Rail transit energy consumption monitoring method, system and electronic device

Abstract

The application provides a rail transit energy consumption monitoring method, system and electronic equipment, and belongs to the field of rail transit. The method comprises the following steps: acquiring real-time data at each configuration point on each device in a to-be-monitored line network; determining basic data according to the real-time data at the configuration points and storing the basic data into a basic data table; determining state standard data, measurement standard data and metering growth standard data according to the basic data in the basic data table, storing the state standard data into a state standard table, storing the measurement standard data into a measurement standard table, and storing the metering growth standard data into a metering growth standard table; and calculating state indexes, measurement indexes and total energy consumption of the to-be-monitored line network according to the data in the state standard table, the measurement standard table and the metering growth standard table. The application integrates energy consumption data of the whole line network, realizes management and index calculation of line network-level energy consumption, and improves the monitoring efficiency of rail transit energy consumption.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of rail transit, in particular to a rail transit energy consumption monitoring method, system and electronic device. BACKGROUND

[0002] With the development of rail transit, it is necessary to promote urban rail informatization, develop intelligent systems, build smart urban rail, and establish a data sharing platform that is technically advanced, accurate, safe and reliable. However, the existing rail transit energy monitoring and control platform can only monitor and control at the line level, and cannot realize the management and index calculation of line network energy consumption.

[0003] Therefore, it is particularly important to build a system that can integrate the entire line network energy consumption and equipment facility data. SUMMARY

[0004] The purpose of the present application is to provide a rail transit energy consumption monitoring method, system and electronic device, which can integrate the entire line network energy consumption data and improve the monitoring efficiency of rail transit energy consumption.

[0005] To achieve the above purpose, the present application provides the following solutions:

[0006] A rail transit energy consumption monitoring method, comprising:

[0007] Obtaining real-time data at each configuration point on each device in the to-be-monitored line network; the real-time data includes state data, metric data and / or meter growth data; the to-be-monitored line network includes multiple lines, each line includes multiple stations, each station includes multiple devices, and each device is provided with multiple configuration points;

[0008] For any configuration point on any device in the to-be-monitored line network, determining basic data according to the real-time data at the configuration point, and storing the basic data into a basic data table in the database; the basic data includes configuration point number, event time and value; the fields of the basic data table include configuration point number, event time and value;

[0009] According to the basic data in the basic data table, state standard data, measurement standard data and metering growth standard data are determined, and the state standard data is stored into a state standard table in the database, the measurement standard data is stored into a measurement standard table in the database, and the metering growth standard data is stored into a metering growth standard table in the database; fields of the state standard table include: unique key, event time, data type, configuration point number, state value and state duration; fields of the measurement standard table include: unique key, event time, data type, configuration point number, measurement value and state; fields of the metering growth standard table include: unique key, event time, data type, configuration point number, monitoring value, difference from last result and abnormal state code;

[0010] According to the data in the state standard table, the measurement standard table and the metering growth standard table, state indicators, measurement indicators and total energy consumption of the to-be-monitored line network are calculated.

[0011] Optionally, real-time data at each configuration point on each device in the to-be-monitored line network is acquired, specifically including:

[0012] Measurement data and metering growth data at each configuration point on each device in the to-be-monitored line network are acquired according to a set time interval;

[0013] When a state at any configuration point on any device in the to-be-monitored line network changes, state data at the configuration point is acquired.

[0014] Optionally, according to the real-time data at the configuration point, basic data is determined, specifically including:

[0015] A data format of the real-time data at the configuration point is normalized to obtain the basic data.

[0016] Optionally, the rail transit energy consumption monitoring method further includes:

[0017] The basic data at each configuration point on each device in the to-be-monitored line network is cached into redis.

[0018] Optionally, according to the basic data in the basic data table, state standard data, measurement standard data and metering growth standard data are determined, specifically including:

[0019] For any piece of basic data in the basic data table, according to a configuration point number of the basic data, a data type of the basic data is determined; the data type is state data, measurement data or metering growth data;

[0020] If the data type of the basic data is state data, a unique key is automatically generated, and a state duration is determined according to a configuration point number of the basic data, an event time of the basic data, and the basic data stored in the redis, so as to generate state standard data; a value of the basic data is a state value of the state standard data;

[0021] If the data type of the basic data is metric data, a unique key is automatically generated, and a state of the basic data is determined according to a value of the basic data and a preset value range, so as to generate metric standard data; the value of the basic data is a measurement value of the metric standard data; the state of the basic data is normal or abnormal;

[0022] If the data type of the basic data is meter growth data, a unique key is automatically generated, and a difference value from a last result and an abnormal state code are determined according to a value of the basic data, an event time of the basic data, and the basic data stored in the redis, so as to generate meter growth standard data; the value of the basic data is a monitoring value of the meter growth standard data.

[0023] Optionally, the rail transit energy consumption monitoring method further comprises:

[0024] obtaining traction energy consumption historical data and lighting electricity historical data of each line in a to-be-monitored line network; the traction energy consumption historical data comprises monthly traction energy consumption per 100 kilometers of a corresponding line; the lighting electricity historical data comprises power lighting electricity of each station on the corresponding line;

[0025] for any line in the to-be-monitored line network, predicting traction unit consumption of the line in a future set period by using a Holt-Winters method according to traction energy consumption historical data of the line;

[0026] predicting power lighting unit consumption of the line in the future set period by using the Holt-Winters method according to lighting electricity historical data of the line.

[0027] Optionally, the rail transit energy consumption monitoring method further comprises:

[0028] obtaining a line index vector of each line in the to-be-monitored line network; the line index vector comprises average train passenger full load, average train motor power, average train auxiliary equipment power, and average station spacing;

[0029] clustering the line index vectors of the lines in the to-be-monitored line network to determine similarity of the lines in the to-be-monitored line network.

[0030] Optionally, the rail transit energy consumption monitoring method further comprises:

[0031] Obtain a site index vector of each site in the to-be-monitored line network; the site index vector comprises power lighting energy consumption, site area, total power of air conditioning equipment, total power of elevator equipment, and site passenger flow set and distribution volume.

[0032] Cluster the site index vector of each site in the to-be-monitored line network, and determine the similarity between sites in the to-be-monitored line network.

[0033] To achieve the above object, the present application further provides the following scheme:

[0034] An urban rail transit energy consumption monitoring system comprises:

[0035] A data acquisition module is configured to acquire real-time data at each configuration point on each device in the to-be-monitored line network; the real-time data comprises state data, measurement data, and / or metering growth data; the to-be-monitored line network comprises a plurality of lines, each line comprises a plurality of sites, each site comprises a plurality of devices, and each device is provided with a plurality of configuration points;

[0036] A basic data determination module is connected to the data acquisition module and is configured to, for any configuration point on any device in the to-be-monitored line network, determine basic data according to the real-time data at the configuration point, and store the basic data into a basic data table in a database; the basic data comprises a configuration point number, an event time, and a value; the fields of the basic data table comprise a configuration point number, an event time, and a value;

[0037] A data classification module is connected to the basic data determination module and is configured to, according to the basic data in the basic data table, determine state standard data, measurement standard data, and metering growth standard data, and store the state standard data into a state standard table in a database, store the measurement standard data into a measurement standard table in the database, and store the metering growth standard data into a metering growth standard table in the database; the fields of the state standard table comprise a unique key, an event time, a data type, a configuration point number, a state value, and a state duration; the fields of the measurement standard table comprise a unique key, an event time, a data type, a configuration point number, a measurement value, and a state; the fields of the metering growth standard table comprise a unique key, an event time, a data type, a configuration point number, a monitoring value, a difference from a last result, and an abnormal state code;

[0038] A calculation module is connected to the data classification module and is configured to calculate a state index, a measurement index, and total energy consumption of the to-be-monitored line network according to the data in the state standard table, the measurement standard table, and the metering growth standard table.

[0039] To achieve the above object, the present application further provides the following scheme:

[0040] An electronic device comprises a memory for storing a computer program and a processor for running the computer program to make the electronic device execute the rail transit energy consumption monitoring method.

[0041] According to the specific embodiments of the present application, the following technical effects are disclosed: the present application acquires real-time data (including state data, metric data and / or meter growth data) at each configuration point on each device in the monitored line network, determines basic data according to the real-time data at each configuration point, and stores the basic data into a basic data table in the database, further determines state standard data, metric standard data and meter growth standard data according to the basic data in the basic data table, stores the state standard data into a state standard table in the database, stores the metric standard data into a metric standard table in the database, and stores the meter growth standard data into a meter growth standard table in the database, and finally calculates the state index, the metric index and the total energy consumption of the monitored line network according to the data in the state standard table, the metric standard table and the meter growth standard table. The energy consumption data of the whole line network is integrated, the management and index calculation of the line network level energy consumption are realized, and the monitoring efficiency of the rail transit energy consumption is improved. BRIEF DESCRIPTION OF DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0043] Figure 1 The flow chart of the rail transit energy consumption monitoring method provided by the present application;

[0044] Figure 2 The schematic diagram of the rail transit energy consumption monitoring system provided by the present application. DETAILED DESCRIPTION

[0045] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0046] The subway line network has a huge and complex data system, and the data complexity is reflected in the huge data volume, multiple professional classifications of data, and wide business coverage. Among them, the most important and largest data volume is energy consumption and equipment and facility data. The purpose of the present application is to provide a rail transit energy consumption monitoring method, system and electronic equipment, which integrates the energy consumption data of the whole line network to form a unified energy consumption index calculation platform.

[0047] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below in combination with the drawings and specific embodiments.

[0048] Embodiment one

[0049] As Figure 1 shown, the present embodiment provides a rail transit energy consumption monitoring method, comprising:

[0050] S1: acquiring real-time data at each configuration point on each device in the line network to be monitored. The data at the configuration point can be temperature, humidity, power consumption, water consumption, current state of the device, etc.

[0051] The line network to be monitored includes multiple lines, each line includes multiple stations, and each station includes multiple devices, and each device is provided with multiple configuration points.

[0052] The configuration point is a point on the device that monitors a certain value and can return information. One device contains one or more configuration points. For example, a simple humidity sensor only collects humidity as a measurement value; an elevator has multiple configuration points such as an electricity meter, elevator speed monitoring, elevator operation state monitoring, and elevator operation temperature monitoring, and collects multiple types of information.

[0053] The real-time data includes state data, measurement data and / or metering growth data.

[0054] In the data collection stage, all configuration points are not distinguished by point type. When collecting, the instantaneous values of each point are collected by polling all configuration points at regular intervals, and then the data is decoded using different protocols. Because the data structures are different between different devices and between different manufacturers of the same device, the decoded data needs to be further processed into a general data structure, and finally the structured data is used for index calculation and big data analysis.

[0055] Specifically, the measurement data and metering growth data at each configuration point on each device in the line network to be monitored are acquired at a set time interval. When the state at any configuration point on any device in the line network to be monitored changes, the state data at the configuration point is acquired.

[0056] S2: For any configuration point on any device in the line network to be monitored, according to real-time data at the configuration point, determine the basic data, and store the basic data into a basic data table in the database. The basic data includes configuration point number, event time and value. The fields of the basic data table include configuration point number (type 32-bit VARCHAR), event time (type DATETIME) and value (type 255-bit VARCHAR).

[0057] Specifically, the data format of the real-time data at the configuration point is normalized to obtain the basic data.

[0058] The detailed process of collecting data is as follows:

[0059] (21) Receive real-time data of configuration point: a service end for collecting real-time data of all configuration points of a certain protocol at the site is arranged on the server of each site, and multiple service ends are arranged for multiple protocols. The service end is independent of the method of the present application as a service for directly collecting configuration point data. The present application obtains the data collected by the service end by means of timing polling. The polling time can be set according to the characteristics of different types of configuration points, but in order to improve the accuracy of the calculated indicators, the interval time should not be too long.

[0060] (22) Decode data: all configuration points on a device are decoded by the same protocol regardless of the type. The commonly used protocol types are modbus and iec104, and when new protocol data is accessed, a new parser will be developed. The binary data of the configuration point is directly collected. There are two ways to decode, one is fixed length, which is decoded by utf-8 according to the different positions and lengths of the data provided by the manufacturer; the other is to use the decoding package provided by the manufacturer, that is, the corresponding decoding package is introduced in the code, the decoding function is called, the binary data is input, and the plaintext data after parsing is output.

[0061] (23) Data caching and persistence: the original data will form a json structured data after decoding, and since the json data structure of each point after decoding may be different, the data format of various configuration points is normalized to a standard structure. The data structure after normalization is as follows:

[0062] The standard data structure has three fields: configuration point number, event time and value. The unified and simplified data structure facilitates subsequent transmission and processing. When used later, only the configuration point number of the data is matched with the field in the configuration point table, and the detailed information of the configuration point can be obtained.

[0063] After data normalization, it will be stored in the data warehouse for backup, and the basic data of each configuration point on each device in the monitoring line network will be cached to redis and sent to kafka for subsequent use by Flink real-time program.

[0064] S3: According to the basic data in the basic data table, determine state standard data, metric standard data and meter growth standard data, and store the state standard data in the state standard table in the database, store the metric standard data in the metric standard table in the database, and store the meter growth standard data in the meter growth standard table in the database.

[0065] The fields of the state standard table include: unique key, event time, data type, configuration point number, state value and state duration. The structure of the state standard table is shown in Table 1.

[0066] Table 1 State standard table

[0067] Unique key VARCHAR 32 Event time DATETIME Data type VARCHAR 255 Configuration point number VARCHAR 255 Measured value VARCHAR 255 State INT

[0068] The fields of the metric standard table include: unique key, event time, data type, configuration point number, measurement value and state. The structure of the metric standard table is shown in Table 2.

[0069] Table 2 Metric standard table

[0070] Energy consumption relationship unique code VARCHAR 32 Party number VARCHAR Energy consumption relationship enable time DATETIME 255 Energy consumption relationship end time DATETIME 255 Relationship name VARCHAR 255 Energy consumption relationship formula VARCHAR

[0071] The fields of the meter growth standard table include: unique key, event time, data type, configuration point number, monitoring value, difference from last result and abnormal state code. The structure of the meter growth standard table is shown in Table 3.

[0072] Table 3 Meter growth standard table

[0073]

[0074]

[0075] In order to realize index calculation, more information needs to be attached to the normalized basic data. Energy consumption data needs to get the meter growth value of the same point in a period of time. The measurement data needs to take the last data in a period of time. The state data needs to know the time when the state changes and the changed state. These three types of data have different data structures and update methods according to their characteristics. In order to shield the differences of different manufacturers and different devices, the measurement data and the meter growth data are unified to update every 5 minutes for each configuration point, that is, every 5 minutes, each measurement data and meter growth data configuration point will generate a data. The state data generates a data every time the state changes.

[0076] The overall data processing flow is as follows: first, the Flink program reads the collected configuration point real-time data from Kafka in real time, and classifies the data according to the point type information in the configuration point table, and distinguishes state data, metric data and meter growth data. These three types of data are combined with different configuration point original information to generate three different standardized data. Offline indicators will first store standardized data in the data warehouse, and then after the daily subway system operation is completed, the scheduling system will uniformly call the indicator calculation script to perform T+1 offline indicator calculation.

[0077] Specifically, S3 includes:

[0078] (31) For any piece of basic data in the basic data table, according to the configuration point number of the basic data, the data type of the basic data is determined.

[0079] The data type is state data, metric data or meter growth data. Wherein, the state data such as the on-off state of the device, the device temperature alarm state, etc. The metric data such as temperature, humidity, etc. The meter growth data such as electric meter, water meter, oil consumption meter, etc. The information to be concerned about the state data includes the configuration point name, the configuration point device classification, the configuration point current instantaneous state value and the collection time. The information to be concerned about the meter growth data includes the configuration point name, the configuration point device classification, the last measurement value of the configuration point, the current measurement value of the configuration point and the collection time, etc.

[0080] (32) If the data type of the basic data is state data, a unique key is automatically generated, and the state duration is determined according to the configuration point number of the basic data, the event time of the basic data and the basic data stored in redis, to generate state standard data. The value of the basic data is the state value of the state standard data.

[0081] (33) If the data type of the basic data is metric data, a unique key is automatically generated, and the state of the basic data is determined according to the value of the basic data and the pre-set value range, to generate metric standard data. The value of the basic data is the measurement value of the metric standard data; the state of the basic data is normal or abnormal.

[0082] (34) If the data type of the basic data is meter growth data, a unique key is automatically generated, and the difference from the last result and the abnormal state code are determined according to the value of the basic data, the event time of the basic data and the basic data stored in redis, to generate meter growth standard data. The value of the basic data is used as the monitoring value of the meter growth standard data.

[0083] In summary, the detailed steps of data processing are as follows:

[0084] 1) Data shunting: The normalized data received by the Flink program has only three fields: configuration point number, event time and value. The program will pre-load all configuration point information in the database into redis for caching when starting. When processing business data, match the configuration point number in the data with the point number of the configuration point information in redis to find the detailed information of the corresponding configuration point. The configuration point detailed information includes configuration point unique sequence number, configuration point corresponding device number, configuration point type, configuration point measurement unit, configuration point value range, etc. The source data can be shunted into state data, measurement data and meter growth data for processing respectively through the configuration point type information of the basic data.

[0085] 2) Data processing: In order to speed up data processing and avoid data loss caused by Flink program crash, redis cache is introduced as a carrier for storing temporary configuration point related information.

[0086] There are four kinds of data stored in redis.

[0087] ① Configuration point data: code table, used to classify and enrich the basic information of normalized data.

[0088] ② Measurement point cache data: records the time and measurement value of the last data at the end of each time window for each configuration point (time window is adjustable, default is 5 minutes).

[0089] ③ State point cache data: records the state value and time when the state of each configuration point changes last time.

[0090] ④ Meter growth point cache data: records the time of the last data at the end of each time window and the growth of measurement value in the last window period (time window is adjustable, default is 5 minutes).

[0091] Measurement data is instantaneous data at a certain point in time. In order to simplify the unified output data, the last data in each time window is selected as the output for each measurement data configuration point. Compared with the historical data in redis, it is ensured that the current configuration point data is the last data in the window period, and the data life cycle of measurement data in redis is 1 day. State data needs to know the time of the current state of the configuration point, which will compare the same point data in redis until the state changes. The data in redis is not set to expire. The meter growth data needs to know the growth of the measurement value of the configuration point in the last time window. In order to avoid the expiration of redis data due to the crash of Flink program, the data period of meter growth data in redis is designed to be 7 days, without setting the data life cycle.

[0092] 21) Metric data processing process: Since the data of each metric point is output every 5 minutes, the rule is that no matter how many data the point receives in the 5-minute window, only the value of the last data of the point is taken as the final output value, and the state is given according to whether the value range is within the normal range. For example, a point measuring the temperature of a subway station hall, the temperature value range is approximately between 5 degrees and 32 degrees, if it exceeds this range, the state value will indicate abnormal; otherwise, the state will show normal.

[0093] For example, a data is received as follows: {“configuration point number”:“P1”, “event time”:“2023-06-1210:02:04”, “value”:“28.5”}, after comparing the configuration point number, it is determined that this data is metric data, then enter the metric data processing process. The time of this data of configuration point P1 is 10:02:04, it is known that the time range of this data collection of the point is between 10:00 and 10:05 (5 minutes as a window, whole point division), the time slice of this data is set to 1005. Get the current system date, for example, the date of collecting data is 2023-06-12, which is Monday, then add “1” to the data information, the purpose is to limit the period of a week to cover the key in redis, to prevent the data in redis from expanding indefinitely, process the data into key-value format, the key is “P1_1_1005”, and the value is “28.5”. When collecting for the first time, check the key in redis, if no record is found, directly write the key-value into redis; if the key is found, compare the time of this data with the time recorded in redis whether they are in the same whole point 5-minute interval, if yes, write the current data into redis; if not, it means that the record in redis is the last data of the point in the previous 5-minute interval, take out this data as the value of P1 point in the 10:00-10:05 time interval, and update the value cache of the configuration point in redis.

[0094] 22) State data processing process: The calculation of state data will use redis to cache the last received data information of each state configuration point, when the same configuration point data is received again, compare the “value” of this data, i.e. the state value, with the last state value of the point saved in redis, if they are the same, it means that the state of the point has not changed, then directly skip the processing of this data; if they are different, it means that the state of the point has changed, at this time, a state change data is output.

[0095] For example, receiving the following data: {“configuration point number”:“P2”,“event time”:“2023-06-1210:02:04”,“value”:“1”}, the cached key-value in redis is as follows: the key is the configuration point number, and the value is the state value and time of the last state of the point, assuming it is {“event time”:“2023-06-1209:02:04”,“value”:“0”}; from which the duration of P2 point state“1” is calculated as 1 hour, the data of P2 point in redis is updated to {“event time”:“2023-06-1210:02:04”,“state value”:“1”}, and a state change data is generated according to the above state data structure.

[0096] 23) Meter growth data processing process: meter growth data and measurement data are similar, and all statistics are put into a 5-minute window for calculation. Normally, the value of each point of the meter growth data increases with time. If the value of the same meter growth point received subsequently is smaller than the value received previously, it indicates that the data is abnormal, and the enumeration value of the abnormal state code is set to abnormal.

[0097] In the redis cache, the key structure of the meter growth data is consistent with that of the measurement data, and the value is the meter base at the end of the last 5 minutes, the value of the last point, and the time of the last record. By comparing whether the values of the same meter growth configuration point this time and last time are positive growth and whether they are in the same 5-minute period, the meter growth value of the meter growth configuration point in the current five minutes is finally obtained, and the information of the point in redis is updated.

[0098] 3) Data output: the three types of data after shunting are written into the state standard table, the measurement standard table and the meter growth standard table in the data warehouse respectively, and the data partition is the current system date. When calculating the indicators, each type of indicator will read the corresponding data for calculation.

[0099] S4: According to the data in the state standard table, the measurement standard table and the meter growth standard table, the state indicators, the measurement indicators and the total energy consumption of the line network to be monitored are calculated.

[0100] Specifically, the state data, the measurement data and the meter growth data are converted into different indicators.

[0101] The state indicators include: device state duration, device alarm frequency, etc. Specifically, the device state duration is determined according to the state change data in the state standard table, and the device fault frequency is counted.

[0102] The measurement indicators include: daily maximum temperature, daily maximum humidity, daily temperature difference, annual average temperature in the station, etc.

[0103] The meter growth type index includes: classified sub-item energy consumption statistics, relationship energy consumption statistics, traction energy consumption statistics, carbon emissions, etc. Among them, the traction energy consumption is calculated according to the energy consumption data collected by the configuration point of the monitored traction energy.

[0104] Because the energy consumption statistics have hierarchical characteristics, that is, the sum of the power consumption of multiple same type devices in a station is the sum of the energy consumption of this type in the station; the sum of the power consumption of multiple devices in the station is the total power consumption of the station; the sum of the power consumption of all stations on the same line is the total power consumption of the line; and the sum of the power consumption of all lines in the line network is the total power consumption of the line network. Therefore, to calculate the relationship energy consumption related index, the configuration point results need to be classified and summarized in combination with the energy consumption relationship information.

[0105] The energy consumption calculation rule is: if there are two elevators in A station, and the configuration point numbers representing the power consumption of the two elevators are P1 and P2 respectively, and the total energy consumption relationship of the elevators in A station is R2, then R2=P1+P2; and the total energy consumption relationship of the air conditioner in A station is R3, the total energy consumption relationship of the lighting in A station is R4, the total energy consumption of the electromechanical in A station is R5, and the total energy consumption of other in A station is R6, then the total energy consumption relationship of A station is R1=R2+R3+R4+R5+R6. Based on this example, in the energy consumption system, starting from the most basic energy consumption configuration point, to the final line network total energy consumption relationship, the complete energy consumption relationship in the line network can be manually set according to the energy consumption unit level, and stored in the energy consumption relationship table. The structure of the energy consumption relationship table is shown in Table 4.

[0106] Table 4 Energy consumption relationship table

[0107] Unit of measurement VARCHAR 32 Figure 2 ​ 255 ​ ​ ​ ​ ​ ​ 255 ​ ​ 255 ​ ​ 255

[0108] The energy consumption relationship start time and the energy consumption relationship end time refer to the effective time of the relationship. Usually, after the station is completed, the energy consumption relationship in the station can be established, and if there is an increase or decrease in energy-using equipment later, the corresponding energy consumption relationship formula is modified. The energy consumption relationship table is manually maintained by artificial, that is, the statistical formula of all energy-using devices in the line network is manually created, and after establishment, the total energy consumption is calculated according to the formula every day.

[0109] After the energy consumption relationship is established, the total energy consumption of each level is calculated every day through the pre-set spark program. Because the meter growth standard data is the value growth of each meter growth configuration point every 5 minutes. Therefore, the basic result of relationship energy consumption statistics is also summarized every 5 minutes. Finally, to calculate the daily statistics of each level energy consumption, only the sum of all 5-minute results of the day is needed.

[0110] Traction energy consumption is the power consumption of the subway in the tunnel, which usually accounts for 50-60% of the total energy consumption of the subway system. The difference between traction energy consumption and station energy consumption is that traction energy consumption can only be counted in units of lines, while station energy consumption can be counted to the station level. The configuration points of traction energy consumption also belong to the metering growth point, and the traction energy data collected through these configuration points are used to calculate the traction energy consumption index.

[0111] The carbon emission index is obtained by multiplying the carbon emission coefficient of the corresponding energy type on the basis of various energy consumption indexes, and the carbon emission coefficient of different energy types is different. For example, the total energy consumption of A station on a certain day is 100000kw.h, and the carbon emission coefficient of electric energy is 0.785, so the carbon emission of electric energy of the station is 100000*0.785=7850kg.

[0112] The running time of the offline index should be set after the subway operation of the previous day, usually between 3am and 6am, and the script is arranged according to the above relationship. All offline scripts import parameters of the previous day (the index calculation script reads the data generated in the previous day partition to calculate the index of the previous day).

[0113] Further, the present application combines historical index data with other basic information for fusion analysis, and realizes the prediction of future line network energy consumption by means of mining algorithm, and the track transportation energy consumption monitoring method further comprises:

[0114] S5: obtaining the traction energy consumption historical data and lighting electricity historical data of each line in the to-be-monitored line network.

[0115] The traction energy consumption historical data includes the monthly traction energy consumption per 100km of the corresponding line.

[0116] The lighting electricity historical data includes the power lighting electricity of each station on the corresponding line.

[0117] S6: for any line in the to-be-monitored line network, according to the traction energy consumption historical data of the line, the Holt-Winters method is used to predict the traction unit consumption of the line in a future set period; according to the lighting electricity historical data of the line, the Holt-Winters method is used to predict the power lighting unit consumption of the line in a future set period. Specifically, by inputting the line number, the monthly traction energy consumption per 100km of the line as the basic data model, the traction unit consumption of the line in the next 12 months is predicted. By inputting the line number, the station / section power lighting electricity of the line as the basic data model, the power lighting unit consumption of the line in the next 12 months is predicted.

[0118] The Holt-Winters method is a time series analysis and prediction method, which is suitable for non-stationary sequences containing linear trend and periodic fluctuations. The model parameters are constantly adapted to the changes of non-stationary sequences by using exponential smoothing method, and the future trend is short-term predicted. The Holt-Winters method introduces the Winters periodic term on the basis of the Holt model, which can be used to process the fixed periodic fluctuation behavior in the time series, so the historical data of the past 12 months are taken as the training input and the cycle length is driven to predict.

[0119] Further, the rail transit energy consumption monitoring method further comprises:

[0120] S7: Obtain a line index vector of each line in the to-be-monitored line network. The line index vector comprises an average train passenger full load, an average train motor power, an average train auxiliary equipment power, and an average station spacing.

[0121] S8: Cluster the line index vector of each line in the to-be-monitored line network to determine the similarity of the lines in the to-be-monitored line network. The present application clusters the line index vector of each line in the to-be-monitored line network based on the KMEANS algorithm.

[0122] Specifically, first, the line index vector of each line is standardized. Then, a specified line code record is taken as a reference, and each index vector is taken as a reference vector. The Euclidean distance between the reference vector and the index vector of other lines is calculated. The smaller the distance, the higher the similarity between the index vectors. Finally, the line index vector with the smallest distance value is taken as the best clustering result.

[0123] S9: Obtain a station index vector of each station in the to-be-monitored line network. The station index vector comprises a dynamic lighting energy consumption, a station area, a total air conditioning equipment power, a total elevator equipment power, and a station passenger flow aggregation. The dynamic lighting energy consumption is directly obtained from the data index provided by the energy management system. The station area is provided by the subway design party or the operation party. The total air conditioning equipment power and the total elevator equipment power are provided by the subway operation party. The station passenger flow aggregation is obtained by counting the sum of the in-station and out-station passenger flow.

[0124] S10: Cluster the station index vector of each station in the to-be-monitored line network to determine the similarity between the stations in the to-be-monitored line network.

[0125] The station index vector of each station is taken as the basis of the data model, and the data is standardized to prevent the influence of the weight of a single factor on the overall model. After completing the data preprocessing, the KMEANS algorithm is used for clustering, and the clustering results are arranged in descending order to obtain the best clustering result with the best discrimination as the final conclusion.

[0126] The application integrates energy consumption data of the whole line network, forms a unified energy consumption index calculation platform, unifies energy consumption and equipment and facility structure of the whole line network and data access, and performs cross-dimension analysis and data mining on energy consumption data combined with other professional data of rail transit, thereby improving monitoring efficiency of rail transit energy consumption.

[0127] Embodiment two

[0128] In order to perform the method corresponding to the above-mentioned embodiment one, to realize the corresponding functions and technical effects, the following provides a rail transit energy consumption monitoring system.

[0129] As ​ shown, the rail transit energy consumption monitoring system provided by the embodiment includes a data acquisition module 1, a basic data determination module 2, a data classification module 3 and a calculation module 4.

[0130] The data acquisition module 1 is configured to acquire real-time data at each configuration point on each device in a to-be-monitored line network. The real-time data includes state data, measurement data and / or meter growth data. The to-be-monitored line network includes multiple lines, each line includes multiple stations, each station includes multiple devices, and each device is provided with multiple configuration points.

[0131] The basic data determination module 2 is connected with the data acquisition module 1. The basic data determination module 2 is configured to determine, for any configuration point on any device in the to-be-monitored line network, basic data according to real-time data at the configuration point, and store the basic data into a basic data table in a database. The basic data includes a configuration point number, an event time and a value. Fields of the basic data table include the configuration point number, the event time and the value.

[0132] The data classification module 3 is connected with the basic data determination module 2. The data classification module 3 is configured to determine, according to the basic data in the basic data table, state standard data, measurement standard data and meter growth standard data, store the state standard data into a state standard table in a database, store the measurement standard data into a measurement standard table in the database, and store the meter growth standard data into a meter growth standard table in the database.

[0133] Fields of the state standard table include a unique key, an event time, a data type, a configuration point number, a state value and a state duration. Fields of the measurement standard table include a unique key, an event time, a data type, a configuration point number, a measurement value and a state. Fields of the meter growth standard table include a unique key, an event time, a data type, a configuration point number, a monitoring value, a difference from a last result and an abnormal state code.

[0134] The computing module 4 is connected with the data classification module 3, and the computing module 4 is used for calculating the state index, the measurement index and the total energy consumption of the line network to be monitored according to the data in the state standard table, the measurement standard table and the meter growth standard table.

[0135] Compared with the prior art, the track transportation energy consumption monitoring system provided by the embodiment has the same beneficial effects as the track transportation energy consumption monitoring method provided by the first embodiment, and thus will not be described here.

[0136] Embodiment three

[0137] The embodiment provides an electronic device, including a memory and a processor, the memory is used for storing a computer program, and the processor runs the computer program to make the electronic device execute the track transportation energy consumption monitoring method of the first embodiment.

[0138] Optionally, the electronic device can be a server.

[0139] In addition, the embodiment of the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the track transportation energy consumption monitoring method of the first embodiment.

[0140] In the specification, each embodiment is described in a progressive manner, and each embodiment focuses on the difference from other embodiments, and the same or similar parts of each embodiment can be referred to each other.

[0141] The principles and implementation manners of the present application are described by using specific examples in the specification, and the above description of the embodiments is only used to help understand the method of the present application and its core idea; meanwhile, for the general technical personnel in the art, according to the idea of the present application, the specific implementation manner and application range can be changed. In conclusion, the content of the specification should not be understood as the limitation of the present application.

Claims

1. A rail transit energy consumption monitoring method, characterized in that, The rail transit energy consumption monitoring method comprises: acquiring real-time data at each configuration point on each device in a to-be-monitored line network; the real-time data comprises state data, measurement data and / or meter growth data; the to-be-monitored line network comprises a plurality of lines, each line comprises a plurality of stations, each station comprises a plurality of devices, and each device is provided with a plurality of configuration points; for any configuration point on any device in the to-be-monitored line network, determining basic data according to the real-time data at the configuration point, and storing the basic data into a basic data table in a database; the basic data comprises a configuration point number, an event time and a value; the fields of the basic data table comprise a configuration point number, an event time and a value; caching the basic data of each configuration point on each device in the to-be-monitored line network into redis; determining state standard data, measurement standard data and meter growth standard data according to the basic data in the basic data table, and storing the state standard data into a state standard table in the database, storing the measurement standard data into a measurement standard table in the database, and storing the meter growth standard data into a meter growth standard table in the database; the fields of the state standard table comprise a unique key, an event time, a data type, a configuration point number, a state value and a state duration; the fields of the measurement standard table comprise a unique key, an event time, a data type, a configuration point number, a measurement value and a state; the fields of the meter growth standard table comprise a unique key, an event time, a data type, a configuration point number, a monitoring value, a difference from a last result and an abnormal state code; wherein, determining the state standard data, the measurement standard data and the meter growth standard data according to the basic data in the basic data table specifically comprises: for any basic data in the basic data table, determining the data type of the basic data according to the configuration point number of the basic data; the data type is state data, measurement data or meter growth data; if the data type of the basic data is state data, automatically generating a unique key, and determining a state duration according to the configuration point number of the basic data, the event time of the basic data and the basic data stored in redis, to generate state standard data; the value of the basic data is the state value of the state standard data; if the data type of the basic data is measurement data, automatically generating a unique key, and determining the state of the basic data according to the value of the basic data and a preset value range, to generate measurement standard data; the value of the basic data is the measurement value of the measurement standard data; the state of the basic data is normal or abnormal; if the data type of the basic data is meter growth data, automatically generating a unique key, and determining a difference from a last result and an abnormal state code according to the value of the basic data, the event time of the basic data and the basic data stored in redis, to generate meter growth standard data; the value of the basic data is the monitoring value of the meter growth standard data; According to the data in the state standard table, the metric standard table and the meter growth standard table, the state indicators, the metric indicators and the total energy consumption of the line network to be monitored are calculated; the energy consumption statistics have hierarchical characteristics, and the sum of the power consumption of multiple same types of devices in a station is the sum of the energy consumption of the same type of devices in the station; the sum of the power consumption of multiple types of devices in a station is the total power consumption of the station; the sum of the power consumption of all stations on the same line is the total power consumption of the line; to calculate the related indicators of energy consumption, the configuration point results need to be hierarchically summarized in combination with the energy consumption relationship information; in the energy consumption system, starting from the most basic energy consumption configuration point, to the final total energy consumption relationship of the line network, the complete energy consumption relationship in the line network is manually set according to the energy consumption unit level, and is stored in the energy consumption relationship table; after the energy consumption relationship is established, the total energy consumption at each level is calculated according to the energy consumption relationship formula through the pre-set spark program every day.

2. The rail transit energy consumption monitoring method according to claim 1, characterized in that, Real-time data at each configuration point on each device in the line network to be monitored is obtained, specifically including: Metric data and meter growth data at each configuration point on each device in the line network to be monitored are obtained at a set time interval; When the state of any configuration point on any device in the line network to be monitored changes, the state data at the configuration point is obtained.

3. The rail transit energy consumption monitoring method according to claim 1, characterized in that, According to the real-time data at the configuration point, the basic data is determined, specifically including: The data format of the real-time data at the configuration point is normalized to obtain the basic data.

4. The rail transit energy consumption monitoring method according to claim 1, characterized in that, The rail transit energy consumption monitoring method further includes: Traction energy consumption historical data and lighting power consumption historical data of each line in the line network to be monitored are obtained; the traction energy consumption historical data includes monthly traction energy consumption per hundred kilometers of the corresponding line; the lighting power consumption historical data includes power lighting power consumption of each station on the corresponding line; For any line in the line network to be monitored, the Holt-Winters method is used to predict the traction specific energy consumption of the line in a future set period according to the traction energy consumption historical data of the line; The Holt-Winters method is used to predict the power lighting specific energy consumption of the line in a future set period according to the lighting power consumption historical data of the line.

5. The rail transit energy consumption monitoring method according to claim 1, wherein, The rail transit energy consumption monitoring method further includes: Line indicator vectors of each line in the line network to be monitored are obtained; the line indicator vectors include average train passenger full load, average train motor power, average train auxiliary equipment power and average station spacing; The line indicator vectors of each line in the line network to be monitored are clustered to determine the similarity of the lines in the line network to be monitored.

6. The rail transit energy consumption monitoring method according to claim 1, wherein, The rail transit energy consumption monitoring method further includes: Station indicator vectors of each station in the line network to be monitored are obtained; the station indicator vectors include power lighting energy consumption, station area, total power of air conditioning equipment, total power of elevator equipment and station passenger flow collection and distribution; The station indicator vectors of each station in the line network to be monitored are clustered to determine the similarity between the stations in the line network to be monitored.

7. A rail transit energy consumption monitoring system applied to the rail transit energy consumption monitoring method of any one of claims 1-6, characterized in that, The rail transit energy consumption monitoring system includes: The data acquisition module is configured to acquire real-time data at each configuration point on each device in the to-be-monitored line network; the real-time data includes state data, measurement data, and / or meter growth data; the to-be-monitored line network includes a plurality of lines, each line includes a plurality of stations, each station includes a plurality of devices, and each device is provided with a plurality of configuration points; The basic data determination module is connected with the data acquisition module and is configured to, for any configuration point on any device in the to-be-monitored line network, determine basic data according to the real-time data at the configuration point, and store the basic data into a basic data table in a database; the basic data includes a configuration point number, an event time, and a value; and fields of the basic data table include the configuration point number, the event time, and the value; The data classification module is connected with the basic data determination module and is configured to, according to the basic data in the basic data table, determine state standard data, measurement standard data, and meter growth standard data, and store the state standard data into a state standard table in the database, store the measurement standard data into a measurement standard table in the database, and store the meter growth standard data into a meter growth standard table in the database; fields of the state standard table include a unique key, an event time, a data type, a configuration point number, a state value, and a state duration; fields of the measurement standard table include a unique key, an event time, a data type, a configuration point number, a measurement value, and a state; and fields of the meter growth standard table include a unique key, an event time, a data type, a configuration point number, a monitoring value, a difference from a last result, and an abnormal state code. The calculation module is connected with the data classification module and is configured to calculate a state index, a measurement index, and total energy consumption of the to-be-monitored line network according to data in the state standard table, the measurement standard table, and the meter growth standard table.

8. An electronic device, comprising: The electronic device includes a memory and a processor; the memory is configured to store a computer program; and the processor is configured to run the computer program to enable the electronic device to perform the rail transit energy consumption monitoring method in any one of claims 1 to 6.

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