Electric energy meter fault positioning method and device, computer equipment, readable storage medium and program product

Abstract

The invention relates to an electric energy meter fault positioning method and device, computer equipment, a computer readable storage medium and a computer program product, and relates to the technical field of electric appliance detection. The method comprises the following steps: establishing communication connection with a to-be-detected electric energy meter through a preset communication protocol, and obtaining various working data of the to-be-detected electric energy meter based on the communication connection; performing fault detection on the to-be-detected electric energy meter according to the working data to obtain an initial fault detection result; under the condition that the initial fault detection result represents that the storage chip of the to-be-detected electric energy meter has a fault, obtaining an impedance value of the storage chip, and inputting the impedance value into the big data model to obtain a fault positioning result of the storage chip; the big data model is obtained by training the historical impedance value of the storage chip and the corresponding fault analysis result. The method can improve the fault detection accuracy of the electric energy meter.

Description

Technical Field

[0001] The present application relates to the field of electrical appliance detection technology, and in particular to an electric energy meter fault location method, device, computer equipment, computer-readable storage medium and computer program product. Background Art

[0002] The functions of electric energy meters are becoming increasingly complex, and memory chips play an important role in storing key operating data and user information. Failure of memory chips may cause data loss or abnormal operation of the electric energy meter. However, traditional fault diagnosis methods often rely on surface inspections and basic functional tests, which are difficult to accurately and quickly locate the fault point, especially when it comes to the complex circuits and data structures inside the memory chip. Therefore, the current electric energy meter fault detection methods have the problem of low accuracy. Summary of the invention

[0003] Based on this, it is necessary to provide an electric energy meter fault locating method, device, computer equipment, computer readable storage medium and computer program product that can improve the fault detection accuracy of the electric energy meter in response to the above technical problems.

[0004] In a first aspect, the present application provides a method for locating an electric energy meter fault, comprising:

[0005] Establishing a communication connection with the electric energy meter to be detected through a preset communication protocol, and obtaining various working data of the electric energy meter to be detected based on the communication connection;

[0006] Perform fault detection on the electric energy meter to be detected according to the working data to obtain an initial fault detection result;

[0007] When the initial fault detection result indicates that the storage chip of the electric energy meter to be detected is faulty, the impedance value of the storage chip is obtained, and the impedance value is input into the big data model to obtain the fault location result of the storage chip; the big data model is trained through the historical impedance values ​​of the storage chip and the corresponding fault analysis results.

[0008] In one embodiment, fault detection is performed on the electric energy meter to be detected according to the working data to obtain an initial fault detection result, including:

[0009] Perform fault detection on the electric energy meter to be detected according to the working data to determine whether the electric energy meter to be detected has a fault;

[0010] When it is determined that the electric energy meter to be detected has a fault, the faulty components of the electric energy meter to be detected are located according to the working data to obtain an initial fault detection result.

[0011] In one embodiment, when it is determined that the electric energy meter to be detected has a fault, locating the faulty component of the electric energy meter to be detected according to the working data to obtain an initial fault detection result includes:

[0012] Determine the read / write status of the memory chip according to the status word included in the working data;

[0013] In the case where the read / write status is abnormal, it is determined that the initial fault detection result is a storage chip failure.

[0014] In one embodiment, when it is determined that the electric energy meter to be detected has a fault, locating the faulty component of the electric energy meter to be detected according to the working data to obtain an initial fault detection result includes:

[0015] Determine the code record corresponding to the storage chip according to the event record included in the working data;

[0016] When there is an abnormality in the code record corresponding to the memory chip, it is determined that the initial fault detection result is a memory chip fault.

[0017] In one embodiment, when it is determined that the electric energy meter to be detected has a fault, locating the faulty component of the electric energy meter to be detected according to the working data to obtain an initial fault detection result includes:

[0018] Determine the update status of the frozen data according to the frozen data included in the working data;

[0019] When the update situation is that the update time exceeds a preset threshold or there is an abnormality in the update data, it is determined that the initial fault detection result is a storage chip failure.

[0020] In one embodiment, the big data model is trained by the following method:

[0021] Obtain historical impedance values ​​of the memory chip;

[0022] Compare the historical impedance values ​​with the preset impedance threshold range in turn to obtain the fault analysis results corresponding to the historical impedance values; the fault analysis results are that the historical impedance values ​​within the preset impedance threshold range are normal impedance values, and the historical impedance values ​​exceeding the preset impedance threshold range are abnormal impedance values;

[0023] The initial big data model is trained according to the historical impedance values ​​and the fault analysis results corresponding to the historical impedance values ​​until the preset training end conditions are met to obtain a trained big data model.

[0024] In a second aspect, the present application also provides an electric energy meter fault locating device, comprising:

[0025] A data acquisition module, used to establish a communication connection with the electric energy meter to be detected through a preset communication protocol, and acquire various working data of the electric energy meter to be detected based on the communication connection;

[0026] The first fault detection module is used to perform fault detection on the electric energy meter to be detected according to the working data to obtain an initial fault detection result;

[0027] The second fault detection module is used to obtain the impedance value of the storage chip when the initial fault detection result indicates that the storage chip of the electric energy meter to be detected has a fault, and input the impedance value into the big data model to obtain the fault location result of the storage chip; the big data model is trained through the historical impedance values ​​of the storage chip and the corresponding fault analysis results.

[0028] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0029] Establishing a communication connection with the electric energy meter to be detected through a preset communication protocol, and obtaining various working data of the electric energy meter to be detected based on the communication connection;

[0030] Perform fault detection on the electric energy meter to be detected according to the working data to obtain an initial fault detection result;

[0031] When the initial fault detection result indicates that the storage chip of the electric energy meter to be detected is faulty, the impedance value of the storage chip is obtained, and the impedance value is input into the big data model to obtain the fault location result of the storage chip; the big data model is trained through the historical impedance values ​​of the storage chip and the corresponding fault analysis results.

[0032] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the following steps are implemented:

[0033] Establishing a communication connection with the electric energy meter to be detected through a preset communication protocol, and obtaining various working data of the electric energy meter to be detected based on the communication connection;

[0034] Perform fault detection on the electric energy meter to be detected according to the working data to obtain an initial fault detection result;

[0035] When the initial fault detection result indicates that the storage chip of the electric energy meter to be detected is faulty, the impedance value of the storage chip is obtained, and the impedance value is input into the big data model to obtain the fault location result of the storage chip; the big data model is trained through the historical impedance values ​​of the storage chip and the corresponding fault analysis results.

[0036] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0037] Establishing a communication connection with the electric energy meter to be detected through a preset communication protocol, and obtaining various working data of the electric energy meter to be detected based on the communication connection;

[0038] Perform fault detection on the electric energy meter to be detected according to the working data to obtain an initial fault detection result;

[0039] When the initial fault detection result indicates that the storage chip of the electric energy meter to be detected is faulty, the impedance value of the storage chip is obtained, and the impedance value is input into the big data model to obtain the fault location result of the storage chip; the big data model is trained through the historical impedance values ​​of the storage chip and the corresponding fault analysis results.

[0040] The above-mentioned electric energy meter fault location method, device, computer equipment, computer-readable storage medium and computer program product establish a communication connection with the electric energy meter to be detected through a preset communication protocol, and obtain various working data of the electric energy meter to be detected based on the communication connection, which is used as the basis for subsequent electric energy meter fault detection. According to the working data, the electric energy meter to be detected is fault detected to obtain an initial fault detection result, which can preliminarily determine whether the electric energy meter has a fault and preliminarily locate the components of the electric energy meter fault. In the case where the initial fault detection result represents the storage chip fault of the electric energy meter to be detected, the impedance value of the storage chip is obtained, and the impedance value is input into the pre-trained big data model to obtain the fault location result of the storage chip. Among them, the big data model is trained by the historical impedance value of the storage chip and the corresponding fault analysis result. According to the impedance value of the storage chip, the fault type of the storage chip is further determined at the physical level, thereby determining the fault type of the components of the electric energy meter to be detected, improving the precision of the fault detection and improving the fault detection accuracy of the electric energy meter. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions 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 related drawings can be obtained based on these drawings without paying creative work.

[0042] FIG1 is a diagram showing an application environment of a method for locating a fault of an electric energy meter according to an embodiment;

[0043] FIG2 is a schematic diagram of a flow chart of a method for locating a fault of an electric energy meter in one embodiment;

[0044] FIG3 is a schematic flow chart of the steps of determining the initial fault detection result in one embodiment;

[0045] FIG4 is a schematic diagram of a flow chart of a method for locating a fault of an electric energy meter in another embodiment;

[0046] FIG5 is a structural block diagram of an electric energy meter fault locating device in one embodiment;

[0047] FIG. 6 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0049] The electric energy meter fault location method provided in the embodiment of the present application can be applied in the application environment shown in FIG1. ​​The application environment includes: an electric energy meter 102 to be detected, a server 104 and a data storage system. Among them, the electric energy meter 102 to be detected communicates with the server 104 through a communication protocol. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The server 104 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. The server 104 establishes a communication connection with the electric energy meter 102 to be detected through a preset communication protocol, obtains a variety of working data of the electric energy meter 102 to be detected based on the communication connection, and then performs preliminary fault detection on the electric energy meter 102 to be detected according to the working data to obtain an initial fault detection result. In the case where the initial fault detection result indicates that the storage chip of the electric energy meter 102 to be detected is faulty, the server 104 obtains the impedance value of the storage chip, and inputs the impedance value into the big data model to obtain the fault location result of the storage chip. Among them, the big data model is trained by storing the historical impedance values ​​of the chip and the corresponding fault analysis results.

[0050] In an exemplary embodiment, as shown in FIG2 , a method for locating an electric energy meter fault is provided, and the method is applied to the server 104 in FIG1 as an example for description, including the following steps S202 to S206 . Among them:

[0051] Step S202: establishing a communication connection with the electric energy meter to be detected through a preset communication protocol, and acquiring various working data of the electric energy meter to be detected based on the communication connection.

[0052] Among them, the communication protocol can be a specification, which is the rules and standards for data exchange and communication between different devices or systems. It defines the data format, transmission method, and how to detect and correct errors, etc., to ensure that different devices can understand each other and exchange information effectively.

[0053] Among them, an energy meter can be an instrument used to measure the consumption of electrical energy, recording the amount of electricity consumed by electrical equipment within a certain period of time, usually displayed in kilowatt-hours (kWh). Energy meters can be mechanical (such as traditional mechanical meters) or digital (such as smart meters). Modern smart meters can not only monitor electrical energy in real time, but also transmit data with power companies or users' equipment through communication protocols, realizing functions such as remote meter reading, electricity consumption monitoring and management.

[0054] The working data may be data generated by the electric energy meter during operation.

[0055] Optionally, the server establishes a communication connection with the electric energy meter to be detected through a preset communication protocol (such as DL / T645-2007 protocol), obtains various working data of the electric energy meter to be detected based on the real-time communication connection, and uses the various working data as the basis for the server to detect the electric energy meter.

[0056] Step S204: perform fault detection on the electric energy meter to be detected according to the working data to obtain an initial fault detection result.

[0057] The initial fault detection result may be whether the electric energy meter to be detected has a fault, and the preliminary location of the type of the fault or the component causing the fault.

[0058] Optionally, the server determines whether the electric energy meter to be detected is faulty according to whether the working data includes abnormal working data, and preliminarily locates the faulty components in the electric energy meter to be detected according to the source and type of the abnormal working data.

[0059] Step 206, when the initial fault detection result indicates that the storage chip of the electric energy meter to be detected is faulty, the impedance value of the storage chip is obtained, and the impedance value is input into the big data model to obtain the fault location result of the storage chip.

[0060] Among them, the big data model is trained by storing the historical impedance values ​​of the chip and the corresponding fault analysis results.

[0061] Among them, the storage chip can be a storage chip of the electric energy meter. Flash memory or EEPROM (Electrically Erasable Programmable Read-Only Memory) is usually used to store data. These storage chips can save information such as metering data, configuration parameters, and historical records of the electric energy meter. Flash memory usually has a higher storage density and write speed, while EEPROM performs well when frequently reading and writing small amounts of data.

[0062] Impedance is a physical quantity used to describe the degree of resistance of circuit components to alternating current (AC) signals. It is a complex number that includes both resistive and reactive components. The unit of impedance is ohm (Ω), usually represented by the letter "Z".

[0063] Optionally, when the initial fault detection result indicates that the storage chip of the energy meter to be detected is faulty, the server measures the resistance and impedance of the storage chip in the energy meter through an impedance analyzer to obtain the impedance value of the storage chip, and inputs the impedance value into the big data model to obtain the fault location result of the storage chip, so as to determine the faulty components of the energy meter and the specific fault type of the components. The analysis can discover potential problems of the storage chip from a physical level, and further confirm the failure and severity of the storage chip in combination with the big data model, providing a scientific basis for subsequent repair or replacement.

[0064] In the above-mentioned electric energy meter fault location method, a communication connection is established with the electric energy meter to be detected through a preset communication protocol, and a variety of working data of the electric energy meter to be detected are obtained based on the communication connection, which is used as the basis for subsequent electric energy meter fault detection. The electric energy meter to be detected is fault-detected according to the working data to obtain an initial fault detection result, which can preliminarily determine whether the electric energy meter has a fault and preliminarily locate the components of the electric energy meter fault. In the case where the initial fault detection result represents the storage chip fault of the electric energy meter to be detected, the impedance value of the storage chip is obtained, and the impedance value is input into the pre-trained big data model to obtain the fault location result of the storage chip. Among them, the big data model is trained by the historical impedance value of the storage chip and the corresponding fault analysis result. According to the impedance value of the storage chip, the fault type of the storage chip is further determined at the physical level, thereby determining the fault type of the components of the electric energy meter to be detected, improving the precision of the fault detection and improving the fault detection accuracy of the electric energy meter.

[0065] In an exemplary embodiment, as shown in FIG3 , step S204 performs fault detection on the electric energy meter to be detected according to the working data to obtain an initial fault detection result, including:

[0066] Step S302: Perform fault detection on the electric energy meter to be detected according to the working data to determine whether the electric energy meter to be detected has a fault.

[0067] Optionally, the server performs fault detection on the electric energy meter to be detected based on the working data, detects whether there is abnormal data in the working data, and determines that the electric energy meter to be detected has a fault if there is abnormal data, and determines that the electric energy meter to be detected has not a fault if there is no abnormal data.

[0068] Step S304, when it is determined that the electric energy meter to be detected has a fault, locate the faulty component of the electric energy meter to be detected according to the working data to obtain an initial fault detection result.

[0069] The faulty component may be a faulty component among all components of the electric energy meter to be tested. The electric energy meter includes multiple components that work together to measure and record the use of electric energy, such as a measurement chip, a power calculation unit, a storage chip, a display screen, a communication module, and a reference clock crystal.

[0070] Optionally, when determining that the electric energy meter to be detected has a fault, the server locates the faulty component of the electric energy meter to be detected according to the source of the abnormal data in the working data, and obtains an initial fault detection result. Furthermore, the fault type of the faulty component can be preliminarily determined according to the content and type of the abnormal data.

[0071] In this embodiment, the server first determines whether the electric energy meter to be detected has a fault based on the working data, and if a fault exists, further locates the faulty components of the electric energy meter based on the working data, obtains an initial fault detection result, and conducts an in-depth analysis of the fault of the electric energy meter, thereby improving the fault detection accuracy of the electric energy meter.

[0072] In an exemplary embodiment, in step S304, when it is determined that the electric energy meter to be detected has a fault, locating the faulty components of the electric energy meter to be detected according to the working data to obtain an initial fault detection result includes:

[0073] The read / write status of the memory chip is determined according to the status word included in the working data; when the read / write status is abnormal, the initial fault detection result is determined to be a memory chip fault.

[0074] Among them, the status word refers to a binary data structure used to represent the current status of a system or device. It contains multiple bits, each of which corresponds to a specific state or condition. The status word is usually used to monitor and control the system, and may include operating status, mode information, and signal indication. Among them, the operating status includes the operating status of each component of the energy meter, such as normal operation, fault state, or maintenance state.

[0075] Among them, the read and write state can be the data storage, reading and updating process designed by the memory chip.

[0076] Optionally, the server queries the read and write status of the storage chip based on the status word included in the working data. When the read and write status indicates that data reading and writing are not normal, it is determined that the initial fault detection result of the electric energy meter to be detected is a storage chip failure, and the fault type of the storage chip is preliminarily determined to be a read and write failure.

[0077] In this embodiment, by monitoring the status word content of the electric energy meter to be tested and determining the read and write status of the storage chip, it is possible to promptly detect abnormalities in the read and write status of the storage chip, thereby locating the faulty components and preliminary fault types of the electric energy meter to be tested, making it easier to take corresponding measures in a timely manner and improve maintenance efficiency and accuracy.

[0078] In an exemplary embodiment, in step S304, when it is determined that the electric energy meter to be detected has a fault, locating the faulty components of the electric energy meter to be detected according to the working data to obtain an initial fault detection result includes:

[0079] According to the event record included in the working data, the code record corresponding to the storage chip is determined; when there is an abnormality in the code record corresponding to the storage chip, the initial fault detection result is determined to be a storage chip fault.

[0080] Among them, event logging can be a log record of important events that occur in a device or system, which may include system status changes, error occurrences, configuration changes, user operations, etc. The main function of event logging is to provide audit tracking, troubleshooting, and system performance monitoring.

[0081] Optionally, the server determines the code record corresponding to the storage chip from the event record based on the event record included in the working data. When there is an abnormality in the code record of the storage chip, such as an error code or an abnormal event in the code record, it is determined that the initial fault detection result of the electric meter to be detected is a storage chip failure.

[0082] In this embodiment, by analyzing the time records of the electric energy meter to be detected, the server can quickly identify the abnormality of the storage chip, so that the fault detection process is changed from empirical judgment to precise positioning based on data, thereby improving the fault detection efficiency and accuracy of the electric energy meter.

[0083] In an exemplary embodiment, in step S304, when it is determined that the electric energy meter to be detected has a fault, locating the faulty components of the electric energy meter to be detected according to the working data to obtain an initial fault detection result includes:

[0084] According to the frozen data included in the working data, the update status of the frozen data is determined; when the update status is that the update time exceeds a preset threshold or there is an abnormality in the updated data, the initial fault detection result is determined to be a storage chip failure.

[0085] Frozen data refers to the power or status data locked by the system at a specific point in time. This data is usually generated and saved under specific conditions for subsequent analysis and reporting. For example, the power meter may freeze the current power consumption data at the end of each billing cycle, and the peak or valley values ​​of the power consumption will also be frozen and stored regularly to ensure that the recorded data will not be affected by subsequent reading and writing.

[0086] The preset threshold may be the length of a freezing time period of the frozen data.

[0087] Optionally, the server determines the update status of the frozen data according to the frozen data included in the working data, for example, determines the update data of the time periods of the most recent updates and the corresponding update time. If the update status is that the update time exceeds a preset threshold, it indicates that the storage chip has not been updated in time. If the update status is that the updated data is abnormal, such as the value of the data updated in the most recent times has not changed, or the change exceeds a preset normal range, it is determined that the initial fault detection result of the electric energy meter to be detected is that the frozen data cannot be updated normally due to a storage chip failure.

[0088] In this embodiment, by monitoring the update of the frozen data of the electric energy meter to be detected, the server can effectively identify the failure of the storage chip, accurately and efficiently locate the fault source of the electric energy meter to be detected, and take the next step of judgment and measures.

[0089] In an exemplary embodiment, the big data model described in the above embodiment is trained by the following method:

[0090] Obtain historical impedance values ​​of the storage chip; compare the historical impedance values ​​with the preset impedance threshold range in turn to obtain fault analysis results corresponding to the historical impedance values; train the initial big data model according to the historical impedance values ​​and the fault analysis results corresponding to the historical impedance values ​​until the preset training end conditions are met to obtain a trained big data model.

[0091] The historical impedance value may be an impedance value recorded in a historical time period of the electric energy meter to be tested.

[0092] The preset threshold range may be an upper limit value and a lower limit value obtained by linearly fitting the historical impedance value and the physical condition of the corresponding memory chip. The physical condition of the memory chip may be that the memory chip is normal, physically damaged, or has performance degradation. Optionally, in one case, the preset threshold range may also be obtained by fitting the impedance characteristics of the memory chip under normal operation.

[0093] The initial big data model can be a model structure that has not been trained in machine learning or deep learning, usually defined by a specific algorithm and architecture. The basic structure of the model can be a neural network including neuron layers, convolutional layers, and recurrent layers.

[0094] The fault analysis result is that the historical impedance value within the preset impedance threshold range is the normal impedance value, and the historical impedance value beyond the preset impedance threshold range is the abnormal impedance value.

[0095] Optionally, the server obtains the historical impedance value of the storage chip, compares the historical impedance value with a preset threshold range once, obtains the fault analysis result corresponding to the historical impedance value, uses the fault analysis result corresponding to each historical impedance value as its actual classification label, and trains the initial big data model based on the historical impedance value and the corresponding actual classification label as training data until the preset training end condition is met. For example, the training data is input into the initial big data model to obtain the initial classification label corresponding to each historical impedance value, and the loss function between the initial classification label and the actual classification label is calculated. If the loss function is higher than the preset threshold, the parameters and weights of the initial big data model are adjusted, and then the loss function is iteratively calculated until the loss function is lower than the preset threshold or the number of iterations reaches the preset number of times, and a trained big data model is obtained.

[0096] In this embodiment, by training a big data model based on the impedance characteristics of the storage chip, the fault of the storage chip is further identified and determined, and potential problems of the storage chip can be discovered at the physical level. The fault and severity of the storage chip can be further determined, and the electric energy meter that deviates from the normal working range can be quickly identified, thereby accelerating the fault diagnosis process. At the same time, the system's automated analysis and data comparison reduce the need for manual operation and improve efficiency. The time required for fault identification and location is greatly reduced, allowing the maintenance team to respond more quickly, effectively reducing the downtime of the electric energy meter.

[0097] In an exemplary embodiment, as shown in FIG4 , another electric energy meter fault location method is provided, including:

[0098] Step 1, by using the electric energy meter verification device to communicate with the electric energy meter, this method uses a standard communication protocol (such as DL / T 645-2007 protocol) to read the control status and data records in the electric energy meter.

[0099] Step 2, read the operating status word: The control status word can provide the current working status of the energy meter, including whether an error has occurred, whether the memory chip can be read and written normally, etc. Step 3, read the event record: The energy meter usually records operation and fault events. A memory chip failure may leave a specific error code or abnormal record.

[0100] Step 4, read frozen data: Some key data such as peak and valley power values ​​will be periodically "frozen" and stored. A storage chip failure may cause these data to fail to update or become corrupted.

[0101] Step 5: Based on the above data, it is possible to preliminarily determine whether the storage chip is faulty and the possible fault type (such as read / write failure, data loss, etc.).

[0102] Step 6: Use an impedance analyzer to accurately measure the resistance and impedance of the storage chip in the electric energy meter. By comparing the impedance values ​​measured by the meter in a normal state and a suspected fault state, the physical condition of the chip can be identified.

[0103] Step 7, impedance analysis of normal meters: Establish a database of standard impedance values, which represent the impedance characteristics of the energy meter storage chip under normal working conditions. Impedance analysis of damaged storage chips: Compare the impedance value of the faulty meter with the impedance database of the normal meter. Differences outside the predetermined range may indicate physical damage or performance degradation of the storage chip. Apply machine learning or data analysis technology, input a large amount of impedance analysis data into the model, and train the model to identify and predict the failure mode of the storage chip.

[0104] In this embodiment, the key data is read by the electric energy meter verification device communicating with the electric energy meter, and the chip is physically tested using an impedance analyzer. The combination of these two methods can significantly improve the accuracy of fault location. The preliminary communication analysis can effectively screen out devices that may have problems, and the subsequent impedance analysis can further confirm the specific type and location of the fault. The problem that the traditional method cannot perform fine fault detection in-depth inside the storage chip is solved, and the accuracy and reliability of fault detection are improved. The impedance analysis method combined with the use of big data models can quickly identify electric energy meters that deviate from the normal working range, thereby accelerating the fault diagnosis process. At the same time, the automated analysis and data comparison of the system reduce the need for manual operation and improve efficiency. The time required for fault identification and location is greatly reduced, allowing the maintenance team to respond more quickly, effectively reducing the downtime of the electric energy meter. Using big data models to process and analyze a large amount of impedance data can adapt to the complex circuits and data structures inside the storage chip and provide more accurate fault mode recognition. The difficulty in fault location caused by the complex internal structure of the storage chip is solved, and the diagnostic capability of complex faults is improved. The proposed impedance test method is a non-destructive test method that can complete fault detection and location without damaging the storage chip. This avoids the damage that traditional physical detection methods may cause to the chip, ensures the reusability of the chip after positioning, and reduces the cost of repair or replacement. In summary, by combining the communication analysis method and the impedance analysis method, the present invention effectively improves the speed and accuracy of fault positioning of the electric energy meter storage chip, while overcoming the various technical difficulties existing in the traditional method, and provides strong technical support for the efficient maintenance of the electric energy meter.

[0105] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0106] Based on the same inventive concept, the embodiment of the present application also provides an electric energy meter fault locating device for implementing the electric energy meter fault locating method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the electric energy meter fault locating device provided below can refer to the limitations of the electric energy meter fault locating method above, and will not be repeated here.

[0107] In an exemplary embodiment, as shown in FIG5 , a power meter fault location device 500 is provided, comprising: a data acquisition module 502 , a first fault detection module 504 and a second fault detection module 506 , wherein:

[0108] The data acquisition module 502 is used to establish a communication connection with the electric energy meter to be detected through a preset communication protocol, and obtain various working data of the electric energy meter to be detected based on the communication connection.

[0109] The first fault detection module 504 is used to perform fault detection on the electric energy meter to be detected according to the working data to obtain an initial fault detection result.

[0110] The second fault detection module 506 is used to obtain the impedance value of the storage chip when the initial fault detection result indicates that the storage chip of the electric energy meter to be detected is faulty, and input the impedance value into the big data model to obtain the fault location result of the storage chip; the big data model is trained through the historical impedance values ​​of the storage chip and the corresponding fault analysis results.

[0111] Furthermore, in one embodiment, the first fault detection module 504 is also used to perform fault detection on the electric energy meter to be detected based on the working data to determine whether the electric energy meter to be detected has a fault; when it is determined that the electric energy meter to be detected has a fault, the faulty components of the electric energy meter to be detected are located based on the working data to obtain an initial fault detection result.

[0112] Furthermore, in one embodiment, the first fault detection module 504 is also used to determine the read / write status of the storage chip according to the status word included in the working data; when the read / write status is abnormal, the initial fault detection result is determined to be a storage chip fault.

[0113] Furthermore, in one embodiment, the first fault detection module 504 is also used to determine the code record corresponding to the storage chip based on the event record included in the working data; when there is an abnormality in the code record corresponding to the storage chip, determine that the initial fault detection result is a storage chip fault.

[0114] Furthermore, in one embodiment, the first fault detection module 504 is also used to determine the update status of the frozen data based on the frozen data included in the working data; when the update status is that the update time exceeds a preset threshold or there is an abnormality in the update data, the initial fault detection result is determined to be a storage chip failure.

[0115] Furthermore, in one embodiment, the electric energy meter fault locating device 500 also includes a model training module, which is used to obtain historical impedance values ​​of the storage chip; compare the historical impedance values ​​with the preset impedance threshold range in turn to obtain fault analysis results corresponding to the historical impedance values; the fault analysis results are that the historical impedance values ​​within the preset impedance threshold range are normal impedance values, and the historical impedance values ​​exceeding the preset impedance threshold range are abnormal impedance values; the initial big data model is trained according to the historical impedance values ​​and the fault analysis results corresponding to the historical impedance values ​​until the preset training end conditions are met to obtain a trained big data model.

[0116] Each module in the above-mentioned electric energy meter fault location device 500 can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module above.

[0117] In an exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be shown in FIG6. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as various working data of the electric energy meter to be detected, the impedance value and historical impedance value of the storage chip of the electric energy meter to be detected. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for locating an electric energy meter fault is implemented.

[0118] Those skilled in the art will understand that the structure shown in FIG. 6 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0119] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0120] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0121] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0122] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0123] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0124] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for locating an electric energy meter fault, characterized in that: The method comprises: Establishing a communication connection with the electric energy meter to be detected through a preset communication protocol, and acquiring various working data of the electric energy meter to be detected based on the communication connection; Performing fault detection on the electric energy meter to be detected according to the working data to obtain an initial fault detection result; When the initial fault detection result indicates that the storage chip of the electric energy meter to be detected has a fault, the impedance value of the storage chip is obtained, and the impedance value is input into a big data model to obtain a fault location result of the storage chip; the big data model is trained through historical impedance values ​​of the storage chip and corresponding fault analysis results.

2. The method according to claim 1, characterized in that The performing fault detection on the electric energy meter to be detected according to the working data to obtain an initial fault detection result includes: Performing fault detection on the electric energy meter to be detected according to the working data to determine whether the electric energy meter to be detected has a fault; When it is determined that the electric energy meter to be detected has a fault, the faulty component of the electric energy meter to be detected is located according to the working data to obtain an initial fault detection result.

3. The method according to claim 2, characterized in that When it is determined that the electric energy meter to be detected has a fault, locating the faulty component of the electric energy meter to be detected according to the working data to obtain an initial fault detection result includes: Determining the read / write status of the storage chip according to the status word included in the working data; In a case where the read / write status is abnormal read / write, it is determined that the initial fault detection result is a fault of the storage chip.

4. The method according to claim 2, characterized in that: When it is determined that the electric energy meter to be detected has a fault, locating the faulty component of the electric energy meter to be detected according to the working data to obtain an initial fault detection result includes: Determining a code record corresponding to the storage chip according to the event record included in the working data; In the case that there is an abnormality in the code record corresponding to the storage chip, it is determined that the initial fault detection result is a fault of the storage chip.

5. The method according to claim 2, characterized in that: When it is determined that the electric energy meter to be detected has a fault, locating the faulty component of the electric energy meter to be detected according to the working data to obtain an initial fault detection result, including: Determining, according to the frozen data included in the working data, an update status of the frozen data; When the update condition is that the update time exceeds a preset threshold or there is an abnormality in the update data, it is determined that the initial fault detection result is a fault of the storage chip.

6. The method according to claim 1, characterized in that The big data model is trained by the following method: Acquiring a historical impedance value of the memory chip; The historical impedance values ​​are sequentially compared with the preset impedance threshold range to obtain the fault analysis results corresponding to the historical impedance values; the fault analysis results are that the historical impedance values ​​within the preset impedance threshold range are normal impedance values, and the historical impedance values ​​beyond the preset impedance threshold range are abnormal impedance values; The initial big data model is trained according to the historical impedance values ​​and the fault analysis results corresponding to the historical impedance values ​​until a preset training end condition is met, thereby obtaining a trained big data model.

7. An electric energy meter fault location device, characterized in that: The device comprises: A data acquisition module, used to establish a communication connection with the electric energy meter to be detected through a preset communication protocol, and acquire various working data of the electric energy meter to be detected based on the communication connection; A first fault detection module, configured to perform fault detection on the electric energy meter to be detected according to the working data to obtain an initial fault detection result; The second fault detection module is used to obtain the impedance value of the storage chip when the initial fault detection result indicates that the storage chip of the electric energy meter to be detected has a fault, and input the impedance value into a big data model to obtain the fault location result of the storage chip; the big data model is trained through the historical impedance values ​​of the storage chip and the corresponding fault analysis results.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. 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 according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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