CN114493926A - Method and system for line loss lean management and electricity theft prevention based on terminal edge computing - Google Patents

Abstract

The invention belongs to the field of power equipment, and particularly relates to a line loss lean management and electricity larceny prevention method and system based on terminal edge calculation. The method comprises the following steps: s1: collecting power information of each power utilization node through a concentrator, and S2: deploying an edge calculation module in each concentrator, and then calculating the hour, day and month of the line loss rate corresponding to the current equipment node and the annual statistical result; s3: the concentrator sends the own equipment number, the real-time collected power information and the calculated line loss data statistical result to the cloud server; s4: the cloud server analyzes whether the line loss fault exists in the whole power utilization transformer area or not, and judges whether the transformer area has electricity stealing behavior or not when the line loss abnormal state exists; s5: and the cloud server sends the line loss fault result and the electricity stealing judgment conclusion to corresponding station area management personnel. The method and the system solve the problems that the line loss analysis is not timely in the conventional power management system, the cloud server has heavy processing tasks, the risk resistance is weak and the like.

Description

Method and system for line loss lean management and electricity theft prevention based on terminal edge computing

technical field

The invention belongs to the field of power equipment, and in particular relates to a method and system for lean management of line losses and anti-power theft based on terminal edge computing.

Background technique

In the power grid, the unreasonable configuration of transmission and distribution transformers will cause the transmission and distribution transformers to be in no-load or light-load operation for a long time, resulting in a substantial increase in power loss. There are three main reasons for the power loss on the line: 1. The three-phase grid is unbalanced and the power factor is low. 2. The grid layout is unreasonable, the power supply line is too long or the accuracy of the measuring equipment is poor. 3. The equipment management and maintenance are not standardized, and there are behaviors such as electricity leakage and electricity theft.

Among them, there is often a three-phase load unbalance phenomenon in distribution lines. When the three-phase load unbalance degree is more than 20%, the current in the line will increase significantly, resulting in a large increase in power loss. For example, in industrial and mining enterprises, many electrical equipment are inductive electrical equipment, and the inductive load will absorb the active power of the system, and at the same time, the power system needs to provide a large amount of reactive power to operate normally. In the process of transportation and distribution of reactive power provided by the system, when there are many transmission lines involved, and it needs to be converted and transmitted by transformers, it is easy to lead to an increase in reactive power loss, resulting in a decrease in the power factor of the grid.

In the construction of power grid, if the transformers are not properly arranged, the power supply point and the load center are greatly deviated, the power supply is supplied in a circuitous way or the power is transmitted near and far, or there are problems such as too small wire cross-section or long power supply radius; Power consumption has increased significantly. At the same time, if the node metering equipment has poor accuracy in metering, it is easy to cause power loss. In some electricity metering, there are often problems such as unreasonable metering methods, high supply and low volume. In addition, there are errors in the magnification of the electric energy meter or transformer, and the unreasonable configuration of the metering circuit will also lead to an increase in electric energy loss to varying degrees.

Irregular power grid management is the most important man-made cause of line loss. If the equipment is not properly maintained, leakage, discharge and other phenomena may occur; accurate statistics are not carried out when copying and checking, and there are phenomena such as estimated copying, under-copying and omission of copying; all of which can cause statistical power consumption and actual power consumption. If the error is large, the statistical result of line loss will be increased. At the same time, there are various behaviors of stealing electricity in various places, which is also one of the reasons for the increasing line loss.

In the existing power management system, it is usually necessary to analyze all power-consuming nodes in the station area on the cloud server side according to large-scale historical data, and then estimate the comprehensive line loss analysis result of the line. By comparing the calculated theoretical line loss with the statistical actual line loss, the rationality of the structure and operation of the power supply network and the scientificity of the power supply management can be identified. And timely find out the problems of metering device, equipment performance, power management, operation mode, theoretical calculation, copying statistics, etc., so as to take loss reduction measures to provide decision support for the economic operation of power grids of power operating enterprises.

However, conventional line loss analysis and evaluation are usually completed in back-end data centers such as cloud servers, and the cloud server needs to comprehensively analyze and process massive amounts of data when the settlement cycle is reached. The data processing process of this management method is cumbersome and highly dependent on the cloud, and it also suffers from monitoring lag.

SUMMARY OF THE INVENTION

In order to solve the problems that the line loss analysis in the existing power management system is not timely, the line loss analysis is highly dependent on the cloud server, the anti-risk capability is weak, the amount of data in the line loss analysis process is too large, and the data processing process is inflexible. The present invention provides a method and system for line loss lean management and electricity theft prevention based on terminal edge computing.

The present invention adopts the following technical solutions to realize:

A method for line loss lean management and electricity theft prevention based on terminal edge computing, the method is used to realize electricity consumption information, statistics and analysis at the terminal of power equipment, and then perform lean management of line loss in the station area, and timely respond to line loss faults and prevent theft of electricity. The line loss lean management and electricity theft prevention method specifically includes the following processes:

S1: According to the preset collection frequency, collect the power information of each power-consuming node through the concentrator, including voltage, current, active power, reactive power, and frozen power.

S2: Deploy an edge computing module in each concentrator, and after each collection is completed, calculate the hourly, daily hourly and daily hours of the line loss rate corresponding to the current equipment node according to the historical information of the collected power information within one hour. , month, and year statistics.

S3: The concentrator sends its own device number and power information collected in real time to the cloud server for backup storage, and sends updated data of the calculated line loss data statistics to the cloud server every hour.

S4: The cloud server analyzes whether there is a line loss fault in the entire station area according to the line loss rate statistics calculated by each concentrator obtained, and judges whether there is an electricity stealing behavior in the station area when the line loss abnormal state occurs;

S5: The cloud server sends the analyzed line loss fault results, power stealing judgment conclusions, and corresponding power-consuming node information to the corresponding station area management personnel, and sends corresponding fault handling suggestions at the same time.

As a further improvement of the present invention, in step S1, the power information collected by the collector also includes hourly frozen data of the electric meter, daily frozen data of the electric meter, hourly frozen data of the circuit breaker, and daily frozen data of the circuit breaker.

As a further improvement of the present invention, the concentrator also includes a storage module. The storage module is used to store the historical information of the power information collected within one hour, and the statistical result of the line loss rate of the current equipment node calculated according to the historical information.

As a further improvement of the present invention, in step S1, the storage module stores the historical information collected by the concentrator and the calculated statistical results in a specific storage unit respectively; and covers the historical information of the stored power information every hour , and update the statistical results of the line loss rate at the same time.

As a further improvement of the present invention, in step S2, the statistical results of the line loss rate respectively include: the starting power of the equipment, the ending power of the equipment, the line loss rate of the last hour, the line loss rate of the last day; the A-phase line of the last day Loss rate, B-phase line loss rate of the previous day, C-phase line loss rate of the previous day, bus loss rate of the current month, A-phase line loss rate of the current month, B-phase line loss rate of the current month, C-phase line loss rate of the current month, and bus loss rate of the current year rate, current year A-phase line loss rate, current year B-phase line loss rate, current year C-phase line loss rate.

As a further improvement of the present invention, the edge computing module in the collector completes the calculation of the statistical results of the line loss rate through a statistical model. Before the statistical model interacts with the data center in the cloud server, the model needs to be registered or updated. Or update process as follows:

S01: The cloud server reads the latest version number and model name of the statistical model.

S02: The cloud server checks the model name corresponding to the current version number in the comparison concentrator and the model name corresponding to the latest version number in the cloud server, judges whether the two are consistent, and makes the following decisions:

(1) When the two are consistent, the model of the current version number is determined to be valid, and the specified model content is read.

(2) When the two are inconsistent, it is determined that the model with the current version number is invalid, and the statistical model in the concentrator is updated according to the latest version of the statistical model in the cloud server.

S03: The cloud server reads the registration information of the statistical model in the concentrator.

S04: The cloud server checks whether the statistical model is registered, and makes the following decisions:

(1) Yes, it is determined that the registration or update process has been completed.

(2) Otherwise, complete the setting of the model registration process again.

S05: After completing the model registration, the concentrator requests the server for parameter information for calculating the statistical result of the line loss rate.

As a further improvement of the present invention, in steps S2 and S3, the data processing process of the statistical model is as follows:

S001: Request the raw data used to calculate the statistical result of line loss rate, and judge whether the data is obtained successfully:

(1) If yes, go to the next step.

(2) Otherwise, the calculation process is interrupted, and an error message including the cause of the data acquisition error is printed.

S002: Calculate the statistical result of the line loss rate according to the original data obtained in the previous step, and judge whether the calculation result is legal:

(1) If yes, go to the next step.

(2) Otherwise, the data upload will be interrupted, and an error message containing an illegal data type will be printed.

S003: Send the statistical result of the line loss rate calculated in the previous step to the cloud server, and judge whether the upload is successful according to the return information of the cloud server:

(1) is the message that the print data is uploaded successfully.

(2) Otherwise, print the reason for upload failure.

As a further improvement of the present invention, in step S4, there is a fault analysis tool running in the cloud server, and the fault analysis tool is used to analyze the possible existence in the whole station area or power supply network according to the historical statistical data of the line loss rate of each node. line damage or electricity theft. And locate the corresponding risk nodes, and issue early warning signals for the risk nodes.

The present invention also includes a line loss lean management and electricity theft prevention system based on terminal edge computing. Manage line damage, deal with line loss faults in a timely manner, and monitor electricity theft. Line loss lean management and anti-theft power system include: concentrator, edge computing module, cloud server.

The concentrator is installed at each power node in the power supply station area, and the concentrator is used to collect power information of each node in the power supply station area, and the power information is sent to an edge computing module and a cloud server.

The edge computing module is electrically connected to the concentrator, and the edge computing module is used to receive the power information collected by the concentrator, and calculate the statistical result of the line loss rate in the current node according to the historical data of the power information in a preset period; and Send the line loss rate statistics to the cloud server.

The cloud server is used to store the power information of each power node and the statistical results of the line loss rate. The cloud server also includes a power stealing behavior analysis tool, which is used to judge whether there is a line loss fault or power stealing behavior in the power supply station area or the entire power supply network according to the statistical results of the line loss rate of each node; and locate the corresponding risk. node, and issue early warning signals for risk nodes.

As a further improvement of the present invention, the line loss lean management and electricity theft prevention system further includes a management terminal, which is connected to the cloud server in communication; the management area of each management terminal corresponds to one or more power consumption nodes in the application radio area. The management terminal is distributed to the corresponding management personnel, and when the cloud server locates the risk node, it sends an early warning signal to the corresponding management terminal; the management terminal is a dedicated terminal, or a general mobile terminal running a power management APP; the management terminal also Used to query the historical data of each node in the management area.

The technical scheme provided by the invention has the following beneficial effects:

The present invention deploys a data processing module with edge computing capability in the concentrator that automatically collects and uploads power information, acquires relevant data when the concentrator uploads the collected data, and uses the relevant data to locally complete the current node center line loss statistics The calculation process of the result. At the same time, upload the line loss data of the current node to the cloud server. Because this implementation can obtain the line loss statistics of each node immediately after the collection data of the concentrator is generated; therefore, this greatly improves the timeliness of the speed limit data, which lays a solid foundation for the rapid processing of line loss faults and the timely detection of electricity theft. Base.

The method of the invention changes the centralized processing of the data processing method for evaluating line loss in the power supply network into a distributed processing mode based on the edge computing capability of the terminal. It can greatly reduce the computing pressure of the back-end data center; at the same time, it can also reduce the impact of failures such as back-end server downtime on the normal operation of the power supply network, and improve the overall security and fault tolerance of the system.

Although the present invention does not complete the line loss data statistics task of each node in the backend server, the backend server still completes the line loss fault analysis of the power supply network and the determination of electricity stealing behavior based on the data of all nodes. And the backend server sends the judgment results to each concentrator or management terminal, which is convenient for management personnel to deal with faults and eliminate risks in a timely manner.

Description of drawings

The accompanying drawings are used to provide a further understanding of the present invention, and constitute a part of the specification, and are used to explain the present invention together with the embodiments of the present invention, and do not constitute a limitation to the present invention. In the attached image:

FIG. 1 is a flowchart of steps of a method for lean management of line losses and preventing electricity theft based on terminal edge computing provided in Embodiment 1 of the present invention;

2 is a flowchart of a registration or update process of a statistical model in Embodiment 1 of the present invention;

FIG. 3 is a flowchart of the data processing process of the statistical model in Embodiment 1 of the present invention.

FIG. 4 is a system module diagram of a system for lean management of line losses and anti-power theft based on terminal edge computing provided in Embodiment 2 of the present invention.

Detailed ways

In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

Example 1

As shown in FIG. 1 , this embodiment provides a method for lean management of line losses and anti-power theft based on terminal edge computing. Lean management of damages, timely response to line damage failures and prevention of electricity theft.

The method provided in this embodiment may be implemented by running on basic micro-applications or software running on Zhixin and Huawei system platforms. During the implementation of the method, key data such as voltage, current, active power, and frozen power of scorpionfish nodes in the entire station area can be monitored in real time through the upgrading of distribution network intelligent equipment and the acquisition of sensing equipment such as centralized comprehensive coverage. At the same time, based on the computing capability of the edge computing module on the terminal side, the method of this embodiment can perform statistical analysis of line loss by station on the spot, and send abnormal conditions such as line loss out of tolerance to the data center of the cloud and the server in time. In turn, it provides a data basis for fault analysis and power theft investigation. Through the processing method provided in this embodiment, real-time supervision of low-voltage line losses can be realized, and work such as line loss management and power theft verification can be effectively supported. It can also reduce the data processing pressure of the cloud server, and eliminate the impact of the downtime or other failures of the server equipment in the cloud on the stability of the power grid operation.

The line loss lean management and electricity theft prevention method provided by this embodiment specifically includes the following processes:

S1: According to the preset collection frequency, collect the power information of each power-consuming node through the concentrator, including voltage, current, active power, reactive power, and frozen power.

S2: Deploy an edge computing module in each concentrator, and after each collection is completed, calculate the hourly, daily hourly and daily hours of the line loss rate corresponding to the current equipment node according to the historical information of the collected power information within one hour. , month, and year statistics.

Specifically, the statistical results of the line loss rate respectively include: the starting power of the equipment, the ending power of the equipment, the line loss rate of the last hour, and the line loss rate of the previous day; the line loss rate of phase A on the previous day and the phase B of the previous day Line loss rate, last day C-phase line loss rate, current month bus line loss rate, current month A-phase line loss rate, current month B-phase line loss rate, current month C-phase line loss rate, current year bus loss rate, current year A-phase line loss rate , the current year B-phase line loss rate, the current year C-phase line loss rate.

Among them, the edge computing model completes the data update of the line loss rate in the previous hour every 1h. Complete the data update of the previous day's line loss rate, the previous day's A-phase line loss rate, the previous day's B-phase line loss rate, and the previous day's C-phase line loss rate; every 28-31d (from the date of the current month) The number is determined) to complete the data update of the current month bus loss rate, the current month A phase line loss rate, the current month B phase line loss rate, and the current month C phase line loss rate; complete the current year bus loss rate, the current year A phase line loss rate, The data of the current year's B-phase line loss rate and the current year's C-phase line loss rate are updated.

S3: The concentrator sends its own device number and power information collected in real time to the cloud server for backup storage, and sends updated data of the calculated line loss data statistics to the cloud server every hour.

S4: The cloud server analyzes whether there is a line loss fault in the entire station area according to the line loss rate statistics calculated by each concentrator obtained, and judges whether there is an electricity stealing behavior in the station area when the line loss abnormal state occurs;

There is a fault analysis tool running in the cloud server. The fault analysis tool is used to analyze the possible line loss faults or electricity stealing behaviors in the entire station area or power supply network according to the historical statistical data of the line loss rate of each node. And locate the corresponding risk nodes, and issue early warning signals for the risk nodes.

S5: The cloud server sends the analyzed line loss fault results, power stealing judgment conclusions, and corresponding power-consuming node information to the corresponding station area management personnel, and sends corresponding fault handling suggestions at the same time.

In the edge computing module of this embodiment, a statistical model is used to complete the calculation of the statistical results of the line loss rate. Before the statistical model interacts with the data center in the cloud server, it is necessary to register or update the model. As shown in FIG. The process of registering or updating a model is as follows:

S01: The cloud server reads the latest version number and model name of the statistical model.

S02: The cloud server checks the model name corresponding to the current version number in the comparison concentrator and the model name corresponding to the latest version number in the cloud server, judges whether the two are consistent, and makes the following decisions:

(1) When the two are consistent, the model of the current version number is determined to be valid, and the specified model content is read.

(2) When the two are inconsistent, it is determined that the model with the current version number is invalid, and the statistical model in the concentrator is updated according to the latest version of the statistical model in the cloud server.

S03: The cloud server reads the registration information of the statistical model in the concentrator.

S04: The cloud server checks whether the statistical model is registered, and makes the following decisions:

(1) Yes, it is determined that the registration or update process has been completed.

(2) Otherwise, complete the setting of the model registration process again.

S05: After completing the model registration, the concentrator requests the server for parameter information for calculating the statistical result of the line loss rate.

After the statistical model is registered, data statistics and calculation tasks can be carried out normally, and the calculation process is completed on the local terminal close to the collection. As shown in Figure 3, the data processing process of the statistical model is roughly as follows:

S001: Request the raw data used to calculate the statistical result of line loss rate, and judge whether the data is obtained successfully:

(1) If yes, go to the next step.

(2) Otherwise, the calculation process is interrupted, and an error message including the cause of the data acquisition error is printed.

S002: Calculate the statistical result of the line loss rate according to the original data obtained in the previous step, and judge whether the calculation result is legal:

(1) If yes, go to the next step.

(2) Otherwise, the data upload will be interrupted, and an error message containing an illegal data type will be printed.

S003: Send the statistical result of the line loss rate calculated in the previous step to the cloud server, and judge whether the upload is successful according to the return information of the cloud server:

(1) is the message that the print data is uploaded successfully.

(2) Otherwise, print the reason for upload failure.

In this example, the collector includes a data storage module. The power information collected by the collector also includes hourly freezing data of the electric meter, daily freezing data of the electric meter, hourly freezing data of the circuit breaker, and daily freezing data of the circuit breaker. The storage module stores the historical information collected by the concentrator and the calculated statistical results in specific storage units respectively; and covers the stored historical information of the power information every hour, and simultaneously updates the statistical results of the line loss rate. In the storage module, the stored data card types are shown in Table 1:

Table 1: Statistics related to the collected and calculated line loss rate

In this embodiment, a data processing module with edge computing capability is deployed in the concentrator that automatically collects and uploads power information, and the concentrator acquires relevant data while uploading the collected data, and uses the relevant data to locally complete the line loss of the current node The calculation process of statistical results. At the same time, upload the line loss data of the current node to the cloud server. Because this implementation can obtain the line loss statistics of each node immediately after the collection data of the concentrator is generated; therefore, this greatly improves the timeliness of the speed limit data, which lays a solid foundation for the rapid processing of line loss faults and the timely detection of electricity theft. Base.

The method provided in this embodiment changes the centralized processing of the data processing method for evaluating line loss in the power supply network into a distributed processing mode based on the edge computing capability of the terminal. It can greatly reduce the computing pressure of the back-end data center; at the same time, it can also reduce the impact of failures such as back-end server downtime on the normal operation of the power supply network, and improve the overall security and fault tolerance of the system.

Although this embodiment does not complete the line loss data statistics task of each node in the background server, the power supply network line loss fault analysis and power stealing behavior determination based on all node data are still completed by the background server. And the backend server sends the judgment results to each concentrator or management terminal, which is convenient for management personnel to deal with faults and eliminate risks in a timely manner.

Example 2

This embodiment provides a line loss lean management and electricity theft prevention system based on terminal edge computing. The system adopts the line loss lean management and electricity theft prevention method based on terminal edge computing as in Manage the line loss, deal with line loss faults in a timely manner and monitor the behavior of electricity theft.

Among them, as shown in Figure 4, the line loss lean management and electricity theft prevention system includes: a concentrator, an edge computing module, and a cloud server.

The concentrator is installed at each power-consuming node in the power supply station area. The concentrator is used to collect power information of each node in the power supply station area, and the power information is sent to an edge computing module and a cloud server. A storage module is also included in the concentrator. The storage module is used to store the historical information of the power information collected within one hour, and the statistical result of the line loss rate of the current equipment node calculated according to the historical information.

The concentrator in this embodiment adopts the existing equipment that can realize remote meter reading. Among them, the concentrator contains a memory for storing the collected data, and a communication system for connecting the cloud server in the background data. The concentrator and cloud server use the communication network based on broadband power carrier communication (HPLC) or electric operator to realize the communication connection.

The edge computing module is electrically connected to the concentrator, and the edge computing module is used to receive the power information collected by the concentrator, and calculate the statistical result of the line loss rate in the current node according to the historical data of the power information in a preset period; and Send the line loss rate statistics to the cloud server.

The edge computing model in this embodiment is a small processor, and the processor can be connected to the concentrator to directly acquire various collection data collected by the concentrator. And complete data processing and operation.

The cloud server is used to store the power information of each power node and the statistical results of the line loss rate. The cloud server also includes a power stealing behavior analysis tool, which is used to determine whether there is a line loss fault or power stealing behavior in the power supply station area or the entire power supply network according to the statistical results of the line loss rate of each node; and locate the corresponding risk. node, and issue early warning signals for risk nodes.

In this embodiment, the system for lean management of line loss and anti-electricity theft further includes a management terminal, which is connected to the cloud server in communication. The management area of each management terminal applies to one or more power-consuming nodes in the radio station area. The management terminal is distributed to the corresponding management personnel, and when the cloud server locates the risk node, it sends an early warning signal to the corresponding management terminal. The management terminal provided in this embodiment may be a dedicated terminal, or a general mobile terminal running a power management APP, such as a mobile phone, a tablet computer, and the like. For different concentrators or power consumption nodes. In the power management APP of this embodiment, node login can also be performed by scanning a code or the like, and then the historical monitoring data of each node in the management area can be queried through the management terminal.

The above descriptions are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present invention shall be included in the protection of the present invention. within the range.

Claims (10)

1. A method for lean management of line loss and anti-power theft based on terminal edge computing, which is used to realize electricity consumption information, statistics and analysis at the terminal of power equipment, and then perform lean management of line loss in the station area, so as to respond to line loss in time It is characterized in that: the method for lean management of line loss and preventing electricity theft includes the following processes: S1: According to the preset collection frequency, collect the power information of each power consumption node through the concentrator, including voltage, current, active power, reactive power and frozen power; S2: Deploy an edge computing module in each concentrator, and after each collection is completed, calculate the hourly, daily hourly and daily hours of the line loss rate corresponding to the current equipment node according to the historical information of the collected power information within one hour. , month, and year statistics; S3: The concentrator sends its own device number and real-time collected power information to the cloud server for backup storage, and at the same time sends the updated data of the calculated line loss data statistics to the cloud server every hour; S4: The cloud server analyzes whether there is a line loss fault in the entire radio station area according to the obtained statistical results of the line loss rate calculated by each concentrator, and judges whether there is an electricity stealing behavior in the station area when there is an abnormal state of line loss; S5: The cloud server sends the analyzed line loss fault results, power stealing judgment conclusions, and corresponding power node information to the corresponding station area management personnel, and sends corresponding fault handling suggestions at the same time. 2. The line loss lean management and the method for preventing electricity theft based on terminal edge computing as claimed in claim 1, characterized in that: in step S1, the power information collected by the collector also includes hourly freezing data of the electric meter, daily freezing of the electric meter Data, hourly freeze data for circuit breakers, daily freeze data for circuit breakers. 3. The line-loss lean management and anti-electricity-stealing method based on terminal edge computing according to claim 1, wherein: the concentrator further comprises a storage module; the storage module is used to store the collected data within one hour. The historical information of the received power information, and the statistical result of the line loss rate of the current equipment node calculated according to the historical information. 4. The line-loss lean management and anti-electricity-stealing method based on terminal edge computing as claimed in claim 3, characterized in that: in step S1, the storage module separates the historical information collected by the concentrator and the calculated statistical result. It is stored in a specific storage unit; and the historical information of the stored power information is overwritten every hour, and the statistical results of the line loss rate are updated at the same time. 5. The line loss lean management and the method for preventing electricity theft based on terminal edge computing as claimed in claim 1, characterized in that: in step S2, the statistical results of the line loss rate respectively include: equipment starting power, equipment ending power , last hour line loss rate, last day line loss rate; last day A phase line loss rate, last day B phase line loss rate, last day C phase line loss rate, current month bus line loss rate, current month A phase line loss rate Line loss rate, current month B phase line loss rate, current month C phase line loss rate, current year bus loss rate, current year A phase line loss rate, current year B phase line loss rate, current year C phase line loss rate. 6. The line loss lean management and the method for preventing electricity theft based on terminal edge computing as claimed in claim 1, it is characterized in that: the edge computing module in the described collector completes the calculation of line loss rate statistical result by a statistical model, so The above statistical model needs to be registered or updated before data interaction with the data center in the cloud server. The registration or update process of the model is as follows: S01: The cloud server reads the latest version number and model name of the statistical model; S02: The cloud server checks the model name corresponding to the current version number in the comparison concentrator and the model name corresponding to the latest version number in the cloud server, judges whether the two are consistent, and makes the following decisions: (1) When the two are consistent, the model of the current version number is determined to be valid, and the specified model content is read; (2) When the two are inconsistent, it is determined that the model of the current version number is invalid, and the statistical model in the concentrator is updated according to the statistical model of the latest version in the cloud server; S03: The cloud server reads the registration information of the statistical model in the concentrator; S04: The cloud server checks whether the statistical model is registered, and makes the following decisions: (1) Yes, it is determined that the registration or update process has been completed; (2) Otherwise, complete the setting of the model registration process again; S05: After completing the model registration, the concentrator requests the server for parameter information for calculating the statistical result of the line loss rate. 7. The line loss lean management and the method for preventing electricity theft based on terminal edge computing as claimed in claim 1, is characterized in that: in step S2 and S3, the data processing process of described statistical model is as follows: S001: Request the original data for calculating the statistical result of the statistical line loss rate, and judge whether the data is obtained successfully: if yes, go to the next step; otherwise, the calculation process is interrupted, and an error message including the cause of the data acquisition error is printed; S002: Calculate the statistical result of the line loss rate according to the original data obtained in the previous step, and judge whether the calculation result is legal: if yes, go to the next step; otherwise, the data upload is interrupted, and an error message containing an illegal data type is printed; S003: Send the statistical result of the line loss rate calculated in the previous step to the cloud server, and judge whether the upload is successful according to the return information of the cloud server: if yes, print a message that the data upload is successful, otherwise, print the reason for the upload failure. 8. The line-loss lean management and anti-electricity-stealing method based on terminal edge computing as claimed in claim 1, wherein in step S4, a fault analysis tool is run in the cloud server, and the fault analysis tool uses Based on the historical statistical data of the line loss rate of each node, it can analyze the possible line loss faults or electricity stealing behaviors in the entire station area or power supply network; and locate the corresponding risk nodes, and issue early warning signals for the risk nodes. 9. A line loss lean management and anti-theft system based on terminal edge computing, characterized in that: it adopts the line loss lean management and anti-theft system based on terminal edge computing as described in any one of claims 1-8 The electricity method is used to manage the line loss of all nodes in the station area, deal with the line loss fault and monitor the electricity stealing behavior in time; the line loss lean management and electricity stealing prevention system includes: a concentrator, which is installed at each power-consuming node in the power supply station area, the concentrator is used to collect power information of each node in the power supply station area, and the power information is sent to an edge computing module and a cloud server; an edge computing module, which is electrically connected to the concentrator, and the edge computing module is used to receive the power information collected by the concentrator, and calculate the line loss in the current node according to the historical data of the power information in a preset period statistics results of line loss rate; and send the statistics results of line loss rate to the cloud server; and The cloud server is used to store the power information of each power-consuming node and the statistical result of the line loss rate; the cloud server also includes a power theft behavior analysis tool, which is used to judge according to the statistical result of the line loss rate of each node Whether there is a line loss fault or electricity stealing behavior in the power supply station area or the entire power supply network; and locate the corresponding risk node, and issue an early warning signal for the risk node. 10. The line-loss lean management and electricity-stealing prevention system based on terminal edge computing according to claim 9, wherein the line-loss lean management and electricity-stealing prevention system further comprises a management terminal, which communicates with the cloud server connection, the management area of each management terminal is to one or more power-consuming nodes in the application radio area; the management terminal is distributed to the corresponding management personnel, and when the cloud server is located to the risk node, it is sent to the corresponding management terminal. An early warning signal is issued; the management terminal is a dedicated terminal, or a general mobile terminal running a power management APP; the management terminal is also used for querying the historical data of each node in the management area.

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