每日0点执行删除sub
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@ -0,0 +1,82 @@
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package com.agri.quartz.task;
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import org.slf4j.Logger;
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import org.slf4j.LoggerFactory;
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import org.springframework.data.redis.connection.RedisConnection;
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import org.springframework.data.redis.connection.RedisConnectionFactory;
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import org.springframework.data.redis.core.Cursor;
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import org.springframework.data.redis.core.ScanOptions;
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import org.springframework.stereotype.Component;
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import javax.annotation.Resource;
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import java.util.ArrayList;
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import java.util.List;
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@Component("agriTask")
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public class AgriTask {
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@Resource
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private RedisConnectionFactory redisConnectionFactory;
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private final static Logger log = LoggerFactory.getLogger(AgriTask.class);
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/**
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* 每日零点执行
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* 删除 redis 所有sub: 开头的数据
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*/
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public void clearInvalidCache() {
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log.info("===== 开始执行Redis sub: 键清理任务 =====");
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RedisConnection connection = null;
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Cursor<byte[]> cursor = null;
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int deletedCount = 0;
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try {
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// 获取Redis连接
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connection = redisConnectionFactory.getConnection();
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// 配置SCAN参数:匹配sub:*,分批遍历(每次1000条)
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ScanOptions scanOptions = ScanOptions.scanOptions()
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.match("sub:*")
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.count(1000)
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.build();
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// 遍历所有匹配的键
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cursor = connection.scan(scanOptions);
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List<byte[]> batchKeys = new ArrayList<>(1000); // 批量删除缓冲区
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while (cursor.hasNext()) {
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batchKeys.add(cursor.next());
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// 每攒1000个键批量删除(减少网络交互)
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if (batchKeys.size() >= 1000) {
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deletedCount += batchKeys.size();
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connection.del(batchKeys.toArray(new byte[0][]));
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log.info("批量删除 {} 个sub: 键", batchKeys.size());
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batchKeys.clear();
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}
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}
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// 删除剩余的键
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if (!batchKeys.isEmpty()) {
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deletedCount += batchKeys.size();
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connection.del(batchKeys.toArray(new byte[0][]));
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log.info("批量删除剩余 {} 个sub: 键", batchKeys.size());
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}
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log.info("===== Redis sub: 键清理完成,总计删除 {} 个键 =====", deletedCount);
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} catch (Exception e) {
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log.error("Redis sub: 键清理失败", e);
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// 可选:添加告警逻辑(如钉钉/邮件通知)
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} finally {
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// 关闭游标和连接
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if (cursor != null) {
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try {
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cursor.close();
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} catch (Exception e) {
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log.error("关闭游标失败", e);
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}
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}
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if (connection != null) {
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connection.close();
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}
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}
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}
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}
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@ -30,7 +30,7 @@ import java.util.stream.Collectors;
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/**
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* 卷膜自动模式定时检查
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* 每五分钟执行一次
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* 每五分钟执行一次 每日时间区间内执行
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* 1、查询大棚列表开启自动模式的大棚,获取imei
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* 2、根据imei查询dtu_data最后一分钟最后一条温度数据
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* 3、查询参数设置获取 风口总长 预留风口长度 在条件内开启指定风口,第一次开 预留风口+条件内设定的风口长度 还是得存日志、、、
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@ -183,6 +183,7 @@ public class RollerAutoTask {
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//判断温度是否在适宜温度内
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TempCommandStatus tempCommandStatus = TempJudgeUtil.judgeTempCommand(currentTemp, term.getTemp());
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// todo 开关指令需要通知用户 推送主题 && 更新数据 前端重新请求消息表
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if (tempCommandStatus == TempCommandStatus.OPEN) {
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// 开指令
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sendOpenCommand(imei,agriName, roller, isFirstRun, term.getVent(), reservedLen);
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