Smart electric meter data management method and system based on big data

Abstract

The invention discloses a smart electric meter data management method and system based on big data, and belongs to the technical field of electric meter management. The system comprises a data acquisition module, a power consumption analysis module, an electric meter management module, a data storage module and a network communication module. The data acquisition module is used for acquiring historical logs and electricity utilization information of an electricity meter; the electricity utilization analysis module is used for setting a sample record and a reference record, establishing a standard power broken line graph and establishing an electricity utilization trend graph for each electricity meter; the ammeter management module analyzes the standard power broken line graph and the power consumption trend graph, calculates a first coefficient between ammeters and a second coefficient between sample records, thereby calculating an abnormal index, defining an abnormal ammeter and performing early warning; the data storage module is used for regularly generating power utilization records and storing the power utilization records into historical logs; and the network communication module is used for receiving an instruction issued by a back-end system through a CoAP protocol, and uploading the power utilization record in the data storage module to the back-end system by using an MQTT protocol.

Description

Technical Field

[0001] The present invention relates to the technical field of electric meter management, and specifically to a method and system for managing smart electric meter data based on big data. Background Art

[0002] With the wide application of smart electric meters, power companies can collect continuous and fine-grained user electricity consumption data. This data is huge in volume and has high frequency. Although it provides detailed electricity consumption information and services for consumers and enterprises, it also brings challenges to data storage, processing, and analysis, highlighting the necessity of data analysis.

[0003] At present, for the abnormal supervision of the number of electric meters, the method of comparing historical electricity consumption records is usually adopted, and there are certain problems with this method. On the one hand, when the electricity consumption object is an office building or other business premises, even the same electric meter has the situation of frequent change of electricity consumption objects. Different electricity consumption objects have different electricity consumption patterns, and simply comparing the electricity quantity threshold or power threshold will cause a large number of false alarms, affecting the accuracy of information. On the other hand, due to industry, scale, and space limitations in the same place, there will be a certain degree of correlation and similarity even among different electricity consumption objects. If the prior art cannot effectively analyze these correlations, it naturally cannot detect abnormal electricity consumption situations in a timely manner. Therefore, at present, an efficient and intelligent technical solution for electric meter data supervision is needed to solve the above problems. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for managing smart electric meter data based on big data to solve the problems raised in the above background art.

[0005] To solve the above technical problems, the present invention provides a method for managing smart electric meter data based on big data, including the following steps:

[0006] S100. Send the serial number and reporting period of the electric meter to be read to the Gateway through the CoAP protocol.

[0007] S200. After receiving the request, the Gateway reads the corresponding electric meter data according to the serial number.

[0008] S300. The Gateway collects the historical logs of the electric meters in the office building and real-time collects the electricity consumption information of the electric meters through the serial port or sensors.

[0009] S400. Obtain the electricity consumption records of each electric meter in the historical logs, set sample records and reference records, establish a standard power line graph through the sample records, and establish an electricity consumption trend graph for each electric meter according to the sample records and reference records.

[0010] S500. Analyze the standard power line chart and power consumption trend chart of each electric meter, calculate the first coefficient based on the relationship between the electric meter and all other electric meters, calculate the second coefficient based on the relationship between all sample records under the electric meter, calculate the anomaly index by synthesizing the first coefficient and the second coefficient, define the abnormal electric meters and issue early warnings.

[0011] S600. Display the power consumption information of each electric meter through a visual large screen, regularly generate power consumption records and store them in the historical log.

[0012] S700. The Gateway uses the MQTT protocol to upload JSON - formatted data to the HES. After the HES receives the data uploaded by MQTT, it transfers the payload into the database for subsequent data analysis and management.

[0013] In S300, the historical log refers to the power consumption records of all electric meters. Each power consumption record includes the power consumption information of an electric meter for one day, and the power consumption information is the power consumption at different times.

[0014] In S400, the specific steps are as follows:

[0015] S401. Obtain all the power consumption records of the electric meter EM u in the historical log, sort these power consumption records in descending order of time. The power consumption record ranked first is used as the sample record, and all the remaining power consumption records are used as ordinary records. Establish a standard power line chart with time as the horizontal axis and power consumption as the vertical axis for the sample record, set the number of sampling points c, evenly set c sampling points in the horizontal axis direction of the standard power line chart, and analyze and calculate the power consumption at the corresponding time of each sampling point.

[0016] Analyze the power change between the corresponding time of the sampling point and the starting time in the power line chart, split the time period according to the duration of different powers, multiply the power by the duration to obtain the power consumption corresponding to this time period, and sum the power consumption of all time periods to obtain the power consumption at the corresponding time of the sampling point.

[0017] S402. Establish a non - standard power line chart for ordinary records, set sampling points and calculate the power consumption at the corresponding time. Set the error amount r, calculate the difference in power consumption between each sampling point in the non - standard power line chart and the corresponding sampling point in the standard power line chart. When the difference in power consumption of all sampling points is less than r, the corresponding ordinary record is used as a reference record for the sample record. Set the reference records in sequence until the condition is not met and then stop setting.

[0018] S403. Analyze the power consumption of each sampling point under the sample record and all reference records, calculate the average value of all power consumptions at the same sampling point as the reference power consumption, and based on the reference power consumption at the corresponding time of each sampling point for the electric meter EM uEstablish an electricity consumption trend chart, and establish a standard power line chart and an electricity consumption trend chart for each electric meter respectively according to the above method.

[0019] In S500, the specific steps are as follows:

[0020] S501. Obtain the standard power line chart and the electricity consumption trend chart of each electric meter, and use the electricity consumption power corresponding to each sampling point on the standard power line chart as the reference power. Calculate the average reference power RFP by obtaining the average value of the reference powers of all sampling points within the standard power line chart. ave , and obtain the average reference electricity consumption RPC by obtaining the average value of the reference electricity consumptions of all sampling points within the electricity consumption trend chart. ave .

[0021] S502. Count the total number v of all electric meters, and obtain the average reference power u and the average reference electricity consumption of the electric meter EM. Count the total number j of all sampling points, and obtain the reference power u corresponding to the i-th sampling point under the electric meter EM and the reference electricity consumption . Substitute them into the formula to calculate the first coefficient u of the electric meter EM.

[0022]

[0023] In the formula, and are respectively the reference power and the reference electricity consumption corresponding to the i-th sampling point under the k-th electric meter except the electric meter EM u . and are respectively the average reference power and the average reference electricity consumption of the k-th electric meter except the electric meter EM u . Calculate the first coefficient of each electric meter respectively.

[0024] S503. Obtain all the electricity consumption records of the electric meter EM u , mark the last ordinary record as the reference record in the order of arrangement, and use the ordinary records after the marked record as sample records, and continue to set reference records for the sample records. And so on, until all ordinary records are set as sample records or reference records.

[0025] Each electric meter may have multiple sample records, and each sample record may also have multiple reference records. According to the order of arrangement of the electricity consumption records, take the electricity consumption record with the earliest order as the sample record, and set the reference record according to the ordinary records after the sample record. The reference record is after the sample record, and multiple reference records under the same sample record are arranged continuously.

[0026] Each electricity consumption record is at least one of the sample record or the reference record. When there is no reference record for the sample record, the sample record itself is used as the reference record, and the standard power fold line chart and the electricity consumption trend chart are the same chart.

[0027] S504. Establish the electricity consumption trend chart of the sample record h based on the sample record h and all its reference records. According to the electricity meter EM u 's standard power fold line chart and electricity consumption trend chart, and the standard power fold line chart and electricity consumption trend chart of each sample record under the electricity meter EM u Substitute them into the first coefficient formula for calculation, and take the calculation result as the second coefficient of the electricity meter EM u 's second coefficient.

[0028] When calculating the first coefficient, each electricity meter has only one standard power fold line chart and one electricity consumption trend chart. By analyzing and calculating with the standard power fold line charts and electricity consumption trend charts of all other electricity meters, the first coefficient is obtained.

[0029] When calculating the second coefficient, there are multiple sample records for the electricity meter, and there are different numbers of reference records under each sample record. Analyze and calculate the standard power fold line chart and electricity consumption trend chart corresponding to the sample record closest to the current time in terms of time, and the standard power fold line charts and electricity consumption trend charts corresponding to all other sample records, to obtain the second coefficient.

[0030] S505. Calculate the second coefficient of each electricity meter respectively, and multiply the first coefficient by the second coefficient to obtain the anomaly index. Set the index threshold yc, and regard the electricity meters with the anomaly index greater than yc as abnormal electricity meters, and warn the information of the abnormal electricity meters to the data center.

[0031] The first coefficient represents the difference in the electricity consumption patterns between the electricity meter and other electricity meters, and the second coefficient represents the difference in the electricity consumption patterns of the electricity meter at different stages of itself. Combining the first coefficient and the second coefficient to obtain the anomaly index can better reflect the abnormal situation information of the electricity meter's electricity consumption.

[0032] In S600, the data center displays the power and electricity consumption of each electricity meter in real time through the visualization large screen, highlights the standard power fold line chart and electricity consumption trend chart of the abnormal electricity meters, and regularly stores the electricity consumption records generated by the power of the electricity meters collected at different times into the historical log.

[0033] A smart electricity meter data management system based on big data includes a data acquisition module, an electricity consumption analysis module, an electricity meter management module, a data storage module, and a network communication module.

[0034] The system execution process includes:

[0035] 1. Data Request: HES sends the serial number of the electricity meter to be read to the Gateway via the CoAP protocol.

[0036] 2. Data Reading: After receiving the request, the Gateway reads the corresponding electricity meter data according to the serial number.

[0037] 3. Data Processing: The Gateway stores and converts the read data into JSON format for subsequent processing.

[0038] 4. Data Upload: The Gateway uses the MQTT protocol to upload the JSON-formatted data to HES.

[0039] 5. Data Storage: After receiving the data uploaded by MQTT, HES transfers the payload into the database for subsequent data analysis and management.

[0040] The data acquisition module is used to collect historical logs and electricity consumption information of each electricity meter.

[0041] The electricity consumption analysis module obtains the electricity consumption records of each electricity meter in the historical logs, sets sample records and reference records, establishes a standard power line graph through the sample records, and establishes an electricity consumption trend graph for each electricity meter according to the sample records and reference records.

[0042] The electricity meter management module analyzes the standard power line graph and electricity consumption trend graph of each electricity meter, calculates the first coefficient according to the relationship between the electricity meter and all other electricity meters, calculates the second coefficient according to the relationship between all sample records under the electricity meter, comprehensively calculates the anomaly index based on the first coefficient and the second coefficient, defines the abnormal electricity meters and issues warnings.

[0043] The data storage module is used to periodically generate electricity consumption records and store them in the historical logs.

[0044] The network communication module receives the instructions issued by the backend system via the CoAP protocol, and uploads the electricity consumption records in the data storage module to the backend system in JSON format using the MQTT protocol.

[0045] The overall system architecture design includes:

[0046] Electricity Meters: Used to monitor electricity consumption in real time, with the ability to receive instructions, and can read electricity consumption records through serial ports or sensors.

[0047] Electricity Meter Gateway (Gateway): As an intermediary, it receives instructions from HES, and according to the types of electricity meters from different manufacturers, uses different protocols, parameters or methods to be responsible for reading data from the electricity meters and uploading it to the backend system (HES).

[0048] Backend system HES (Head End System): It has the parameters of all electricity meters, is used to send instructions and parameters to the electricity meter gateway, receive and store electricity meter data, and perform data analysis and management.

[0049] Communication protocol: CoAP (Constrained Application Protocol): A lightweight network protocol suitable for low-bandwidth and high-latency environments. It is used to transmit instructions between the HES and the gateway.

[0050] MQTT (Message Queuing Telemetry Transport): A lightweight messaging protocol suitable for low-bandwidth and high-latency network environments, ensuring data reliability and security. It is used to transmit electricity meter data between the HES and the gateway.

[0051] Data format: JSON (JavaScript Object Notation): A lightweight data interchange format that is easy for humans to read and write, and is also easy for machines to parse and generate. It is used to format the read electricity meter data and upload it to the HES.

[0052] The mechanism for data reading through the electricity meter gateway (Gateway) aims to solve the problems of high latency and high overhead in the traditional electricity meter architecture in a low-bandwidth wireless communication network environment. A lightweight Internet of Things communication protocol is adopted to improve the efficiency and reliability of data transmission.

[0053] The data acquisition module includes a historical data acquisition unit and an electricity meter data acquisition unit.

[0054] The historical data acquisition unit is used to collect the electricity consumption records of all electricity meters. Each electricity consumption record includes the electricity consumption information of an electricity meter for one day, and the electricity consumption information is the electricity consumption power at different times.

[0055] The electricity meter data acquisition unit is used to collect the electricity consumption information of all electricity meters in real time.

[0056] The electricity consumption analysis module includes a record classification unit and a trend analysis unit.

[0057] The record classification unit is used to set sample records and reference records.

[0058] First, sort all the electricity consumption records of the electricity meter EM u in descending order of time. Take the first electricity consumption record as the sample record, and all the remaining electricity consumption records as ordinary records. Establish a standard power line graph with time as the horizontal axis and electricity consumption power as the vertical axis for the sample record, and establish a non-standard power line graph for the ordinary records.

[0059] Secondly, set the number of sampling points c, evenly set c sampling points in the horizontal direction of the standard power line graph and the non-standard power line graph, and analyze and calculate the power consumption at each sampling point corresponding time. Set the error amount r, and calculate the difference in power consumption between each sampling point in the non-standard power line graph and the corresponding sampling point in the standard power line graph.

[0060] Finally, when the difference in power consumption of all sampling points is less than r, use the corresponding ordinary record as the reference record of the sample record; set the reference records in order of arrangement until the condition is not met and then stop setting.

[0061] The trend analysis unit is used to establish an electricity consumption trend graph.

[0062] First, analyze the power consumption of each sampling point under the sample records and all reference records, and calculate the average value of all power consumptions at the same sampling point as the reference power consumption.

[0063] Then, based on the reference power consumption at each sampling point corresponding time for the electricity meter EM u Establish an electricity consumption trend graph, and establish a standard power line graph and an electricity consumption trend graph for each electricity meter according to the above method.

[0064] The electricity meter management module includes an abnormal analysis unit and a risk warning unit.

[0065] The abnormal analysis module is used to calculate the first coefficient of each electricity meter.

[0066] First, obtain the standard power line graph and the electricity consumption trend graph of each electricity meter, and use the power consumption corresponding to each sampling point on the standard power line graph as the reference power. Calculate the average value of the reference powers of all sampling points in the standard power line graph to obtain the average reference power RFP ave .

[0067] Secondly, calculate the average value of the reference power consumptions of all sampling points in the electricity consumption trend graph to obtain the average reference power consumption RPC ave , count the number of all electricity meters v, and obtain the average reference power u of the electricity meter EM and the average reference power consumption Count the number of all sampling points j, and obtain the reference power u corresponding to the i-th sampling point under the electricity meter EM and the reference power consumption

[0068] Finally, according to the formula Calculate the first coefficient u of the electricity meter EM Calculate the first coefficient of each electricity meter respectively.

[0069] Among them, and They are the reference power and reference power consumption corresponding to the i-th sampling point under the k-th electric meter except the electric meter EM u respectively, and and They are the average reference power and average reference power consumption of the k-th electric meter except the electric meter EM u respectively.

[0070] The risk warning unit is used to screen abnormal electric meters.

[0071] First, obtain all the electricity consumption records of the electric meter EM u . Mark the last ordinary record as the reference record in the order of arrangement, and regard the ordinary records after the marked record as sample records. Continue to set reference records for the sample records. And so on until all ordinary records are set as sample records or reference records.

[0072] Second, establish the electricity consumption trend chart of the sample record h through the sample record h and all its reference records. According to the standard power line chart and electricity consumption trend chart of the electric meter EM u , and the standard power line chart and electricity consumption trend chart of each sample record under the electric meter EM u , substitute them into the first coefficient formula for calculation, and take the calculation result as the second coefficient of the electric meter EM u .

[0073] Finally, calculate the second coefficient of each electric meter respectively, and multiply the first coefficient by the second coefficient to obtain the abnormal index. Set the index threshold yc, regard the electric meters with abnormal index greater than yc as abnormal electric meters, and warn the information of the abnormal electric meters to the data center.

[0074] The data storage module displays the power and electricity consumption of each electric meter, as well as the standard power line chart and electricity consumption trend chart of the abnormal electric meters in real time through the visual big screen, and regularly generates electricity consumption records of the electricity consumption information collected from each electric meter and stores them in the historical log.

[0075] According to the setting of the backend system HES, regularly generate electricity consumption records of the electricity consumption information collected from each electric meter and report them to the backend system HES, and modify the electric meter settings in real time according to the importance of the information, and warn the information of the abnormal electric meters to the data center.

[0076] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0077] 1. Low latency and low cost: By using the CoAP and MQTT protocols, instant meter reading and remote control are realized, reducing the operation latency. And due to the small data volume and lightweight protocol, it is suitable for low-bandwidth wireless communication networks, reducing the overall operation cost.

[0078] 2. Efficient integration and data formatting: Edge computing improves data processing efficiency, reduces the load on the HES, and facilitates integration with other systems. It can effectively enhance the performance and reliability of the smart meter system in a low-bandwidth environment, meeting the requirements of modern smart grids. After reading the meter data, the Gateway converts it into JSON format, which is easy to parse and store, facilitating subsequent data management and analysis.

[0079] 3. High reliability and security: The use of the MQTT(S) protocol to upload data ensures the reliability and security of data transmission, meeting the requirements of modern smart grids. Through features such as lightweight communication protocols, instantaneity, data formatting, high reliability, integration, and edge computing, the high latency and high overhead problems of traditional meter architectures in low-bandwidth environments are solved, improving the overall performance of the smart meter system.

[0080] The present invention solves the following problems in the prior art:

[0081] 1. High overhead and large data volume: The traditional architecture has a high overhead during data upload, and the data volume is large, resulting in high bandwidth requirements and affecting system efficiency.

[0082] 2. Incompatibility with low-bandwidth environments: The traditional architecture is not suitable for low-bandwidth wireless communication network environments such as NB-IoT, which limits its use in certain application scenarios.

[0083] 3. High latency: The traditional architecture has a high latency during meter reading and remote power cut-off and restoration operations for users, affecting the user experience and system response speed.

[0084] By adopting lightweight IoT communication protocols (such as CoAP and MQTT) and optimizing data formats (such as JSON), the present invention can effectively reduce the data volume and bandwidth requirements, achieve instant meter reading and remote control, and improve the overall system performance.

[0085] First, analyze the electricity consumption records of each meter to set sample records and reference records. Then, establish a standard power line graph through the sample records, and establish an electricity consumption trend graph based on the sample records and all reference records.

[0086] Secondly, through analysis and calculation with the standard power line graphs and electricity consumption trend graphs of all other meters, obtain the first coefficient for each meter. Generate multiple sample records for each meter, and perform analysis and calculation on the standard power line graph and electricity consumption trend graph corresponding to the sample record closest to the current time of each meter, and the standard power line graphs and electricity consumption trend graphs corresponding to all other sample records, to obtain the second coefficient.

[0087] Finally, calculate the anomaly index for each electricity meter by integrating the first coefficient and the second coefficient, define the abnormal electricity meters and issue early warnings. Compared with the simple threshold judgment based on historical data in the prior art, the detection method for analyzing whether there is an anomaly is more intelligent and efficient, and can quickly detect abnormal electricity consumption information and issue early warnings in a timely manner.

[0088] In summary, compared with the traditional technology, the present invention has the advantages of efficient and intelligent supervision of electricity meter data, and can improve the detection efficiency of abnormal electricity consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention, and do not constitute a limitation to the present invention. In the drawings:

[0090] Figure 1 is a schematic flowchart of a method for managing smart electricity meter data based on big data according to the present invention;

[0091] Figure 2 is a schematic structural diagram of a smart electricity meter data management system based on big data according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0092] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0093] Please refer to Figure 1 , the present invention provides a method for managing smart electricity meter data based on big data, including the following steps:

[0094] S100. Send the serial number and reporting period of the electricity meter to be read to the Gateway through the CoAP protocol.

[0095] S200. After the Gateway receives the request, read the corresponding electricity meter data according to the serial number.

[0096] S300. The Gateway collects the historical logs of the electricity meters in the office building, and real-time collects the electricity consumption information of the electricity meters through the serial port or sensors.

[0097] S400. Obtain the electricity consumption records of each electricity meter in the historical logs, set the sample records and reference records, establish a standard power line graph through the sample records, and establish an electricity consumption trend graph for each electricity meter according to the sample records and reference records.

[0098] S500. Analyze the standard power line chart and power consumption trend chart of each electricity meter, calculate the first coefficient based on the relationship between the electricity meter and all other electricity meters, calculate the second coefficient according to the relationship between all sample records under the electricity meter, calculate the anomaly index by integrating the first coefficient and the second coefficient, define the abnormal electricity meters and issue early warnings.

[0099] S600. Display the power consumption information of each electricity meter through a visual large screen, regularly generate power consumption records and store them in the historical log.

[0100] S700. The Gateway uses the MQTT protocol to upload JSON-formatted data to the HES. After receiving the MQTT-uploaded data, the HES transfers the payload into the database for subsequent data analysis and management.

[0101] In S300, the historical log refers to the power consumption records of all electricity meters. Each power consumption record includes the power consumption information of an electricity meter for one day, and the power consumption information is the power consumption at different times.

[0102] In S400, the specific steps are as follows:

[0103] S401. Obtain all the power consumption records of the electricity meter EM u in the historical log, sort these power consumption records in descending order of time, and take the power consumption record ranked first as the sample record, and all the remaining power consumption records as ordinary records. Establish a standard power line chart with time as the horizontal axis and power consumption as the vertical axis for the sample record, set the number of sampling points c, evenly set c sampling points in the horizontal axis direction of the standard power line chart, and analyze and calculate the power consumption at the corresponding time of each sampling point.

[0104] Analyze the power change between the corresponding time of the sampling point and the starting time corresponding time in the power line chart, split the time period according to the duration of different powers, multiply the power by the duration to obtain the power consumption corresponding to this time period, and sum the power consumption of all time periods to obtain the power consumption at the corresponding time of the sampling point.

[0105] S402. Establish a non-standard power line chart for ordinary records, set sampling points and calculate the power consumption at the corresponding time. Set the error amount r, calculate the difference in power consumption between each sampling point in the non-standard power line chart and the corresponding sampling point in the standard power line chart. When the difference in power consumption of all sampling points is less than r, take the corresponding ordinary record as the reference record of the sample record. Set the reference records in order of arrangement until the condition is not met and then stop setting.

[0106] S403. Analyze the power consumption of each sampling point under the sample record and all reference records, calculate the average value of all power consumptions at the same sampling point as the reference power consumption, and based on the reference power consumption at the corresponding time of each sampling point, for the electricity meter EM uEstablish an electricity consumption trend graph. For each electric meter, establish a standard power line graph and an electricity consumption trend graph respectively according to the above method.

[0107] In S500, the specific steps are as follows:

[0108] S501. Obtain the standard power line graph and the electricity consumption trend graph of each electric meter, and use the electricity consumption power corresponding to each sampling point on the standard power line graph as the reference power. Calculate the average value of the reference powers of all sampling points within the standard power line graph to obtain the average reference power RFP ave , and obtain the average reference electricity quantity RPC by calculating the average value of the reference electricity quantities of all sampling points within the electricity consumption trend graph. ave .

[0109] S502. Count the total number v of all electric meters, and obtain the average reference power u and the average reference electricity quantity of the electric meter EM. Count the total number j of all sampling points, and obtain the reference power u corresponding to the i-th sampling point under the electric meter EM and the reference electricity quantity . Substitute them into the formula to calculate the first coefficient u of the electric meter EM.

[0110]

[0111] In the formula, and are respectively the reference power and the reference electricity quantity corresponding to the i-th sampling point under the k-th electric meter except the electric meter EM u . and are respectively the average reference power and the average reference electricity quantity of the k-th electric meter except the electric meter EM u . Calculate the first coefficient of each electric meter respectively.

[0112] S503. Obtain all the electricity consumption records of the electric meter EM u . Mark the last ordinary record in the arranged order as the reference record, and use the ordinary records after the marked record as the sample records. Continue to set the reference records for the sample records. And so on, until all ordinary records are set as sample records or reference records.

[0113] Each electric meter may have multiple sample records, and each sample record may also have multiple reference records. According to the arranged order of the electricity consumption records, take the electricity consumption record with the earliest sorting as the sample record, and set the reference record according to the ordinary records after the sample record. The reference record is after the sample record, and multiple reference records under the same sample record are arranged continuously.

[0114] Each electricity consumption record is at least one of a sample record or a reference record. When there is no reference record for a sample record, the sample record itself is used as the reference record, and the standard power line chart and the electricity consumption trend chart are the same chart.

[0115] S504. Establish an electricity consumption trend chart for sample record h based on sample record h and all its reference records. According to the electricity meter EM u 's standard power line chart and electricity consumption trend chart, and the standard power line chart and electricity consumption trend chart of each sample record under the electricity meter EM u are substituted into the first coefficient formula for calculation, and the calculation result is used as the second coefficient of the electricity meter EM u .

[0116] When calculating the first coefficient, each electricity meter has only one standard power line chart and one electricity consumption trend chart. By analyzing and calculating with the standard power line charts and electricity consumption trend charts of all other electricity meters, the first coefficient is obtained.

[0117] When calculating the second coefficient, there are multiple sample records for the electricity meter, and there are different numbers of reference records under each sample record. The standard power line chart and electricity consumption trend chart corresponding to the sample record closest to the current time in terms of time are analyzed and calculated with the standard power line charts and electricity consumption trend charts corresponding to all other sample records to obtain the second coefficient.

[0118] S505. Calculate the second coefficient of each electricity meter respectively, and multiply the first coefficient by the second coefficient to obtain the anomaly index. Set an index threshold yc, and regard the electricity meters with an anomaly index greater than yc as abnormal electricity meters, and warn the information of the abnormal electricity meters to the data center.

[0119] The first coefficient represents the difference in the electricity consumption patterns between an electricity meter and other electricity meters, and the second coefficient represents the difference in the electricity consumption patterns of an electricity meter at different stages of itself. The anomaly index is obtained by combining the first coefficient and the second coefficient, which can better reflect the information on the abnormal situation of the electricity meter's electricity consumption.

[0120] In S600, the data center uses a visualization big screen to display the power and electricity consumption of each electricity meter in real time, highlights the standard power line chart and electricity consumption trend chart of the abnormal electricity meters, and regularly stores the electricity consumption records generated by the power of the electricity meters collected at different times into the historical log.

[0121] Please refer to Figure 2 , the present invention provides a big data-based intelligent electricity meter data management system, including a data acquisition module, an electricity consumption analysis module, an electricity meter management module, a data storage module, and a network communication module.

[0122] The system execution process includes:

[0123] 1. Data Request: HES sends the serial number of the electricity meter to be read to the Gateway via the CoAP protocol.

[0124] 2. Data Reading: After receiving the request, the Gateway reads the corresponding electricity meter data according to the serial number.

[0125] 3. Data Processing: The Gateway stores and converts the read data into JSON format for subsequent processing.

[0126] 4. Data Upload: The Gateway uses the MQTT protocol to upload the JSON-formatted data to HES.

[0127] 5. Data Storage: After receiving the data uploaded by MQTT, HES transfers the payload into the database for subsequent data analysis and management.

[0128] The data acquisition module is used to collect historical logs and power consumption information of each electricity meter.

[0129] The power consumption analysis module obtains the power consumption records of each electricity meter in the historical logs, sets sample records and reference records, establishes a standard power line graph through the sample records, and establishes a power consumption trend graph for each electricity meter according to the sample records and reference records.

[0130] The electricity meter management module analyzes the standard power line graph and power consumption trend graph of each electricity meter, calculates the first coefficient based on the relationship between the electricity meter and all other electricity meters, calculates the second coefficient according to the relationship between all sample records under the electricity meter, comprehensively calculates the anomaly index based on the first coefficient and the second coefficient, defines the abnormal electricity meters and issues warnings.

[0131] The data storage module is used to periodically generate power consumption records and store them in the historical logs.

[0132] The network communication module receives the instructions issued by the backend system via the CoAP protocol, and uploads the power consumption records in the data storage module to the backend system in JSON format using the MQTT protocol.

[0133] The overall system architecture design includes:

[0134] Electricity Meter: Used to monitor power consumption in real time, capable of receiving instructions, and can read power consumption records through the serial port or sensors.

[0135] Electricity Meter Gateway: As an intermediary, it receives HES instructions, and according to the types of electricity meters from different manufacturers, uses different protocols, parameters or methods to be responsible for reading data from the electricity meters and uploading it to the backend system (HES).

[0136] Backend system HES (Head End System): It has the parameters of all electricity meters, is used to send instructions and parameters to the electricity meter gateway Gateway, receive and store electricity meter data, and perform data analysis and management.

[0137] Communication protocol: CoAP (Constrained Application Protocol): A lightweight network protocol suitable for low-bandwidth and high-latency environments. It is used to transmit instructions between the HES and the gateway.

[0138] MQTT (Message Queuing Telemetry Transport): A lightweight messaging protocol suitable for low-bandwidth and high-latency network environments, ensuring data reliability and security. It is used to transmit electricity meter data between the HES and the gateway.

[0139] Data format: JSON (JavaScript Object Notation): A lightweight data interchange format that is easy for humans to read and write, and also easy for machines to parse and generate. It is used to format the read electricity meter data and upload it to the HES.

[0140] The mechanism for reading data through the electricity meter gateway Gateway aims to solve the problems of high latency and high overhead in the traditional electricity meter architecture in a low-bandwidth wireless communication network environment. A lightweight Internet of Things communication protocol is adopted to improve the efficiency and reliability of data transmission.

[0141] The mechanism for the data acquisition module to read electricity meter data through the smart electricity meter gateway Gateway adopts lightweight IoT communication protocols (CoAP and MQTT) to improve the overall performance.

[0142] The specific solution includes: The Gateway reads the electricity meter data using the electricity meter protocol (such as DLMS) according to the CoAP protocol instructions of the HES, converts it into the JSON format, and then uploads it to the HES through the MQTT protocol.

[0143] The specific units include: The historical data acquisition unit and the electricity meter data acquisition unit.

[0144] The historical data acquisition unit is used to collect the electricity consumption records of all electricity meters. Each electricity consumption record includes the electricity consumption information of an electricity meter for one day, and the electricity consumption information is the electricity consumption power at different times.

[0145] The electricity meter data acquisition unit is used to collect the electricity consumption information of all electricity meters in real time.

[0146] This process realizes data transmission in a low-bandwidth environment, reduces the data volume and latency, and at the same time improves the reliability and integration of the system, meeting the requirements of modern smart power grids.

[0147] The power consumption analysis module includes a record classification unit and a trend analysis unit.

[0148] The record classification unit is used to set sample records and reference records.

[0149] First, sort all the power consumption records of the electricity meter EM u in descending order of time. Take the first power consumption record as the sample record, and the remaining all power consumption records as ordinary records. Establish a standard power line graph with time as the horizontal axis and power consumption as the vertical axis for the sample record, and establish a non-standard power line graph for the ordinary records.

[0150] Second, set the number of sampling points c. Uniformly set c sampling points in the horizontal direction of the standard power line graph and the non-standard power line graph, and analyze and calculate the power consumption at each sampling point corresponding time. Set the error amount r, and calculate the difference in power consumption between each sampling point in the non-standard power line graph and the corresponding sampling point in the standard power line graph.

[0151] Finally, when the difference in power consumption at all sampling points is less than r, take the corresponding ordinary record as the reference record of the sample record; set the reference records in order of arrangement until the condition is not met and then stop setting.

[0152] The trend analysis unit is used to establish a power consumption trend graph.

[0153] First, analyze the power consumption at each sampling point under the sample record and all reference records, and calculate the average value of all power consumptions at the same sampling point as the reference power consumption.

[0154] Then, based on the reference power consumption at each sampling point corresponding time, establish a power consumption trend graph for the electricity meter EM u and establish a standard power line graph and a power consumption trend graph for each electricity meter respectively according to the above method.

[0155] The electricity meter management module includes an anomaly analysis unit and a risk warning unit.

[0156] The anomaly analysis module is used to calculate the first coefficient of each electricity meter.

[0157] First, obtain the standard power line graph and the power consumption trend graph of each electricity meter, and take the power consumption corresponding to each sampling point on the standard power line graph as the reference power. Calculate the average value of the reference powers of all sampling points in the standard power line graph to obtain the average reference power RFP ave .

[0158] Secondly, calculate the average reference power consumption RPC of all sampling points in the power consumption trend graph ave , count the number v of all electric meters, and obtain the average reference power u and average reference power consumption of the electric meter EM Count the number j of all sampling points, and obtain the reference power u corresponding to the i-th sampling point under the electric meter EM and reference power consumption

[0159] Finally, according to the formula calculate the first coefficient u of the electric meter EM Calculate the first coefficient of each electric meter respectively.

[0160] Among them, and are the reference power and reference power consumption corresponding to the i-th sampling point under the k-th electric meter except the electric meter EM u respectively, and and are the average reference power and average reference power consumption of the k-th electric meter except the electric meter EM u respectively.

[0161] The risk warning unit is used to screen abnormal electric meters.

[0162] First, obtain all power consumption records of the electric meter EM u , mark the last ordinary record as the reference record in the order of arrangement, and use the ordinary records after the marked record as sample records, and continue to set reference records for the sample records. And so on until all ordinary records are set as sample records or reference records.

[0163] Secondly, establish a power consumption trend graph of the sample record h through the sample record h and all its reference records, and according to the standard power line graph and power consumption trend graph of the electric meter EM u , and the standard power line graph and power consumption trend graph of each sample record under the electric meter EM u , substitute them into the first coefficient formula for calculation, and take the calculation result as the second coefficient of the electric meter EM u .

[0164] Finally, calculate the second coefficient of each electric meter respectively, and multiply the first coefficient by the second coefficient to obtain the abnormal index. Set the index threshold yc, and regard the electric meters with abnormal index greater than yc as abnormal electric meters, and warn the information of the abnormal electric meters to the data center.

[0165] The data storage module displays the power and power consumption of each electric meter in real time through a visual large screen, as well as the standard power line chart and power consumption trend chart of abnormal electric meters, and regularly generates power consumption records of the electric meters collected and stores them in the historical log.

[0166] According to the settings of the backend system HES, regularly generate power consumption records of the electric meters collected and report them to the backend system HES, and modify the electric meter settings in real time according to the importance of the information, and warn the data center of the information of abnormal electric meters.

[0167] Example 1:

[0168] Suppose there are a total of 3 electric meters, A1, A2, and A3, and 3 electric meter serial numbers, S1, S2, and S3.

[0169] HES sends the serial numbers S1, S2, and S3 of the electric meters to be read to the Gateway through the CoAP protocol.

[0170] After receiving the request, the Gateway reads the corresponding electric meter data according to the serial number.

[0171] For the 3 electric meters A1, A2, and A3, their average reference powers are 3kw, 5kw, and 2kw respectively, and their average reference power consumptions are 24kwh, 30kwh, and 16kwh respectively. The reference powers and reference power consumptions corresponding to each sampling point under these electric meters are:

[0172] Electric meter A1:

[0173] Sampling point 1: reference power 2kw: reference power consumption 6kwh; Sampling point 2: reference power 3kw: reference power consumption 12kwh;

[0174] Electric meter A2:

[0175] Sampling point 1: reference power 2kw: reference power consumption 4kwh; Sampling point 2: reference power 1kw: reference power consumption 8kwh;

[0176] Electric meter A3:

[0177] Sampling point 1: reference power 3kw: reference power consumption 8kwh; Sampling point 2: reference power 4kw: reference power consumption 18kwh;

[0178] Substitute into the formula to calculate the first coefficient of electric meter A1:

[0179]

[0180] Then the first coefficient of electric meter A1 is 0.24.

[0181] The Gateway stores and converts the read data into JSON format, and uploads the JSON-formatted data to HES using the MQTT protocol.

[0182] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device.

[0183] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for managing smart meter data based on big data, characterized in that: The method includes the following steps: S100. Send the serial number and reporting period of the electricity meter to be read to the Gateway through the CoAP protocol; S200. After receiving the request, the Gateway reads the corresponding electricity meter data according to the serial number; S300. The Gateway collects the historical logs of the electricity meters in the office building and real-time collects the electricity consumption information of the electricity meters through the serial port or sensors; S400. Obtain the electricity consumption records of each electricity meter in the historical logs, set sample records and reference records, establish a standard power line graph through the sample records, and establish an electricity consumption trend graph for each electricity meter according to the sample records and reference records; S500. Analyze the standard power line graph and electricity consumption trend graph of each electricity meter, calculate the first coefficient through the relationship between the electricity meter and all other electricity meters, calculate the second coefficient according to the relationship between all sample records under the electricity meter, comprehensively calculate the abnormal index from the first coefficient and the second coefficient, define the abnormal electricity meters and give early warnings; S600. Display the electricity consumption information of each electricity meter through a visual large screen, regularly generate electricity consumption records and store them in the historical logs; S700. The Gateway uses the MQTT protocol to upload the JSON-formatted data to the HES. After receiving the MQTT-uploaded data, the HES transfers the payload into the database for subsequent data analysis and management.

2. The method for managing smart meter data based on big data according to claim 1, wherein: In S300, the historical log refers to the electricity consumption records of all electricity meters. Each electricity consumption record includes the electricity consumption information of an electricity meter for one day, and the electricity consumption information is the electricity consumption power at different times.

3. The method for managing smart meter data based on big data according to claim 2, characterized in that: In S400, the specific steps are as follows: S401. Obtain all the electricity consumption records of the electricity meter EM in the historical log, sort these electricity consumption records in descending order of time, take the electricity consumption record ranked first as the sample record, and the remaining all electricity consumption records as ordinary records; establish a standard power line chart with time as the horizontal axis and electricity power as the vertical axis for the sample record, set the number of sampling points c, evenly set c sampling points in the horizontal axis direction of the standard power line chart, and analyze and calculate the electricity consumption at the corresponding time of each sampling point. u ​ S402. Establish a non-standard power line graph for ordinary records, set sampling points and calculate the electricity consumption at the corresponding time; set the error amount r, calculate the difference in electricity consumption between each sampling point in the non-standard power line graph and the corresponding sampling point in the standard power line graph. When the difference in electricity consumption of all sampling points is less than r, use the corresponding ordinary record as the reference record of the sample record; set the reference records in sequence until the condition is not met and then stop setting; S403. Analyze the electricity consumption at each sampling point in the sample record and all reference records, calculate the average value of all electricity consumptions at the same sampling point as the reference electricity consumption, and use the reference electricity consumption corresponding to each sampling point at the corresponding time as the electricity meter EM u Establish an electricity consumption trend chart, and establish a standard power line chart and an electricity consumption trend chart for each electricity meter respectively according to the above method.

4. A method for managing smart meter data based on big data according to claim 3, characterized in that: In S500, the specific steps are as follows: S501. Obtain the standard power line chart and power consumption trend chart of each electric meter, and use the power consumption corresponding to each sampling point on the standard power line chart as the reference power; calculate the average reference power RFP by averaging the reference powers of all sampling points within the standard power line chart ave , and obtain the average reference power consumption RPC by averaging the reference power consumptions of all sampling points within the power consumption trend chart ave ; S502. Count the total number of electric meters \(v\), and obtain the average reference power of the electric meter EM u and the average reference power consumption and the average reference electricity quantity Count the total number of sampling points \(j\), and obtain the reference power u corresponding to the \(i\)-th sampling point under the electric meter EM and the reference electricity quantity Substitute into the formula to calculate the first coefficient of the electric meter EM u ​ Wherein, and are the reference power and reference electricity quantity corresponding to the i-th sampling point under the k-th electricity meter except the electricity meter EM u respectively, and are the average reference power and average reference electricity quantity of the k-th electricity meter except the electricity meter EM u respectively; calculate the first coefficient of each electricity meter; S503. Obtain the electricity meter EM u Obtain all electricity consumption records, mark the last ordinary record in the arranged order as the reference record, use the ordinary records after the marked record as sample records, and continue to set reference records for the sample records; and so on until all ordinary records are set as sample records or reference records; S504. Establish an electricity consumption trend graph of the sample record h based on the sample record h and all its reference records. According to the standard power broken line graph of the electricity meter EM u and the electricity consumption trend graph, and the standard power broken line graph and electricity consumption trend graph of each sample record under the electricity meter EM u , substitute them into the first coefficient formula for calculation, and use the calculation result as the second coefficient of the electricity meter EM u . S505. Calculate the second coefficient of each electricity meter respectively, and multiply the first coefficient by the second coefficient to obtain the abnormal index; set the index threshold yc, regard the electricity meters with abnormal index greater than yc as abnormal electricity meters, and warn the information of the abnormal electricity meters to the data center.

5. A method for managing smart meter data based on big data according to claim 4, characterized in that: In S600, the data center displays the power and electricity consumption of each electricity meter in real time through the visual large screen, highlights the standard power line graph and electricity consumption trend graph of the abnormal electricity meters, and regularly generates electricity consumption records of the power of the electricity meters at different times and stores them in the historical logs.

6. A big data-based intelligent electricity meter data management system, characterized in that: The system includes a data acquisition module, an electricity consumption analysis module, an electricity meter management module, a data storage module and a network communication module; The data acquisition module is used to collect historical logs and the electricity consumption information of each electricity meter; The electricity consumption analysis module obtains the electricity consumption records of each electricity meter in the historical logs, sets sample records and reference records, establishes a standard power line graph through the sample records, and establishes an electricity consumption trend graph for each electricity meter according to the sample records and reference records; The electricity meter management module analyzes the standard power line graph and power consumption trend graph of each electricity meter, calculates the first coefficient based on the relationship between the electricity meter and all other electricity meters, calculates the second coefficient based on the relationship between all sample records under the electricity meter, calculates the anomaly index by synthesizing the first coefficient and the second coefficient, defines the abnormal electricity meters and issues early warnings; The data storage module is used to periodically generate power consumption records and store them in the historical log; The network communication module receives the instructions sent by the backend system through the CoAP protocol, and uploads the power consumption records in the data storage module to the backend system in JSON format using the MQTT protocol.

7. The intelligent electric meter data management system based on big data according to claim 6, wherein: The data acquisition module includes a historical data acquisition unit and an electricity meter data acquisition unit; The historical data acquisition unit is used to acquire the power consumption records of all electricity meters. Each power consumption record includes the power consumption information of an electricity meter for one day, and the power consumption information is the power consumption at different times; The electricity meter data acquisition unit is used to acquire the power consumption information of all electricity meters in real time.

8. A big data-based intelligent electric meter data management system according to claim 7, characterized in that: The power consumption analysis module includes a record classification unit and a trend analysis unit; The record classification unit is used to set sample records and reference records; First, sort all the electricity consumption records of the electricity meter EM in descending order of time, take the first electricity consumption record as the sample record, and the remaining all electricity consumption records as ordinary records; establish a standard power line chart with time as the horizontal axis and electricity consumption power as the vertical axis for the sample record, and establish a non-standard power line chart for the ordinary records; u ​ Secondly, set the number of sampling points c, evenly set c sampling points in the horizontal direction of the standard power line graph and the non-standard power line graph, and analyze and calculate the power consumption at the corresponding time of each sampling point; set the error amount r, and calculate the difference in power consumption between each sampling point in the non-standard power line graph and the corresponding sampling point in the standard power line graph; Finally, when the difference in power consumption of all sampling points is less than r, use the corresponding ordinary record as the reference record of the sample record; set the reference records in sequence according to the arrangement order until the condition is not met and then stop setting; The trend analysis unit is used to establish a power consumption trend graph; First, analyze the power consumption of each sampling point under the sample records and all reference records, and calculate the average value of all power consumptions at the same sampling point as the reference power consumption; Then, based on the reference power consumption corresponding to each sampling point at the corresponding time, for the electricity meter EM u An electricity consumption trend graph is established, and a standard power line graph and an electricity consumption trend graph are established for each electricity meter respectively according to the above method.

9. A big data-based intelligent electric meter data management system according to claim 8, characterized in that: The electricity meter management module includes an anomaly analysis unit and a risk warning unit; The anomaly analysis module is used to calculate the first coefficient of each electricity meter; First, obtain the standard power line graph and power consumption trend graph of each electricity meter, and use the power consumption corresponding to each sampling point on the standard power line graph as the reference power; Calculate the average reference power RFP by obtaining the average reference power of all sampling points within the standard power fold line graph ave ; Secondly, calculate the average reference power consumption RPC by averaging the reference power consumptions of all sampling points in the power consumption trend graph ave , count the total number of electricity meters v, and obtain the average reference power u and average reference power consumption of the electricity meter EM Count the total number of sampling points j, and obtain the reference power u corresponding to the i-th sampling point under the electricity meter EM and reference power consumption Finally, according to the formula calculate the first coefficient of the electricity meter EM u Calculate the first coefficient of each electricity meter separately; ​ Wherein, and are respectively the reference power and reference power consumption corresponding to the i-th sampling point under the k-th electricity meter except the electricity meter EM u ; and are respectively the average reference power and average reference power consumption of the k-th electricity meter except the electricity meter EM u ; The risk warning unit is used to screen abnormal electricity meters; First, obtain all the electricity consumption records of the electricity meter EM u Arrange them in order and mark the last ordinary record as the reference record. Take the ordinary records after the marked record as sample records, and continue to set reference records for the sample records; and so on until all ordinary records are set as sample records or reference records; Secondly, an electricity consumption trend graph of the sample record h is established through the sample record h and all its reference records, and according to the standard power broken line graph of the electricity meter EM u and the electricity consumption trend graph, and the standard power broken line graph and the electricity consumption trend graph of each sample record under the electricity meter EM u are substituted into the first coefficient formula for calculation, and the calculation result is used as the second coefficient of the electricity meter EM u ; Finally, calculate the second coefficient of each electricity meter respectively, and multiply the first coefficient by the second coefficient to obtain the anomaly index; set the index threshold yc, and use the electricity meters with an anomaly index greater than yc as abnormal electricity meters, and warn the information of the abnormal electricity meters to the data center.

10. A big data-based intelligent electric meter data management system according to claim 9, characterized in that: The data storage module displays the power and power consumption of each electricity meter, as well as the standard power line graph and power consumption trend graph of the abnormal electricity meters in real time through the visualization big screen, and periodically generates power consumption records of the power consumption information collected from each electricity meter and stores them in the historical log; according to the setting of the backend system HES, periodically report the power consumption information collected from each electricity meter to the backend system HES, and modify the electricity meter settings in real time according to the importance of the information, and warn the information of the abnormal electricity meters to the data center.

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