State monitoring system and method for electric energy metering device
By comprehensively considering the complexity and environmental impact of the metering data in the status monitoring of the electric energy metering device, a state monitoring method is designed, which solves the problem of large analysis errors in the prior art and improves the accuracy of evaluation.
Patent Information
- Application Number
- CN202510629071.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-16
- Publication Date
- 2025-06-13
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The prior art fails to take into account the complexity of the measurement data and the impact of the environment on the device in the state analysis of the electric energy metering device, resulting in large analysis errors and low evaluation accuracy.
A condition monitoring method is designed to avoid errors and improve evaluation accuracy by acquiring measurement data, actual power use data and environmental data during the monitoring cycle.
By comprehensively evaluating the operating status of the metering device, analysis errors caused by the complexity of the metering data and environmental impact are avoided, and the accuracy of the metering device's operating status evaluation is improved.
Smart Images
Figure CN120143045A_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of condition monitoring, specifically a condition monitoring system and method for electric energy metering devices. Background Art
[0002] An electric energy metering device refers to a device used to measure and record the consumption of electric energy. It includes electric energy meters, transformers, secondary circuits, electric energy metering cabinets (boxes), and related auxiliary equipment. The main function of the electric energy metering device is to accurately measure the active and reactive electric energy flowing in the power grid and record the relevant electric energy usage data for purposes such as electricity bill calculation, energy management, and power system analysis. These devices are designed and manufactured in accordance with specific standards and specifications to ensure the accuracy of measurement and the reliability of data. During the use of electric energy metering devices, it is often necessary to monitor the status of the electric energy metering devices to analyze the quality of the electric energy metering devices. At this time, a condition monitoring system is required to monitor the status of the electric energy metering devices; In the prior art, during the process of analyzing the status of metering devices, the complexity of metering data and the influence of the environment on metering devices are not taken into account simultaneously, resulting in a large analysis error often occurring due to the complexity of metering data and the influence of the environment on metering devices during status analysis, thereby leading to a low accuracy in evaluating the operating status of metering devices. Most of the prior art has the above problems; To solve the problems proposed in this background art, this application designs a condition monitoring system and method for electric energy metering devices. Summary of the Invention
[0003] To solve the deficiencies in the prior art mentioned in the background art, this application proposes a condition monitoring system and method for electric energy metering devices. This application proposes that during the process of analyzing the status of metering devices, the complexity of metering data and the influence of the environment on metering devices are taken into account simultaneously, and then a comprehensive evaluation of the operating status of metering devices is carried out, avoiding the analysis error caused by the complexity of metering data and the influence of the environment on metering devices during status analysis, and improving the accuracy of evaluating the operating status of metering devices.
[0004] To achieve the above object, this application provides the following technical solutions: In the first aspect, this application provides a condition monitoring method for electric energy metering devices, which includes the following specific steps: S1. Obtain the metering data and actual electric energy usage data of the electric energy metering device during the monitoring period, and at the same time obtain the environmental data where the electric energy metering device is located; S2. Import the actual electric energy usage data during the monitoring period into the metering data difficulty analysis strategy for metering data difficulty analysis; S3. Perform metering difficulty analysis based on the metering data difficulty analysis results and the environmental data where the electric energy metering device is located; S4. Perform metering accuracy analysis based on the metering data of the electric energy metering device and the actual electric energy consumption data within the monitoring period; S5. Perform metering device status analysis based on the metering difficulty analysis results and the metering accuracy analysis results.
[0005] As a preferred technical solution of the status monitoring method for the electric energy metering device, the specific content of obtaining the metering data of the electric energy metering device and the actual electric energy consumption data within the monitoring period, and simultaneously obtaining the environmental data where the electric energy metering device is located is as follows: S11. Obtain the electricity metering data of the electric energy metering device for the electrical equipment within the monitoring period, and store it in the first storage component for analyzing the impact of the fluctuations and anomalies of the electricity metering data on the metering of the electric energy metering device; S12. Obtain the actual electric energy consumption data of the electrical equipment within the monitoring period and store it in the second storage component; S13. Collect the environmental data of the environment where the electric energy metering device is located through the environmental data acquisition terminal and store it in the third storage component, where the environmental data includes environmental data types such as temperature and humidity that affect the operation accuracy of the electric energy metering device.
[0006] As a preferred technical solution of the status monitoring method for the electric energy metering device, the metering data difficulty analysis strategy in S2 includes the following specific steps: S21. Obtain the change curve of the actual electric energy consumption data within the monitoring period, and calculate the difference between the electric energy consumption data at each moment and the actual previous moment within the monitoring period; S22. Import the difference between the electric energy consumption data at each moment and the actual previous moment within the monitoring period into the metering data complexity coefficient calculation formula to calculate the metering data complexity coefficient, where the metering data complexity coefficient calculation formula is: , where \(x_t\) is the actual electric energy consumption data at the \(t\)th moment within the monitoring period, \(x_{(t - 1)}\) is the actual electric energy consumption data at the \((t - 1)\)th moment within the monitoring period, \(T\) is the duration of the monitoring period, \(dt\) is the time integral, and in this formula, the complexity coefficient of the metering data is evaluated by the magnitude of the fluctuation of the metering data over time; S23. Obtain the change curve of the actual electric energy consumption data within the monitoring period and the safe range data of the electric energy change speed that the metering device can count, and at the same time import the calculated metering data complexity coefficient into the metering data difficulty calculation formula to calculate the metering data difficulty, where the metering data difficulty calculation formula is: , where c is the change detection difficulty factor, xm is the median of the power change speed safety range data that the metering device can count, xmax is the maximum value of the power change speed safety range data that the metering device can count, and xmin is the minimum value of the power change speed safety range data that the metering device can count. In this formula, the power change speed safety range data that the metering device can count is the power situation from the minimum load to the maximum load when the metering device is designed.
[0007] As a preferred technical solution of the state monitoring method for the power metering device, the metering difficulty analysis based on the metering data difficulty analysis result and the environmental data where the power metering device is located includes the following specific steps: S31. Obtain the environmental data where the power metering device is located, and import the environmental data where the power metering device is located into the environmental impact value calculation formula to calculate the environmental impact value. The environmental impact value calculation formula is: , where N is the number of environmental types, ai is the impact factor of the i-th environmental type, fti is the value of the i-th environmental type data at time t, and fmi is the median of the safety range of the i-th environmental type data; S32. Obtain the calculated metering data difficulty and the environmental impact value, and sum them after weighting to obtain the metering difficulty.
[0008] As a preferred technical solution of the state monitoring method for the power metering device, the metering accuracy analysis based on the metering data of the power metering device and the actual power usage data within the monitoring period includes the following specific contents: Obtain the metering data of the power metering device and the actual power usage data within the monitoring period, and import them into the metering accuracy calculation formula to calculate the metering accuracy. The metering accuracy calculation formula is: , where xtz is the metering data of the power metering device at time t.
[0009] As a preferred technical solution of the state monitoring method for the power metering device, the metering device state analysis based on the metering difficulty analysis result and the metering accuracy analysis result includes the following specific contents: Obtain the metering difficulty and the metering accuracy of the metering device within the monitoring period that are calculated. Divide the metering accuracy by the metering difficulty to obtain the state value of the metering device. If the calculated state value of the metering device is greater than or equal to the set state threshold, it means that the state of the metering device is normal and no maintenance is required; if the calculated state value of the metering device is less than the set state threshold, it means that the state of the metering device is abnormal and maintenance is required.
[0010] Second aspect, the present application provides a state monitoring system for an electric energy metering device, which is implemented based on the above-mentioned state monitoring method for an electric energy metering device, and specifically includes a data acquisition module, a metering data difficulty analysis module, a metering difficulty analysis module, a metering accuracy analysis module, and a state analysis module; wherein, the data acquisition module is used to acquire the metering data and actual electric energy usage data of the electric energy metering device within a monitoring period, and at the same time acquire the environmental data where the electric energy metering device is located; the metering data difficulty analysis module is used to import the actual electric energy usage data within the monitoring period into a metering data difficulty analysis strategy for metering data difficulty analysis; the metering difficulty analysis module is used to perform metering difficulty analysis based on the metering data difficulty analysis result and the environmental data where the electric energy metering device is located; the metering accuracy analysis module is used to perform metering accuracy analysis based on the metering data and actual electric energy usage data of the electric energy metering device within the monitoring period; the state analysis module performs metering device state analysis based on the metering difficulty analysis result and the metering accuracy analysis result. The output end of the data acquisition module is electrically connected to the metering data difficulty analysis module, the metering difficulty analysis module, and the metering accuracy analysis module. The output end of the metering data difficulty analysis module is electrically connected to the metering difficulty analysis module. The output end of the metering difficulty analysis module is electrically connected to the metering accuracy analysis module. The output ends of the metering difficulty analysis module and the accuracy analysis module are jointly connected to the state analysis module.
[0011] Third aspect, the present application provides an electronic device, including: a processor and a memory, wherein, a computer program that can be called by the processor is stored in the memory; The processor executes the above-mentioned state monitoring method for an electric energy metering device by calling the computer program stored in the memory.
[0012] Fourth aspect, the present application provides a computer-readable storage medium, storing instructions, when the instructions run on a computer, causing the computer to execute the state monitoring method for an electric energy metering device as described above.
[0013] Compared with the prior art, the beneficial effects of the present application are as follows: First, the actual power consumption data within the monitoring period is imported into the metering data difficulty analysis strategy for metering data difficulty analysis. Second, metering difficulty analysis is performed based on the metering data difficulty analysis result and the environmental data where the power metering device is located. Then, metering accuracy analysis is carried out based on the metering data of the power metering device and the actual power consumption data within the monitoring period. Finally, metering device status analysis is performed based on the metering difficulty analysis result and the metering accuracy analysis result. During the process of metering device status analysis, the complexity of the metering data and the influence of the environment on the metering device are taken into account, so as to comprehensively evaluate the operating status of the metering device, avoiding analysis errors caused by the complexity of the metering data and the influence of the environment on the metering device during status analysis, and improving the accuracy of the evaluation of the operating status of the metering device. BRIEF DESCRIPTION OF THE DRAWINGS
[0014] Other features, objects, and advantages of the present application will become more apparent by reading the detailed description of the non-limiting embodiments with reference to the following drawings; Figure 1 It is a schematic diagram of the overall process of the method for monitoring the status of a power metering device of the present application; Figure 2 It is a schematic diagram of step S2 of the method for monitoring the status of a power metering device of the present application; Figure 3 It is a schematic diagram of step S5 of the method for monitoring the status of a power metering device of the present application; Figure 4 It is a schematic diagram of the overall framework of the system for monitoring the status of a power metering device of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0015] To better understand the present application, more detailed descriptions of various aspects of the present application will be made with reference to the drawings. It should be understood that these detailed descriptions are only descriptions of the exemplary embodiments of the present application and do not limit the scope of the present application in any way. Throughout the specification, the same reference numerals refer to the same elements. The expression "and / or" includes any and all combinations of one or more of the associated listed items.
[0016] In the accompanying drawings, for ease of illustration, the sizes, dimensions, and shapes of the elements have been slightly adjusted. The drawings are for illustrative purposes only and are not drawn to an exact scale. As used herein, terms such as "substantially", "approximately", and similar terms are used as terms indicating approximation, rather than terms indicating degree, and are intended to account for the inherent deviations in measured or calculated values that would be recognized by a person of ordinary skill in the art. Additionally, in this application, the order in which the steps of each process are described does not necessarily represent the order in which these processes occur in actual operation, unless otherwise specifically limited or derivable from the context. It should also be understood that expressions such as "comprising", "including", "having", "containing", and / or "including with" are open-ended rather than closed-ended expressions in this specification, which means that the stated features, elements, and / or components exist, but do not exclude the existence of one or more other features, elements, components, and / or their combinations. Furthermore, when an expression such as "at least one of..." appears after a list of listed features, it modifies the entire list of features, rather than just an individual element in the list. Additionally, when describing the embodiments of this application, the use of "may" indicates "one or more embodiments of this application". And the term "exemplary" is intended to refer to an example or illustration. Unless otherwise defined, all terms used herein (including engineering terms and technical terms) have the same meaning as the ordinary understanding of a person of ordinary skill in the art to which this application pertains. It should also be understood that unless specifically stated in this application, words defined in common dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art, and should not be interpreted in an idealized or overly formal sense.
[0017] Embodiment 1 To solve the technical problems presented in the background art, this application provides a preferred embodiment: As Figures 1 - 3 shown, a method for monitoring the state of an electric energy metering device includes the following specific steps: S1. Obtain the metering data and actual power consumption data of the electric energy metering device during the monitoring period, and simultaneously obtain the environmental data where the electric energy metering device is located; In one specific embodiment, the specific content of obtaining the metering data and actual power consumption data of the electric energy metering device during the monitoring period and simultaneously obtaining the environmental data where the electric energy metering device is located is as follows: S11. Obtain the power consumption metering data of the electric energy metering device for electrical equipment during the monitoring period and store it in the first storage component for analyzing the impact of fluctuations and anomalies in the power consumption metering data on the metering of the electric energy metering device; S12. Obtain the actual power consumption data of the electrical equipment during the monitoring period and store it in the second storage component; S13. Collect the environmental data of the environment where the power metering device is located through the environmental data collection terminal and store it in the third storage component. Among them, the environmental data includes types of environmental data that affect the operation accuracy of the power metering device, such as temperature, humidity, etc. For example, the change in environmental temperature will affect the internal resistance value of the electricity meter, thereby affecting the measurement accuracy; the change in humidity may cause the electrical components inside the electricity meter to be affected by moisture, resulting in a decrease in insulation performance and affecting the measurement accuracy; the change in air pressure may have an impact on some sensitive power metering devices, especially in high-altitude areas; mechanical vibration or impact may cause damage to the internal structure of the electricity meter, affecting the measurement accuracy. S2. Import the actual power consumption data within the monitoring period into the metering data difficulty analysis strategy for metering data difficulty analysis. In one embodiment, the metering data difficulty analysis strategy in S2 includes the following specific steps: S21. Obtain the change curve of the actual power consumption data within the monitoring period, and calculate the difference between the power consumption data at each moment and the actual previous moment within the monitoring period. S22. Import the difference between the power consumption data at each moment and the actual previous moment within the monitoring period into the metering data complexity coefficient calculation formula to calculate the metering data complexity coefficient. Among them, the metering data complexity coefficient calculation formula is: , where xt is the actual power consumption data at time t within the monitoring period, x(t - 1) is the actual power consumption data at time t - 1 within the monitoring period, T is the duration of the monitoring period, dt is the time integral. In this formula, the complexity coefficient of the metering data is evaluated by the fluctuation magnitude of the metering data over time. S23. Obtain the change curve of the actual power consumption data within the monitoring period and the safe range data of the power change speed that the metering device can count. At the same time, import the calculated metering data complexity coefficient into the metering data difficulty calculation formula to calculate the metering data difficulty. Among them, the metering data difficulty calculation formula is: , where c is the change detection difficulty factor, xm is the median value of the safe range data of the power change speed that the metering device can count, xmax is the maximum value of the safe range data of the power change speed that the metering device can count, xmin is the minimum value of the safe range data of the power change speed that the metering device can count. In this formula, the safe range data of the power change speed that the metering device can count is the power situation from the minimum load to the maximum load when the metering device is designed. For example, the minimum load to the maximum load that the metering device can count is from 1 Wh to 1 kMh. Thus, the metering of power changes less than 1 Wh or close to 1 Wh is relatively difficult. Therefore, in this formula, the distance from the power change speed to the median value of the safe range data of the power change speed that the metering device can count is used to analyze the metering difficulty of power changes. S3. Conduct measurement difficulty analysis based on the measurement data difficulty analysis results and the environmental data where the electric energy measurement device is located; In one embodiment, S3 includes the following specific steps: S31. Obtain the environmental data where the electric energy measurement device is located, and import the environmental data where the electric energy measurement device is located into the environmental impact value calculation formula to calculate the environmental impact value. The environmental impact value calculation formula is: , where N is the number of environmental types, ai is the impact factor of the i-th environmental type, fti is the value of the i-th environmental type data at time t, and fmi is the median of the safety range of the i-th environmental type data; S32. Obtain the calculated measurement data difficulty and the environmental impact value, and sum them after weighting to obtain the measurement difficulty; S4. Conduct measurement accuracy analysis based on the measurement data of the electric energy measurement device and the actual electric energy usage data within the monitoring period; In one embodiment, S4 includes the following specific content: Obtain the measurement data of the electric energy measurement device and the actual electric energy usage data within the monitoring period, and import them into the measurement accuracy calculation formula to calculate the measurement accuracy. The measurement accuracy calculation formula is: , where xtz is the measurement data of the electric energy measurement device at time t; S5. Conduct measurement device status analysis based on the measurement difficulty analysis results and the measurement accuracy analysis results; In one embodiment, S5 includes the following specific content: Obtain the measurement difficulty and the measurement accuracy of the measurement device within the monitoring period that are calculated. Divide the measurement accuracy by the measurement difficulty to obtain the status value of the measurement device. If the calculated status value of the measurement device is greater than or equal to the set status threshold, it indicates that the status of the measurement device is normal and does not require maintenance; if the calculated status value of the measurement device is less than the set status threshold, it indicates that the status of the measurement device is abnormal and requires maintenance, and transmit the information of the corresponding measurement device to the maintenance personnel.
[0018] It should be specifically noted in this embodiment that the value-taking methods of the set parameters in this embodiment (such as the state threshold, the change detection difficulty factor, and the weight influence factors of each parameter, etc.) are obtained by those skilled in the art through experiments. Preferred experimental methods are as follows: Obtain at least 500 groups of measurement data of the power metering device, actual power consumption data, and environmental data where the power metering device is located. Calculate the state values of the corresponding measurement devices for each group through the obtained data. At the same time, obtain the judgment results of experts on whether the states of these power metering devices are abnormal. Import the calculated state values of the corresponding measurement devices for each group and the judgment results on whether the device states are abnormal into the fitting software, and continuously iterate to obtain the value-taking of the set parameters that meets the maximum judgment accuracy rate.
[0019] Finally, it should be noted in this example that this embodiment has the following advantages compared with the prior art: First, import the actual power consumption data within the monitoring period into the measurement data difficulty analysis strategy for measurement data difficulty analysis. Secondly, conduct measurement difficulty analysis based on the measurement data difficulty analysis results and the environmental data where the power metering device is located. Then, conduct measurement accuracy analysis based on the measurement data and actual power consumption data of the power metering device within the monitoring period. Finally, conduct measurement device state analysis based on the measurement difficulty analysis results and the measurement accuracy analysis results. During the process of conducting measurement device state analysis, take into account the complexity of the measurement data and the influence of the environment on the measurement device, and then comprehensively evaluate the operating state of the measurement device, avoiding analysis errors caused by the complexity of the measurement data and the influence of the environment on the measurement device during state analysis, and improving the accuracy of the evaluation of the operating state of the measurement device.
[0020] Embodiment 2 As Figure 4As shown in the figure, this embodiment provides a status monitoring system for an electric energy metering device, which is implemented based on the above-mentioned status monitoring method for an electric energy metering device, and specifically includes a data acquisition module, a metering data difficulty analysis module, a metering difficulty analysis module, a metering accuracy analysis module, and a status analysis module; among them, the data acquisition module is used to acquire the metering data and actual electric energy usage data of the electric energy metering device during the monitoring period, and at the same time acquire the environmental data where the electric energy metering device is located; the metering data difficulty analysis module is used to import the actual electric energy usage data during the monitoring period into the metering data difficulty analysis strategy for metering data difficulty analysis; the metering difficulty analysis module is used to perform metering difficulty analysis based on the metering data difficulty analysis result and the environmental data where the electric energy metering device is located; the metering accuracy analysis module is used to perform metering accuracy analysis based on the metering data and actual electric energy usage data of the electric energy metering device during the monitoring period; the status analysis module performs metering device status analysis based on the metering difficulty analysis result and the metering accuracy analysis result. The output end of the data acquisition module is electrically connected to the metering data difficulty analysis module, the metering difficulty analysis module, and the metering accuracy analysis module. The output end of the metering data difficulty analysis module is electrically connected to the metering difficulty analysis module. The output end of the metering difficulty analysis module is electrically connected to the metering accuracy analysis module. The output ends of the metering difficulty analysis module and the accuracy analysis module are jointly connected to the status analysis module; at the same time, the data transmission directions of the various modules in this embodiment are as Figure 4 shown by the arrow directions in the figure. At the same time, the specific functional steps of each module in this embodiment have been described in detail in the above method embodiment, and will not be elaborated here.
[0021] Embodiment 3 This embodiment provides an electronic device, including: a processor and a memory, where the memory stores a computer program that can be called by the processor; The processor executes the above-mentioned status monitoring method for an electric energy metering device by calling the computer program stored in the memory.
[0022] This electronic device may have relatively large differences due to different configurations or performances, and can include one or more processors and one or more memories. Among them, at least one computer program is stored in the memory, and this computer program is loaded and executed by the processor to implement the status monitoring method for an electric energy metering device provided by the above method embodiment. This electronic device can also include other components for implementing device functions. For example, this electronic device can also have components such as wired or wireless network interfaces and input / output interfaces for data input and output. This embodiment will not be elaborated here.
[0023] Embodiment 4 This embodiment proposes a computer-readable storage medium, on which a rewritable computer program is stored; When the computer program runs on a computer device, the computer device is caused to execute the above-described method for monitoring the state of the electric energy metering device.
[0024] For example, the computer-readable storage medium can be a read-only memory, a random access memory, a read-only optical disc, magnetic tape, floppy disk, and optical data storage device, etc.
[0025] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions according to the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that contains one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.
[0026] The term "comprising", "including", or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or device that includes a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or device.
[0027] The above description is only a preferred embodiment of the present application and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the application involved in the present application is not limited to the technical solution formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the foregoing application concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) having similar functions applied in the present application.
Claims
1. A method for monitoring the state of an electric energy metering device, characterized in that: It includes the following specific steps: S1. Obtain the metering data and actual power usage data of the electric energy metering device during the monitoring period, and simultaneously obtain the environmental data of the electric energy metering device; S2. Importing the actual power usage data within the monitoring period into the metering data difficulty analysis strategy to perform metering data difficulty analysis; S3. Performing a metering difficulty analysis based on the metering data difficulty analysis results and the environmental data of the electric energy metering device; S4. Performing metering accuracy analysis based on metering data of the electric energy metering device and actual electric energy usage data within the monitoring period; S5. Perform measurement device status analysis based on measurement difficulty analysis results and measurement accuracy analysis results.
2. The state monitoring method for an electric energy metering device according to claim 1, characterized in that: The measurement data difficulty analysis strategy in S2 includes the following specific steps: S21, obtaining a curve of actual power usage data change within a monitoring period, and calculating the difference between the power usage data at each moment within the monitoring period and the actual power usage data at the previous moment; S22, obtaining the difference between the electric energy usage data at each moment in the monitoring period and the actual previous moment and importing it into the metering data complexity coefficient calculation formula to calculate the metering data complexity coefficient, and evaluating the metering data complexity coefficient by the fluctuation of the metering data over time; S23. Obtain the actual electric energy usage data change curve within the monitoring period and the electric energy change speed safety range data that can be counted by the metering device, and at the same time obtain the calculated metering data complexity coefficient and import it into the metering data difficulty calculation formula to calculate the metering data difficulty.
3. The state monitoring method for an electric energy metering device according to claim 2, characterized in that: The calculation formula of the measurement data complexity coefficient in S22 is: , where xt is the actual power usage data at time t in the monitoring period, x(t-1) is the actual power usage data at time t-1 in the monitoring period, T is the duration of the monitoring period, and dt is the time integral.
4. The state monitoring method for an electric energy metering device according to claim 3, characterized in that: The calculation formula for the measurement data difficulty in S23 is: , where c is the change detection difficulty factor, xm is the median value of the safe range data of the electric energy change speed that can be counted by the metering device, xmax is the maximum value of the safe range data of the electric energy change speed that can be counted by the metering device, and xmin is the minimum value of the safe range data of the electric energy change speed that can be counted by the metering device.
5. The state monitoring method for an electric energy metering device according to claim 4, characterized in that: The metering difficulty analysis based on the metering data difficulty analysis result and the environmental data of the electric energy metering device includes the following specific steps: S31, obtaining the environmental data of the electric energy metering device, and importing the environmental data of the electric energy metering device into the environmental impact value calculation formula to calculate the environmental impact value, wherein the environmental impact value calculation formula is: , where N is the number of environmental types, ai is the influencing factor of the i-th environmental type, fti is the value of the i-th environmental type data at time t, and fmi is the median value of the safety range of the i-th environmental type data; S32. Obtain the calculated measurement data difficulty and environmental impact value, add them up after weighting to obtain the measurement difficulty.
6. The state monitoring method for an electric energy metering device according to claim 5, characterized in that: The measurement accuracy analysis based on the measurement data of the electric energy metering device and the actual electric energy usage data during the monitoring period includes the following specific contents: Obtain the metering data and actual power usage data of the power metering device within the monitoring period, and import them into the metering accuracy calculation formula to calculate the metering accuracy, where the metering accuracy calculation formula is: , where xtz is the metering data of the electric energy metering device at time t.
7. The state monitoring method for an electric energy metering device according to claim 6, characterized in that: The measurement device status analysis based on the measurement difficulty analysis result and the measurement accuracy analysis result includes the following specific contents: The metering difficulty and metering accuracy of the metering device within the monitoring period are obtained by calculation, and the status value of the metering device is obtained by dividing the metering accuracy by the metering difficulty. If the calculated status value of the metering device is greater than or equal to the set status threshold, it means that the status of the metering device is normal and no maintenance is required; if the calculated status value of the metering device is less than the set status threshold, it means that the status of the metering device is abnormal and maintenance is required.
8. A state monitoring system for an electric energy metering device, which is implemented based on the state monitoring method for an electric energy metering device according to any one of claims 1 to 7, characterized in that: It specifically includes a data acquisition module, a measurement data difficulty analysis module, a measurement difficulty analysis module, a measurement accuracy analysis module and a status analysis module; The data acquisition module is used to acquire the metering data and actual power usage data of the power metering device during the monitoring period, and to acquire the environmental data of the power metering device; The metering data difficulty analysis module is used to import the actual power usage data within the monitoring period into the metering data difficulty analysis strategy to perform metering data difficulty analysis; The metering difficulty analysis module is used to perform metering difficulty analysis based on the metering data difficulty analysis result and the environmental data of the electric energy metering device; The metering accuracy analysis module is used to perform metering accuracy analysis based on metering data of the electric energy metering device and actual electric energy usage data within a monitoring period; The state analysis module performs state analysis of the metering device based on the metering difficulty analysis result and the metering accuracy analysis result; the output end of the data acquisition module is electrically connected to the metering data difficulty analysis module, the metering difficulty analysis module and the metering accuracy analysis module; the output end of the metering data difficulty analysis module is electrically connected to the metering difficulty analysis module; the output end of the metering difficulty analysis module is electrically connected to the metering accuracy analysis module; the output end of the metering difficulty analysis module and the output end of the accuracy analysis module are commonly connected to the state analysis module.
9. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; It is characterized in that the processor executes the state monitoring method for an electric energy metering device as described in any one of claims 1 to 7 by calling a computer program stored in the memory.
10. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer executes the state monitoring method for an electric energy metering device as described in any one of claims 1 to 7.