merged myems-cleaning
13621160019@163.com
parent
3adb582972
commit
2265b440ce
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MIT License
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Copyright (c) 2020 MyEMS
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Copyright (c) 2021 MyEMS
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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MIT License
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Copyright (c) 2021 MyEMS
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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# myems-cleaning
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MyEMS Cleaning Service
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MyEMS 数据清洗服务
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### Introduction
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This service is a component of MyEMS and it cleans the historical data.
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[](https://app.codacy.com/gh/myems/myems-cleaning?utm_source=github.com&utm_medium=referral&utm_content=myems/myems-cleaning&utm_campaign=Badge_Grade)
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[](https://scrutinizer-ci.com/g/myems/myems-cleaning/?branch=master)
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[](https://codeclimate.com/github/myems/myems-cleaning/maintainability)
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[](https://lgtm.com/projects/g/myems/myems-cleaning/alerts/)
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### Prerequisites
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mysql.connector
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### Installation
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Download and install MySQL Connector:
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```
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$ cd ~/tools
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$ wget https://dev.mysql.com/get/Downloads/Connector-Python/mysql-connector-python-8.0.20.tar.gz
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$ tar xzf mysql-connector-python-8.0.20.tar.gz
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$ cd ~/tools/mysql-connector-python-8.0.20
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$ sudo python3 setup.py install
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```
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Install myems-cleaning service
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```bash
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$ cd ~
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$ git clone https://github.com/myems.git
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$ sudo git checkout master (or the latest release tag)
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$ sudo cp -R ~/myems/myems-cleaning /myems-cleaning
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$ cd /myems-cleaning
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```
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Open config file and edit database configuration
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```bash
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$ sudo nano config.py
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```
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Setup systemd service:
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```bash
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$ sudo cp myems-cleaning.service /lib/systemd/system/
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```
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Enable the service:
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```bash
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$ sudo systemctl enable myems-cleaning.service
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```
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Start the service:
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```bash
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$ sudo systemctl start myems-cleaning.service
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```
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### References
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1. https://myems.io
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2. https://dev.mysql.com/doc/connector-python/en/
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import mysql.connector
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import config
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import time
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from datetime import datetime, timedelta
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import schedule
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def job(logger):
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cnx = None
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cursor = None
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try:
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cnx = mysql.connector.connect(**config.myems_historical_db)
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cursor = cnx.cursor()
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except Exception as e:
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logger.error("Error in clean analog value process " + str(e))
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if cursor:
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cursor.close()
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if cnx:
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cnx.close()
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return
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expired_utc = datetime.utcnow() - timedelta(days=config.live_in_days)
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try:
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cursor.execute(" DELETE FROM tbl_analog_value WHERE utc_date_time < %s ", (expired_utc,))
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cnx.commit()
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except Exception as e:
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logger.error("Error in delete_expired_trend process " + str(e))
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finally:
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if cursor:
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cursor.close()
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if cnx:
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cnx.close()
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logger.info("Deleted trend before date time in UTC: " + expired_utc.isoformat()[0:19])
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def process(logger):
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if config.is_debug:
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# run the job immediately
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job(logger)
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return
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schedule.every(8).hours.do(job, logger)
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while True:
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schedule.run_pending()
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time.sleep(60)
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import mysql.connector
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import config
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import time
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from datetime import datetime, timedelta
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import schedule
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def job(logger):
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cnx = None
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cursor = None
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try:
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cnx = mysql.connector.connect(**config.myems_historical_db)
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cursor = cnx.cursor()
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except Exception as e:
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logger.error("Error in clean digital value process " + str(e))
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if cursor:
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cursor.close()
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if cnx:
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cnx.close()
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return
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expired_utc = datetime.utcnow() - timedelta(days=config.live_in_days)
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try:
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cursor.execute(" DELETE FROM tbl_digital_value WHERE utc_date_time < %s ", (expired_utc,))
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cnx.commit()
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except Exception as e:
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logger.error("Error in delete_expired_trend process " + str(e))
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finally:
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if cursor:
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cursor.close()
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if cnx:
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cnx.close()
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logger.info("Deleted trend before date time in UTC: " + expired_utc.isoformat()[0:19])
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def process(logger):
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if config.is_debug:
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# run the job immediately
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job(logger)
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return
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schedule.every(8).hours.do(job, logger)
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while True:
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schedule.run_pending()
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time.sleep(60)
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import mysql.connector
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import config
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import time
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from datetime import datetime
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########################################################################################################################
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# This procedure will find and tag the bad energy values.
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#
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# Step 1: get the time slot to clean.
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# Step 2: check bad case class 1 with high limits and low limits.
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# Step 3: check bad case class 2 which is in concave shape model.
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# Step 4: tag the is_bad property of energy values.
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########################################################################################################################
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def process(logger):
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while True:
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# the outermost loop to reconnect server if there is a connection error
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cnx = None
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cursor = None
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try:
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cnx = mysql.connector.connect(**config.myems_historical_db)
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cursor = cnx.cursor()
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except Exception as e:
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logger.error("Error at the begin of clean_energy_value.process " + str(e))
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if cursor:
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cursor.close()
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if cnx:
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cnx.close()
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time.sleep(60)
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continue
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# Note:
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# the default value of unchecked values' is_bad property is NULL
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# if a value is checked and the result is bad then is_bad would be set to TRUE
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# else if a value is checked and the result is good then is_bad would be set to FALSE
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################################################################################################################
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# Step 1: get the time slot to clean.
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################################################################################################################
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min_datetime = None
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max_datetime = None
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try:
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query = (" SELECT MIN(utc_date_time), MAX(utc_date_time) "
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" FROM tbl_energy_value "
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" WHERE is_bad IS NULL ")
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cursor.execute(query, ())
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row_datetime = cursor.fetchone()
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if row_datetime is not None and len(row_datetime) == 2 and \
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isinstance(row_datetime[0], datetime) and isinstance(row_datetime[1], datetime):
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min_datetime = row_datetime[0]
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max_datetime = row_datetime[1]
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except Exception as e:
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logger.error("Error in Step 1 of clean_energy_value.process " + str(e))
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if cursor:
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cursor.close()
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if cnx:
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cnx.close()
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time.sleep(60)
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continue
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print("min_datetime: " + min_datetime.isoformat()[0:19])
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print("max_datetime: " + max_datetime.isoformat()[0:19])
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################################################################################################################
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# Step 2: check bad case class 1 with high limits and low limits.
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################################################################################################################
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################################################################################################################
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# bad case 1.1
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# id point_id utc_date_time actual_value is_bad (expected)
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# 104814811 3333 2018-01-31 16:45:04 115603.0078125 good
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# 104814588 3333 2018-01-31 16:44:00 115603.0078125 good
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# 104815007 3333 2018-01-31 16:46:09 1.832278249396618e21 bad
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# 104815226 3333 2018-01-31 16:47:13 1.832278249396618e21 bad
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# 104815423 3333 2018-01-31 16:48:17 1.832278249396618e21 bad
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# 104815643 3333 2018-01-31 16:49:22 1.832278249396618e21 bad
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# 104815820 3333 2018-01-31 16:50:26 1.832278249396618e21 bad
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# 104816012 3333 2018-01-31 16:51:30 1.832278249396618e21 bad
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# 104816252 3333 2018-01-31 16:52:34 1.832278249396618e21 bad
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# 104816446 3333 2018-01-31 16:53:38 1.832278249396618e21 bad
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# 104816667 3333 2018-01-31 16:54:43 1.832278249396618e21 bad
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# 104816860 3333 2018-01-31 16:55:47 1.832278249396618e21 bad
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# 104817065 3333 2018-01-31 16:56:51 1.832278249396618e21 bad
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# 104817284 3333 2018-01-31 16:57:55 1.832278249396618e21 bad
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# 104817482 3333 2018-01-31 16:58:59 1.832278249396618e21 bad
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# 104817723 3333 2018-01-31 17:00:04 1.832278249396618e21 bad
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# 104817940 3333 2018-01-31 17:01:08 115749.0078125 good
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# 104818142 3333 2018-01-31 17:02:11 115749.0078125 good
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# 104818380 3333 2018-01-31 17:03:16 115749.0078125 good
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# 104818596 3333 2018-01-31 17:04:20 115749.0078125 good
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################################################################################################################
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################################################################################################################
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# bad case 1.2:
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# id point_id utc_date_time actual_value is_bad (expected)
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# 3333 2018-01-31 17:27:53 115823.0078125 good
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# 3333 2018-01-31 17:28:57 115823.0078125 good
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# 3333 2018-01-31 17:30:02 115823.0078125 good
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# 3333 2018-01-31 17:31:06 115823.0078125 good
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# 3333 2018-01-31 17:32:11 0 bad
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# 3333 2018-01-31 17:33:15 0 bad
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# 3333 2018-01-31 17:34:19 0 bad
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# 3333 2018-01-31 17:35:24 0 bad
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# 3333 2018-01-31 17:36:28 0 bad
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# 3333 2018-01-31 17:37:32 0 bad
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# 3333 2018-01-31 17:38:36 0 bad
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# 3333 2018-01-31 17:39:41 0 bad
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# 3333 2018-01-31 17:40:44 0 bad
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# 3333 2018-01-31 17:41:49 0 bad
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# 3333 2018-01-31 17:43:57 0 bad
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# 3333 2018-01-31 17:42:53 0 bad
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# 3333 2018-01-31 17:45:01 0 bad
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# 3333 2018-01-31 17:46:06 0 bad
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# 3333 2018-01-31 17:47:10 0 bad
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# 3333 2018-01-31 17:48:14 115969.0078125 good
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# 3333 2018-01-31 17:49:18 115969.0078125 good
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# 3333 2018-01-31 17:50:22 115969.0078125 good
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################################################################################################################
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################################################################################################################
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# bad case 1.3:
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# id point_id utc_date_time actual_value is_bad (expected)
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# 3333 2018-02-04 07:00:38 139968 good
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# 3333 2018-02-04 07:01:42 139968 good
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# 3333 2018-02-04 07:03:54 -7.068193740872921e-3 bad
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# 3333 2018-02-04 07:04:58 -7.068193740872921e-3 bad
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# 3333 2018-02-04 07:06:03 -7.068193740872921e-3 bad
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# 3333 2018-02-04 07:07:06 -7.068193740872921e-3 bad
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# 3333 2018-02-04 07:08:10 -7.068193740872921e-3 bad
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# 3333 2018-02-04 07:09:13 -7.068193740872921e-3 bad
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# 3333 2018-02-04 07:10:17 -7.068193740872921e-3 bad
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# 3333 2018-02-04 07:11:21 -7.068193740872921e-3 bad
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# 3333 2018-02-04 07:12:25 -7.068193740872921e-3 bad
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# 3333 2018-02-04 07:13:29 -7.068193740872921e-3 bad
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# 3333 2018-02-04 07:14:33 -7.068193740872921e-3 bad
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# 3333 2018-02-04 07:15:37 -7.068193740872921e-3 bad
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# 3333 2018-02-04 07:16:41 -7.068193740872921e-3 bad
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# 3333 2018-02-04 07:17:45 140114 good
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# 3333 2018-02-04 07:18:49 140114 good
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# 3333 2018-02-04 07:19:53 140114 good
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################################################################################################################
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################################################################################################################
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# bad case 1.4:
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# id point_id utc_date_time actual_value is_bad (expected)
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# 3333 2018-02-08 01:16:38 165746.015625 good
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# 3333 2018-02-08 01:15:34 165746.015625 good
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# 3333 2018-02-08 01:14:30 165746.015625 good
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# 3333 2018-02-08 01:13:27 0.00303281145170331 bad
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# 3333 2018-02-08 01:12:22 0.00303281145170331 bad
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# 3333 2018-02-08 01:11:19 0.00303281145170331 bad
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# 3333 2018-02-08 01:10:15 0.00303281145170331 bad
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# 3333 2018-02-08 01:09:11 0.00303281145170331 bad
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# 3333 2018-02-08 01:08:06 0.00303281145170331 bad
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# 3333 2018-02-08 01:07:02 0.00303281145170331 bad
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# 3333 2018-02-08 01:05:58 0.00303281145170331 bad
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# 3333 2018-02-08 01:04:54 0.00303281145170331 bad
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# 3333 2018-02-08 01:03:50 0.00303281145170331 bad
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# 3333 2018-02-08 01:02:46 0.00303281145170331 bad
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# 3333 2018-02-08 01:01:42 0.00303281145170331 bad
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# 3333 2018-02-08 01:00:39 0.00303281145170331 bad
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# 3333 2018-02-08 00:59:34 0.00303281145170331 bad
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# 3333 2018-02-08 00:58:31 0.00303281145170331 bad
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# 3333 2018-02-08 00:57:27 165599.015625 good
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# 3333 2018-02-08 00:56:23 165599.015625 good
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# 3333 2018-02-08 00:55:20 165599.015625 good
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# 3333 2018-02-08 00:54:16 165599.015625 good
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################################################################################################################
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print("Step 2: Processing bad case 1.x")
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try:
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cnx_system = mysql.connector.connect(**config.myems_system_db)
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cursor_system = cnx_system.cursor(dictionary=True)
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query = (" SELECT id, high_limit, low_limit "
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" FROM tbl_points "
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" WHERE object_type='ENERGY_VALUE'")
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cursor_system.execute(query)
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rows_points = cursor_system.fetchall()
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point_dict = dict()
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if rows_points is not None and len(rows_points) > 0:
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for row in rows_points:
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point_dict[row['id']] = {"high_limit": row['high_limit'],
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"low_limit": row['low_limit']}
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except Exception as e:
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logger.error("Error in step 2.1 of clean_energy_value.process " + str(e))
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time.sleep(60)
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continue
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finally:
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if cursor_system:
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cursor_system.close()
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if cnx_system:
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cnx_system.close()
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try:
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query = (" SELECT id, point_id, actual_value "
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" FROM tbl_energy_value "
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" WHERE utc_date_time >= %s AND utc_date_time <= %s AND is_bad IS NOT TRUE ")
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cursor.execute(query, (min_datetime, max_datetime,))
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rows_energy_values = cursor.fetchall()
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except Exception as e:
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logger.error("Error in step 2.2 of clean_energy_value.process " + str(e))
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if cursor:
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cursor.close()
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if cnx:
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cnx.close()
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time.sleep(60)
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continue
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bad_list = list()
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if rows_energy_values is not None and len(rows_energy_values) > 0:
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for row_energy_value in rows_energy_values:
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point_id = row_energy_value[1]
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actual_value = row_energy_value[2]
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point = point_dict.get(point_id, None)
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if point is None or actual_value > point['high_limit'] or actual_value < point['low_limit']:
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bad_list.append(row_energy_value[0])
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print('bad list: ' + str(bad_list))
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if len(bad_list) > 0:
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try:
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update = (" UPDATE tbl_energy_value "
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" SET is_bad = TRUE "
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" WHERE id IN (" + ', '.join(map(str, bad_list)) + ")")
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cursor.execute(update, )
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cnx.commit()
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except Exception as e:
|
||||
logger.error("Error in step 2.3 of clean_energy_value.process " + str(e))
|
||||
if cursor:
|
||||
cursor.close()
|
||||
if cnx:
|
||||
cnx.close()
|
||||
time.sleep(60)
|
||||
continue
|
||||
|
||||
################################################################################################################
|
||||
# Step 3: check bad case class 2 which is in concave shape model.
|
||||
################################################################################################################
|
||||
print("Step 3: Processing bad case 2.x")
|
||||
################################################################################################################
|
||||
# bad case 2.1
|
||||
# id point_id utc_date_time actual_value is_bad (expected)
|
||||
# 3333 2018-02-05 04:55:45 146129.015 good
|
||||
# 3333 2018-02-05 04:56:49 146129.015 good
|
||||
# 3333 2018-02-05 04:57:54 146129.015 good
|
||||
# 3333 2018-02-05 05:22:52 145693.015 bad
|
||||
# 3333 2018-02-05 05:25:01 146274 good
|
||||
# 3333 2018-02-05 05:26:03 146274 good
|
||||
# 3333 2018-02-05 05:27:05 146274 good
|
||||
# 3333 2018-02-05 05:29:30 146274 good
|
||||
################################################################################################################
|
||||
|
||||
################################################################################################################
|
||||
# bad case 2.2
|
||||
# id point_id utc_date_time actual_value is_bad (expected)
|
||||
# 3321 2018-05-15 15:09:54 33934040 good
|
||||
# 3321 2018-05-15 15:08:51 33934040 good
|
||||
# 3321 2018-05-15 15:07:47 33934040 good
|
||||
# 3321 2018-05-15 15:06:44 33934040 good
|
||||
# 3321 2018-05-15 15:05:40 33934040 good
|
||||
# 3321 2018-05-15 15:04:36 33934040 good
|
||||
# 3321 2018-05-15 09:09:00 33928880 bad
|
||||
# 3321 2018-05-15 09:05:23 33933568 good
|
||||
# 3321 2018-05-15 09:04:20 33933568 good
|
||||
# 3321 2018-05-15 09:03:16 33933568 good
|
||||
# 3321 2018-05-15 09:02:13 33933560 good
|
||||
# 3321 2018-05-15 09:01:09 33933560 good
|
||||
# 3321 2018-05-15 09:00:04 33933560 good
|
||||
################################################################################################################
|
||||
|
||||
################################################################################################################
|
||||
# bad case 2.3
|
||||
# id point_id utc_date_time actual_value is_bad (expected)
|
||||
# 554 2018-05-19 15:32:52 24001 good
|
||||
# 554 2018-05-19 15:30:45 24001 good
|
||||
# 554 2018-05-19 15:28:39 24001 good
|
||||
# 554 2018-05-19 15:26:32 24001 good
|
||||
# 554 2018-05-19 15:24:25 24001 good
|
||||
# 554 2018-05-19 15:22:18 24001 good
|
||||
# 554 2018-05-19 15:20:10 24001 good
|
||||
# 554 2018-05-19 15:18:04 24001 good
|
||||
# 554 2018-05-19 15:15:58 24001 good
|
||||
# 554 2018-05-19 15:13:51 24001 good
|
||||
# 554 2018-05-19 15:11:43 24001 good
|
||||
# 554 2018-05-19 15:09:37 24001 good
|
||||
# 554 2018-05-19 15:07:29 24000 good
|
||||
# 554 2018-05-19 15:05:22 23000 bad
|
||||
# 554 2018-05-19 15:03:14 23999 good
|
||||
# 554 2018-05-19 15:01:06 23999 good
|
||||
# 554 2018-05-19 14:58:59 23999 good
|
||||
# 554 2018-05-19 14:56:52 23998 good
|
||||
# 554 2018-05-19 14:54:45 23998 good
|
||||
# 554 2018-05-19 14:52:39 23998 good
|
||||
################################################################################################################
|
||||
# todo bad case 2.3.1
|
||||
# "id", "point_id", "utc_date_time", "actual_value", "is_bad" (actual)
|
||||
# 68504700, 2, "2021-01-09 03:40:12.0", 40454414.063, 0
|
||||
# 68507243, 2, "2021-01-09 03:43:12.0", 40454476.563, 0
|
||||
# 68510030, 2, "2021-01-09 03:47:17.0", 40428074.219, 0 ?
|
||||
# 68512573, 2, "2021-01-09 03:50:18.0", 40454621.094, 0
|
||||
# 68515421, 2, "2021-01-09 03:54:23.0", 40454703.125, 0
|
||||
# 68517964, 2, "2021-01-09 03:57:23.0", 40454761.719, 0
|
||||
|
||||
################################################################################################################
|
||||
# bad case 2.4
|
||||
# id point_id utc_date_time actual_value is_bad (expected)
|
||||
# 104373141 3336 2018-01-30 03:04:12 216463.015625 good
|
||||
# 104373337 3336 2018-01-30 03:05:15 216463.015625 good
|
||||
# 104373555 3336 2018-01-30 03:06:20 216463.015625 good
|
||||
# 104373750 3336 2018-01-30 03:07:25 192368.015625 bad
|
||||
# 104373957 3336 2018-01-30 03:08:29 192368.015625 bad
|
||||
# 104374175 3336 2018-01-30 03:09:33 192368.015625 bad
|
||||
# 104374382 3336 2018-01-30 03:10:38 192368.015625 bad
|
||||
# 104374604 3336 2018-01-30 03:11:42 192368.015625 bad
|
||||
# 104374792 3336 2018-01-30 03:12:47 192368.015625 bad
|
||||
# 104375010 3336 2018-01-30 03:13:51 192368.015625 bad
|
||||
# 104375200 3336 2018-01-30 03:14:55 192368.015625 bad
|
||||
# 104375418 3336 2018-01-30 03:16:00 192368.015625 bad
|
||||
# 104375617 3336 2018-01-30 03:17:04 192368.015625 bad
|
||||
# 104375837 3336 2018-01-30 03:18:08 192368.015625 bad
|
||||
# 104376023 3336 2018-01-30 03:19:12 192368.015625 bad
|
||||
# 104376216 3336 2018-01-30 03:20:16 192368.015625 bad
|
||||
# 104376435 3336 2018-01-30 03:21:21 192368.015625 bad
|
||||
# 104376634 3336 2018-01-30 03:22:25 192368.015625 bad
|
||||
# 104376853 3336 2018-01-30 03:23:30 192368.015625 bad
|
||||
# 104377071 3336 2018-01-30 03:24:34 192368.015625 bad
|
||||
# 104377274 3336 2018-01-30 03:25:38 192368.015625 bad
|
||||
# 104377501 3336 2018-01-30 03:26:42 216574.015625 good
|
||||
# 104377714 3336 2018-01-30 03:27:47 216574.015625 good
|
||||
################################################################################################################
|
||||
|
||||
################################################################################################################
|
||||
# bad case 2.5
|
||||
# id point_id utc_date_time actual_value is_bad (expected)
|
||||
# 104370839 3334 2018-01-30 02:52:23 844966.0625 good
|
||||
# 104371064 3334 2018-01-30 02:53:27 844966.0625 good
|
||||
# 104371261 3334 2018-01-30 02:54:32 844966.0625 good
|
||||
# 104371479 3334 2018-01-30 02:55:36 826142.0625 bad
|
||||
# 104371672 3334 2018-01-30 02:56:41 826142.0625 bad
|
||||
# 104371884 3334 2018-01-30 02:57:45 826142.0625 bad
|
||||
# 104372110 3334 2018-01-30 02:58:49 826142.0625 bad
|
||||
# 104372278 3334 2018-01-30 02:59:54 845019.0625 good
|
||||
# 104372512 3334 2018-01-30 03:00:58 845019.0625 good
|
||||
# 104372704 3334 2018-01-30 03:02:03 845019.0625 good
|
||||
################################################################################################################
|
||||
|
||||
################################################################################################################
|
||||
# bad case 2.6
|
||||
# 394084273 1001444 2019-08-22 03:39:44 38969028 good
|
||||
# 394083709 1001444 2019-08-22 03:38:43 38968876 good
|
||||
# 394083145 1001444 2019-08-22 03:37:43 28371884 bad
|
||||
# 394082019 1001444 2019-08-22 03:35:42 28371884 bad
|
||||
# 394081456 1001444 2019-08-22 03:34:42 28371884 bad
|
||||
# 394080892 1001444 2019-08-22 03:33:42 28371884 bad
|
||||
# 394079200 1001444 2019-08-22 03:30:38 28371884 bad
|
||||
# 394077511 1001444 2019-08-22 03:27:37 38968408 good
|
||||
# 394076947 1001444 2019-08-22 03:26:37 38968236 good
|
||||
# 394076384 1001444 2019-08-22 03:25:37 38968060 good
|
||||
################################################################################################################
|
||||
|
||||
################################################################################################################
|
||||
# bad case 2.7
|
||||
# id point_id utc_date_time actual_value is_bad (expected)
|
||||
# 17303260 11 2020-3-15 05:43:52 33600 good
|
||||
# 17303399 11 2020-3-15 05:44:58 33600 good
|
||||
# 17303538 11 2020-3-15 05:46:04 33600 good
|
||||
# 17303677 11 2020-3-15 05:47:10 33500 bad
|
||||
# 17303816 11 2020-3-15 05:48:15 33500 bad
|
||||
# 17303955 11 2020-3-15 05:49:21 33600 good
|
||||
# 17304094 11 2020-3-15 05:50:27 33600 good
|
||||
# 17304233 11 2020-3-15 05:51:33 33600 good
|
||||
################################################################################################################
|
||||
|
||||
try:
|
||||
query = (" SELECT point_id, id, utc_date_time, actual_value "
|
||||
" FROM tbl_energy_value "
|
||||
" WHERE utc_date_time >= %s AND utc_date_time <= %s AND is_bad IS NOT TRUE "
|
||||
" ORDER BY point_id, utc_date_time ")
|
||||
cursor.execute(query, (min_datetime, max_datetime,))
|
||||
rows_energy_values = cursor.fetchall()
|
||||
except Exception as e:
|
||||
logger.error("Error in step 3.1 of clean_energy_value.process " + str(e))
|
||||
if cursor:
|
||||
cursor.close()
|
||||
if cnx:
|
||||
cnx.close()
|
||||
time.sleep(60)
|
||||
continue
|
||||
|
||||
point_value_dict = dict()
|
||||
current_point_value_list = list()
|
||||
current_point_id = 0
|
||||
|
||||
if rows_energy_values is not None and len(rows_energy_values) > 0:
|
||||
for row_energy_value in rows_energy_values:
|
||||
previous_point_id = current_point_id
|
||||
current_point_id = row_energy_value[0]
|
||||
if current_point_id not in point_value_dict.keys():
|
||||
# new point id found
|
||||
# save previous point values
|
||||
if len(current_point_value_list) > 0:
|
||||
point_value_dict[previous_point_id] = current_point_value_list
|
||||
current_point_value_list = list()
|
||||
|
||||
current_point_value_list.append({'id': row_energy_value[1],
|
||||
'actual_value': row_energy_value[3]})
|
||||
else:
|
||||
current_point_value_list.append({'id': row_energy_value[1],
|
||||
'actual_value': row_energy_value[3]})
|
||||
# end of for loop
|
||||
# save rest point values
|
||||
if len(current_point_value_list) > 0:
|
||||
point_value_dict[current_point_id] = current_point_value_list
|
||||
|
||||
bad_list = list()
|
||||
|
||||
for point_id, point_value_list in point_value_dict.items():
|
||||
if len(point_value_list) <= 1:
|
||||
continue
|
||||
elif len(point_value_list) == 2:
|
||||
if point_value_list[1]['actual_value'] < point_value_list[0]['actual_value']:
|
||||
bad_list.append(point_value_list[1]['id'])
|
||||
continue
|
||||
else:
|
||||
base_point_value = point_value_list[0]['actual_value']
|
||||
concave_point_value_list = list()
|
||||
for i in range(len(point_value_list)):
|
||||
if point_value_list[i]['actual_value'] < base_point_value:
|
||||
# candidate concave value found
|
||||
concave_point_value_list.append(point_value_list[i]['id'])
|
||||
else:
|
||||
# normal value found
|
||||
if len(concave_point_value_list) > 0:
|
||||
# save confirmed concave value(s) to bad value(s)
|
||||
bad_list.extend(concave_point_value_list)
|
||||
|
||||
# prepare for next candidate concave value list
|
||||
base_point_value = point_value_list[i]['actual_value']
|
||||
concave_point_value_list.clear()
|
||||
continue
|
||||
|
||||
print('bad list: ' + str(bad_list))
|
||||
if len(bad_list) > 0:
|
||||
try:
|
||||
update = (" UPDATE tbl_energy_value "
|
||||
" SET is_bad = TRUE "
|
||||
" WHERE id IN (" + ', '.join(map(str, bad_list)) + ")")
|
||||
cursor.execute(update, )
|
||||
cnx.commit()
|
||||
except Exception as e:
|
||||
logger.error("Error in step 3.2 of clean_energy_value.process " + str(e))
|
||||
if cursor:
|
||||
cursor.close()
|
||||
if cnx:
|
||||
cnx.close()
|
||||
time.sleep(60)
|
||||
continue
|
||||
|
||||
################################################################################################################
|
||||
# TODO: bad case 2.8
|
||||
# id point_id utc_date_time actual_value is_bad (expected)
|
||||
# 105752070 3333 2018-02-04 00:27:15 138144 good
|
||||
# 105752305 3333 2018-02-04 00:28:19 138144 good
|
||||
# 105752523 3333 2018-02-04 00:29:22 138144 good
|
||||
# 105752704 3333 2018-02-04 00:30:26 138144 good
|
||||
# 105752924 3333 2018-02-04 00:31:30 138144 good
|
||||
# 105753138 3333 2018-02-04 00:32:34 138144 good
|
||||
# 105753351 3333 2018-02-04 00:33:38 138144 good
|
||||
# 105753577 3333 2018-02-04 00:34:42 52776558592 bad?
|
||||
# 105753794 3333 2018-02-04 00:35:46 52776558592 bad?
|
||||
# 105753999 3333 2018-02-04 00:36:50 52776558592 bad?
|
||||
# 105754231 3333 2018-02-04 00:37:54 52776558592 bad?
|
||||
# 105754443 3333 2018-02-04 00:38:58 52776558592 bad?
|
||||
# 105754655 3333 2018-02-04 00:40:01 52776558592 bad?
|
||||
# 105754878 3333 2018-02-04 00:41:06 52776558592 bad?
|
||||
# 105755092 3333 2018-02-04 00:42:09 52776558592 bad?
|
||||
# 105755273 3333 2018-02-04 00:43:14 52776558592 bad?
|
||||
# 105755495 3333 2018-02-04 00:44:17 52776558592 bad?
|
||||
# 105755655 3333 2018-02-04 00:45:21 52776558592 bad?
|
||||
# 105755854 3333 2018-02-04 00:46:25 52776558592 bad?
|
||||
# 105756073 3333 2018-02-04 00:47:29 52776558592 bad?
|
||||
# 105756272 3333 2018-02-04 00:48:34 52776558592 bad?
|
||||
# 105756489 3333 2018-02-04 00:49:38 52776558592 bad?
|
||||
################################################################################################################
|
||||
|
||||
################################################################################################################
|
||||
# TODO: bad case 2.10
|
||||
# id point_id utc_date_time actual_value is_bad (expected)
|
||||
# 106363135 3336 2018-02-06 04:45:57 253079.015625 good
|
||||
# 106363776 3336 2018-02-06 04:49:09 253079.015625 good
|
||||
# 106364381 3336 2018-02-06 04:52:21 253079.015625 good
|
||||
# 106364603 3336 2018-02-06 04:53:25 253079.015625 good
|
||||
# 106365213 3336 2018-02-06 04:56:37 253079.015625 good
|
||||
# 106365634 3336 2018-02-06 04:58:45 253079.015625 good
|
||||
# 106366055 3336 2018-02-06 05:00:53 253079.015625 good
|
||||
# 106367097 3336 2018-02-06 05:06:12 259783.015625 bad?
|
||||
# 106367507 3336 2018-02-06 05:08:21 259783.015625 bad?
|
||||
# 106368318 3336 2018-02-06 05:12:37 259783.015625 bad?
|
||||
# 106368732 3336 2018-02-06 05:14:44 259783.015625 bad?
|
||||
# 106368952 3336 2018-02-06 05:15:48 259783.015625 bad?
|
||||
# 106369145 3336 2018-02-06 05:16:52 259783.015625 bad?
|
||||
# 106369353 3336 2018-02-06 05:17:56 259783.015625 bad?
|
||||
################################################################################################################
|
||||
|
||||
################################################################################################################
|
||||
# TODO: bad case 2.11
|
||||
# id point_id utc_date_time actual_value is_bad (expected)
|
||||
# 14784589 21 2020-03-05 07:22:22 17990 good
|
||||
# 14784450 21 2020-03-05 07:21:17 17990 good
|
||||
# 14784311 21 2020-03-05 07:20:10 17990 good
|
||||
# 14784172 21 2020-03-05 07:19:04 17990 good
|
||||
# 14784033 21 2020-03-05 07:17:58 18990 bad
|
||||
# 14783894 21 2020-03-05 07:16:52 17990 good
|
||||
# 14783755 21 2020-03-05 07:15:46 17990 good
|
||||
# 14783616 21 2020-03-05 07:14:40 17990 good
|
||||
# 14783477 21 2020-03-05 07:13:34 17990 good
|
||||
# 14783338 21 2020-03-05 07:12:28 17990 good
|
||||
# 14783199 21 2020-03-05 07:11:22 17990 good
|
||||
################################################################################################################
|
||||
|
||||
################################################################################################################
|
||||
# TODO: bad case 2.12
|
||||
# id point_id utc_date_time actual_value is_bad (expected)
|
||||
# 3337308 21 2020-01-07 09:02:18 7990 good
|
||||
# 3337174 21 2020-01-07 09:01:13 7990 good
|
||||
# 3337040 21 2020-01-07 09:00:08 7990 good
|
||||
# 3336906 21 2020-01-07 08:59:04 7990 good
|
||||
# 3336772 21 2020-01-07 08:57:59 7990 good
|
||||
# 3336638 21 2020-01-07 08:56:54 8990 bad
|
||||
# 3336504 21 2020-01-07 08:55:49 7990 good
|
||||
# 3336370 21 2020-01-07 08:54:44 7990 good
|
||||
# 3336236 21 2020-01-07 08:53:39 7990 good
|
||||
# 3336102 21 2020-01-07 08:52:34 7990 good
|
||||
# 3335968 21 2020-01-07 08:51:30 7990 good
|
||||
################################################################################################################
|
||||
# Step 4: tag the is_bad property of energy values.
|
||||
################################################################################################################
|
||||
try:
|
||||
update = (" UPDATE tbl_energy_value "
|
||||
" SET is_bad = FALSE "
|
||||
" WHERE utc_date_time >= %s AND utc_date_time < %s AND is_bad IS NULL ")
|
||||
# NOTE: use '<' instead of '<=' in WHERE statement because there may be some new inserted values
|
||||
cursor.execute(update, (min_datetime, max_datetime,))
|
||||
cnx.commit()
|
||||
except Exception as e:
|
||||
logger.error("Error in step 4 of clean_energy_value.process " + str(e))
|
||||
time.sleep(60)
|
||||
continue
|
||||
finally:
|
||||
if cursor:
|
||||
cursor.close()
|
||||
if cnx:
|
||||
cnx.close()
|
||||
|
||||
time.sleep(900)
|
||||
|
|
@ -0,0 +1,24 @@
|
|||
# definition of the database
|
||||
myems_system_db = {
|
||||
'user': 'root',
|
||||
'password': '!MyEMS1',
|
||||
'host': '127.0.0.1',
|
||||
'database': 'myems_system_db',
|
||||
'port': 3306,
|
||||
}
|
||||
|
||||
myems_historical_db = {
|
||||
'user': 'root',
|
||||
'password': '!MyEMS1',
|
||||
'host': '127.0.0.1',
|
||||
'database': 'myems_historical_db',
|
||||
'port': 3306,
|
||||
}
|
||||
|
||||
# indicates how long analog values and digital values will be kept in database
|
||||
# the longer days the more memory and disc space needed.
|
||||
live_in_days = 365
|
||||
# note: By default, energy values in historical db will never be deleted automatically.
|
||||
|
||||
# indicates if the program is in debug mode
|
||||
is_debug = False
|
||||
|
|
@ -0,0 +1,37 @@
|
|||
import logging
|
||||
from logging.handlers import RotatingFileHandler
|
||||
from multiprocessing import Process
|
||||
import clean_analog_value
|
||||
import clean_digital_value
|
||||
import clean_energy_value
|
||||
|
||||
|
||||
def main():
|
||||
"""main"""
|
||||
# create logger
|
||||
logger = logging.getLogger('myems-cleaning')
|
||||
# specifies the lowest-severity log message a logger will handle,
|
||||
# where debug is the lowest built-in severity level and critical is the highest built-in severity.
|
||||
# For example, if the severity level is INFO, the logger will handle only INFO, WARNING, ERROR, and CRITICAL
|
||||
# messages and will ignore DEBUG messages.
|
||||
logger.setLevel(logging.ERROR)
|
||||
# create file handler which logs messages
|
||||
fh = RotatingFileHandler('myems-cleaning.log', maxBytes=1024*1024, backupCount=1)
|
||||
# create formatter and add it to the handlers
|
||||
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
|
||||
fh.setFormatter(formatter)
|
||||
# add the handlers to logger
|
||||
logger.addHandler(fh)
|
||||
|
||||
# clean analog values
|
||||
Process(target=clean_analog_value.process, args=(logger,)).start()
|
||||
# clean digital values
|
||||
Process(target=clean_digital_value.process, args=(logger,)).start()
|
||||
# clean energy values
|
||||
Process(target=clean_energy_value.process, args=(logger,)).start()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,15 @@
|
|||
[Unit]
|
||||
Description=myems-cleaning daemon
|
||||
After=network.target
|
||||
|
||||
[Service]
|
||||
User=root
|
||||
Group=root
|
||||
ExecStart=/usr/bin/python3 /myems-cleaning/main.py
|
||||
ExecReload=/bin/kill -s HUP $MAINPID
|
||||
ExecStop=/bin/kill -s TERM $MAINPID
|
||||
PrivateTmp=true
|
||||
Restart=always
|
||||
|
||||
[Install]
|
||||
WantedBy=multi-user.target
|
||||
|
|
@ -1,6 +1,6 @@
|
|||
MIT License
|
||||
|
||||
Copyright (c) 2020 MyEMS
|
||||
Copyright (c) 2021 MyEMS
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
|
|
|
|||
Loading…
Reference in New Issue