Electric energy meter current overload prevention method and device, server and storage medium

Abstract

The invention relates to the technical field of overload prevention, and provides an electric energy meter current overload prevention method and device, a server and a storage medium. The current information of the target area is acquired, branch classification is performed according to the source of the information, the power consumption trend of each branch is analyzed carefully based on the real-time current information, the preset prediction period and the trend prediction model, and corresponding historical power consumption data is acquired according to the time label. The method comprises the following steps: acquiring the current power consumption trend of each branch, performing comprehensive analysis on each branch through a preset prevention model in combination with historical power consumption data and the current power consumption trend to judge whether abnormal power consumption exists in each branch, and performing overload control on the abnormal branch according to a preset prevention control mode when abnormal power consumption is detected in the branches. By monitoring and predicting the power consumption trend in real time, potential overload risks can be found and processed in time, and therefore safety accidents such as fire disasters and equipment damage caused by overload of the electric energy meter are effectively avoided.

Description

Technical Field

[0001] The present application relates to the technical field of overload prevention, and in particular to a method, device, server and storage medium for preventing current overload in an electric energy meter. Background Art

[0002] In the power industry, electric energy meters used to measure electric energy consumption and collect and upload relevant data such as current and voltage can efficiently convert analog signals into digital data and upload them to the data acquisition layer through channels, thereby realizing the automatic collection of user electricity consumption information.

[0003] However, there are still significant challenges in preventing current overloads in electric energy meters. Current overload, which is caused by factors such as increased voltage at the power supply end, an increase in the number of electrical devices in the circuit or an increase in power, and increased resistance due to aging wires, will not only accelerate the aging of wires, sockets and other equipment, affecting the collection of electricity consumption data and normal use by users, but may also cause potential risks such as fires due to heating of the lines.

[0004] At present, the problem of current overload is solved by setting a current threshold in the energy meter. When the monitored current exceeds this preset threshold, the alarm mechanism will be triggered. However, the threshold-based alarm mechanism cannot predict the overload trend of the energy meter current in advance, but can only issue an alarm after the current actually exceeds the threshold, which makes the implementation of preventive measures passive and may lag behind. Summary of the invention

[0005] In view of this, the present application provides a method, device, server and storage medium for preventing current overload of an electric energy meter to solve the problem of preventing current overload.

[0006] In a first aspect, the present application provides a method for preventing current overload in an electric energy meter, the method comprising: Acquire the current information of the target area in real time according to the collection time interval, and classify the branches according to the source of the current information; analyze the current power consumption trend of each branch according to the current information, the preset prediction period and the preset trend prediction model; Acquiring historical electricity consumption data according to a time tag, wherein the prediction period includes a time tag; Determine whether there is abnormal power consumption in each branch according to the preset prevention model, the historical power consumption data and the current power consumption trend; When there is abnormal power consumption in the branch, overload control is performed on the branch with abnormal power consumption according to a preset preventive control method.

[0007] In an optional implementation, within a prediction period, analyzing the current power consumption trend of each branch according to the current information, a preset prediction period, and a preset trend prediction model includes: Obtaining the current electricity consumption impact coefficient according to the time tag; Performing power consumption trend fitting according to the trend prediction model and the current information; Obtaining an average change in electricity consumption according to the electricity consumption trend fitting line; According to the average change in electricity consumption and the current electricity consumption influence coefficient, a reasonable current range for the next collection time interval is obtained.

[0008] In an optional implementation, obtaining the average change in electricity consumption according to the electricity consumption trend fitting line includes: calculating the average change in electricity consumption by the following formula: Among them, △E is the average change in electricity consumption, I1, I2, ....I n are all current changes in adjacent acquisition time intervals, n is the number of current changes, and T is the acquisition time interval.

[0009] In an optional implementation, obtaining the current power consumption impact coefficient according to the time tag includes: Acquire current weather information and time information according to the time tag; The current electricity consumption impact coefficient is obtained according to the current weather information and the time information.

[0010] In an optional implementation, obtaining a reasonable current range for a next collection time interval according to the average change in power consumption and the current power consumption impact coefficient includes: Obtaining a first predicted current for the next collection time interval according to the current information obtained during the current collection time interval and the average change in power consumption; Obtaining a second predicted current according to the first predicted current and the current power consumption impact coefficient; The reasonable current range is obtained according to the first predicted current and the second predicted current.

[0011] In an optional implementation, the acquiring of historical electricity usage data according to the time tag includes: Acquire a historical current data set according to the current weather information and the time information; A mean filtering calculation is performed on the historical current data set to obtain the historical power consumption data.

[0012] In an optional implementation, the overload control of the branch with abnormal power consumption according to a preset preventive control method includes: Acquiring overload control parameters according to the historical power consumption data and the current power consumption trend; comparing the overload control parameter with a preset control threshold; An overload control strategy is acquired according to the threshold comparison result, so as to perform overload control on the branch with abnormal power consumption according to the overload control strategy.

[0013] A second aspect of the present application provides a current overload prevention device for an electric energy meter, the device comprising: A classification acquisition module is used to acquire the current information of the target area in real time according to the acquisition time interval, and classify the branches according to the source of the current information; A trend prediction module, used to analyze the current power consumption trend of each branch according to the current information, a preset prediction period and a preset trend prediction model; A feature acquisition module, used to acquire historical power consumption data according to a time tag, wherein the prediction period includes a time tag; an abnormality judgment module, used to judge whether there is abnormal power consumption in each branch according to a preset prevention model, the historical power consumption data and the current power consumption trend; The overload prevention module is used to perform overload control on the branch with abnormal power consumption according to a preset prevention control method when there is abnormal power consumption in the branch.

[0014] A third aspect of the present application provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the electric energy meter current overload prevention method as described above when executing the computer program.

[0015] A fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the electric energy meter current overload prevention method as described above.

[0016] In summary, this application at least includes the following beneficial technical effects: 1. Through real-time monitoring and prediction of electricity consumption trends, potential overload risks can be discovered and handled in a timely manner, thereby effectively avoiding safety accidents such as fire and equipment damage caused by overload of electricity meters.

[0017] 2. Timely implementation of overload control measures can protect electricity meters and other electrical equipment from damage caused by current overload, extend equipment service life and reduce maintenance costs.

[0018] 3. Through the analysis of electricity consumption trends and comparison of historical data, users can better understand their own electricity consumption, so as to formulate reasonable electricity consumption plans and achieve energy conservation and optimized management.

[0019] 4. It combines multiple technical means such as real-time data collection, trend forecasting, and intelligent analysis, reflects the application and development trend of smart grid and Internet of Things technologies in the field of energy management, and improves the level of intelligence in energy management. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 It is a flow chart of a method for preventing current overload of an electric energy meter provided in an embodiment of the present application; Figure 2 It is a functional module diagram of a current overload prevention device for an electric energy meter provided in an embodiment of the present application; Figure 3 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only 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.

[0023] The electric energy meter current overload prevention method provided in the embodiment of the present application is executed by an electronic device, and accordingly, the electric energy meter current overload prevention device operates in the electronic device. The electric energy meter current overload prevention method provided in the embodiment of the present application is described below from the perspective of the electric energy meter current overload prevention device combined with the current monitoring and prevention process.

[0024] like Figure 1 FIG. 1 is a flow chart of a method for preventing electric energy meter current overload provided in an embodiment of the present application. The method for preventing electric energy meter current overload provided in an embodiment of the present application comprises the following steps.

[0025] Step S11, acquiring the current information of the target area in real time according to the collection time interval, and classifying the branches according to the source of the current information.

[0026] The current information is collected in real time by the smart meters installed in each branch. The current information includes but is not limited to the measured current value, and the data is transmitted to the central processor. Then, according to the source of the current information (i.e., the smart meter that transmits the current information), the collected current information is identified and classified according to the branch according to the preset mapping table between the branch and the information source.

[0027] Step S12: Analyze the current power consumption trend of each branch according to the current information, the preset prediction period and the preset trend prediction model.

[0028] The prediction period includes but is not limited to current information and corresponding time tags.

[0029] It should be understood that environmental information has an important impact on the power consumption of the corresponding user. Among them, the current weather information (such as temperature, humidity, sunny or rainy conditions, etc.) and time information (such as season, time period, etc.) have a crucial impact on the user's use of electrical equipment and the choice of power consumption. For example, when the temperature is high in summer, users tend to use cooling equipment such as air conditioners and electric fans, resulting in a significant increase in power consumption.

[0030] After obtaining the time tag, the date and time corresponding to the collected current information are obtained by parsing the time tag. The current weather information and time information are queried according to the date and time. Thus, according to the current weather information and time information, a mapping table between preset environmental information and power consumption impact coefficient is queried to obtain the current power consumption impact coefficient.

[0031] At the same time, the power consumption trend is fitted according to the current value and time information contained in the current information to obtain the power consumption trend fitting line that can directly reflect the power consumption change. And the average change in power consumption used to represent the average change trend of the current within a given collection time interval is calculated according to the following formula: Among them, △E is the average change in electricity consumption, I1, I2, ....I n are all current changes in adjacent acquisition time intervals, n is the number of current changes, and T is the acquisition time interval.

[0032] After obtaining the average change in electricity consumption, the first predicted current is calculated based on the current value obtained in the current collection time interval and the average change in electricity consumption. The first predicted current is the theoretical current value of the next collection time interval inferred based on the average change in electricity consumption. Further, taking into account the actual impact of the environment on the applied electricity, the second predicted current is calculated based on the first predicted current and the current electricity consumption impact coefficient. The second predicted current is the theoretical current value of the next collection time interval inferred after comprehensive environmental coefficients. Then, based on the first predicted current and the second predicted current, the reasonable current range is obtained.

[0033] For example, the average change in electricity consumption is E, the current collection time interval is T, the current electricity consumption impact coefficient is θ, and the current collection current value is I n And, according to the first predicted current calculation formula, the first predicted current is obtained as (I n +ET), the second predicted current obtained by further calculation is θ(I n +ET) and the first predicted current is less than the second predicted current, so according to the first predicted current and the second predicted current, [(I n +ET),θ(I n +ET)].

[0034] Step S13: Acquire historical electricity consumption data according to the time tag.

[0035] Using the time tag as the query condition, the historical current data records close to the current time are retrieved from the database. Furthermore, considering the impact of weather on power consumption, the historical current data records are screened according to the similarity between the current weather information and the historical weather records to obtain a historical current data set that is closer to the current situation.

[0036] Furthermore, after obtaining the historical current data set, the historical power consumption data is obtained according to the preset mean filter calculation. The historical power consumption data is used to represent historical data that is closer to the current power consumption situation. The specific operation is as follows: The N historical current data obtained are arranged from large to small to obtain a sorted data set, and then de-extreme value is performed according to the arrangement order in the sorted data set to remove the maximum and minimum values ​​in the sorted data set and reduce abnormal values ​​generated in special power consumption conditions.

[0037] Then, the historical electricity consumption data is calculated by the following formula: in, is the historical electricity consumption data, are all historical current data in the sorted data set, and (N-2) is the number of data in the sorted data set after depolarization.

[0038] Step S14: judging whether there is abnormal power consumption in each branch according to the preset prevention model, the historical power consumption data and the current power consumption trend.

[0039] After obtaining historical electricity consumption data and current electricity consumption trends, Compare with the reasonable current range to determine whether there is abnormal power consumption in the corresponding branch. When the current is greater than the maximum value of the reasonable range, it is determined that abnormal power consumption exists in the branch.

[0040] Step S15: When there is abnormal power consumption in the branch, overload control is performed on the branch with abnormal power consumption according to a preset preventive control method.

[0041] according to The maximum value of the reasonable current range is calculated, and the difference between the two is calculated (i.e., the overload control parameter). Further, the overload control parameter is compared with the preset control threshold, and the overload control strategy is determined based on it. Among them, the control threshold is designed according to the severity of the overload obtained in the experiment. When the overload control parameter is in a certain threshold range, the overload control strategy is determined according to the mapping table between the preset control threshold and the overload control strategy. Then, the branch with abnormal power consumption is overloaded according to the overload control strategy.

[0042] For example, suppose that in a certain power system, there is a branch with a real-time current value of 120A, and the reasonable current range of the branch is [80A, 100A]. At this time, it is detected that the branch has abnormal power consumption, and the overload control parameter is calculated to be 120A-100A=20A. Among them, the current overload prevention device of the electric energy meter is preset with [0A, 10A], [10A, 20A], [20A, 30A]. By comparing the overload control parameter with the control threshold, it is determined that the overload control parameter 20A falls within the [10A, 20A] interval. According to the preset mapping table, the system finds that the overload control strategy corresponding to the [10A, 20A] interval is "send a warning signal and limit the current to 110A". Therefore, a warning signal is sent to the branch through the automation equipment, and the current limiter is adjusted to limit the current to less than 110A to alleviate the overload situation. At the same time, the system continues to monitor the power consumption of the branch and adjusts the control strategy as needed.

[0043] This application is used in the field of overload prevention. By obtaining the current information of the target area and classifying the branches according to the source of the information, the power consumption trend of each branch is analyzed in detail based on the real-time current information, the preset prediction period and the trend prediction model. At the same time, the corresponding historical power consumption data is obtained according to the time tag, and then the preset prevention model is combined with the historical power consumption data and the current power consumption trend to conduct a comprehensive analysis of each branch to determine whether there is abnormal power consumption in each branch. When abnormal power consumption is detected in the branch, the abnormal branch is overloaded according to the preset prevention control method. This application can timely discover and deal with potential overload risks by real-time monitoring and predicting power consumption trends, thereby effectively avoiding safety accidents such as fire and equipment damage caused by overload of the energy meter.

[0044] like Figure 2 As shown, it is a functional module diagram of a current overload prevention device for an electric energy meter provided in an embodiment of the present application.

[0045] In some embodiments, the electric energy meter current overload prevention device 2 may include a plurality of functional modules composed of computer program segments. The computer programs of the various program segments in the electric energy meter current overload prevention device 2 may be stored in a memory of a server and executed by at least one processor to execute (see Figure 1 Description) Functionality of current overload prevention method for electric energy meter.

[0046] In this embodiment, the electric energy meter current overload prevention device 2 can be divided into multiple functional modules according to the functions it performs. The functional modules may include: a classification acquisition module 21, a trend prediction module 22, a feature acquisition module 23, an abnormality judgment module 24 and an overload prevention module 25. The module referred to in the present invention refers to a series of computer program segments that can be executed by at least one processor and can complete fixed functions, which are stored in a memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0047] The classification acquisition module 21 is used to acquire the current information of the target area in real time according to the acquisition time interval, and classify the branches according to the source of the current information.

[0048] The trend prediction module 22 is used to analyze the current power consumption trend of each branch according to the current information, a preset prediction period and a preset trend prediction model.

[0049] In an optional implementation, within a prediction period, the trend prediction module 22 is specifically used to: Obtaining the current electricity consumption impact coefficient according to the time tag; Performing power consumption trend fitting according to the trend prediction model and the current information; Obtaining an average change in electricity consumption according to the electricity consumption trend fitting line; According to the average change in electricity consumption and the current electricity consumption influence coefficient, a reasonable current range for the next collection time interval is obtained.

[0050] In an optional implementation, the trend prediction module 22 is further used for: The average change in electricity consumption is calculated using the following formula: Among them, △E is the average change in electricity consumption, I1, I2, ....I n are all current changes in adjacent acquisition time intervals, n is the number of current changes, and T is the acquisition time interval.

[0051] In an optional implementation, the trend prediction module 22 is further used for: Acquire current weather information and time information according to the time tag; The current electricity consumption impact coefficient is obtained according to the current weather information and the time information.

[0052] In an optional implementation, the trend prediction module 22 is further used for: Obtaining a first predicted current for the next collection time interval according to the current information obtained during the current collection time interval and the average change in power consumption; Obtaining a second predicted current according to the first predicted current and the current power consumption impact coefficient; The reasonable current range is obtained according to the first predicted current and the second predicted current.

[0053] The feature acquisition module 23 is used to acquire historical power consumption data according to a time tag, wherein the prediction period includes a time tag.

[0054] In an optional implementation, the feature acquisition module 23 is specifically used to: Acquire a historical current data set according to the current weather information and the time information; A mean filtering calculation is performed on the historical current data set to obtain the historical power consumption data.

[0055] The abnormality judgment module 24 is used to judge whether there is abnormal power consumption in each branch according to a preset prevention model, the historical power consumption data and the current power consumption trend.

[0056] The overload prevention module 25 is used to perform overload control on the branch with abnormal power consumption according to a preset prevention control method when there is abnormal power consumption in the branch.

[0057] In an optional implementation, the overload prevention module 25 is specifically used for: Acquiring overload control parameters according to the historical power consumption data and the current power consumption trend; comparing the overload control parameter with a preset control threshold; An overload control strategy is acquired according to the threshold comparison result, so as to perform overload control on the branch with abnormal power consumption according to the overload control strategy.

[0058] It should be understood that the various variations and specific embodiments of the methods provided in the above embodiments are also applicable to the electric energy meter current overload prevention device of the present embodiment. Through the above detailed description of the electric energy meter current overload prevention method, those skilled in the art can clearly know the implementation method of the electric energy meter current overload prevention device in the present embodiment. For the sake of brevity of the specification, it will not be described in detail here.

[0059] like Figure 3 , which is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0060] In a preferred embodiment of the present invention, the electronic device 3 may include, but is not limited to: a memory 31 , at least one processor 32 and at least one communication bus 33 .

[0061] Those skilled in the art should understand that Figure 3 The structure of the electronic device 3 shown does not constitute a limitation of the embodiment of the present invention, and the electronic device 3 may also include more or less other hardware or software than shown in the figure, or a different arrangement of components.

[0062] In some embodiments, the electronic device 3 is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application-specific integrated circuits, programmable gate arrays, digital processors and embedded devices.

[0063] It should be noted that the electronic device 3 is only an example, and other existing or future electronic products that are suitable for the present application should also be included in the protection scope of the present application and included here by reference.

[0064] In some embodiments, the memory 31 stores a computer program, and when the computer program is executed by the at least one processor 32, all or part of the steps in the electric energy meter current overload prevention method as described above are implemented. The memory 31 includes a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc memory, magnetic disk memory, magnetic tape memory, or any other computer-readable medium that can be used to carry or store data. Further, the computer-readable storage medium may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, and the like.

[0065] In some embodiments, the at least one processor 32 is the control core (ControlUnit) of the electronic device 3, and uses various interfaces and lines to connect various components of the entire electronic device 3, and executes various functions and processes data of the electronic device 3 by running or executing programs or modules stored in the memory 31, and calling data stored in the memory 31. For example, when the at least one processor 32 executes the computer program stored in the memory 31, it implements all or part of the steps of the electric energy meter current overload prevention method described in the embodiment of the present application; or implements all or part of the functions of the electric energy meter current overload prevention device. The at least one processor 32 can be composed of an integrated circuit, for example, it can be composed of a single packaged integrated circuit, or it can be composed of multiple integrated circuits with the same function or different functions, including one or more central processing units (CPU), microprocessors, digital processing chips, graphics processors, and various control chips.

[0066] In some embodiments, the at least one communication bus 33 is configured to realize the connection and communication between the memory 31 and the at least one processor 32, etc. Although not shown, the electronic device 3 may also include a power supply (such as a battery) for powering each component. Preferably, the power supply may be logically connected to the at least one processor 32 through a power management device, so as to realize the functions of managing charging, discharging, and power consumption management through the power management device. The power supply may also include any components such as one or more DC or AC power supplies, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, etc. The electronic device 3 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0067] The above-mentioned integrated unit implemented in the form of a software function module can be stored in a computer-readable storage medium. The above-mentioned software function module is stored in a storage medium, including a number of instructions for enabling an electronic device (which can be a personal computer, electronic device, or network device, etc.) or a processor to execute part of the method described in each embodiment of the present application.

[0068] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0069] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0070] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for preventing current overload of an electric energy meter, characterized in that: The method comprises: Acquire the current information of the target area in real time according to the collection time interval, and classify the branches according to the source of the current information; analyze the current power consumption trend of each branch according to the current information, the preset prediction period and the preset trend prediction model; Acquiring historical electricity consumption data according to a time tag, wherein the prediction period includes a time tag; Determine whether there is abnormal power consumption in each branch according to the preset prevention model, the historical power consumption data and the current power consumption trend; When there is abnormal power consumption in the branch, overload control is performed on the branch with abnormal power consumption according to a preset preventive control method.

2. The method for preventing electric energy meter current overload according to claim 1, characterized in that: In a prediction cycle, the current power consumption trend of each branch is analyzed according to the current information, the preset prediction cycle and the preset trend prediction model, including: Obtaining the current electricity consumption impact coefficient according to the time tag; Performing power consumption trend fitting according to the trend prediction model and the current information; Obtaining an average change in electricity consumption according to the electricity consumption trend fitting line; According to the average change in electricity consumption and the current electricity consumption influence coefficient, a reasonable current range for the next collection time interval is obtained.

3. The method for preventing electric energy meter current overload according to claim 2, characterized in that: The obtaining of the average change in electricity consumption according to the electricity consumption trend fitting line comprises: The average change in electricity consumption is calculated using the following formula: Among them, △E is the average change in electricity consumption, I1, I2, ....I n are all current changes in adjacent acquisition time intervals, n is the number of current changes, and T is the acquisition time interval.

4. The method for preventing electric energy meter current overload according to claim 2, characterized in that: The obtaining of the current power consumption impact coefficient according to the time tag comprises: Acquire current weather information and time information according to the time tag; The current electricity consumption impact coefficient is obtained according to the current weather information and the time information.

5. The method for preventing current overload of an electric energy meter according to any one of claims 3 to 4, characterized in that: The obtaining of a reasonable current range for the next collection time interval according to the average change in power consumption and the current power consumption influence coefficient includes: Obtaining a first predicted current for the next collection time interval according to the current information obtained during the current collection time interval and the average change in power consumption; Obtaining a second predicted current according to the first predicted current and the current power consumption impact coefficient; The reasonable current range is obtained according to the first predicted current and the second predicted current.

6. The method for preventing electric energy meter current overload according to claim 4, characterized in that: The obtaining of historical electricity consumption data according to the time tag includes: Acquire a historical current data set according to the current weather information and the time information; A mean filtering calculation is performed on the historical current data set to obtain the historical power consumption data.

7. The method for preventing electric energy meter current overload according to claim 1, characterized in that: The overload control of the branch with abnormal power consumption according to the preset preventive control method includes: Acquiring overload control parameters according to the historical power consumption data and the current power consumption trend; comparing the overload control parameter with a preset control threshold; An overload control strategy is acquired according to the threshold comparison result, so as to perform overload control on the branch with abnormal power consumption according to the overload control strategy.

8. A current overload prevention device for an electric energy meter, characterized in that: The device comprises: A classification acquisition module is used to acquire the current information of the target area in real time according to the acquisition time interval, and classify the branches according to the source of the current information; A trend prediction module, used to analyze the current power consumption trend of each branch according to the current information, a preset prediction period and a preset trend prediction model; A feature acquisition module, used to acquire historical power consumption data according to a time tag, wherein the prediction period includes a time tag; An abnormality judgment module, used to judge whether there is abnormal power consumption in each branch according to a preset prevention model, the historical power consumption data and the current power consumption trend; The overload prevention module is used to perform overload control on the branch with abnormal power consumption according to a preset prevention control method when there is abnormal power consumption in the branch.

9. An electronic device, characterized in that: The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the electric energy meter current overload prevention method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for preventing current overload of an electric energy meter according to any one of claims 1 to 7 are implemented.

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