垃圾桶的空闲爆满情况/利用率分析

2024-02-21 18:38

本文主要是介绍垃圾桶的空闲爆满情况/利用率分析,希望对大家解决编程问题提供一定的参考价值,需要的开发者们随着小编来一起学习吧!

满载:
select m.DEVICECODE,m.SYS_KEY,m.GARDENNAME,m.GARBAGETYPE,m.THROWTIME,m.WEIGHT from  
(select DEVICECODE,SYS_KEY,GARDENNAME,GARBAGETYPE,THROWTIME,to_number(WEIGHT) as WEIGHT from TFJL_COPY) m 
where m.WEIGHT BETWEEN 23.2265 and 27.29 order by m.DEVICECODE,m.GARBAGETYPE,m.THROWTIME,m.WEIGHT asc;空闲:
select m.DEVICECODE,m.SYS_KEY,m.GARDENNAME,m.GARBAGETYPE,m.THROWTIME,m.WEIGHT from 
(select DEVICECODE,SYS_KEY,GARDENNAME,GARBAGETYPE,THROWTIME,to_number(WEIGHT) as WEIGHT from TFJL_COPY) m 
where m.WEIGHT BETWEEN 0.2 and 13.745 order by m.DEVICECODE,m.GARBAGETYPE,m.THROWTIME,m.WEIGHT asc;select m.DEVICECODE,m.SYS_KEY,m.GARDENNAME,m.GARBAGETYPE,m.THROWTIME,m.WEIGHT,
row_number() over(partition by m.GARDENNAME,m.THROWTIME order by m.WEIGHT desc) from 
(select DEVICECODE,SYS_KEY,GARDENNAME,GARBAGETYPE,THROWTIME,to_number(WEIGHT) as WEIGHT from TFJL_COPY) m 
order by m.DEVICECODE,m.SYS_KEY,m.GARBAGETYPE,m.THROWTIME,m.WEIGHT asc;select m.SYS_KEY,m.GARDENNAME,m.GARBAGETYPE,m.THROWTIME,m.WEIGHT,
row_number() over(partition by m.GARDENNAME,m.THROWTIME order by m.WEIGHT desc) from 
(select SYS_KEY,GARDENNAME,GARBAGETYPE,THROWTIME,to_number(WEIGHT) as WEIGHT from TFJL_COPY) m 
order by m.GARDENNAME,m.GARBAGETYPE,m.SYS_KEY,m.THROWTIME,m.WEIGHT asc;按照垃圾分类求重量最大值、最小值、空闲、满载:
select p.GARBAGETYPE,max(p.WEIGHT) as zd,min(p.WEIGHT) as zx,((max(p.WEIGHT)+min(p.WEIGHT))*0.5) as kx,(0.85*max(p.WEIGHT)+0.15*min(p.WEIGHT)) as mz from 
(select SYS_KEY,GARDENNAME,GARBAGETYPE,THROWTIME,to_number(WEIGHT) as WEIGHT from TFJL_COPY) p
GROUP BY p.GARBAGETYPE;按照垃圾分类求重量满载:
select m.DEVICECODE,m.SYS_KEY,m.GARDENNAME,m.GARBAGETYPE,m.THROWTIME,m.WEIGHT from  
(select DEVICECODE,SYS_KEY,GARDENNAME,GARBAGETYPE,THROWTIME,to_number(WEIGHT) as WEIGHT from TFJL_COPY) m,
(select p.GARBAGETYPE,max(p.WEIGHT) as zd,min(p.WEIGHT) as zx,((max(p.WEIGHT)+min(p.WEIGHT))*0.5) as kx,
(0.85*max(p.WEIGHT)+0.15*min(p.WEIGHT)) as mz from 
(select SYS_KEY,GARDENNAME,GARBAGETYPE,THROWTIME,to_number(WEIGHT) as WEIGHT from TFJL_COPY) p
GROUP BY p.GARBAGETYPE) n  
where m.GARBAGETYPE = n.GARBAGETYPE and m.WEIGHT BETWEEN n.mz and n.zd order by m.DEVICECODE,m.GARBAGETYPE,m.THROWTIME,m.WEIGHT asc;按照垃圾分类求重量空闲:
select m.DEVICECODE,m.SYS_KEY,m.GARDENNAME,m.GARBAGETYPE,m.THROWTIME,m.WEIGHT from  
(select DEVICECODE,SYS_KEY,GARDENNAME,GARBAGETYPE,THROWTIME,to_number(WEIGHT) as WEIGHT from TFJL_COPY) m,
(select p.GARBAGETYPE,max(p.WEIGHT) as zd,min(p.WEIGHT) as zx,((max(p.WEIGHT)+min(p.WEIGHT))*0.5) as kx,
(0.85*max(p.WEIGHT)+0.15*min(p.WEIGHT)) as mz from 
(select SYS_KEY,GARDENNAME,GARBAGETYPE,THROWTIME,to_number(WEIGHT) as WEIGHT from TFJL_COPY) p
GROUP BY p.GARBAGETYPE) n  
where m.GARBAGETYPE = n.GARBAGETYPE and m.WEIGHT BETWEEN n.zx and n.kx order by m.DEVICECODE,m.GARBAGETYPE,m.THROWTIME,m.WEIGHT asc;求满载次数:
select q.DEVICECODE,q.GARDENNAME,q.GARBAGETYPE,q.THROWTIME,count(*) as mz_cs from 
(select m.DEVICECODE,m.SYS_KEY,m.GARDENNAME,m.GARBAGETYPE,m.THROWTIME,m.WEIGHT from  
(select DEVICECODE,SYS_KEY,GARDENNAME,GARBAGETYPE,THROWTIME,to_number(WEIGHT) as WEIGHT from TFJL_COPY) m,
(select p.GARBAGETYPE,max(p.WEIGHT) as zd,min(p.WEIGHT) as zx,((max(p.WEIGHT)+min(p.WEIGHT))*0.5) as kx,
(0.85*max(p.WEIGHT)+0.15*min(p.WEIGHT)) as mz from 
(select SYS_KEY,GARDENNAME,GARBAGETYPE,THROWTIME,to_number(WEIGHT) as WEIGHT from TFJL_COPY) p
GROUP BY p.GARBAGETYPE) n  
where m.GARBAGETYPE = n.GARBAGETYPE and m.WEIGHT BETWEEN n.mz and n.zd order by m.DEVICECODE,m.GARBAGETYPE,m.THROWTIME,m.WEIGHT asc) q 
GROUP BY q.DEVICECODE,q.GARDENNAME,q.GARBAGETYPE,q.THROWTIME order by q.DEVICECODE;
求空闲次数:
select q.DEVICECODE,q.GARDENNAME,q.GARBAGETYPE,q.THROWTIME,count(*) as kx_cs from 
(select m.DEVICECODE,m.SYS_KEY,m.GARDENNAME,m.GARBAGETYPE,m.THROWTIME,m.WEIGHT from  
(select DEVICECODE,SYS_KEY,GARDENNAME,GARBAGETYPE,THROWTIME,to_number(WEIGHT) as WEIGHT from TFJL_COPY) m,
(select p.GARBAGETYPE,max(p.WEIGHT) as zd,min(p.WEIGHT) as zx,((max(p.WEIGHT)+min(p.WEIGHT))*0.5) as kx,
(0.85*max(p.WEIGHT)+0.15*min(p.WEIGHT)) as mz from 
(select SYS_KEY,GARDENNAME,GARBAGETYPE,THROWTIME,to_number(WEIGHT) as WEIGHT from TFJL_COPY) p
GROUP BY p.GARBAGETYPE) n  
where m.GARBAGETYPE = n.GARBAGETYPE and m.WEIGHT BETWEEN n.zx and n.kx order by m.DEVICECODE,m.GARBAGETYPE,m.THROWTIME,m.WEIGHT asc) q
GROUP BY q.DEVICECODE,q.GARDENNAME,q.GARBAGETYPE,q.THROWTIME order by q.DEVICECODE求一个月内空闲次数:
select q.DEVICECODE,q.GARDENNAME,q.GARBAGETYPE,q.THROWTIME,count(*) as kx_cs from 
(select m.DEVICECODE,m.SYS_KEY,m.GARDENNAME,m.GARBAGETYPE,m.THROWTIME,m.WEIGHT from  
(select DEVICECODE,SYS_KEY,GARDENNAME,GARBAGETYPE,THROWTIME,to_number(WEIGHT) as WEIGHT from TFJL_COPY) m,
(select p.GARBAGETYPE,max(p.WEIGHT) as zd,min(p.WEIGHT) as zx,((max(p.WEIGHT)+min(p.WEIGHT))*0.5) as kx,
(0.85*max(p.WEIGHT)+0.15*min(p.WEIGHT)) as mz from 
(select SYS_KEY,GARDENNAME,GARBAGETYPE,THROWTIME,to_number(WEIGHT) as WEIGHT from TFJL_COPY) p
GROUP BY p.GARBAGETYPE) n  
where m.GARBAGETYPE = n.GARBAGETYPE and m.WEIGHT BETWEEN n.zx and n.kx order by m.DEVICECODE,m.GARBAGETYPE,m.THROWTIME,m.WEIGHT asc) q
GROUP BY q.DEVICECODE,q.GARDENNAME,q.GARBAGETYPE,q.THROWTIME having substr(q.THROWTIME,1,7) = substr(TO_CHAR(sysdate,'yyyy-mm-dd hh24:mi:ss'),1,7);求一周内空闲次数:
select q.DEVICECODE,q.GARDENNAME,q.GARBAGETYPE,q.THROWTIME,count(*) as kx_cs from 
(select m.DEVICECODE,m.SYS_KEY,m.GARDENNAME,m.GARBAGETYPE,m.THROWTIME,m.WEIGHT from  
(select DEVICECODE,SYS_KEY,GARDENNAME,GARBAGETYPE,THROWTIME,to_number(WEIGHT) as WEIGHT from TFJL_COPY) m,
(select p.GARBAGETYPE,max(p.WEIGHT) as zd,min(p.WEIGHT) as zx,((max(p.WEIGHT)+min(p.WEIGHT))*0.5) as kx,
(0.85*max(p.WEIGHT)+0.15*min(p.WEIGHT)) as mz from 
(select SYS_KEY,GARDENNAME,GARBAGETYPE,THROWTIME,to_number(WEIGHT) as WEIGHT from TFJL_COPY) p
GROUP BY p.GARBAGETYPE) n  
where m.GARBAGETYPE = n.GARBAGETYPE and m.WEIGHT BETWEEN n.zx and n.kx order by m.DEVICECODE,m.GARBAGETYPE,m.THROWTIME,m.WEIGHT asc) q
GROUP BY q.DEVICECODE,q.GARDENNAME,q.GARBAGETYPE,q.THROWTIME 
having trunc(TO_DATE(THROWTIME, 'yyyy-mm-dd hh24:mi:ss'))<=trunc(Sysdate) and trunc(TO_DATE(THROWTIME, 'yyyy-mm-dd hh24:mi:ss'))>= trunc(sysdate-7);求上一周的数据
Select * From TFJL_COPY a Where trunc(TO_DATE(THROWTIME, 'yyyy-mm-dd hh24:mi:ss'))>=trunc(Sysdate,'d')
AND trunc(TO_DATE(THROWTIME, 'yyyy-mm-dd hh24:mi:ss'))<= Next_day(trunc(sysdate,'d'),7);求当前日期前七天的数据
Select * From TFJL_COPY a Where trunc(TO_DATE(THROWTIME, 'yyyy-mm-dd hh24:mi:ss'))<=trunc(Sysdate) 
and trunc(TO_DATE(THROWTIME, 'yyyy-mm-dd hh24:mi:ss'))>= trunc(sysdate-7);删除多字段重复数据
DELETE FROM TFJL_COPY_COPY a
WHERE (a.DEVICECODE, a.THROWTIME,a.GARBAGETYPE,a.WEIGHT) IN 
(SELECT DEVICECODE,THROWTIME,GARBAGETYPE,WEIGHT FROM TFJL_COPY_COPY GROUP BY DEVICECODE,THROWTIME,GARBAGETYPE,WEIGHT HAVING COUNT(*) > 1)
AND ROWID NOT IN (SELECT MIN(ROWID) FROM TFJL_COPY_COPY GROUP BY DEVICECODE,THROWTIME,GARBAGETYPE,WEIGHT HAVING COUNT(*) > 1);查找多字段重复数据
SELECT * FROM TFJL_COPY_COPY a WHERE (a.DEVICECODE, a.THROWTIME,a.GARBAGETYPE,a.WEIGHT) IN (SELECT DEVICECODE,THROWTIME,GARBAGETYPE,WEIGHT
FROM TFJL_COPY_COPY GROUP BY DEVICECODE,THROWTIME,GARBAGETYPE,WEIGHT HAVING COUNT(*) > 1);

 

这篇关于垃圾桶的空闲爆满情况/利用率分析的文章就介绍到这儿,希望我们推荐的文章对编程师们有所帮助!



http://www.chinasem.cn/article/732700

相关文章

Android 缓存日志Logcat导出与分析最佳实践

《Android缓存日志Logcat导出与分析最佳实践》本文全面介绍AndroidLogcat缓存日志的导出与分析方法,涵盖按进程、缓冲区类型及日志级别过滤,自动化工具使用,常见问题解决方案和最佳实... 目录android 缓存日志(Logcat)导出与分析全攻略为什么要导出缓存日志?按需过滤导出1. 按

Linux中的HTTPS协议原理分析

《Linux中的HTTPS协议原理分析》文章解释了HTTPS的必要性:HTTP明文传输易被篡改和劫持,HTTPS通过非对称加密协商对称密钥、CA证书认证和混合加密机制,有效防范中间人攻击,保障通信安全... 目录一、什么是加密和解密?二、为什么需要加密?三、常见的加密方式3.1 对称加密3.2非对称加密四、

MySQL中读写分离方案对比分析与选型建议

《MySQL中读写分离方案对比分析与选型建议》MySQL读写分离是提升数据库可用性和性能的常见手段,本文将围绕现实生产环境中常见的几种读写分离模式进行系统对比,希望对大家有所帮助... 目录一、问题背景介绍二、多种解决方案对比2.1 原生mysql主从复制2.2 Proxy层中间件:ProxySQL2.3

python使用Akshare与Streamlit实现股票估值分析教程(图文代码)

《python使用Akshare与Streamlit实现股票估值分析教程(图文代码)》入职测试中的一道题,要求:从Akshare下载某一个股票近十年的财务报表包括,资产负债表,利润表,现金流量表,保存... 目录一、前言二、核心知识点梳理1、Akshare数据获取2、Pandas数据处理3、Matplotl

python panda库从基础到高级操作分析

《pythonpanda库从基础到高级操作分析》本文介绍了Pandas库的核心功能,包括处理结构化数据的Series和DataFrame数据结构,数据读取、清洗、分组聚合、合并、时间序列分析及大数据... 目录1. Pandas 概述2. 基本操作:数据读取与查看3. 索引操作:精准定位数据4. Group

MySQL中EXISTS与IN用法使用与对比分析

《MySQL中EXISTS与IN用法使用与对比分析》在MySQL中,EXISTS和IN都用于子查询中根据另一个查询的结果来过滤主查询的记录,本文将基于工作原理、效率和应用场景进行全面对比... 目录一、基本用法详解1. IN 运算符2. EXISTS 运算符二、EXISTS 与 IN 的选择策略三、性能对比

MySQL 内存使用率常用分析语句

《MySQL内存使用率常用分析语句》用户整理了MySQL内存占用过高的分析方法,涵盖操作系统层确认及数据库层bufferpool、内存模块差值、线程状态、performance_schema性能数据... 目录一、 OS层二、 DB层1. 全局情况2. 内存占js用详情最近连续遇到mysql内存占用过高导致

深度解析Nginx日志分析与499状态码问题解决

《深度解析Nginx日志分析与499状态码问题解决》在Web服务器运维和性能优化过程中,Nginx日志是排查问题的重要依据,本文将围绕Nginx日志分析、499状态码的成因、排查方法及解决方案展开讨论... 目录前言1. Nginx日志基础1.1 Nginx日志存放位置1.2 Nginx日志格式2. 499

Olingo分析和实践之EDM 辅助序列化器详解(最佳实践)

《Olingo分析和实践之EDM辅助序列化器详解(最佳实践)》EDM辅助序列化器是ApacheOlingoOData框架中无需完整EDM模型的智能序列化工具,通过运行时类型推断实现灵活数据转换,适用... 目录概念与定义什么是 EDM 辅助序列化器?核心概念设计目标核心特点1. EDM 信息可选2. 智能类

Olingo分析和实践之OData框架核心组件初始化(关键步骤)

《Olingo分析和实践之OData框架核心组件初始化(关键步骤)》ODataSpringBootService通过初始化OData实例和服务元数据,构建框架核心能力与数据模型结构,实现序列化、URI... 目录概述第一步:OData实例创建1.1 OData.newInstance() 详细分析1.1.1