Abnormal event recording method and system of intelligent electric energy meter

Abstract

The invention relates to the technical field of intelligent electric energy meters, in particular to an abnormal event recording method and system of an intelligent electric energy meter, and the method comprises the steps: obtaining historical abnormal event information, according to the historical abnormal event information, determining an initial time period when an electric power abnormal event occurs in the intelligent electric energy meter and an electric power parameter change in the initial time period, and according to the electric power abnormal event, the abnormal degree, and the initial time period and the electric power parameter change corresponding to the electric power abnormal event, generating an abnormal event monitoring standard, and collecting the real-time monitoring electric power parameters within a preset time, determining whether an abnormal event has occurred in the real-time monitoring electric power parameters based on the abnormal event monitoring standard, if the abnormal event has occurred, recording and storing the occurred abnormal event, and controlling to display the recorded and stored occurred abnormal event. The power management efficiency and the response speed of the intelligent electric energy meter are improved.

Description

Technical Field

[0001] The present application relates to the technical field of smart electric energy meters, and in particular to a method and system for recording abnormal events of smart electric energy meters. Background Art

[0002] With the continuous development of smart grid technology, smart energy meters are increasingly used in power systems. Smart energy meters can not only measure and monitor power parameters in real time, but also have data recording and analysis functions, which play an important role in the stability, safety and economy of power systems. However, in actual operation, due to various reasons (such as equipment failure, human operation errors, etc.), abnormal events such as voltage surge and current overload may occur in the power system. If these abnormal events are not discovered and handled in time, they may cause serious damage to power equipment and even affect the stable operation of the entire power system. Therefore, how to effectively record and analyze these abnormal events has become a problem that needs to be solved urgently.

[0003] Although traditional electric energy meters can measure power parameters, they often lack the automatic recording and alarm functions for abnormal events. This results in the power workers being unable to obtain relevant information in a timely manner when abnormal events occur, thus delaying the troubleshooting time. In addition, even if some high-end electric energy meters have simple recording functions, due to the lack of intelligent data processing and analysis capabilities, the recorded data is often difficult to use effectively and cannot meet the needs of modern power systems for rapid fault location and prevention, thereby reducing power management efficiency and response speed. Summary of the invention

[0004] In order to solve at least one of the above technical problems, the present application provides a method and system for recording abnormal events of a smart electric energy meter.

[0005] In a first aspect, the present application provides a method for recording abnormal events of a smart electric energy meter, which adopts the following technical solution: Acquire historical abnormal event information, wherein the historical abnormal event information is the power abnormal event that occurred in the smart electric energy meter within a historical period, the abnormal degree of the power abnormal event, and the power parameter change of the smart electric energy meter corresponding to the power abnormal event; Determine the starting time period when the power abnormal event occurs in the smart electric energy meter and the power parameter changes in the starting time period according to the historical abnormal event information; Generate an abnormal event monitoring standard according to the abnormal power event, the abnormal degree, the starting time period corresponding to the abnormal power event, and the power parameter change; Collect real-time monitoring power parameters within a preset time, and determine whether an abnormal event has occurred in the real-time monitoring power parameters based on the abnormal event monitoring standard. If an abnormal event has occurred, record and store the abnormal event, and control the display of the abnormal event after recording and storage.

[0006] In a possible implementation, determining whether an abnormal event has occurred in the real-time monitored power parameter based on the abnormal event monitoring standard further includes: If the abnormal event does not exist, determine whether the real-time monitored power parameter has an abnormal event that is prone to occur based on the abnormal event monitoring standard; if the abnormal event that is prone to occur exists, determine the probability of occurrence of the abnormal event corresponding to the abnormal event that is prone to occur, the start time period of the event that occurs corresponding to the abnormal event probability, and the abnormality degree of the abnormal event that is prone to occur; Matching the abnormality level of the event with the preset abnormality level standard to determine the prevention priority score of the probability of occurrence of each abnormal event; Performing a product operation on the prevention priority score and the probability of occurrence of the abnormal event, and sorting the abnormal events that are prone to occur according to the product operation scores to obtain a sequence of events that are prone to occur; The starting time period of the time occurrence is added to the prone event sequence to obtain a prone event record.

[0007] In a possible implementation, generating an abnormal event monitoring standard according to the abnormal power event, the abnormal degree, the starting time period corresponding to the abnormal power event, and the power parameter change includes: According to the power abnormal event, abnormal coordinate systems of different types of abnormal events are created respectively, wherein the X-axis of the abnormal coordinate system is different time nodes, and the Y-axis of the abnormal coordinate system is different power parameter values; The abnormal coordinate system is labeled according to the event name of the abnormal power event, and the starting time period and the power parameter change are mapped to the abnormal coordinate system after corresponding labeling to obtain abnormal event waveform diagrams of different types of abnormal events in the abnormal power event; Collect the highest peak value, the lowest peak value and the period time corresponding to different peak value change cycles in each abnormal event waveform diagram, and calculate the difference between the highest peak value and the lowest peak value to obtain the power difference corresponding to each peak value change cycle; The power difference is used as a numerator and the period time period is used as a denominator to calculate to obtain the power change mean range corresponding to each peak change period in the abnormal event waveform diagram; The abnormal power change range is sorted according to a preset sorting standard, and the sorted power change mean range is bound to the abnormal degree corresponding to the power change mean range and the event name corresponding to the power change mean range to generate an event monitoring standard.

[0008] In a possible implementation, the collecting of real-time monitoring power parameters within a preset time and determining whether an abnormal event has occurred in the real-time monitoring power parameters based on the abnormal event monitoring standard include: Create a real-time power event coordinate system, and map the real-time monitored power parameters to the real-time power event coordinate system according to time nodes to obtain a real-time power waveform diagram, wherein the X-axis of the real-time power event coordinate system is different time nodes, and the Y-axis of the real-time power event coordinate system is different power parameter values; Collecting the highest critical value, the lowest critical value and the critical period time corresponding to different peak value change cycles in the real-time power waveform diagram, and performing a difference calculation between the highest critical value and the lowest critical value to obtain the real-time power difference corresponding to each peak value change cycle; The real-time power difference is used as a numerator and the duration corresponding to the critical cycle time period is used as a denominator to calculate to obtain the real-time power mean value of the real-time power waveform diagram; The real-time power average is matched with the power change average range in the event monitoring standard to determine whether an abnormal event has occurred in the real-time monitoring power parameter corresponding to the real-time power average.

[0009] In a possible implementation, determining whether the real-time monitored power parameter has an abnormal event that is prone to occur based on the abnormal event monitoring standard includes: Determine a change in the mean value of the real-time power mean value within the preset time, and determine a change trend of the mean value based on the change in the mean value; Predicting the current mean value within a future preset time period according to the mean value change trend to obtain the future power mean value within the future preset time period; The future power mean is matched with the power change mean range in the event monitoring standard to determine whether the real-time monitored power parameter has an abnormal event that is prone to occur.

[0010] In a possible implementation, predicting the current mean value in a future preset time period according to the mean value change trend to obtain the future power mean value in the future preset time period includes: Based on the peak change period, unsupervised time series data sorting is performed on the mean change trend to obtain mean matrix data; Inputting the mean matrix data into a trained feature extraction model to perform vector feature extraction to obtain feature dimensions and the number of feature dimensions, and combining the obtained feature dimensions and the number of feature dimensions with the mean matrix data to generate future mean matrix data; The future matrix data is input into a preset algorithm model for data extrapolation to obtain the future power mean value within a preset time period.

[0011] In a possible implementation, the inputting the mean matrix data into a trained feature extraction model to extract vector features to obtain the number of feature dimensions includes: Determine the periodic time periods of different peak value change periods in the real-time monitored power parameter and the real-time power mean values ​​corresponding to the periodic time periods based on the mean value matrix data; Inputting the periodic time period and the real-time power mean into the feature extraction model to extract vector features, respectively, to obtain a time vector feature corresponding to the periodic time period and a data vector feature corresponding to the real-time power mean; Quantitative statistics are performed on the data vector features and the time vector features to obtain feature dimensions and the number of feature dimensions.

[0012] In a second aspect, the present application provides an abnormal event recording system for a smart electric energy meter, which adopts the following technical solution: An abnormal event recording system for a smart electric energy meter, comprising: An information acquisition module is used to acquire historical abnormal event information, wherein the historical abnormal event information is the power abnormal event that occurred in the smart electric energy meter within a historical period, the abnormal degree of the power abnormal event, and the power parameter change of the smart electric energy meter corresponding to the power abnormal event; A data monitoring module, used to determine the starting time period when the power abnormal event occurs in the smart electric energy meter and the power parameter changes in the starting time period according to the historical abnormal event information; A standard generation module, configured to generate an abnormal event monitoring standard according to the abnormal power event, the abnormal degree, the starting time period corresponding to the abnormal power event, and the power parameter change; The recording and storage module is used to collect real-time monitoring power parameters within a preset time, and determine whether an abnormal event has occurred in the real-time monitoring power parameters based on the abnormal event monitoring standard. If the abnormal event has occurred, the abnormal event that has occurred is recorded and stored, and the abnormal event that has occurred after recording and storage is controlled to be displayed.

[0013] In a possible implementation, the system further includes: an event determination module, a degree matching module, an event sorting module, and an event recording module, wherein: The event judgment module is used to determine whether there is an abnormal event that is prone to occur in the real-time monitored power parameter based on the abnormal event monitoring standard when there is no abnormal event that has occurred, and if there is an abnormal event that is prone to occur, determine the probability of occurrence of the abnormal event corresponding to the abnormal event that is prone to occur, the start time period of the event that occurs corresponding to the abnormal event probability, and the abnormality degree of the abnormal event that is prone to occur; The degree matching module is used to match the abnormal degree of the event with a preset abnormal degree standard to determine the prevention priority score of the probability of occurrence of each abnormal event; The event sorting module is used to perform a product operation on the prevention priority score and the probability of occurrence of the abnormal event, and sort the abnormal events that are prone to occur according to the product operation score to obtain a sequence of events that are prone to occur; The event recording module is used to add the time starting time period to the prone event sequence to obtain a prone event record.

[0014] In another possible implementation, when the standard generation module generates the abnormal event monitoring standard according to the power abnormal event, the abnormal degree, the starting time period corresponding to the power abnormal event, and the power parameter change, it is specifically used to: According to the power abnormal event, abnormal coordinate systems of different types of abnormal events are created respectively, wherein the X-axis of the abnormal coordinate system is different time nodes, and the Y-axis of the abnormal coordinate system is different power parameter values; The abnormal coordinate system is labeled according to the event name of the abnormal power event, and the starting time period and the power parameter change are mapped to the abnormal coordinate system after corresponding labeling to obtain abnormal event waveform diagrams of different types of abnormal events in the abnormal power event; Collect the highest peak value, the lowest peak value and the period time corresponding to different peak value change cycles in each abnormal event waveform diagram, and calculate the difference between the highest peak value and the lowest peak value to obtain the power difference corresponding to each peak value change cycle; The power difference is used as a numerator and the period time period is used as a denominator to calculate to obtain the power change mean range corresponding to each peak change period in the abnormal event waveform diagram; The abnormal power change range is sorted according to a preset sorting standard, and the sorted power change mean range is bound to the abnormal degree corresponding to the power change mean range and the event name corresponding to the power change mean range to generate an event monitoring standard.

[0015] In another possible implementation, when the recording and storage module collects the real-time monitored power parameters within a preset time and determines whether an abnormal event has occurred in the real-time monitored power parameters based on the abnormal event monitoring standard, it is specifically used to: Create a real-time power event coordinate system, and map the real-time monitored power parameters to the real-time power event coordinate system according to time nodes to obtain a real-time power waveform diagram, wherein the X-axis of the real-time power event coordinate system is different time nodes, and the Y-axis of the real-time power event coordinate system is different power parameter values; Collecting the highest critical value, the lowest critical value and the critical period time corresponding to different peak value change cycles in the real-time power waveform diagram, and performing a difference calculation between the highest critical value and the lowest critical value to obtain the real-time power difference corresponding to each peak value change cycle; The real-time power difference is used as a numerator and the duration corresponding to the critical cycle time period is used as a denominator to calculate to obtain the real-time power mean value of the real-time power waveform diagram; The real-time power average is matched with the power change average range in the event monitoring standard to determine whether an abnormal event has occurred in the real-time monitoring power parameter corresponding to the real-time power average.

[0016] In another possible implementation, when the event determination module determines whether the real-time monitored power parameter has an abnormal event that is prone to occur based on the abnormal event monitoring standard, it is specifically used to: Determine a change in the mean value of the real-time power mean value within the preset time, and determine a change trend of the mean value based on the change in the mean value; Predicting the current mean value within a future preset time period according to the mean value change trend to obtain the future power mean value within the future preset time period; The future power mean is matched with the power change mean range in the event monitoring standard to determine whether the real-time monitored power parameter has an abnormal event that is prone to occur.

[0017] In another possible implementation, when the event determination module predicts the current mean value in a future preset time period according to the mean value change trend to obtain the future power mean value in the future preset time period, it is specifically used to: Based on the peak change period, unsupervised time series data sorting is performed on the mean change trend to obtain mean matrix data; Inputting the mean matrix data into a trained feature extraction model to perform vector feature extraction to obtain feature dimensions and the number of feature dimensions, and combining the obtained feature dimensions and the number of feature dimensions with the mean matrix data to generate future mean matrix data; The future matrix data is input into a preset algorithm model for data extrapolation to obtain the future power mean value within a preset time period.

[0018] In another possible implementation, when the event determination module inputs the mean matrix data into a trained feature extraction model to extract vector features and obtain the number of feature dimensions, it is specifically used to: Determine the periodic time periods of different peak value change periods in the real-time monitored power parameter and the real-time power mean values ​​corresponding to the periodic time periods based on the mean value matrix data; Inputting the periodic time period and the real-time power mean into the feature extraction model to extract vector features, respectively, to obtain a time vector feature corresponding to the periodic time period and a data vector feature corresponding to the real-time power mean; Quantitative statistics are performed on the data vector features and the time vector features to obtain feature dimensions and the number of feature dimensions.

[0019] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute an abnormal event recording method for a smart electric energy meter as described in any one of the first aspects.

[0020] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium stores a computer program, which, when executed in a computer, causes the computer to execute any method for recording abnormal events of a smart electric energy meter according to the first aspect.

[0021] In summary, the present application includes at least one of the following beneficial technical effects: When recording abnormal events of the smart electric energy meter during operation, historical abnormal event information is obtained, and the historical abnormal event information includes abnormal power events that occurred in the smart electric energy meter within the historical period, the abnormal degree of the abnormal power events, and the corresponding power parameter changes of the smart electric energy meter when the abnormal power events occurred. Then, the starting time period of the abnormal power event in the smart electric energy meter and the power parameter changes within the starting time period are determined according to the historical abnormal event information. Then, the abnormal event monitoring standard is generated according to the abnormal power event, the abnormal degree, and the starting time period and power parameter changes corresponding to the abnormal power event. Then, the real-time monitoring power parameters within the preset time are collected, and based on the abnormal event monitoring standard, it is determined whether there is an abnormal event that has occurred in the real-time monitoring power parameters. If there is an abnormal event that has occurred, the abnormal event that has occurred is recorded and stored, and the abnormal event that has occurred after the storage is controlled to be displayed. The staff maintains the smart electric energy meter according to the abnormal events that have been recorded and stored in time, thereby improving the power management efficiency and response speed of the smart electric energy meter. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic flow chart of a method for recording abnormal events of a smart electric energy meter provided in an embodiment of the present application.

[0023] Figure 2 A schematic diagram of the structure of an abnormal event recording system for a smart electric energy meter provided in an embodiment of the present application.

[0024] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The following is combined with Figure 1-3 This application is described in further detail.

[0026] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, a person skilled in the art may make non-creative modifications to the present embodiment as needed, but such modifications are protected by the patent law as long as they are within the scope of the present application.

[0027] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0028] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0029] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0030] The embodiment of the present application provides a method for recording abnormal events of a smart energy meter, which is executed by an electronic device, wherein the electronic device can be an independent physical electronic device, or an electronic device cluster or distributed system composed of multiple physical electronic devices, or a cloud electronic device that provides cloud computing services. The embodiment of the present application is not limited here, such as Figure 1 As shown, the method includes: Step S10: Obtain historical abnormal event information.

[0031] The historical abnormal event information includes abnormal power events that occurred in the smart power meter during the historical period, the abnormal degree of the abnormal power events, and the corresponding power parameter changes of the smart power meter when the abnormal power events occurred.

[0032] For the embodiment of the present application, the historical abnormal event information represents the collection of all abnormal power events recorded by the smart energy meter system in a certain period or certain time periods in the past. This information plays a vital role in the subsequent analysis of the stability of the power system and the prediction of possible circuit problems in the future. The historical period refers to a specific time period, which is used to define which abnormal power events are regarded as historical events. This time period can be set according to actual needs, for example: one month or one year. An abnormal power event refers to an abnormal situation that occurs during the supply or use of electricity, such as voltage fluctuations, current overloads, and reduced power quality, that is, an event that affects power equipment and user electricity consumption. The degree of abnormality is used to quantify the severity of abnormal power events, and is evaluated by specific pre-set abnormality standards, such as judging by the size and duration of the voltage deviation from the normal value. The change in power parameters refers to the change in power parameters (such as voltage, current, power factor, etc.) monitored by the smart energy meter when an abnormal power event occurs.

[0033] In the embodiment of the present application, the electronic device will use the power data management platform to query. The specific steps include: first, log in to the power data management platform and navigate to the historical abnormal event information query interface; second, set the query conditions on the interface, including historical period, abnormal event type, etc.; finally, submit the query request and wait for the platform to return the historical abnormal event information data.

[0034] Step S11: determining the starting time period when the power abnormal event occurs in the smart electric energy meter and the power parameter changes in the starting time period according to the historical abnormal event information.

[0035] For the embodiment of the present application, the electronic device extracts relevant data of the target power abnormal event from the historical abnormal event information, including the timestamp of its occurrence, event type, etc. Then, the starting time period of the abnormal event is determined based on these data, that is, the time range in which the abnormal event begins to occur and lasts. Finally, within the starting time period, the changes in power parameters are obtained and analyzed by querying the recorded data of the smart power meter.

[0036] Step S12: Generate an abnormal event monitoring standard according to the abnormal power event, the degree of abnormality, the starting time period corresponding to the abnormal power event, and the change of power parameters.

[0037] Specifically, abnormal coordinate systems of different types of abnormal events are created according to abnormal power events, the X-axis of the abnormal coordinate system is different time nodes, and the Y-axis of the abnormal coordinate system is different power parameter values. The abnormal coordinate system is labeled according to the event name of the abnormal power event, and the starting time period and the power parameter change are mapped to the corresponding labeled abnormal coordinate system to obtain abnormal event waveforms of different types of abnormal events in the abnormal power event, and then the highest peak value, the lowest peak value and the cycle time period corresponding to different peak change periods in each abnormal event waveform are collected, and the highest peak value and the lowest peak value are calculated to obtain the power difference corresponding to each peak change period, and the power difference is used as the numerator and the cycle time period is used as the denominator to obtain the power change mean range corresponding to each peak change period in the abnormal event waveform, and the power change abnormal range is sorted according to the preset sorting standard, and the sorted power change mean range is bound to the abnormal degree corresponding to the power change mean range and the event name corresponding to the power change mean range, and finally the event monitoring standard is generated.

[0038] Step S13, collect real-time monitoring power parameters within a preset time, and determine whether an abnormal event has occurred in the real-time monitoring power parameters based on the abnormal event monitoring standard. If an abnormal event has occurred, the abnormal event will be recorded and stored, and the abnormal event that has occurred after the record is stored will be controlled to be displayed.

[0039] Specifically, when collecting real-time monitored power parameters within a preset time and determining whether an abnormal event has occurred in the real-time monitored power parameters based on the abnormal event monitoring standard, it includes: creating a real-time power event coordinate system, and mapping the real-time monitored power parameters to the real-time power event coordinate system according to the time nodes to obtain a real-time power waveform diagram, the X-axis of the real-time power event coordinate system is different time nodes, and the Y-axis of the real-time power event coordinate system is different power parameter values, collecting the highest critical value, the lowest critical value and the critical cycle time period corresponding to different peak change cycles in the real-time power waveform diagram, and calculating the difference between the highest critical value and the lowest critical value to obtain the real-time power difference corresponding to each peak change cycle, and then using the real-time power difference as the numerator and the duration corresponding to the critical cycle time period as the denominator to calculate, to obtain the real-time power mean of the real-time power waveform diagram, and then matching the real-time power mean with the power change mean range in the event monitoring standard to determine whether an abnormal event has occurred in the real-time monitored power parameters corresponding to the real-time power mean.

[0040] In an embodiment of the present application, when recording abnormal events of a smart electric energy meter during operation, historical abnormal event information is obtained, where the historical abnormal event information includes abnormal power events that occurred in the smart electric energy meter within a historical period, the abnormal degree of the abnormal power events, and the corresponding power parameter changes of the smart electric energy meter when the abnormal power events occurred. Then, the starting time period of the abnormal power event in the smart electric energy meter and the power parameter changes within the starting time period are determined based on the historical abnormal event information. Then, an abnormal event monitoring standard is generated based on the abnormal power event, the abnormal degree, and the starting time period and power parameter changes corresponding to the abnormal power event. Then, real-time monitoring power parameters within a preset time are collected, and based on the abnormal event monitoring standard, it is determined whether an abnormal event has occurred in the real-time monitoring power parameters. If an abnormal event has occurred, the abnormal event will be recorded and stored, and the display of the abnormal event after recording and storage will be controlled. The staff maintains the smart electric energy meter based on the abnormal events that have been recorded and stored in a timely manner, thereby improving the power management efficiency and response speed of the smart electric energy meter.

[0041] A possible implementation method of the embodiment of the present application is to determine whether an abnormal event has occurred in real-time monitoring of power parameters based on abnormal event monitoring standards, and also include: if no abnormal event has occurred, then determine whether there is an abnormal event that is prone to occur in real-time monitoring of power parameters based on the abnormal event monitoring standards; if there is an abnormal event that is prone to occur, then determine the probability of occurrence of the abnormal event corresponding to the abnormal event that is prone to occur, as well as the starting time period of the event corresponding to the probability of occurrence of the abnormal event and the degree of abnormality of the abnormal event that is prone to occur, match the degree of abnormality of the event with the preset abnormality standard, determine the prevention priority score of the probability of occurrence of each abnormal event, multiply the prevention priority score by the probability of occurrence of the abnormal event, and sort the abnormal events that are prone to occur according to the product operation scores to obtain a sequence of events that are prone to occur, add the starting time period of time to the sequence of events that are prone to occur, and obtain a record of events that are prone to occur.

[0042] Specifically, based on the abnormal event monitoring standard, it is determined whether the real-time monitored power parameters are prone to abnormal events, including: determining the mean change of the real-time power mean within the preset time, and determining the mean change trend based on the mean change, predicting the current mean within the future preset time period according to the mean change trend, and obtaining the future power mean within the future preset time period, matching the future power mean with the power change mean range in the event monitoring standard, and determining whether the real-time monitored power parameters are prone to abnormal events.

[0043] Specifically, the current mean within a future preset time period is predicted according to the mean change trend to obtain the future power mean within the future preset time period, including: unsupervised time series data sorting of the mean change trend based on the peak change cycle to obtain mean matrix data, inputting the mean matrix data into a trained feature extraction model for vector feature extraction to obtain feature dimensions and the number of feature dimensions, and combining the obtained feature dimensions and the number of feature dimensions with the mean matrix data to generate future mean matrix data, inputting the future matrix data into a preset algorithm model for data calculation to obtain the future power mean within the future preset time period.

[0044] Specifically, the mean matrix data is input into the trained feature extraction model for vector feature extraction to obtain the number of feature dimensions, including: determining the periodic time periods of different peak change periods in the real-time monitoring power parameters and the real-time power mean values ​​corresponding to the periodic time periods based on the mean matrix data, and then respectively inputting the periodic time periods and the real-time power mean values ​​into the feature extraction model for vector feature extraction to obtain the time vector features corresponding to the periodic time periods and the data vector features corresponding to the real-time power mean values, and then performing quantitative statistics on the data vector features and the time vector features to obtain the feature dimensions and the number of feature dimensions.

[0045] For the embodiment of the present application, the probability of abnormal event occurrence is obtained based on the proportion of the mean change trend. Since the mean change trend has different trend changes in different peak change cycles, in the process of changing trend prediction, the mean change trend prediction of multiple units is performed with 20 peak change cycles as units, and after obtaining the future power averages of multiple units, they are matched with the event monitoring standards respectively, and the probability of abnormal event occurrence is determined according to the corresponding unit number in the matching results.

[0046] The following is an introduction to an abnormal event recording system of a smart electric energy meter provided in an embodiment of the present application. The abnormal event recording system of the smart electric energy meter described below and the abnormal event recording method of the smart electric energy meter described above can be referred to each other. Please refer to Figure 2 , Figure 2 1 is a schematic diagram of the structure of an abnormal event recording system 20 of a smart electric energy meter provided in an embodiment of the present application, including: The information acquisition module 21 is used to acquire historical abnormal event information, where the historical abnormal event information includes abnormal power events that occurred in the smart electric energy meter within a historical period, the abnormal degree of the abnormal power events, and the corresponding power parameter changes of the smart electric energy meter when the abnormal power events occurred; The data monitoring module 22 is used to determine the starting time period of the power abnormal event of the smart electric energy meter and the power parameter changes within the starting time period according to the historical abnormal event information; A standard generation module 23, for generating an abnormal event monitoring standard according to the abnormal power event, the abnormal degree, and the starting time period corresponding to the abnormal power event and the power parameter change; The recording and storage module 24 is used to collect real-time monitoring power parameters within a preset time, and determine whether an abnormal event has occurred in the real-time monitoring power parameters based on the abnormal event monitoring standard. If an abnormal event has occurred, the abnormal event will be recorded and stored, and the abnormal event that has occurred after recording and storage will be controlled to be displayed.

[0047] In a possible implementation of the embodiment of the present application, the system 20 further includes: an event determination module, a degree matching module, an event sorting module, and an event recording module, wherein: An event judgment module is used to determine whether there is an abnormal event that is prone to occur in the real-time monitoring power parameters based on the abnormal event monitoring standard when there is no abnormal event that has occurred. If there is an abnormal event that is prone to occur, the probability of occurrence of the abnormal event corresponding to the abnormal event that is prone to occur, the starting time period of the event that corresponds to the probability of occurrence of the abnormal event, and the abnormal degree of the event that is prone to occur; A degree matching module is used to match the abnormal degree of an event with a preset abnormal degree standard and determine the prevention priority score of the probability of occurrence of each abnormal event; An event sorting module is used to perform a product operation on the prevention priority score and the probability of occurrence of abnormal events, and sort the abnormal events that are prone to occur according to the product operation scores to obtain a sequence of events that are prone to occur; The event recording module is used to add the starting time period of the time occurrence to the prone event sequence to obtain the prone event record.

[0048] In another possible implementation of the embodiment of the present application, when the standard generation module 23 generates the abnormal event monitoring standard according to the power abnormal event, the abnormal degree, and the starting time period corresponding to the power abnormal event and the power parameter change, it is specifically used to: According to the power abnormal events, abnormal coordinate systems of different types of abnormal events are created respectively, the X-axis of the abnormal coordinate system is different time nodes, and the Y-axis of the abnormal coordinate system is different power parameter values; The abnormal coordinate system is marked according to the event name of the abnormal power event, and the starting time period and the power parameter change are mapped to the abnormal coordinate system after the corresponding marking, so as to obtain the abnormal event waveform diagrams of different types of abnormal events in the abnormal power event; Collect the highest peak value, lowest peak value and period time corresponding to different peak value change cycles in each abnormal event waveform, and calculate the difference between the highest peak value and the lowest peak value to obtain the power difference corresponding to each peak value change cycle; The power difference is used as the numerator and the period time period is used as the denominator to calculate and obtain the power change mean range corresponding to each peak change cycle in the abnormal event waveform diagram; The abnormal power change range is sorted according to the preset sorting standard, and the sorted power change mean range is bound with the abnormal degree corresponding to the power change mean range and the event name corresponding to the power change mean range to generate an event monitoring standard.

[0049] Another possible implementation in the embodiment of the present application is that the recording storage module 24 collects the real-time monitoring power parameters within the preset time, and determines whether an abnormal event has occurred in the real-time monitoring power parameters based on the abnormal event monitoring standard, specifically for: Create a real-time power event coordinate system, and map the real-time monitored power parameters to the real-time power event coordinate system according to the time nodes to obtain a real-time power waveform diagram, where the X-axis of the real-time power event coordinate system is different time nodes, and the Y-axis of the real-time power event coordinate system is different power parameter values; Collect the highest critical value, the lowest critical value and the critical period corresponding to different peak change cycles in the real-time power waveform, and calculate the difference between the highest critical value and the lowest critical value to obtain the real-time power difference corresponding to each peak change cycle; The real-time power difference is used as the numerator and the duration corresponding to the critical cycle time period is used as the denominator to calculate and obtain the real-time power mean value of the real-time power waveform diagram; The real-time power average is matched with the power change average range in the event monitoring standard to determine whether an abnormal event has occurred in the real-time monitoring power parameter corresponding to the real-time power average.

[0050] In another possible implementation of the embodiment of the present application, when the event judgment module determines whether the real-time monitored power parameters are prone to abnormal events based on the abnormal event monitoring standard, it is specifically used to: Determine the average change of the real-time power average within a preset time, and determine the average change trend based on the average change; The current mean value in a future preset time period is predicted according to the mean value change trend to obtain the future power mean value in the future preset time period; Match the future power average with the power change average range in the event monitoring standard to determine whether the real-time monitoring power parameters are prone to abnormal events.

[0051] In another possible implementation of the embodiment of the present application, when the event judgment module predicts the current mean value in the future preset time period according to the mean value change trend and obtains the future power mean value in the future preset time period, it is specifically used to: Based on the peak change cycle, the mean change trend is sorted out in an unsupervised time series data to obtain the mean matrix data; The mean matrix data is input into the trained feature extraction model to extract vector features, and the feature dimension and the number of feature dimensions are obtained. The obtained feature dimension and the number of feature dimensions are combined with the mean matrix data to generate future mean matrix data. The future matrix data is input into the preset algorithm model for data extrapolation to obtain the future power mean within the preset time period.

[0052] Another possible implementation method in the embodiment of the present application is that when the event judgment module inputs the mean matrix data into the trained feature extraction model to extract vector features and obtains the number of feature dimensions, it is specifically used to: Determine the periodic time periods of different peak value change periods in the real-time monitoring power parameters and the real-time power mean values ​​corresponding to the periodic time periods based on the mean matrix data; The periodic time period and the real-time power mean are respectively input into the feature extraction model for vector feature extraction, so as to obtain the time vector feature corresponding to the periodic time period and the data vector feature corresponding to the real-time power mean; Perform quantitative statistics on data vector features and time vector features to obtain feature dimensions and the number of feature dimensions.

[0053] The present application embodiment provides an electronic device, such as Figure 3 As shown, Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application, Figure 3 The electronic device 300 shown includes: a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, such as through a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in actual applications, the transceiver 304 is not limited to one, and the structure of the electronic device 300 does not constitute a limitation on the embodiments of the present application.

[0054] The processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of the embodiments of the present application. The processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0055] The bus 302 may include a path to transmit information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0056] The memory 303 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0057] The memory 303 is used to store application code for executing the solution of the embodiment of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the above method embodiment.

[0058] Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0059] A computer-readable storage medium provided in an embodiment of the present application is introduced below. The computer-readable storage medium described below and the method described above can be referenced to each other.

[0060] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the abnormal event recording method of the smart electric energy meter are implemented.

[0061] Since the embodiments of the computer-readable storage medium part and the embodiments of the method part correspond to each other, the embodiments of the computer-readable storage medium part refer to the description of the embodiments of the method part.

[0062] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0063] The above are only some implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for recording abnormal events of a smart electric energy meter, characterized in that: include: Acquire historical abnormal event information, wherein the historical abnormal event information is the power abnormal event that occurred in the smart electric energy meter within a historical period, the abnormal degree of the power abnormal event, and the power parameter change of the smart electric energy meter corresponding to the power abnormal event; Determine the starting time period when the power abnormal event occurs in the smart electric energy meter and the power parameter changes in the starting time period according to the historical abnormal event information; Generate an abnormal event monitoring standard according to the abnormal power event, the abnormal degree, the starting time period corresponding to the abnormal power event, and the power parameter change; Collect real-time monitoring power parameters within a preset time, and determine whether an abnormal event has occurred in the real-time monitoring power parameters based on the abnormal event monitoring standard. If an abnormal event has occurred, record and store the abnormal event, and control the display of the abnormal event after recording and storage.

2. The abnormal event recording method of a smart electric energy meter according to claim 1, characterized in that: The determining whether an abnormal event has occurred in the real-time monitored power parameter based on the abnormal event monitoring standard further includes: If the abnormal event does not exist, determine whether the real-time monitored power parameter has an abnormal event that is prone to occur based on the abnormal event monitoring standard; if the abnormal event that is prone to occur exists, determine the probability of occurrence of the abnormal event corresponding to the abnormal event that is prone to occur, the start time period of the event that occurs corresponding to the abnormal event probability, and the abnormality degree of the abnormal event that is prone to occur; Matching the abnormality level of the event with the preset abnormality level standard to determine the prevention priority score of the probability of occurrence of each abnormal event; Performing a product operation on the prevention priority score and the probability of occurrence of the abnormal event, and sorting the abnormal events that are prone to occur according to the product operation scores to obtain a sequence of events that are prone to occur; The starting time period of the time occurrence is added to the prone event sequence to obtain a prone event record.

3. The abnormal event recording method of a smart electric energy meter according to claim 2, characterized in that: The generating of the abnormal event monitoring standard according to the abnormal power event, the abnormal degree, the starting time period corresponding to the abnormal power event, and the power parameter change includes: According to the power abnormal event, abnormal coordinate systems of different types of abnormal events are created respectively, wherein the X-axis of the abnormal coordinate system is different time nodes, and the Y-axis of the abnormal coordinate system is different power parameter values; The abnormal coordinate system is labeled according to the event name of the abnormal power event, and the starting time period and the power parameter change are mapped to the abnormal coordinate system after corresponding labeling to obtain abnormal event waveform diagrams of different types of abnormal events in the abnormal power event; Collect the highest peak value, the lowest peak value and the periodic time corresponding to different peak value change cycles in each abnormal event waveform diagram, and calculate the difference between the highest peak value and the lowest peak value to obtain the power difference corresponding to each peak value change cycle; The power difference is used as a numerator and the period time period is used as a denominator to calculate to obtain the power change mean range corresponding to each peak change period in the abnormal event waveform diagram; The abnormal power change range is sorted according to a preset sorting standard, and the sorted power change mean range is bound to the abnormal degree corresponding to the power change mean range and the event name corresponding to the power change mean range to generate an event monitoring standard.

4. The abnormal event recording method of a smart electric energy meter according to claim 3, characterized in that: The collecting of real-time monitoring power parameters within a preset time, and determining whether an abnormal event has occurred in the real-time monitoring power parameters based on the abnormal event monitoring standard, includes: Create a real-time power event coordinate system, and map the real-time monitored power parameters to the real-time power event coordinate system according to time nodes to obtain a real-time power waveform diagram, wherein the X-axis of the real-time power event coordinate system is different time nodes, and the Y-axis of the real-time power event coordinate system is different power parameter values; Collecting the highest critical value, the lowest critical value and the critical period time corresponding to different peak value change cycles in the real-time power waveform diagram, and performing a difference calculation between the highest critical value and the lowest critical value to obtain the real-time power difference corresponding to each peak value change cycle; The real-time power difference is used as a numerator and the duration corresponding to the critical cycle time period is used as a denominator to calculate to obtain the real-time power mean value of the real-time power waveform diagram; The real-time power average is matched with the power change average range in the event monitoring standard to determine whether an abnormal event has occurred in the real-time monitoring power parameter corresponding to the real-time power average.

5. The abnormal event recording method of a smart electric energy meter according to claim 4, characterized in that: The determining, based on the abnormal event monitoring standard, whether the real-time monitored power parameter has an abnormal event that is prone to occur includes: Determine a change in the mean value of the real-time power mean value within the preset time, and determine a change trend of the mean value based on the change in the mean value; Predicting the current mean value within a future preset time period according to the mean value change trend to obtain the future power mean value within the future preset time period; The future power mean is matched with the power change mean range in the event monitoring standard to determine whether the real-time monitored power parameter has an abnormal event that is prone to occur.

6. The abnormal event recording method of a smart electric energy meter according to claim 5, characterized in that: The method of predicting the current mean value in a future preset time period according to the mean value change trend to obtain the future power mean value in the future preset time period includes: Based on the peak change period, unsupervised time series data sorting is performed on the mean change trend to obtain mean matrix data; Inputting the mean matrix data into a trained feature extraction model to perform vector feature extraction to obtain feature dimensions and the number of feature dimensions, and combining the obtained feature dimensions and the number of feature dimensions with the mean matrix data to generate future mean matrix data; The future matrix data is input into a preset algorithm model for data extrapolation to obtain the future power mean value within a preset time period.

7. The abnormal event recording method of a smart electric energy meter according to claim 6, characterized in that: The mean matrix data is input into the trained feature extraction model to extract vector features to obtain the number of feature dimensions, including: Determine the periodic time periods of different peak value change periods in the real-time monitored power parameter and the real-time power mean values ​​corresponding to the periodic time periods based on the mean value matrix data; Inputting the periodic time period and the real-time power mean into the feature extraction model to extract vector features, respectively, to obtain a time vector feature corresponding to the periodic time period and a data vector feature corresponding to the real-time power mean; Quantitative statistics are performed on the data vector features and the time vector features to obtain feature dimensions and the number of feature dimensions.

8. An abnormal event recording system for a smart electric energy meter, characterized in that: include: An information acquisition module is used to acquire historical abnormal event information, wherein the historical abnormal event information is the power abnormal event that occurred in the smart electric energy meter within a historical period, the abnormal degree of the power abnormal event, and the power parameter change of the smart electric energy meter corresponding to the power abnormal event; A data monitoring module, used to determine the starting time period when the power abnormal event occurs in the smart electric energy meter and the power parameter changes in the starting time period according to the historical abnormal event information; A standard generation module, configured to generate an abnormal event monitoring standard according to the abnormal power event, the abnormal degree, the starting time period corresponding to the abnormal power event, and the power parameter change; The recording and storage module is used to collect real-time monitoring power parameters within a preset time, and determine whether an abnormal event has occurred in the real-time monitoring power parameters based on the abnormal event monitoring standard. If the abnormal event has occurred, the abnormal event that has occurred is recorded and stored, and the abnormal event that has occurred after recording and storage is controlled to be displayed.

9. An electronic device, characterized in that: The electronic device includes: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute an abnormal event recording method for a smart electric energy meter according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: include: A computer program is stored which can be loaded by a processor and executes a method for recording abnormal events of a smart electric energy meter as described in any one of claims 1 to 7.

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