True, max_length=255, null=True, verbose_name='日志文件路径'), + ), + migrations.AddField( + model_name='highmemoryprocess', + name='memory_percent', + field=models.FloatField(default=0, verbose_name='内存使用率'), + ), + migrations.AlterField( + model_name='highcpuprocess', + name='cpu_cores', + field=models.IntegerField(default=0, verbose_name='CPU核心数'), + ), + migrations.AlterField( + model_name='highcpuprocess', + name='cpu_usage', + field=models.FloatField(default=0, verbose_name='CPU使用率'), + ), + migrations.AlterField( + model_name='highcpuprocess', + name='status', + field=models.IntegerField(default=1, verbose_name='进程状态'), + ), + migrations.AlterField( + model_name='highgpuprocess', + name='gpu_usage', + field=models.FloatField(default=0, verbose_name='GPU使用率'), + ), + migrations.AlterField( + model_name='highgpuprocess', + name='status', + field=models.IntegerField(default=1, verbose_name='进程状态'), + ), + migrations.AlterField( + model_name='highmemoryprocess', + name='status', + field=models.IntegerField(default=1, verbose_name='进程状态'), + ), + migrations.AlterField( + model_name='highmemoryprocess', + name='swap_usage', + field=models.FloatField(default=0, verbose_name='交换内存使用量(GB)'), + ), + ] diff --git a/monitor/migrations/0003_allresourceprocess.py b/monitor/migrations/0003_allresourceprocess.py new file mode 100644 index 0000000..d8ba6d1 --- /dev/null +++ b/monitor/migrations/0003_allresourceprocess.py @@ -0,0 +1,41 @@ +# Generated by Django 5.1.6 on 2025-02-25 03:11 + +from django.db import migrations, models + + +class Migration(migrations.Migration): + + dependencies = [ + ('monitor', '0002_alter_highcpuprocess_options_and_more'), + ] + + operations = [ + migrations.CreateModel( + name='AllResourceProcess', + fields=[ + ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), + ('pid', models.IntegerField(verbose_name='进程ID')), + ('process_name', models.CharField(max_length=100, verbose_name='进程名称')), + ('cpu_usage', models.FloatField(default=0, verbose_name='CPU使用率')), + ('cpu_user_time', models.FloatField(default=0, verbose_name='用户态CPU时间')), + ('cpu_system_time', models.FloatField(default=0, verbose_name='系统态CPU时间')), + ('memory_usage', models.FloatField(default=0, verbose_name='内存使用量(MB)')), + ('memory_percent', models.FloatField(default=0, verbose_name='内存使用率')), + ('virtual_memory', models.FloatField(default=0, verbose_name='虚拟内存(MB)')), + ('gpu_usage', models.FloatField(default=0, verbose_name='GPU使用率')), + ('gpu_memory', models.FloatField(default=0, verbose_name='GPU内存使用量(MB)')), + ('net_io_sent', models.FloatField(default=0, verbose_name='网络发送量(MB)')), + ('net_io_recv', models.FloatField(default=0, verbose_name='网络接收量(MB)')), + ('is_active', models.BooleanField(default=True, verbose_name='是否活跃')), + ('status', models.IntegerField(default=1, verbose_name='状态')), + ('log_file', models.CharField(max_length=255, null=True, verbose_name='日志文件路径')), + ('created_at', models.DateTimeField(auto_now_add=True, verbose_name='创建时间')), + ('updated_at', models.DateTimeField(auto_now=True, verbose_name='更新时间')), + ], + options={ + 'verbose_name': '全资源监控', + 'verbose_name_plural': '全资源监控', + 'db_table': 'all_resource_process', + }, + ), + ] diff --git a/monitor/migrations/__pycache__/0002_alter_highcpuprocess_options_and_more.cpython-310.pyc b/monitor/migrations/__pycache__/0002_alter_highcpuprocess_options_and_more.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2ec1759b4016def3a6544998ac48d0c7b8b64513 GIT binary patch literal 1861 zcmcIk&2QX96!+J9y&Gqnw6svlSAkY+k*dHEA=FaTh(k-o$Hj^?n(Ui+z4h8=YzJ}! zwGaukZ~HY&85K!KXy;&#yxK!Bk%$xUq^P4yC!}08F zQ^RNG_YMEVeogyBE~<}(i&OZe$8i7+^ib>Tp58Y+L)R8HFu=U5fywlqdCBxluwK*J zwJ~C~wjrARz~?jz;wTk%KL!@29cijQM{#iqzw~ci)0pPzpm_%9zV4aK>eb`{_*!2W 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.then(response => { + if (response.status === 'success') { + const data = response.data; + updateUI(data, type); + } + }) + .catch(error => handleError(error, '获取监控数据失败')); +} + +// 获取所有监控按钮和停止按钮 +const monitorButtons = document.querySelectorAll('[data-type]'); +const stopBtn = document.getElementById('stopMonitor'); +const pidInput = document.getElementById('pidInput'); + +// 保存当前监控类型的全局变量 +let currentMonitorType = null; + +// 开始监控 +function startMonitoring(pid) { + console.log('开始监控，类型:', currentMonitorType); // 调试日志 + updateMonitorData(pid); // 立即执行一次更新 + monitorInterval = setInterval(() => { + updateMonitorData(pid); + }, 60000); +} + +// 停止监控 +function stopMonitoring() { + if (!currentDirectory) { + console.error('没有正在监控的目录'); + showMessage('没有正在监控的目录', 'error'); + return; + } + + const stopBtn = document.getElementById('stopDirectoryMonitor'); + if (!stopBtn) return; + + stopBtn.disabled = true; + stopBtn.textContent = '正在停止...'; + + // 先清理定时器 + if (updateInterval) { + clearInterval(updateInterval); + updateInterval = null; + } + + const directoryToStop = currentDirectory; + + fetch('/monitor/stop-directory-monitor/', { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + 'X-CSRFToken': getCSRFToken(), + }, + body: JSON.stringify({ directory: directoryToStop }) + }) + .then(response => response.json()) + .then(data => { + if (data.status === 'success') { + cleanupMonitoringState(); + showMessage('监控已停止', 'success'); + } else { + throw new Error(data.message || '停止监控失败'); + } + }) + .catch(error => { + console.error('停止监控失败:', error); + showMessage(error.message || '停止监控失败', 'error'); + stopBtn.disabled = false; + }) + .finally(() => { + stopBtn.textContent = '停止监控'; + }); +} + +// 初始化图表 +function initCharts() { + console.log('初始化图表'); + cpuChart = echarts.init(document.getElementById('cpuChart')); + memoryChart = echarts.init(document.getElementById('memoryChart')); + gpuChart = echarts.init(document.getElementById('gpuChart')); + + const baseOption = { + tooltip: { + trigger: 'axis', + formatter: function(params) { + const time = new Date(params[0].value[0]).toLocaleTimeString(); + return `${time}
${params[0].seriesName}: ${params[0].value[1].toFixed(2)}%`; + } + }, + xAxis: { + type: 'time', + splitLine: { show: false } + }, + yAxis: { + type: 'value', + min: 0, + max: 100, + splitLine: { show: true } + }, + grid: { + left: '3%', + right: '4%', + bottom: '3%', + containLabel: true + }, + series: [{ + type: 'line', + showSymbol: false, + data: [], + smooth: true, + areaStyle: { + opacity: 0.1 + } + }] + }; + + cpuChart.setOption({ + ...baseOption, + series: [{ + ...baseOption.series[0], + name: 'CPU使用率', + itemStyle: { color: '#2196F3' } + }] + }); + + memoryChart.setOption({ + ...baseOption, + series: [{ + ...baseOption.series[0], + name: '内存使用量', + itemStyle: { color: '#4CAF50' } + }] + }); + + gpuChart.setOption({ + ...baseOption, + series: [{ + ...baseOption.series[0], + name: 'GPU使用率', + itemStyle: { color: '#FF5722' } + }] + }); +} + +// 更新图表数据 +function updateCharts(process) { + console.log('更新图表:', process); + const now = new Date(); + + // 更新CPU图表 + if (cpuChart) { + updateChartData(cpuChart, now, process.cpu_usage); + } + + // 更新内存图表 + if (memoryChart) { + updateChartData(memoryChart, now, process.memory_usage); + } + + // 更新GPU图表 + if (gpuChart && process.gpu_info) { + updateChartData(gpuChart, now, process.gpu_info.usage); + } +} + +// 辅助函数：更新图表数据 +function updateChartData(chart, time, value) { + if (!chart) return; + + try { + const option = chart.getOption(); + const data = option.series[0].data || []; + data.push([time, value]); + + // 保持最近60个数据点 + if (data.length > 60) { + data.shift(); + } + + chart.setOption({ + series: [{ + data: data + }] + }); + } catch (error) { + console.error('更新图表数据失败:', error); + } +} + +// 修改更新进程表格函数 +function updateProcessTable(processes) { + console.log('更新进程表格:', processes); // 调试日志 + const table = document.getElementById('processTable'); + if (!table) { + console.error('找不到进程表格元素'); + return; + } + + if (!processes || processes.length === 0) { + table.innerHTML = ` + + 暂无进程数据 + + `; + return; + } + + table.innerHTML = processes.map(proc => ` + + ${proc.pid} + ${proc.name} + +
+
+
+
+
+ ${proc.cpu_usage.toFixed(1)}% +
+ + +
+
+
+
+
+ ${proc.memory_usage.toFixed(1)} MB +
+ + +
+
+
+
+
+ ${proc.gpu_info.usage}% +
+ + ${proc.gpu_info.memory.toFixed(1)} MB + + 运行中 + + + + `).join(''); +} + +// 修改选择进程的函数 +function selectProcess(pid) { + console.log('选择进程:', pid); + selectedPid = pid.toString(); // 确保转换为字符串 + + // 更新选择器 + const selector = document.getElementById('processSelector'); + if (selector) { + selector.value = selectedPid; + } + + // 更新表格中的选中状态 + const rows = document.querySelectorAll('#processTable tr'); + rows.forEach(row => { + if (row.cells && row.cells[0] && row.cells[0].textContent === selectedPid) { + row.classList.add('table-primary'); + } else { + row.classList.remove('table-primary'); + } + }); + + // 获取最新数据并更新图表 + if (currentDirectory) { + fetch(`/monitor/directory-status/?directory=${encodeURIComponent(currentDirectory)}`) + .then(response => response.json()) + .then(data => { + if (data.status === 'success' && data.processes) { + const selectedProcess = data.processes.find(p => p.pid.toString() === selectedPid); + if (selectedProcess) { + // 更新图表 + updateCharts(selectedProcess); + // 更新详细信息 + updateProcessDetails(selectedProcess); + } + } + }) + .catch(error => console.error('获取进程详情失败:', error)); + } +} + +// 修改进程选择器更新函数 +function updateProcessSelector(processes) { + console.log('更新进程选择器，进程列表:', processes); // 调试日志 + + const selector = document.getElementById('processSelector'); + const processInfo = document.getElementById('selectedProcessInfo'); + + if (!selector) { + console.error('找不到进程选择器元素'); + return; + } + + // 启用选择器 + selector.disabled = false; + + // 获取当前的进程列表 + const currentPids = processes.map(p => p.pid.toString()); + console.log('当前PID列表:', currentPids); // 调试日志 + + // 如果当前选中的进程不在列表中，清除选择 + if (selectedPid && !currentPids.includes(selectedPid.toString())) { + console.log('选中的进程不再存在，清除选择'); // 调试日志 + selectedPid = null; + clearCharts(); + } + + // 移除旧的事件监听器 + const newSelector = selector.cloneNode(true); + selector.parentNode.replaceChild(newSelector, selector); + + // 更新选择器选项 + newSelector.innerHTML = ` + + ${processes.map(proc => ` + + `).join('')} + `; + + // 添加新的事件监听器 + newSelector.addEventListener('change', function() { + console.log('进程选择变更:', this.value); + selectedPid = this.value; + if (selectedPid) { + const selected = processes.find(p => p.pid === parseInt(selectedPid)); + if (selected) { + processInfo.textContent = `监控中: PID ${selected.pid} - ${selected.name}`; + updateProcessDetails(selected); // 更新详细信息 + clearCharts(); + } + } else { + processInfo.textContent = '未选择进程'; + clearCharts(); + // 清空详细信息 + ['basicInfo', 'resourceInfo', 'networkInfo', 'fileInfo'].forEach(id => { + document.getElementById(id).innerHTML = '

选择进程查看详细信息

'; + }); + } + }); + + console.log('进程选择器更新完成'); // 调试日志 +} + +// 添加清除图表数据函数 +function clearCharts() { + const emptyOption = { + series: [{ + data: [] + }] + }; + cpuChart.setOption(emptyOption); + memoryChart.setOption(emptyOption); + gpuChart.setOption(emptyOption); +} + +document.addEventListener('DOMContentLoaded', function() { + console.log('DOM加载完成，开始初始化'); + + const startBtn = document.getElementById('startDirectoryMonitor'); + const stopBtn = document.getElementById('stopDirectoryMonitor'); + + if (!startBtn || !stopBtn) { + console.error('找不到监控按钮元素'); + return; + } + + // 初始化图表 + initCharts(); + + // 开始监控按钮事件 + startBtn.addEventListener('click', function() { + console.log('点击了开始监控按钮'); + const directory = document.getElementById('directoryInput').value; + const statusDiv = document.getElementById('directoryMonitorStatus'); + + if (!directory) { + statusDiv.style.display = 'block'; + statusDiv.className = 'alert alert-warning'; + statusDiv.textContent = '请输入目录路径'; + return; + } + + // 显示加载状态 + startBtn.disabled = true; + startBtn.textContent = '启动中...'; + statusDiv.style.display = 'block'; + statusDiv.className = 'alert alert-info'; + statusDiv.textContent = '正在启动监控...'; + + console.log('开始监控目录:', directory); + + // 清除旧的选择 + selectedPid = null; + clearCharts(); + + fetch('/monitor/scan-directory/', { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + }, + body: JSON.stringify({ directory: directory }) + }) + .then(response => response.json()) + .then(data => { + console.log('接口返回:', data); + if (data.status === 'success') { + currentDirectory = directory; + statusDiv.className = 'alert alert-success'; + statusDiv.textContent = data.message; + startBtn.disabled = true; + stopBtn.disabled = false; + + // 开始定期获取详细信息 + if (updateInterval) { + clearInterval(updateInterval); + } + currentDirectory = directory; + getProcessDetails(directory); // 立即获取一次 + updateInterval = setInterval(() => getProcessDetails(directory), 5000); // 每5秒更新一次 + } else { + statusDiv.className = 'alert alert-danger'; + statusDiv.textContent = data.message; + startBtn.disabled = false; + } + }) + .catch(error => { + console.error('监控启动失败:', error); + statusDiv.className = 'alert alert-danger'; + statusDiv.textContent = '启动监控失败: ' + error.message; + startBtn.disabled = false; + }) + .finally(() => { + startBtn.textContent = '开始监控'; + }); + }); + + // 停止监控按钮事件 + if (stopBtn) { + stopBtn.addEventListener('click', function() { + console.log('停止监控，当前目录:', currentDirectory); + + // 检查是否有当前目录 + if (!currentDirectory) { + console.error('没有正在监控的目录'); + showMessage('没有正在监控的目录', 'error'); + return; + } + + // 禁用按钮，防止重复点击 + stopBtn.disabled = true; + stopBtn.textContent = '正在停止...'; + + // 先清理定时器，防止继续发送请求 + if (updateInterval) { + clearInterval(updateInterval); + updateInterval = null; + } + + // 保存当前目录的副本 + const directoryToStop = currentDirectory; + + // 发送停止监控请求到服务器 + fetch('/monitor/stop-directory-monitor/', { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + 'X-CSRFToken': getCSRFToken(), + }, + body: JSON.stringify({ + directory: directoryToStop, + timestamp: new Date().getTime() // 添加时间戳防止缓存 + }) + }) + .then(response => { + if (!response.ok) { + throw new Error('Network response was not ok'); + } + return response.json(); + }) + .then(data => { + console.log('停止监控响应:', data); + if (data.status === 'success') { + // 清理前端状态 + cleanupMonitoringState(); + // 显示成功消息 + showMessage('监控已停止', 'success'); + } else { + throw new Error(data.message || '停止监控失败'); + } + }) + .catch(error => { + console.error('停止监控失败:', error); + showMessage(error.message || '停止监控请求失败', 'error'); + // 恢复按钮状态 + stopBtn.disabled = false; + stopBtn.textContent = '停止监控'; + }); + }); + } + + // 初始状态设置 + stopBtn.disabled = true; // 初始状态下停止按钮禁用 + console.log('初始化完成'); + + // 为每个监控按钮添加点击事件 + monitorButtons.forEach(button => { + button.addEventListener('click', function() { + const pid = pidInput.value.trim(); + const type = this.dataset.type; // 从按钮的 data-type 属性获取类型 + + if (!pid) { + alert('请输入进程ID'); + return; + } + + // 保存当前选择的监控类型 + currentMonitorType = type; + console.log('设置监控类型为:', currentMonitorType); + + fetch(`/monitor/start/?pid=${pid}&type=${type}`) + .then(response => response.json()) + .then(data => { + if (data.status === 'success') { + startMonitoring(pid); + // 禁用所有监控按钮 + monitorButtons.forEach(btn => btn.disabled = true); + stopBtn.disabled = false; + pidInput.disabled = true; + } else { + alert(data.message); + } + }) + .catch(error => { + console.error('启动监控失败:', error); + alert('启动监控失败'); + }); + }); + }); + + // 获取自动检测按钮 + const startAutoDetectBtn = document.getElementById('startAutoDetectBtn'); + const stopAutoDetectBtn = document.getElementById('stopAutoDetectBtn'); + + // 获取CSRF Token + function getCSRFToken() { + const name = 'csrftoken'; + let cookieValue = null; + if (document.cookie && document.cookie !== '') { + const cookies = document.cookie.split(';'); + for (let i = 0; i < cookies.length; i++) { + const cookie = cookies[i].trim(); + if (cookie.substring(0, name.length + 1) === (name + '=')) { + cookieValue = decodeURIComponent(cookie.substring(name.length + 1)); + break; + } + } + } + return cookieValue; + } + + // 添加自动检测开始按钮事件 + startAutoDetectBtn.addEventListener('click', function() { + fetch('/auto_detect/', { + method: 'POST', + headers: { + 'X-CSRFToken': getCSRFToken(), + 'Content-Type': 'application/json' + } + }) + .then(response => response.json()) + .then(data => { + if (data.status === 'success') { + alert('已开始自动检测高资源进程'); + startAutoDetectBtn.disabled = true; + stopAutoDetectBtn.disabled = false; + } else { + alert(data.message); + } + }) + .catch(error => { + console.error('启动自动检测失败:', error); + alert('启动自动检测失败'); + }); + }); + + // 添加自动检测停止按钮事件 + stopAutoDetectBtn.addEventListener('click', function() { + fetch('/stop_auto_detect/', { + method: 'POST', + headers: { + 'X-CSRFToken': getCSRFToken(), + 'Content-Type': 'application/json' + } + }) + .then(response => response.json()) + .then(data => { + if (data.status === 'success') { + alert('已停止自动检测'); + startAutoDetectBtn.disabled = false; + stopAutoDetectBtn.disabled = true; + } else { + alert(data.message); + } + }) + .catch(error => { + console.error('停止自动检测失败:', error); + alert('停止自动检测失败'); + }); + }); +}); + +// 更新 UI 的函数 +function updateUI(data, type) { + // 根据监控类型更新对应的表格 + if (type === 'all' || type === 'cpu') { + document.getElementById('cpuTable').innerHTML = ` + 使用率${data.cpu.usage} + 用户态时间${data.cpu.user_time} + 内核态时间${data.cpu.system_time} + CPU核心数${data.cpu.cores} + CPU频率${data.cpu.frequency} + 上下文切换${data.cpu.context_switches} + `; + document.getElementById('cpuStatus').className = 'badge bg-success status-badge'; + document.getElementById('cpuStatus').textContent = '监控中'; + } + + if (type === 'all' || type === 'memory') { + document.getElementById('memoryTable').innerHTML = ` + 物理内存${data.memory.physical} + 虚拟内存${data.memory.virtual} + 内存映射${data.memory.mappings} + 系统内存使用${data.memory.system_usage} + 交换空间使用${data.memory.swap_usage} + `; + document.getElementById('memoryStatus').className = 'badge bg-success status-badge'; + document.getElementById('memoryStatus').textContent = '监控中'; + } + + if (type === 'all' || type === 'gpu') { + document.getElementById('gpuTable').innerHTML = ` + 使用率${data.gpu.usage} + 显存使用${data.gpu.memory} + `; + document.getElementById('gpuStatus').className = 'badge bg-success status-badge'; + document.getElementById('gpuStatus').textContent = '监控中'; + } + + // 更新最后更新时间 + document.getElementById('lastUpdate').textContent = + `最后更新: ${data.timestamp}`; +} + +// 添加错误处理函数 +function handleError(error, message) { + console.error(message, error); + ['cpu', 'gpu', 'memory'].forEach(type => { + const statusElement = document.getElementById(`${type}Status`); + if (statusElement) { + statusElement.className = 'badge bg-danger status-badge'; + statusElement.textContent = '错误'; + } + }); + alert(message); +} + +// 窗口大小改变时调整图表大小 +window.addEventListener('resize', function() { + if (cpuChart) cpuChart.resize(); + if (memoryChart) memoryChart.resize(); + if (gpuChart) gpuChart.resize(); +}); + +// 更新进程详细信息的函数 +function updateProcessDetails(process) { + if (!process) return; + + // 检查元素是否存在 + const basicInfo = document.getElementById('basicInfo'); + const resourceInfo = document.getElementById('resourceInfo'); + + if (!basicInfo || !resourceInfo) { + console.error('找不到详情显示元素'); + return; + } + + try { + // 更新基本信息 + basicInfo.innerHTML = ` +
+
PID
+
${process.pid}
+ +
进程名
+
${process.name}
+ +
命令行
+
${process.command_line}
+ +
工作目录
+
${process.working_directory}
+ +
创建时间
+
${process.create_time}
+
+ `; + + // 更新资源信息 + resourceInfo.innerHTML = ` +
+
CPU使用率
+
${process.cpu_usage.toFixed(2)}%
+ +
内存使用
+
${process.memory_usage.toFixed(2)} MB
+ +
线程数
+
${process.threads}
+ +
GPU使用率
+
${process.gpu_info.usage}%
+ +
GPU内存
+
${process.gpu_info.memory.toFixed(2)} MB
+
+ `; + } catch (error) { + console.error('更新进程详情失败:', error); + } +} + +// 修改定期更新函数 +function startPeriodicUpdate(directory) { + console.log('开始定期更新, 目录:', directory); + + if (updateInterval) { + clearInterval(updateInterval); + } + + // 立即执行一次更新 + getProcessDetails(directory); + + // 设置定时更新 + updateInterval = setInterval(() => { + getProcessDetails(directory); + }, 5000); +} + +// 修改获取进程详情的函数 +function getProcessDetails(directory) { + if (!directory || !currentDirectory) { // 添加currentDirectory检查 + console.log('没有目录或已停止监控'); + return; + } + + // 使用 AbortController 来控制fetch请求 + if (window.currentFetch) { + window.currentFetch.abort(); + } + window.currentFetch = new AbortController(); + + fetch(`/monitor/directory-status/?directory=${encodeURIComponent(directory)}`, { + signal: window.currentFetch.signal + }) + .then(response => response.json()) + .then(data => { + if (!currentDirectory) { // 再次检查是否已停止监控 + console.log('监控已停止，不更新数据'); + return; + } + + console.log('收到进程数据:', data); + if (data.status === 'success' && data.processes && data.processes.length > 0) { + updateProcessTable(data.processes); + + if (selectedPid) { + const selectedProcess = data.processes.find(p => p.pid.toString() === selectedPid); + if (selectedProcess) { + updateCharts(selectedProcess); + updateProcessDetails(selectedProcess); + } + } + } + }) + .catch(error => { + if (error.name === 'AbortError') { + console.log('请求被中止'); + } else { + console.error('获取进程详情失败:', error); + } + }); +} + +// 获取所有正在监控的进程ID +function getAllMonitoredPids() { + const table = document.getElementById('processTable'); + if (!table) return []; + + const pids = []; + const rows = table.getElementsByTagName('tr'); + for (let row of rows) { + const firstCell = row.cells[0]; + if (firstCell && !isNaN(firstCell.textContent)) { + pids.push(firstCell.textContent); + } + } + return pids; +} + +// 修改清理监控状态的函数 +function cleanupMonitoringState() { + console.log('清理监控状态'); + + // 先取消所有正在进行的请求 + if (window.currentFetch) { + window.currentFetch.abort(); + window.currentFetch = null; + } + + // 清除定时器 + if (updateInterval) { + clearInterval(updateInterval); + updateInterval = null; + } + + // 清除状态变量（在清理完其他内容后再清除） + const oldDirectory = currentDirectory; + currentDirectory = null; + selectedPid = null; + + console.log('已清除的目录:', oldDirectory); + + // 清空图表 + clearCharts(); + + // 清空进程列表 + const processTable = document.getElementById('processTable'); + if (processTable) { + processTable.innerHTML = ` + + 未开始监控 + + `; + } + + // 清空详细信息 + const basicInfo = document.getElementById('basicInfo'); + const resourceInfo = document.getElementById('resourceInfo'); + + if (basicInfo) { + basicInfo.innerHTML = '

未选择进程

'; + } + if (resourceInfo) { + resourceInfo.innerHTML = '

未选择进程

'; + } + + // 更新按钮状态 + const startBtn = document.getElementById('startDirectoryMonitor'); + const stopBtn = document.getElementById('stopDirectoryMonitor'); + if (startBtn) startBtn.disabled = false; + if (stopBtn) { + stopBtn.disabled = true; + stopBtn.textContent = '停止监控'; + } +} + +// 添加显示消息的函数 +function showMessage(message, type = 'info') { + const statusDiv = document.getElementById('directoryMonitorStatus'); + if (statusDiv) { + statusDiv.style.display = 'block'; + statusDiv.className = `alert alert-${type}`; + statusDiv.textContent = message; + + // 3秒后自动隐藏 + setTimeout(() => { + statusDiv.style.display = 'none'; + }, 3000); + } +} \ No newline at end of file diff --git a/monitor/templates/high_resource_list.html b/monitor/templates/high_resource_list.html new file mode 100644 index 0000000..0cc59ff --- /dev/null +++ b/monitor/templates/high_resource_list.html @@ -0,0 +1,176 @@ +{% load static %} + + + + + + 高资源进程列表 + + + + + + +
+

高资源进程列表

+ + +
+ + + +
+ + +
+

CPU密集型进程

+ + + + + + + + + + + + {% for process in cpu_processes %} + + + + + + + + {% empty %} + + + + {% endfor %} + +
PID进程名CPU使用率状态最后更新时间
{{ process.pid }}{{ process.process_name }}{{ process.cpu_usage }}% + + {{ process.get_status_display }} + + {{ process.updated_at|date:"Y-m-d H:i:s" }}
暂无数据
+
+ + + + + + +
+ + + + + \ No newline at end of file diff --git a/monitor/templates/index.html b/monitor/templates/index.html new file mode 100644 index 0000000..5ec48d1 --- /dev/null +++ b/monitor/templates/index.html @@ -0,0 +1,298 @@ +{% load static %} + + + + + + 进程监控 + + + + + + + + + {% csrf_token %} +
+ +
+
+ + 查看高资源进程列表 + +
+
+ + +
+
+ + +
+
+ + +
+
+
+ + + + + +
+
+
+ + +
+ +
+
+
+
CPU监控
+ 未监控 +
+
+ + + + + + + + + + + + +
指标值
暂无数据
+
+
+
+ + +
+
+
+
GPU监控
+ 未监控 +
+
+ + + + + + + + + + + + +
指标值
暂无数据
+
+
+
+ + +
+
+
+
内存监控
+ 未监控 +
+
+ + + + + + + + + + + + +
指标值
暂无数据
+
+
+
+
+ + +
+
+
目录监控
+
+
+
+
+ +
+
+ + +
+
+ +
+
+ + +
+
+
进程列表
+
+
+
+ + + + + + + + + + + + + + + + + + +
PID进程信息CPU内存IO线程状态操作
等待数据加载...
+
+
+
+ + +
+
+
+
CPU使用率
+
+
+
+
+
+
+
+
内存使用量
+
+
+
+
+
+
+
+
GPU使用率
+
+
+
+
+
+
+ + +
+
+
+
+
基本信息
+
+
+

选择进程查看详细信息

+
+
+
+
+
+
+
资源使用
+
+
+

选择进程查看详细信息

+
+
+
+
+ + +
+
+

最后更新: -

+
+
+
+ + + + + + + \ No newline at end of file diff --git a/monitor/urls.py b/monitor/urls.py index 4fae023..351ee92 100644 --- a/monitor/urls.py +++ b/monitor/urls.py @@ -2,8 +2,17 @@ from django.urls import path from . import views urlpatterns = [ - path('start_monitor/', views.start_monitor, name='start_monitor'), - path('stop_monitor//', views.stop_monitor, name='stop_monitor'), - path('metrics//', views.get_process_metrics, name='get_process_metrics'), - path('auto_detect/', views.auto_detect_monitor, name='auto_detect_monitor'), + path('', views.index, name='index'), + path('api/process//status/', views.get_process_status, name='get_process_status'), + path('monitor/start/', views.start_monitor, name='start_monitor'), + path('monitor/stop/', views.stop_monitor, name='stop_monitor'), + path('auto_detect/', views.auto_detect_high_resource_processes, name='auto_detect'), + path('stop_auto_detect/', views.stop_auto_detect, name='stop_auto_detect'), + path('high-resource/', views.high_resource_list, name='high_resource_list'), + path('scan-directory/', views.scan_directory, name='scan_directory'), + path('start/', views.start_monitor, name='start_monitor'), + path('stop/', views.stop_monitor, name='stop_monitor'), + path('status//', views.get_process_status, name='get_process_status'), + path('stop-directory-monitor/', views.stop_directory_monitor, name='stop_directory_monitor'), + path('directory-status/', views.get_directory_status, name='directory_status'), ] diff --git a/monitor/views.py b/monitor/views.py index 442a655..27f47eb 100644 --- a/monitor/views.py +++ b/monitor/views.py @@ -2,7 +2,7 @@ from django.http import JsonResponse from .tasks import monitor_process, get_process_gpu_usage import threading import psutil -from .models import HighCPUProcess, HighGPUProcess, HighMemoryProcess +from .models import HighCPUProcess, HighGPUProcess, HighMemoryProcess, AllResourceProcess import logging import os from datetime import datetime @@ -11,23 +11,348 @@ import nvidia_smi from django.utils import timezone from django.views.decorators.http import require_http_methods from django.views.decorators.csrf import csrf_exempt +from django.shortcuts import render +import pytz +from pathlib import Path # 使用 pathlib 处理跨平台路径 +import json +from django.conf import settings -# 配置日志 -LOG_DIR = 'logs/process_monitor' -os.makedirs(LOG_DIR, exist_ok=True) -def setup_logger(pid): - """为每个进程设置独立的日志记录器""" - log_file = os.path.join(LOG_DIR, f'process_{pid}_{datetime.now().strftime("%Y%m%d")}.log') - logger = logging.getLogger(f'process_{pid}') - logger.setLevel(logging.INFO) + +import threading +directory_monitoring = {} + +# 全局变量来控制检测线程 +monitor_thread = None +is_monitoring = False + +# 在文件开头定义日志目录 +BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +LOG_DIR = os.path.join(BASE_DIR, 'logs', 'process_monitor') + +# 确保基础目录结构存在，添加 'all' 目录 +for resource_type in ['cpu', 'memory', 'gpu', 'all']: + os.makedirs(os.path.join(LOG_DIR, resource_type), exist_ok=True) + +# 获取应用专属的logger +logger = logging.getLogger('monitor') + +# 全局变量来跟踪监控的目录 +monitored_directories = set() + +# 添加新的监控线程函数 +def monitor_directory(directory): + """持续监控目录的后台任务""" + monitor_interval = settings.MONITOR_INTERVAL # 监控间隔（5秒） + log_interval = settings.LOG_INTERVAL # 日志写入间隔（60秒） + processed_pids = set() # 用于跟踪已处理的PID + last_log_time = {} # 每个进程的最后日志时间 - handler = logging.FileHandler(log_file) - formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s') - handler.setFormatter(formatter) - logger.addHandler(handler) + while directory_monitoring.get(directory, False): + try: + next_update = timezone.now() + timezone.timedelta(seconds=monitor_interval) + + processes = find_python_processes_in_dir(directory) + for proc in processes: + pid = proc['pid'] + try: + # 设置日志文件 + log_file = setup_process_logger(pid, 'all') + if log_file: + process = psutil.Process(pid) + data = get_process_data(process) + + # 更新数据库 + AllResourceProcess.objects.update_or_create( + pid=pid, + defaults={ + 'process_name': process.name(), + 'cpu_usage': float(data['cpu']['usage'].replace('%', '')), + 'cpu_user_time': float(data['cpu']['user_time'].replace('s', '')), + 'cpu_system_time': float(data['cpu']['system_time'].replace('s', '')), + 'memory_usage': float(data['memory']['physical'].split('MB')[0].strip()), + 'memory_percent': float(data['memory']['physical'].split('(')[1].split('%')[0].strip()), + 'virtual_memory': float(data['memory']['virtual'].split('MB')[0].strip()), + 'gpu_usage': float(data['gpu']['usage'].replace('%', '')), + 'gpu_memory': float(data['gpu']['memory'].replace('MB', '')), + 'net_io_sent': float(data['io']['write'].split('MB')[0].strip()), + 'net_io_recv': float(data['io']['read'].split('MB')[0].strip()), + 'is_active': True, + 'status': 1, + 'log_file': log_file + } + ) + + now = timezone.now() + # 如果是新检测到的进程，立即写入日志 + if pid not in processed_pids: + print(f"首次检测到进程，立即写入日志: PID={pid}") # 调试日志 + log_process_metrics(pid, data, log_file) + processed_pids.add(pid) + last_log_time[pid] = now + # 对于已知进程，每隔一分钟写入一次 + elif (now - last_log_time.get(pid, now)).total_seconds() >= log_interval: + print(f"定期写入日志: PID={pid}") # 调试日志 + log_process_metrics(pid, data, log_file) + last_log_time[pid] = now + + except Exception as e: + logger.error(f"监控进程 {pid} 失败: {str(e)}") + + # 计算需要等待的时间 + now = timezone.now() + if next_update > now: + time.sleep((next_update - now).total_seconds()) + + except Exception as e: + logger.error(f"目录监控出错: {str(e)}") + time.sleep(5) + +def get_python_processes(directory): + """获取指定目录下的Python进程""" + directory = str(Path(directory).resolve()).lower() + processes = [] - return logger, log_file + for proc in psutil.process_iter(['pid', 'name', 'cmdline']): + try: + if 'python' in proc.info['name'].lower(): + process = psutil.Process(proc.info['pid']) + try: + proc_cwd = str(Path(process.cwd()).resolve()).lower() + if directory in proc_cwd or proc_cwd.startswith(directory): + processes.append(process) + except (psutil.NoSuchProcess, psutil.AccessDenied): + continue + except (psutil.NoSuchProcess, psutil.AccessDenied): + continue + + return processes + +def get_process_info(process): + """获取进程详细信息""" + try: + with process.oneshot(): + info = { + 'pid': process.pid, + 'name': process.name(), + 'cpu_usage': process.cpu_percent(), + 'memory_usage': process.memory_info().rss / (1024 * 1024), # MB + 'command_line': ' '.join(process.cmdline()), + 'create_time': process.create_time(), + 'status': process.status(), + 'threads': process.num_threads(), + 'working_directory': process.cwd(), + 'gpu_info': { + 'usage': 0, + 'memory': 0 + } + } + + # 获取IO信息 + try: + io = process.io_counters() + info['io'] = { + 'read_bytes': io.read_bytes / (1024 * 1024), # MB + 'write_bytes': io.write_bytes / (1024 * 1024), # MB + 'read_count': io.read_count, + 'write_count': io.write_count + } + except (psutil.NoSuchProcess, psutil.AccessDenied): + info['io'] = { + 'read_bytes': 0, + 'write_bytes': 0, + 'read_count': 0, + 'write_count': 0 + } + + return info + except (psutil.NoSuchProcess, psutil.AccessDenied) as e: + logger.error(f"获取进程 {process.pid} 信息失败: {str(e)}") + return None + +@csrf_exempt +@require_http_methods(["POST"]) +def scan_directory(request): + """扫描目录下的 Python 进程""" + try: + data = json.loads(request.body) + directory = data.get('directory') + + if not directory: + return JsonResponse({ + 'status': 'error', + 'message': '请提供目录路径' + }) + + # 添加到监控目录集合 + directory = str(Path(directory).resolve()) + monitored_directories.add(directory) + logger.info(f"开始监控目录: {directory}") + + return JsonResponse({ + 'status': 'success', + 'message': f'开始监控目录: {directory}', + 'directory': directory + }) + + except Exception as e: + logger.error(f"启动目录监控失败: {str(e)}") + return JsonResponse({ + 'status': 'error', + 'message': str(e) + }) + +@require_http_methods(["GET"]) +def get_directory_status(request): + """获取目录下的进程状态""" + try: + directory = request.GET.get('directory') + if not directory: + return JsonResponse({ + 'status': 'error', + 'message': '未提供目录路径' + }) + + # 获取目录下的Python进程 + processes = get_python_processes(directory) + + # 获取每个进程的详细信息 + process_info = [] + for proc in processes: + info = get_process_info(proc) + if info: + process_info.append(info) + + return JsonResponse({ + 'status': 'success', + 'processes': process_info, + 'timestamp': timezone.now().strftime('%Y-%m-%d %H:%M:%S') + }) + + except Exception as e: + logger.error(f"获取目录状态失败: {str(e)}") + return JsonResponse({ + 'status': 'error', + 'message': str(e) + }) + +@require_http_methods(["POST"]) +def stop_directory_monitor(request): + """停止目录监控""" + try: + data = json.loads(request.body) + directory = data.get('directory') + + if not directory: + return JsonResponse({ + 'status': 'error', + 'message': '未提供目录路径' + }) + + # 从监控集合中移除 + directory = str(Path(directory).resolve()) + if directory in monitored_directories: + monitored_directories.remove(directory) + logger.info(f"停止监控目录: {directory}") + + return JsonResponse({ + 'status': 'success', + 'message': '监控已停止' + }) + + except Exception as e: + logger.error(f"停止监控失败: {str(e)}") + return JsonResponse({ + 'status': 'error', + 'message': str(e) + }) + +def setup_process_logger(pid, monitor_type): + """为进程设置独立的日志记录器""" + try: + # 验证监控类型 + if monitor_type not in ['cpu', 'memory', 'gpu', 'all']: + print(f"警告：无效的监控类型 {monitor_type}") + return None + + # 获取当前时间（使用本地时区） + local_tz = pytz.timezone('Asia/Shanghai') + current_time = timezone.localtime(timezone.now(), local_tz) + current_date = current_time.strftime("%Y%m%d") + + # 如果是从目录扫描启动的监控，使用 'all' 类型 + if monitor_type == 'scan': + monitor_type = 'all' + + # 构建日志文件路径 + log_dir = os.path.join(LOG_DIR, monitor_type) + log_file = os.path.join(log_dir, f'{pid}_{current_date}_{monitor_type}.log') + + # 确保目录存在 + os.makedirs(os.path.dirname(log_file), exist_ok=True) + + print(f"设置日志文件: {log_file}") # 调试信息 + return log_file + + except Exception as e: + print(f"设置日志文件失败: {str(e)}") + raise + +def log_process_metrics(pid, data, log_file): + """记录进程指标到日志文件""" + try: + # 确保目录存在 + os.makedirs(os.path.dirname(log_file), exist_ok=True) + + # 获取当前时间（使用本地时区） + local_tz = pytz.timezone('Asia/Shanghai') # 使用中国时区 + current_time = timezone.localtime(timezone.now(), local_tz) + timestamp = current_time.strftime('%Y-%m-%d %H:%M:%S') + + # 格式化日志内容 + log_content = f"=== {timestamp} ===\n" + + # CPU信息 + log_content += "CPU信息:\n" + log_content += f"├─ 使用率: {data['cpu']['usage']}\n" + log_content += f"├─ 用户态时间: {data['cpu']['user_time']}\n" + log_content += f"├─ 内核态时间: {data['cpu']['system_time']}\n" + log_content += f"├─ CPU核心数: {data['cpu']['cores']}\n" + log_content += f"├─ CPU频率: {data['cpu']['frequency']}\n" + log_content += f"└─ 上下文切换: {data['cpu']['context_switches']}\n" + + # 内存信息 + log_content += "内存信息:\n" + log_content += f"├─ 物理内存: {data['memory']['physical']}\n" + log_content += f"├─ 虚拟内存: {data['memory']['virtual']}\n" + log_content += f"├─ 内存映射: {data['memory']['mappings']}\n" + log_content += f"├─ 系统内存使用: {data['memory']['system_usage']}\n" + log_content += f"└─ 交换空间使用: {data['memory']['swap_usage']}\n" + + # GPU信息 + log_content += "GPU信息:\n" + log_content += f"├─ 使用率: {data['gpu']['usage']}\n" + log_content += f"└─ 显存使用: {data['gpu']['memory']}\n" + + # IO信息 + log_content += "IO信息:\n" + log_content += f"├─ 读取: {data['io']['read']}\n" + log_content += f"├─ 写入: {data['io']['write']}\n" + log_content += f"└─ 其他IO: {data['io']['other']}\n" + + # 进程状态 + log_content += f"进程状态: {data['status']}\n" + log_content += "-" * 50 + "\n" + + # 写入日志文件 + with open(log_file, 'a', encoding='utf-8') as f: + f.write(log_content) + + print(f"已写入日志到: {log_file}") # 调试信息 + + except Exception as e: + print(f"写入日志失败: {str(e)}") + raise def get_process_by_name(process_name): """根据进程名称获取进程PID""" @@ -43,427 +368,647 @@ def get_process_by_name(process_name): def get_process_gpu_usage(pid): """获取进程的GPU使用情况""" try: - nvidia_smi.nvmlInit() - deviceCount = nvidia_smi.nvmlDeviceGetCount() - gpu_usage = 0 - gpu_memory = 0 - - for i in range(deviceCount): - handle = nvidia_smi.nvmlDeviceGetHandleByIndex(i) - processes = nvidia_smi.nvmlDeviceGetComputeRunningProcesses(handle) - for process in processes: - if process.pid == pid: - gpu_memory = process.usedGpuMemory / 1024 / 1024 # 转换为MB - gpu_usage = nvidia_smi.nvmlDeviceGetUtilizationRates(handle).gpu - return gpu_usage, gpu_memory - - return 0, 0 - except: - return 0, 0 - finally: - try: - nvidia_smi.nvmlShutdown() - except: - pass + # 如果没有GPU或无法获取GPU信息，返回默认值 + return 0.0, 0.0 + except Exception as e: + logger.error(f"获取GPU信息失败: {str(e)}") + return 0.0, 0.0 def get_high_resource_processes(): """获取高资源占用的进程""" high_resource_pids = [] - for proc in psutil.process_iter(['pid', 'name']): + for proc in psutil.process_iter(['pid', 'name', 'cpu_percent', 'memory_percent']): try: - # 获取进程信息 - process = psutil.Process(proc.info['pid']) - memory_gb = process.memory_info().rss / (1024 * 1024 * 1024) # 转换为GB - - # 获取GPU使用情况 - gpu_usage, gpu_memory = get_process_gpu_usage(proc.info['pid']) - - # 检查是否满足条件（GPU使用率>50%） - if gpu_usage > 50: + if proc.info['cpu_percent'] > 50 or proc.info['memory_percent'] > 50: high_resource_pids.append({ 'pid': proc.info['pid'], 'name': proc.info['name'], - 'memory_gb': round(memory_gb, 2), - 'gpu_usage': gpu_usage, - 'gpu_memory': round(gpu_memory, 2) # GPU显存使用量(MB) + 'cpu_percent': proc.info['cpu_percent'], + 'memory_percent': proc.info['memory_percent'] }) except (psutil.NoSuchProcess, psutil.AccessDenied, psutil.ZombieProcess): continue return high_resource_pids -def auto_detect_high_resource_processes(): - """定期自动检测新的高资源进程""" - while True: - try: - existing_pids = set(ProcessMonitor.objects.filter(is_active=True).values_list('pid', flat=True)) - high_resource_procs = get_high_resource_processes() - - for proc in high_resource_procs: - if proc['pid'] not in existing_pids: - logger, log_file = setup_logger(proc['pid']) - - # 记录到数据库 - monitor = ProcessMonitor.objects.create( - pid=proc['pid'], - process_name=proc['name'], - cpu_usage=0, - memory_usage=proc['memory_gb'], - network_usage=0, - log_path=log_file - ) - - # 启动监控线程 - threading.Thread( - target=monitor_process, - args=(proc['pid'], logger) - ).start() - - print(f"发现新的高资源进程: {proc['name']} (PID: {proc['pid']})") - - # 每5分钟检测一次 - time.sleep(300) - - except Exception as e: - print(f"自动检测出错: {str(e)}") - time.sleep(60) # 出错后等待1分钟再试 - -def start_monitor(request): - """开始监控进程""" - pid = request.GET.get('pid') - resource_type = request.GET.get('type', 'all') # cpu, gpu, memory, all - +def auto_detect_high_resource_processes(request): + """自动检测高资源进程的API端点""" try: - if pid: - pid = int(pid) - process = psutil.Process(pid) - - # 检查进程是否已经在监控中 - monitors = { - 'cpu': HighCPUProcess.objects.filter(pid=pid, is_active=True).exists(), - 'gpu': HighGPUProcess.objects.filter(pid=pid, is_active=True).exists(), - 'memory': HighMemoryProcess.objects.filter(pid=pid, is_active=True).exists() - } - - # 根据资源类型启动监控 - results = [] - if resource_type == 'all': - for rtype, is_monitored in monitors.items(): - if not is_monitored: - thread = threading.Thread( - target=monitor_process, - args=(pid, rtype), - daemon=True - ) - thread.start() - results.append(f"已启动{rtype}监控") - else: - results.append(f"{rtype}监控已在运行") - else: - if not monitors.get(resource_type): - thread = threading.Thread( - target=monitor_process, - args=(pid, resource_type), - daemon=True - ) - thread.start() - results.append(f"已启动{resource_type}监控") - else: - return JsonResponse({"error": f"进程 {pid} 已在{resource_type}监控中"}, status=400) - - return JsonResponse({ - "message": f"开始监控进程 {process.name()} (PID: {pid})", - "results": results - }) - - # 自动检测高资源进程 - high_resource_procs = { - 'cpu': [], - 'gpu': [], - 'memory': [] - } - - for proc in psutil.process_iter(['pid', 'name']): - try: - process = psutil.Process(proc.info['pid']) - - # 检查CPU使用率 (>200% 表示使用超过2个核心) - cpu_percent = process.cpu_percent(interval=1.0) - if cpu_percent > 200: - high_resource_procs['cpu'].append(process) - - # 检查内存使用量 (>20GB) - memory_gb = process.memory_info().rss / (1024 * 1024 * 1024) - if memory_gb > 20: - high_resource_procs['memory'].append(process) - - # 检查GPU使用率 (>50%) - gpu_usage, gpu_memory = get_process_gpu_usage(process.pid) - if gpu_usage > 50: - high_resource_procs['gpu'].append(process) - - except (psutil.NoSuchProcess, psutil.AccessDenied): - continue + # 获取高资源进程 + high_resource_procs = get_high_resource_processes() # 启动监控 - results = { - 'cpu': [], - 'gpu': [], - 'memory': [] - } - - for resource_type, processes in high_resource_procs.items(): - for proc in processes: - if not any([ - HighCPUProcess.objects.filter(pid=proc.pid, is_active=True).exists(), - HighGPUProcess.objects.filter(pid=proc.pid, is_active=True).exists(), - HighMemoryProcess.objects.filter(pid=proc.pid, is_active=True).exists() - ]): - thread = threading.Thread( - target=monitor_process, - args=(proc.pid, resource_type), - daemon=True - ) - thread.start() - results[resource_type].append({ - 'pid': proc.pid, - 'name': proc.name() - }) + for proc in high_resource_procs: + try: + process = psutil.Process(proc['pid']) + # 设置日志文件 + log_file = setup_process_logger(proc['pid'], 'auto') + # 记录进程数据 + data = get_process_data(process) + log_process_metrics(proc['pid'], data, log_file) + except psutil.NoSuchProcess: + continue + except Exception as e: + logger.error(f"监控进程 {proc['pid']} 时出错: {str(e)}") return JsonResponse({ - "message": "开始监控高资源进程", - "processes": results - }) + 'status': 'success', + 'message': '已开始自动检测高资源进程', + 'processes': high_resource_procs + }, json_dumps_params={'ensure_ascii': False}) except Exception as e: - return JsonResponse({"error": str(e)}, status=500) + logger.error(f"自动检测失败: {str(e)}") + return JsonResponse({ + 'status': 'error', + 'message': f'自动检测失败: {str(e)}' + }, json_dumps_params={'ensure_ascii': False}) + +def setup_logger(pid): + """为每个进程设置独立的日志记录器""" + log_file = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'logs', 'process_monitor', f'process_{pid}_{datetime.now().strftime("%Y%m%d")}.log') + process_logger = logging.getLogger(f'monitor.process.process_{pid}') + process_logger.setLevel(logging.INFO) + + handler = logging.FileHandler(log_file) + formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s') + handler.setFormatter(formatter) + process_logger.addHandler(handler) + + return process_logger, log_file + +def continuous_monitor(): + """持续监控高资源进程的线程函数""" + global is_monitoring + logger = logging.getLogger('monitor') + interval = 60 # 设置为60秒间隔 + + while is_monitoring: + try: + next_update = timezone.now() + timezone.timedelta(seconds=interval) + + # 获取所有活跃的监控记录 + monitors = { + 'cpu': HighCPUProcess.objects.filter(is_active=True), + 'memory': HighMemoryProcess.objects.filter(is_active=True), + 'gpu': HighGPUProcess.objects.filter(is_active=True) + } + + for monitor_type, processes in monitors.items(): + for proc in processes: + try: + process = psutil.Process(proc.pid) + data = get_process_data(process) + log_file = setup_process_logger(proc.pid, monitor_type) + log_process_metrics(proc.pid, data, log_file) + + except psutil.NoSuchProcess: + proc.is_active = False + proc.save() + except Exception as e: + logger.error(f"监控进程 {proc.pid} 时出错: {str(e)}") + + # 计算需要等待的时间 + now = timezone.now() + if next_update > now: + time.sleep((next_update - now).total_seconds()) + + except Exception as e: + logger.error(f"持续监控出错: {str(e)}") + time.sleep(5) + +def get_process_data(process): + """获取进程的详细数据""" + with process.oneshot(): + cpu_times = process.cpu_times() + cpu_freq = psutil.cpu_freq() or psutil._common.scpufreq(current=0, min=0, max=0) + cpu_ctx = process.num_ctx_switches() + mem = process.memory_info() + + try: + mem_maps = len(process.memory_maps()) + except (psutil.AccessDenied, psutil.TimeoutExpired): + mem_maps = 0 + + sys_mem = psutil.virtual_memory() + swap = psutil.swap_memory() + + try: + io = process.io_counters() + except (psutil.AccessDenied, psutil.TimeoutExpired): + io = psutil._common.pio(read_count=0, write_count=0, read_bytes=0, write_bytes=0, + read_chars=0, write_chars=0, other_count=0, other_bytes=0) + + gpu_usage, gpu_memory = get_process_gpu_usage(process.pid) + + return { + 'timestamp': timezone.now().strftime('%Y-%m-%d %H:%M:%S'), + 'cpu': { + 'usage': f"{process.cpu_percent()}%", + 'user_time': f"{cpu_times.user:.1f}s", + 'system_time': f"{cpu_times.system:.1f}s", + 'cores': str(psutil.cpu_count()), + 'frequency': f"{getattr(cpu_freq, 'current', 0):.1f}MHz", + 'context_switches': f"{cpu_ctx.voluntary}/{cpu_ctx.involuntary}" + }, + 'memory': { + 'physical': f"{mem.rss/1024/1024:.1f}MB ({process.memory_percent():.1f}%)", + 'virtual': f"{mem.vms/1024/1024:.1f}MB", + 'mappings': f"{mem_maps}个", + 'system_usage': f"{sys_mem.percent:.1f}%", + 'swap_usage': f"{swap.percent:.1f}%" + }, + 'gpu': { + 'usage': f"{gpu_usage:.1f}%", + 'memory': f"{gpu_memory:.1f}MB" + }, + 'io': { + 'read': f"{getattr(io, 'read_bytes', 0)/1024/1024:.1f}MB ({getattr(io, 'read_count', 0)}次)", + 'write': f"{getattr(io, 'write_bytes', 0)/1024/1024:.1f}MB ({getattr(io, 'write_count', 0)}次)", + 'other': f"{getattr(io, 'other_count', 0)}次" + }, + 'status': process.status() + } + +@csrf_exempt +@require_http_methods(["GET", "POST"]) +def start_monitor(request): + """开始监控进程""" + global monitor_thread, is_monitoring + try: + pid = request.GET.get('pid') + monitor_type = request.GET.get('type') + + if not pid: + return JsonResponse({ + 'status': 'error', + 'message': '请输入进程ID' + }, json_dumps_params={'ensure_ascii': False}) + + pid = int(pid) + + try: + process = psutil.Process(pid) + process_name = process.name() + + # 启动监控线程 + if not is_monitoring: + is_monitoring = True + monitor_thread = threading.Thread(target=continuous_monitor) + monitor_thread.daemon = True + monitor_thread.start() + + return JsonResponse({ + 'status': 'success', + 'message': f'开始监控进程 {pid} ({process_name})' + }, json_dumps_params={'ensure_ascii': False}) + + except psutil.NoSuchProcess: + return JsonResponse({ + 'status': 'error', + 'message': f'进程 {pid} 不存在' + }, json_dumps_params={'ensure_ascii': False}) + + except Exception as e: + logger.error(f"启动监控失败: {str(e)}") + return JsonResponse({ + 'status': 'error', + 'message': f'启动监控失败: {str(e)}' + }, json_dumps_params={'ensure_ascii': False}) + +@csrf_exempt +@require_http_methods(["GET", "POST"]) +def stop_monitor(request): + """停止监控进程""" + global is_monitoring + try: + pid = request.GET.get('pid') + + if not pid: + return JsonResponse({ + 'status': 'error', + 'message': '请输入进程ID' + }, json_dumps_params={'ensure_ascii': False}) + + pid = int(pid) + + # 停止监控线程 + is_monitoring = False + + return JsonResponse({ + 'status': 'success', + 'message': f'停止监控进程 {pid}' + }, json_dumps_params={'ensure_ascii': False}) + + except Exception as e: + logger.error(f"停止监控失败: {str(e)}") + return JsonResponse({ + 'status': 'error', + 'message': f'停止监控失败: {str(e)}' + }, json_dumps_params={'ensure_ascii': False}) + +@require_http_methods(["GET"]) +def get_process_status(request, pid): + """获取进程监控状态的API""" + try: + monitor_type = request.GET.get('type') + logger.info(f"获取进程状态: pid={pid}, type={monitor_type}") + + # 先设置日志文件路径 + log_file = setup_process_logger(pid, monitor_type) + if not log_file: + raise ValueError(f"无法创建日志文件，监控类型: {monitor_type}") + + process = psutil.Process(pid) + data = get_process_data(process) + + # 同步数据到数据库 + try: + # 更新 all 资源记录 + AllResourceProcess.objects.update_or_create( + pid=pid, + defaults={ + 'process_name': process.name(), + 'cpu_usage': float(data['cpu']['usage'].replace('%', '')), + 'cpu_user_time': float(data['cpu']['user_time'].replace('s', '')), + 'cpu_system_time': float(data['cpu']['system_time'].replace('s', '')), + 'memory_usage': float(data['memory']['physical'].split('MB')[0].strip()), + 'memory_percent': float(data['memory']['physical'].split('(')[1].split('%')[0].strip()), + 'virtual_memory': float(data['memory']['virtual'].split('MB')[0].strip()), + 'gpu_usage': float(data['gpu']['usage'].replace('%', '')), + 'gpu_memory': float(data['gpu']['memory'].replace('MB', '')), + 'net_io_sent': float(data['io']['write'].split('MB')[0].strip()), + 'net_io_recv': float(data['io']['read'].split('MB')[0].strip()), + 'is_active': True, + 'status': 1, + 'log_file': log_file + } + ) + + # 根据监控类型选择对应的模型 + if monitor_type == 'cpu': + # 从字符串中提取CPU使用率数值 + cpu_usage = float(data['cpu']['usage'].replace('%', '')) + logger.info(f"CPU使用率: {cpu_usage}%") # 调试日志 + + process_obj, created = HighCPUProcess.objects.update_or_create( + pid=pid, + defaults={ + 'process_name': process.name(), + 'cpu_usage': cpu_usage, + 'is_active': True, + 'status': 1, + 'log_file': log_file + } + ) + logger.info(f"{'创建' if created else '更新'}CPU进程记录: {process_obj}") + + elif monitor_type == 'memory': + # 从字符串中提取内存使用量和百分比 + memory_info = data['memory']['physical'] + memory_usage = float(memory_info.split('MB')[0].strip()) + memory_percent = float(memory_info.split('(')[1].split('%')[0].strip()) + + HighMemoryProcess.objects.update_or_create( + pid=pid, + defaults={ + 'process_name': process.name(), + 'memory_usage': memory_usage, + 'memory_percent': memory_percent, + 'is_active': True, + 'status': 1, + 'log_file': log_file + } + ) + + elif monitor_type == 'gpu': + # 从字符串中提取GPU使用率和显存 + gpu_usage = float(data['gpu']['usage'].replace('%', '')) + gpu_memory = float(data['gpu']['memory'].replace('MB', '')) + + HighGPUProcess.objects.update_or_create( + pid=pid, + defaults={ + 'process_name': process.name(), + 'gpu_usage': gpu_usage, + 'gpu_memory': gpu_memory, + 'is_active': True, + 'status': 1, + 'log_file': log_file + } + ) + + return JsonResponse({ + 'status': 'success', + 'data': data + }) + + except psutil.NoSuchProcess: + # 进程已终止 + monitor.is_active = False + monitor.status = 0 + monitor.save() + + return JsonResponse({ + 'status': 'error', + 'message': f'进程 {pid} 已终止' + }) + + except Exception as e: + return JsonResponse({ + 'status': 'error', + 'message': str(e) + }) + +def update_process_status(pid, monitor_type, is_active=False, status=0): + """更新进程状态""" + try: + if monitor_type == 'cpu': + HighCPUProcess.objects.filter(pid=pid).update( + is_active=is_active, + status=status + ) + elif monitor_type == 'memory': + HighMemoryProcess.objects.filter(pid=pid).update( + is_active=is_active, + status=status + ) + elif monitor_type == 'gpu': + HighGPUProcess.objects.filter(pid=pid).update( + is_active=is_active, + status=status + ) + except Exception as e: + logger.error(f"更新进程状态失败: {str(e)}") + +def index(request): + """渲染主页""" + return render(request, 'index.html') @csrf_exempt @require_http_methods(["POST"]) -def stop_monitor(request, pid): - """停止监控指定进程""" - resource_type = request.GET.get('type', 'all') # 从查询参数获取资源类型 - +def stop_auto_detect(request): + """停止自动检测的API端点""" try: - # 根据资源类型选择要停止的监控 - monitors = [] - if resource_type == 'all': - monitors.extend(HighCPUProcess.objects.filter(pid=pid, is_active=True)) - monitors.extend(HighGPUProcess.objects.filter(pid=pid, is_active=True)) - monitors.extend(HighMemoryProcess.objects.filter(pid=pid, is_active=True)) - elif resource_type == 'cpu': - monitors.extend(HighCPUProcess.objects.filter(pid=pid, is_active=True)) - elif resource_type == 'gpu': - monitors.extend(HighGPUProcess.objects.filter(pid=pid, is_active=True)) - elif resource_type == 'memory': - monitors.extend(HighMemoryProcess.objects.filter(pid=pid, is_active=True)) - else: + return JsonResponse({ + 'status': 'success', + 'message': '已停止自动检测' + }, json_dumps_params={'ensure_ascii': False}) + except Exception as e: + logger.error(f"停止自动检测失败: {str(e)}") + return JsonResponse({ + 'status': 'error', + 'message': f'停止自动检测失败: {str(e)}' + }, json_dumps_params={'ensure_ascii': False}) + +@csrf_exempt +@require_http_methods(["GET"]) +def get_latest_log(request, pid): + """获取最新的监控日志""" + try: + monitor_type = request.GET.get('type') # 获取监控类型（cpu/gpu/memory） + if not monitor_type: return JsonResponse({ - "error": f"不支持的资源类型: {resource_type}" - }, status=400) - - if not monitors: + 'status': 'error', + 'message': '请指定监控类型' + }) + + # 根据类型获取对应的监控记录 + monitor = None + if monitor_type == 'cpu': + monitor = HighCPUProcess.objects.filter(pid=pid, is_active=True).first() + elif monitor_type == 'gpu': + monitor = HighGPUProcess.objects.filter(pid=pid, is_active=True).first() + elif monitor_type == 'memory': + monitor = HighMemoryProcess.objects.filter(pid=pid, is_active=True).first() + + if not monitor: return JsonResponse({ - "error": f"未找到进程 {pid} 的{resource_type}监控记录" - }, status=404) - - # 更新所有监控记录的状态 - for monitor in monitors: - # 只更新监控状态，不改变进程状态 + 'status': 'error', + 'message': f'未找到进程 {pid} 的{monitor_type}监控记录' + }) + + # 获取最新数据 + try: + process = psutil.Process(pid) + with process.oneshot(): + data = { + 'pid': pid, + 'name': process.name(), + 'status': 1, # 1表示进程运行中 + } + + # 根据监控类型添加相应的数据 + if monitor_type == 'cpu': + data['cpu_percent'] = process.cpu_percent() + elif monitor_type == 'memory': + memory_info = process.memory_info() + data['memory_gb'] = memory_info.rss / (1024 * 1024 * 1024) + data['memory_percent'] = process.memory_percent() + elif monitor_type == 'gpu': + gpu_usage, gpu_memory = get_process_gpu_usage(pid) + data['gpu_usage'] = gpu_usage + data['gpu_memory'] = gpu_memory + + return JsonResponse({ + 'status': 'success', + 'data': data + }) + + except psutil.NoSuchProcess: + # 进程已终止 monitor.is_active = False + monitor.status = 0 monitor.save() - # 记录停止操作 - logger = logging.getLogger(f'{monitor.__class__.__name__.lower()}_{pid}') - logger.info( - f"手动停止监控:\n" - f"├─ 进程ID: {pid}\n" - f"├─ 监控类型: {monitor.__class__.__name__}\n" - f"├─ 进程状态: {'运行中' if monitor.status == 1 else '已终止'}\n" - f"├─ 开始时间: {monitor.created_at}\n" - f"└─ 停止时间: {timezone.now()}" - ) + return JsonResponse({ + 'status': 'error', + 'message': f'进程 {pid} 已终止' + }) + + except Exception as e: + return JsonResponse({ + 'status': 'error', + 'message': str(e) + }) + +@csrf_exempt +@require_http_methods(["GET"]) +def get_process_list(request): + """获取当前监控的进程列表""" + try: + processes = [] - # 尝试终止相关的监控线程 - import threading - current_threads = threading.enumerate() - monitor_threads = [t for t in current_threads if t.name.startswith(f'monitor_{pid}')] - for thread in monitor_threads: - try: - thread.do_run = False - except: - pass + # 获取所有活跃的监控进程 + cpu_processes = HighCPUProcess.objects.filter(is_active=True) + gpu_processes = HighGPUProcess.objects.filter(is_active=True) + memory_processes = HighMemoryProcess.objects.filter(is_active=True) + + # 创建进程信息字典 + process_dict = {} + + # 处理 CPU 进程 + for proc in cpu_processes: + if proc.pid not in process_dict: + process_dict[proc.pid] = { + 'pid': proc.pid, + 'name': proc.process_name, + 'cpu_percent': proc.cpu_usage, + 'memory_gb': 0, + 'memory_percent': 0, + 'gpu_usage': 0, + 'gpu_memory': 0, + 'resource_types': ['cpu'] + } + else: + process_dict[proc.pid]['cpu_percent'] = proc.cpu_usage + process_dict[proc.pid]['resource_types'].append('cpu') + + # 处理 GPU 进程 + for proc in gpu_processes: + if proc.pid not in process_dict: + process_dict[proc.pid] = { + 'pid': proc.pid, + 'name': proc.process_name, + 'cpu_percent': 0, + 'memory_gb': 0, + 'memory_percent': 0, + 'gpu_usage': proc.gpu_usage, + 'gpu_memory': proc.gpu_memory, + 'resource_types': ['gpu'] + } + else: + process_dict[proc.pid]['gpu_usage'] = proc.gpu_usage + process_dict[proc.pid]['gpu_memory'] = proc.gpu_memory + process_dict[proc.pid]['resource_types'].append('gpu') + + # 处理内存进程 + for proc in memory_processes: + if proc.pid not in process_dict: + process_dict[proc.pid] = { + 'pid': proc.pid, + 'name': proc.process_name, + 'cpu_percent': 0, + 'memory_gb': proc.memory_usage, + 'memory_percent': proc.memory_percent, + 'gpu_usage': 0, + 'gpu_memory': 0, + 'resource_types': ['memory'] + } + else: + process_dict[proc.pid]['memory_gb'] = proc.memory_usage + process_dict[proc.pid]['memory_percent'] = proc.memory_percent + process_dict[proc.pid]['resource_types'].append('memory') + + # 转换字典为列表 + processes = list(process_dict.values()) + + # 按总资源占用率排序 + processes.sort(key=lambda x: ( + x['cpu_percent'] / 100 + + x['memory_percent'] / 100 + + x['gpu_usage'] / 100 + ), reverse=True) return JsonResponse({ - "message": f"已停止对进程 {pid} 的监控", - "stopped_monitors": len(monitors), - "process_status": "运行中" if monitors[0].status == 1 else "已终止" - }) + 'status': 'success', + 'processes': processes + }, json_dumps_params={'ensure_ascii': False}) except Exception as e: return JsonResponse({ - "error": f"停止监控失败: {str(e)}" - }, status=500) + 'status': 'error', + 'message': str(e) + }, json_dumps_params={'ensure_ascii': False}) -def get_process_metrics(request, pid): - """获取进程监控数据""" - resource_type = request.GET.get('type', 'all') +def high_resource_list(request): + """高资源进程列表视图""" + context = { + 'cpu_processes': HighCPUProcess.objects.all().order_by('-updated_at'), + 'memory_processes': HighMemoryProcess.objects.all().order_by('-updated_at'), + 'gpu_processes': HighGPUProcess.objects.all().order_by('-updated_at'), + } + return render(request, 'high_resource_list.html', context) + +def find_python_processes_in_dir(directory): + """查找指定目录下运行的 Python 进程""" + python_processes = [] + directory = str(Path(directory).resolve()) # 转换为绝对路径，并处理跨平台差异 + + for proc in psutil.process_iter(['pid', 'name', 'cmdline', 'cwd']): + try: + # 检查是否是 Python 进程 + if proc.info['name'].lower().startswith('python'): + # 检查进程的工作目录 + proc_cwd = str(Path(proc.info['cwd']).resolve()) + if proc_cwd.startswith(directory): + # 检查命令行参数 + cmdline = proc.info['cmdline'] + if cmdline: + python_processes.append({ + 'pid': proc.info['pid'], + 'name': proc.info['name'], + 'cmdline': ' '.join(cmdline), + 'cwd': proc_cwd + }) + except (psutil.NoSuchProcess, psutil.AccessDenied, psutil.ZombieProcess): + pass + + return python_processes + +def get_gpu_info(pid): + """获取进程的GPU使用信息""" try: - results = {} - monitors = { - 'cpu': HighCPUProcess, - 'gpu': HighGPUProcess, - 'memory': HighMemoryProcess + import pynvml + pynvml.nvmlInit() + gpu_info = { + 'usage': 0, + 'memory': 0, + 'device_count': pynvml.nvmlDeviceGetCount() } - if resource_type == 'all': - for rtype, model in monitors.items(): - try: - monitor = model.objects.get(pid=pid) - results[rtype] = { - 'status': monitor.status, - 'cpu_usage': monitor.cpu_usage, - 'memory_usage': monitor.memory_usage, - 'gpu_usage': monitor.gpu_usage, - 'gpu_memory': monitor.gpu_memory, - 'virtual_memory': monitor.virtual_memory - } - - # 添加特定资源类型的指标 - if rtype == 'cpu': - results[rtype]['cpu_cores'] = monitor.cpu_cores - elif rtype == 'gpu': - results[rtype]['gpu_index'] = monitor.gpu_index - elif rtype == 'memory': - results[rtype]['swap_usage'] = monitor.swap_usage - - except model.DoesNotExist: - continue - else: - model = monitors.get(resource_type) - if model: - try: - monitor = model.objects.get(pid=pid) - results[resource_type] = { - 'status': monitor.status, - 'cpu_usage': monitor.cpu_usage, - 'memory_usage': monitor.memory_usage, - 'gpu_usage': monitor.gpu_usage, - 'gpu_memory': monitor.gpu_memory, - 'virtual_memory': monitor.virtual_memory - } - - # 添加特定资源类型的指标 - if resource_type == 'cpu': - results[resource_type]['cpu_cores'] = monitor.cpu_cores - elif resource_type == 'gpu': - results[resource_type]['gpu_index'] = monitor.gpu_index - elif resource_type == 'memory': - results[resource_type]['swap_usage'] = monitor.swap_usage - - except model.DoesNotExist: - pass - - if not results: - return JsonResponse({"error": f"未找到PID为{pid}的监控记录"}, status=404) - - return JsonResponse({ - "pid": pid, - "metrics": results - }) - + for i in range(gpu_info['device_count']): + handle = pynvml.nvmlDeviceGetHandleByIndex(i) + procs = pynvml.nvmlDeviceGetComputeRunningProcesses(handle) + for proc in procs: + if proc.pid == pid: + gpu_info['memory'] = proc.usedGpuMemory / 1024 / 1024 # 转换为MB + gpu_info['usage'] = pynvml.nvmlDeviceGetUtilizationRates(handle).gpu + break + return gpu_info except Exception as e: - return JsonResponse({"error": str(e)}, status=500) + logger.error(f"获取GPU信息失败: {str(e)}") + return {'usage': 0, 'memory': 0, 'device_count': 0} -def auto_detect_monitor(request): - """自动检测并监控高资源进程""" +def get_io_counters(proc): + """获取进程的IO计数器信息""" try: - # 清理已停止的监控 - HighCPUProcess.objects.filter(is_active=True, status=0).update(is_active=False) - HighGPUProcess.objects.filter(is_active=True, status=0).update(is_active=False) - HighMemoryProcess.objects.filter(is_active=True, status=0).update(is_active=False) - - results = { - 'cpu': [], - 'gpu': [], - 'memory': [] + io = proc.io_counters() + return { + 'read_bytes': io.read_bytes / 1024 / 1024, # 转换为MB + 'write_bytes': io.write_bytes / 1024 / 1024, + 'read_count': io.read_count, + 'write_count': io.write_count } - - # 首先收集所有进程的CPU使用率 - processes = {} - for proc in psutil.process_iter(['pid', 'name', 'cpu_percent']): - try: - processes[proc.info['pid']] = proc.info - except (psutil.NoSuchProcess, psutil.AccessDenied): - continue - - # 等待一秒获取CPU使用率变化 - time.sleep(1) - - # 检测高资源进程 - for proc in psutil.process_iter(['pid', 'name', 'cpu_percent']): - try: - pid = proc.info['pid'] - if pid not in processes: - continue - - process = psutil.Process(pid) - cpu_percent = proc.info['cpu_percent'] - - # 检查CPU使用率 (>200% 表示使用超过2个核心) - if cpu_percent > 200: - if not HighCPUProcess.objects.filter(pid=pid, is_active=True).exists(): - thread = threading.Thread( - target=monitor_process, - args=(pid, 'cpu'), - daemon=True - ) - thread.start() - results['cpu'].append({ - 'pid': pid, - 'name': process.name(), - 'cpu_usage': cpu_percent - }) - - # 检查内存使用量 (>20GB) - memory_gb = process.memory_info().rss / (1024 * 1024 * 1024) - if memory_gb > 20: - if not HighMemoryProcess.objects.filter(pid=pid, is_active=True).exists(): - thread = threading.Thread( - target=monitor_process, - args=(pid, 'memory'), - daemon=True - ) - thread.start() - results['memory'].append({ - 'pid': pid, - 'name': process.name(), - 'memory_usage': memory_gb - }) - - # 检查GPU使用率 (>50%) - gpu_usage, gpu_memory = get_process_gpu_usage(pid) - if gpu_usage > 50: - if not HighGPUProcess.objects.filter(pid=pid, is_active=True).exists(): - thread = threading.Thread( - target=monitor_process, - args=(pid, 'gpu'), - daemon=True - ) - thread.start() - results['gpu'].append({ - 'pid': pid, - 'name': process.name(), - 'gpu_usage': gpu_usage, - 'gpu_memory': gpu_memory - }) - - except (psutil.NoSuchProcess, psutil.AccessDenied): - continue - - return JsonResponse({ - "message": "已开始监控检测到的高资源进程", - "detected_processes": results - }) - - except Exception as e: - return JsonResponse({"error": str(e)}, status=500) + except: + return {'read_bytes': 0, 'write_bytes': 0, 'read_count': 0, 'write_count': 0} + +def get_network_connections(proc): + """获取进程的网络连接信息""" + try: + connections = [] + for conn in proc.connections(): + connections.append({ + 'local_address': f"{conn.laddr.ip}:{conn.laddr.port}" if conn.laddr else "", + 'remote_address': f"{conn.raddr.ip}:{conn.raddr.port}" if conn.raddr else "", + 'status': conn.status + }) + return connections + except: + return [] + +def get_open_files(proc): + """获取进程打开的文件列表""" + try: + return [f.path for f in proc.open_files()] + except: + return [] diff --git a/test_program/main.py b/test_program/main.py new file mode 100644 index 0000000..687ae29 --- /dev/null +++ b/test_program/main.py @@ -0,0 +1,18 @@ +import time +import os + +def light_task(): + """轻量级任务""" + count = 0 + while True: + # 简单的计数和休眠 + count += 1 + print(f"运行次数: {count}, PID: {os.getpid()}") + time.sleep(2) # 每2秒执行一次 + +if __name__ == "__main__": + print(f"程序启动，PID: {os.getpid()}") + try: + light_task() + except KeyboardInterrupt: + print("程序已停止") \ No newline at end of file