大家好我是Java1234_小锋老师分享一套锋哥原创的基于Spark实时物联网设备故障预警 数据分析与预测 系统(Java版本可视化大屏KafkaSpringBootVue3)项目介绍随着工业互联网与智能制造的快速发展物联网设备在生产现场的部署规模持续扩大。传统依赖人工巡检与阈值告警的运维方式难以应对高频、多维、持续到达的设备传感器数据容易出现故障发现滞后、误报漏报较多、运维成本居高不下等问题。针对上述背景本文设计并实现了一套基于 Spark 思想的实时物联网设备故障预警系统综合运用 Java、Spring Boot、Vue3、Kafka、MySQL 等主流技术完成了从数据采集、实时分析、风险评分、故障预警到可视化展示与风险预测的完整闭环。系统后端采用 Spring Boot 构建 RESTful 服务结合 Spring Security 与 JWT 实现管理员身份认证与接口鉴权前端采用 Vue3、Element Plus 与 ECharts 实现管理后台与数据可视化大屏数据采集侧通过 Kafka 消息主题device_telemetry 缓冲设备遥测数据并在 Kafka 不可用时自动降级为纯 Java 写库保证演示与实验环境的可用性。系统以窗口聚合方式统计设备数量、告警数量、平均温度、平均振动、故障率与风险评分基于温度、振动、电流等指标计算设备故障风险并利用多元线性回归对风险评分序列进行预测输出 RMSE、MAE、MAPE 等误差指标。数据库采用 MySQL库名为 db_iot_fault核心数据表包括管理员表、设备类型表、设备表、传感器数据表、故障预警表、实时统计表、预测结果表与误差指标表。系统实现了登录认证、首页统计、设备管理、传感器数据查询、故障预警处理、实时分析、预测分析、可视化大屏以及个人中心资料修改、头像上传、密码修改等功能。测试结果表明系统能够稳定完成实时数据采集、预警生成与预测分析界面交互流畅满足本科毕业设计对完整性、实用性与技术综合性的要求。源码下载链接: https://pan.baidu.com/s/1YtIlK_Xw-u7Z8Qu_Mr16Bw?pwd1234提取码: 1234系统展示核心代码package com.java1234.spark; import com.java1234.config.AppProperties; import org.apache.kafka.clients.producer.KafkaProducer; import org.apache.kafka.clients.producer.ProducerConfig; import org.apache.kafka.clients.producer.ProducerRecord; import org.apache.kafka.common.serialization.StringSerializer; import org.springframework.stereotype.Component; import java.util.List; import java.util.Properties; /** * Kafka 遥测生产者可选失败时降级 */ Component public class KafkaTelemetryProducer { private final AppProperties appProperties; public KafkaTelemetryProducer(AppProperties appProperties) { this.appProperties appProperties; } /** * 尝试发送事件到 Kafka */ public boolean trySend(ListTelemetryEvent events) { Properties props new Properties(); props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, 127.0.0.1:9092); props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName()); props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName()); props.put(ProducerConfig.REQUEST_TIMEOUT_MS_CONFIG, 3000); props.put(ProducerConfig.DELIVERY_TIMEOUT_MS_CONFIG, 5000); props.put(ProducerConfig.MAX_BLOCK_MS_CONFIG, 500); props.put(ProducerConfig.RETRIES_CONFIG, 0); try (KafkaProducerString, String producer new KafkaProducer(props)) { for (TelemetryEvent event : events) { String json toJson(event); producer.send(new ProducerRecord(appProperties.getKafkaTopic(), json)).get(); } return true; } catch (Exception ex) { System.out.println([Simulator] Kafka 发送失败使用纯 Java 写库: ex.getMessage()); return false; } } /** * 将遥测事件序列化为 JSON */ private String toJson(TelemetryEvent event) { return String.format( {\device_id\:%d,\temperature\:%s,\vibration\:%s,\current\:%s,\voltage\:%s,\humidity\:%s,\is_fault\:%d,\risk_score\:%s,\event_time\:\%s\}, event.getDeviceId(), event.getTemperature(), event.getVibration(), event.getCurrent(), event.getVoltage(), event.getHumidity(), event.getIsFault(), event.getRiskScore(), event.getEventTime().format(java.time.format.DateTimeFormatter.ofPattern(yyyy-MM-dd HH:mm:ss)) ); } }template div classpage-container div classpage-card div classpage-title故障风险预测分析/div div classerror-cards div classerror-carddiv classmetric-labelRMSE (均方根误差)/divdiv classmetric-value{{ errorMetric.rmse }}/div/div div classerror-carddiv classmetric-labelMAE (平均绝对误差)/divdiv classmetric-value{{ errorMetric.mae }}/div/div div classerror-carddiv classmetric-labelMAPE (平均绝对百分比误差 %)/divdiv classmetric-value{{ errorMetric.mape }}%/div/div /div div refcompareRef classpred-chart/div div refresidualRef classpred-chart pred-residual/div el-table :datatableData stripe border el-table-column propwindow_time label时间窗口 min-width170 template #default{ row }{{ formatWindowTime(row.window_time) }}/template /el-table-column el-table-column proptrue_score label真实风险分 min-width120 template #default{ row }span stylecolor:#409eff;font-weight:600{{ row.true_score }}/span/template /el-table-column el-table-column proppred_score label预测风险分 min-width120 template #default{ row }span stylecolor:#67c23a;font-weight:600{{ row.pred_score }}/span/template /el-table-column el-table-column label误差 min-width100 template #default{ row } span :style{ color: Math.abs(row.true_score - row.pred_score) 5 ? #f56c6c : #909399 } {{ (row.true_score - row.pred_score).toFixed(2) }} /span /template /el-table-column el-table-column propcreate_time label生成时间 min-width170 template #default{ row }{{ formatDateTime(row.create_time) }}/template /el-table-column /el-table el-pagination stylemargin-top:16px;justify-content:flex-end v-model:current-pagepage v-model:page-sizesize :totaltotal layouttotal, prev, pager, next changeloadTable / /div /div /template script setup /** * 预测分析页面真实 vs 预测对比 误差分析 */ import { ref, onMounted, onUnmounted } from vue import * as echarts from echarts import request from /utils/request import { formatDateTime, formatWindowTime } from /utils/format const errorMetric ref({ rmse: 0, mae: 0, mape: 0 }) const tableData ref([]) const page ref(1) const size ref(10) const total ref(0) const compareRef ref(null) const residualRef ref(null) let charts [] let pollTimer null function axisLabel() { return { rotate: 30, interval: auto, formatter(val) { const t formatWindowTime(val); return t.length 16 ? ${t.slice(0,10)}\n${t.slice(11)} : t } } } function initCompareChart(data) { if (!compareRef.value) return const chart echarts.init(compareRef.value) const labels data.map(d formatWindowTime(d.window_time)) chart.setOption({ title: { text: 真实风险分 vs 预测风险分 对比, left: center, textStyle: { fontSize: 15 } }, tooltip: { trigger: axis }, legend: { data: [真实风险分, 预测风险分], top: 32 }, grid: { left: 20, right: 24, bottom: 28, top: 72, containLabel: true }, xAxis: { type: category, data: labels, axisLabel: axisLabel() }, yAxis: { type: value, name: 风险评分 }, series: [ { name: 真实风险分, type: line, smooth: true, data: data.map(d Number(d.true_score)), itemStyle: { color: #409eff }, lineStyle: { width: 3 } }, { name: 预测风险分, type: line, smooth: true, data: data.map(d Number(d.pred_score)), itemStyle: { color: #67c23a }, lineStyle: { width: 3, type: dashed } }, ], }) charts.push(chart) } function initResidualChart(data) { if (!residualRef.value) return const chart echarts.init(residualRef.value) const labels data.map(d formatWindowTime(d.window_time)) const residuals data.map(d Number((Number(d.true_score) - Number(d.pred_score)).toFixed(2))) chart.setOption({ title: { text: 预测残差分析 (真实值 - 预测值), left: center, textStyle: { fontSize: 15 } }, tooltip: { trigger: axis }, grid: { left: 20, right: 24, bottom: 28, top: 56, containLabel: true }, xAxis: { type: category, data: labels, axisLabel: axisLabel() }, yAxis: { type: value, name: 残差 }, series: [{ type: bar, data: residuals.map(v ({ value: v, itemStyle: { color: v 0 ? #409eff : #f56c6c } })), barWidth: 20 }], }) charts.push(chart) } async function loadData() { const [errorRes, compareRes] await Promise.all([ request.get(/prediction/error), request.get(/prediction/compare), ]) errorMetric.value errorRes.data charts.forEach(c c.dispose()) charts [] initCompareChart(compareRes.data || []) initResidualChart(compareRes.data || []) } async function loadTable() { const res await request.get(/prediction/list, { params: { page: page.value, size: size.value } }) tableData.value res.data.items total.value res.data.total } onMounted(() { loadData() loadTable() pollTimer setInterval(loadData, 5000) }) onUnmounted(() { clearInterval(pollTimer); charts.forEach(c c.dispose()) }) /script style scoped .pred-chart { width: 100%; height: 420px; margin-bottom: 24px; } .pred-residual { height: 360px; } /style