Electric energy meter fault prediction method and system

Abstract

The invention discloses an electric energy meter fault prediction method and system, and relates to the technical field of electric energy meter detection, and the method comprises the steps: collecting operation response data formed in the operation command execution process of a target electric energy meter in a business system, constructing a response record set Rec, forming a similar effective set Vrc on the basis, and further constructing a dispersion sequence Vrs, and continuously analyzing the response consistency change of the same type of operation commands to obtain a difference result Trd, so that the gradual discrete change trend of the response duration Rtm of the same type of operation commands can be identified in advance under the conditions that the operation commands are still normally returned and the execution result Res does not fail, and the operation command response time Rtm is accurately and accurately determined. And a service system can find the potential degradation state of the electric energy meter in the aspect of communication processing or internal response capability. And a prediction result is output before communication thorough abnormity or operation timeout occurs, and an advanced disposal basis is provided for operation and maintenance personnel, so that problems caused by degradation and accumulation of response capability are avoided.

Description

Technical Field

[0001] This invention relates to the field of electricity meter testing technology, specifically to an electricity meter fault prediction method and system. Background Technology

[0002] As power metering systems continue to evolve towards centralization and remote operation, electricity meters no longer simply perform the function of measuring electricity consumption. They also frequently participate in various remote business interactions, such as data reading, parameter querying, parameter distribution, and status acquisition. Therefore, as a large number of terminal devices in the power system with long operating cycles, the stability of electricity meters' operating status directly affects the reliability of metering data and the normal operation of business systems. This makes fault prediction based on electricity meter operating behavior an important research direction in this field.

[0003] In existing electricity meter operation and management processes, business systems typically issue various types of operation commands to electricity meters, such as periodic reading commands, status query commands, and parameter configuration commands. For these operations, current technologies focus more on whether the command is executed successfully, or on alarm handling after failure, but lack further analysis of the response process of the same electricity meter to similar operation commands under normal communication conditions. In actual operation, even if the operation command is successfully executed, the response time to the same type of command may gradually differ at different times. However, existing systems usually ignore such differences as network fluctuations or occasional factors, failing to continuously monitor the changing trends of response characteristics, thus making it difficult to detect potential operational anomalies of the electricity meter in a timely manner. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for predicting electricity meter faults, solving the problems mentioned in the background section.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for predicting electricity meter faults, comprising the following steps: S1. Collect the operation response data generated by the target energy meter during the execution of operation commands in the business system, including command type Typ, issuance time Snd, response time Ack, execution result Res, link identifier Lnk and target identifier Mid, and obtain the response duration Rtm based on the issuance time Snd and response time Ack, and construct the response record set Rec. S2. Based on the command category Typ, merge the response record set Rec into similar categories, and filter the merged response records to obtain a valid set Vrc representing the response characteristics of similar operation commands. S3. Perform statistical analysis on the valid sets Vrc of the same type, construct a dispersion index for the consistency of responses to similar operation commands, and form a dispersion sequence Vrs that reflects the changing trend of response characteristics in chronological order. S4. Perform evolutionary analysis on the discrete sequence Vrs to determine whether the response characteristics of similar operation commands have degraded in consistency, generate the difference result Trd, and issue it.

[0006] Preferably, S1 includes S11; S11. During the process of the business system issuing operation commands to the target energy meter, the operation command interaction information is obtained by monitoring and recording the command interaction process between the business system and the target energy meter. Based on the acquired operation command interaction information, the system parses the command issuance records generated by the business system to obtain the command category Typ corresponding to the operation command and the issuance time Snd when the command was sent to the target energy meter; it parses the response message returned by the target energy meter to obtain the response time Ack generated by the target energy meter for the operation command and the corresponding execution result Res; it parses the session information recorded by the business system during the command interaction process to obtain the link identifier Lnk used for the operation command interaction; and it parses the device identifier information pre-established in the business system to obtain the target identifier Mid of the target energy meter corresponding to the operation command interaction. The acquired command category (Typ), issuance time (Snd), response time (Ack), execution result (Res), link identifier (Lnk), and target identifier (Mid) are structured and organized to form the raw operation response data.

[0007] Preferably, S1 further includes S12; S12. Based on the command category Typ in the original operation response data, and under the constraints of the same target identifier Mid and the same link identifier Lnk, merge the operation command interaction information belonging to the same command category Typ to form a sequence of similar response samples. For each response sample in the same type of response sample sequence, the response duration Rtm of the response sample is calculated based on the issuance time Snd and the response time Ack corresponding to the response sample. The command category Typ, response duration Rtm, execution result Res, link identifier Lnk and target identifier Mid of the response sample are combined to form the response sample record Rcr. Multiple response sample records Rcr generated at different times under the same target identifier Mid are aggregated, and an indexable aggregation structure is established according to the command category Typ to obtain a set of response records Rec representing the operational response behavior of the target energy meter.

[0008] Preferably, S2 includes S21; S21. Extract multiple response sample records Rcr from the response record set Rec as the analysis object; Then, using the command category Typ contained in the response sample record Rcr as the basic criterion for class-based analysis, multiple response sample records Rcr with the same command category Typ are divided into the same analysis group; Within each analysis group, the analysis object is limited to the same target energy meter based on the target identifier Mid, and the analysis object is limited to the same communication link condition based on the link identifier Lnk. This ensures that the multiple response sample records Rcr within the same analysis group are consistent in terms of operating environment and communication conditions. This forms a set of multiple similar response sample groups that represent the operational response behavior characteristics of the target energy meter under specific command category Typ, target identifier Mid, and link identifier Lnk.

[0009] Preferably, S2 further includes S22; S22. For each analysis group in the same type of response sample grouping set, perform validity screening processing to remove response sample records Rcr that do not meet the response consistency analysis conditions; The validity screening process includes: removing response sample records Rcr that failed to execute, did not return, or had incomplete status based on the execution result Res in the response sample record Rcr; removing response sample records Rcr with missing response duration based on the response duration Rtm in the response sample record Rcr; and verifying the consistency of communication links within the same type of response sample group based on the link identifier Lnk in the response sample record Rcr. After completing the validity screening, the multiple response sample records Rcr that were selected and retained were summarized to construct a similar valid set Vrc for response consistency quantification and evolution analysis.

[0010] Preferably, S3 includes S31; S31. For multiple response sample records Rcr belonging to the same command category Typ, target identifier Mid and link identifier Lnk constraint in the same valid set Vrc, extract the response duration Rtm corresponding to the response sample record Rcr as the basis for the discreteness calculation object. Based on the extracted multiple response durations Rtm, according to the preset dispersion calculation rules, the degree of difference in the values ​​of response duration Rtm within the statistical interval is statistically calculated, the deviation of the central tendency of response duration Rtm is quantified, and a dispersion index Var representing the consistency of responses to similar operation commands is constructed. The dispersion index Var is used to reflect the degree of dispersion between the response times of multiple operations under the same command category Typ. After the dispersion index Var is calculated, it is associated with the corresponding command category Typ, target identifier Mid, and link identifier Lnk to form a single dispersion calculation result.

[0011] Preferably, S3 further includes S32; S32. For the multiple dispersion indices Var calculated in different time intervals, arrange and aggregate the dispersion indices Var in the time dimension according to the preset time organization rules. Among them, the time organization rule is used to determine the corresponding position of each dispersion index Var on the time axis, and the corresponding position is at least associated with the time window formed by the response sample record Rcr that constitutes the dispersion index Var. According to the time organization rules, multiple dispersion indicators Var are arranged in chronological order to form a dispersion sequence Vrs that reflects the trend of consistency of response to similar operation commands over time.

[0012] Preferably, S4 includes S41; S41. For the discrete sequence Vrs, according to the preset evolution analysis rules, the change relationship between adjacent time positions of the discrete sequence Vrs is compared and analyzed segment by segment to obtain the evolution characteristics of the discrete sequence Vrs. The evolutionary analysis rules are as follows: By comparing the values ​​of the dispersion index Var at adjacent time positions in the dispersion sequence Vrs, the direction of change of the dispersion sequence Vrs at continuous time positions is determined. Specifically, when the dispersion index Var value at a later time position is greater than that at a previous time position, the dispersion sequence Vrs is determined to show an increasing direction of the dispersion index Var, indicating a decreasing direction of change in the consistency of responses to similar operation commands. When the dispersion index Var value at a later time position is less than that at a previous time position, the dispersion sequence Vrs is determined to show a decreasing direction of the dispersion index Var, indicating an increasing direction of change in the consistency of responses to similar operation commands. When the dispersion index Var values ​​at adjacent time positions are within a preset allowable fluctuation range, the dispersion sequence Vrs is determined to be in a stable direction, indicating a state where the consistency of responses to similar operation commands has not changed. Meanwhile, by statistically analyzing the differences in the values ​​of the dispersion index Var corresponding to the dispersion sequence Vrs between adjacent time positions, the magnitude of the change in the dispersion sequence Vrs at continuous time positions is assessed, representing the strength of the change in the dispersion index Var value. By counting the number of consecutive time positions in the discrete sequence Vrs that satisfy the same direction of change, we can determine the time span during which the direction of change in the discrete sequence Vrs is maintained, and thus indicate the persistence of the trend of change in the discrete sequence Vrs. When the discreteness sequence Vrs simultaneously shows that the discreteness index Var increases in both direction and magnitude at multiple consecutive time points, and the direction of change remains consistent, it is determined that the consistency of the response to similar operation commands shows a deterioration trend, and this is recorded as a consistency degradation feature.

[0013] Preferably, S4 further includes S42; S42. Based on the consistency degradation characteristics, the difference amplification judgment is made on the response consistency change process reflected by the discrete sequence Vrs, and the difference result Trd is generated as the output of the energy meter fault prediction result and displayed in the business system. The determination of the amplification of differences is as follows: Determination condition 1: Based on the change direction conclusion corresponding to the consistency degradation feature, determine whether the discreteness sequence Vrs has a continuous time period within the target time range in which the discreteness index Var increases; Determination condition 2: Based on the change magnitude conclusion corresponding to the consistency degradation feature, determine whether the degree of difference in the discreteness index Var value is in an enhancing state within the continuous time period in which the discreteness index Var increases; Determination condition 3: Based on the change persistence conclusion corresponding to the consistency degradation feature, determine whether the increase direction of the discreteness index Var meets the preset persistence requirement; When all three conditions are met, a difference result Trd representing the amplification of differences in responses to similar operation commands is generated, and the difference result Trd is determined as a valid difference amplification result. When any of the decision conditions is not met, a difference result Trd is generated that represents the non-amplified difference between the responses to similar operation commands.

[0014] An electricity meter fault prediction system includes an electricity meter data acquisition module, a data filtering module, a data trend analysis module, and a prediction output module; The electricity meter data acquisition module collects operation response data generated by the target electricity meter during the execution of operation commands in the business system, including command type Typ, issuance time Snd, response time Ack, execution result Res, link identifier Lnk, and target identifier Mid. Based on the issuance time Snd and response time Ack, it obtains the response duration Rtm and constructs a response record set Rec. The data filtering module merges the response record set Rec based on the command category Typ, and filters the merged response records to obtain a valid set Vrc representing the response characteristics of similar operation commands. The data trend analysis module performs statistical analysis on the same effective set Vrc, constructs a dispersion index of the consistency of response to the same operation command, and forms a dispersion sequence Vrs that reflects the changing trend of response characteristics in chronological order. The prediction output module performs evolutionary analysis on the discrete sequence Vrs, determines whether the response characteristics of similar operation commands show consistency degradation, generates a difference result Trd, and sends it out.

[0015] This invention provides a method and system for predicting electricity meter faults, which has the following beneficial effects: (1) By collecting operation response data generated during the execution of operation commands by the target energy meter in the business system, a response record set Rec is constructed, and a similar valid set Vrc is formed on this basis. Further, a discrete sequence Vrs is constructed to continuously analyze the consistency changes of the responses of similar operation commands, thereby obtaining the difference result Trd. This allows for the early identification of the gradual discrete change trend of the response time Rtm of similar operation commands when the operation command still returns normally and the execution result Res does not fail. This enables the business system to discover the potential degradation state of the energy meter in terms of communication processing or internal response capability. The prediction result is output before the communication is completely abnormal or the operation timeout occurs, providing maintenance personnel with a basis for early handling, thereby avoiding concentrated disconnection, batch timeout or business interruption caused by the accumulation of response capability degradation.

[0016] (2) By performing targeted classification and validity screening of response sample records Rcr in the response record set Rec, the data entering the subsequent statistical analysis stage are highly consistent in terms of business semantics, operating environment, and communication conditions. Specifically, by using the command category Typ as the basic judgment condition for classification analysis, and simultaneously introducing the target identifier Mid and link identifier Lnk as constraints, response sample records Rcr from different operation types, different energy meters, or different communication links are effectively distinguished, avoiding the statistical result distortion problem caused by mixing incomparable response data in the prior art; furthermore, by removing response sample records Rcr with abnormal execution results Res or missing response duration Rtm from the same type of response sample group set, a valid set Vrc of the same type is constructed, so that the subsequent analysis is only based on valid samples that truly reflect the normal response capability of the energy meter, effectively improving the reliability and interpretability of the response consistency analysis results, and providing a reliable data foundation for the construction of the subsequent discrete sequence Vrs and the determination of the difference result Trd.

[0017] (3) By introducing clear evolutionary analysis rules and judgment conditions on the basis of the obtained discrete sequence Vrs, the change process of the consistency of the response of similar operation commands is transformed into a predictive conclusion with clear business semantics. Specifically, by comparing and analyzing the discrete index Var corresponding to the discrete sequence Vrs at adjacent time positions segment by segment, the evolution of response consistency is characterized from three dimensions: direction of change, magnitude of change, and duration of change. This eliminates the reliance on manual experience for consistency degradation, allowing it to be automatically identified by repeatable analysis rules. Furthermore, by setting up a multi-condition joint judgment mechanism based on consistency degradation characteristics, a difference result Trd is generated. The prediction conclusion is only output when the discrete index Var continues to increase and the trend of change is stable, thus avoiding misjudging short-term network jitter or occasional service fluctuations as electricity meter faults. When the discrete sequence Vrs continuously shows an increasing direction and increased magnitude of the discrete index Var within multiple consecutive time windows, the difference result Trd will be identified as a valid difference amplification result and a prompt will be output in the business system. This allows maintenance personnel to conduct targeted checks on the electricity meter's operating status before communication becomes completely abnormal or command timeouts occur frequently, improving the accuracy, interpretability, and practical maintenance value of electricity meter fault prediction conclusions. Attached Figure Description

[0018] Figure 1 This is a schematic diagram illustrating the steps of a method for predicting electricity meter faults according to the present invention. Figure 2 This is a schematic block diagram of an electricity meter fault prediction system according to the present invention. Detailed Implementation

[0019] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. Example 1

[0020] This invention provides a method for predicting electricity meter faults. Please refer to [link / reference]. Figure 1 This includes the following steps: S1. Collect the operation response data generated by the target energy meter during the execution of operation commands in the business system, including command type Typ, issuance time Snd, response time Ack, execution result Res, link identifier Lnk and target identifier Mid, and obtain the response duration Rtm based on the issuance time Snd and response time Ack, and construct the response record set Rec. S2. Based on the command category Typ, merge the response record set Rec into similar categories, and filter the merged response records to obtain a valid set Vrc representing the response characteristics of similar operation commands. S3. Perform statistical analysis on the valid sets Vrc of the same type, construct a dispersion index for the consistency of responses to similar operation commands, and form a dispersion sequence Vrs that reflects the changing trend of response characteristics in chronological order. S4. Perform evolutionary analysis on the discrete sequence Vrs to determine whether the response characteristics of similar operation commands have degraded in consistency, generate the difference result Trd, and issue it.

[0021] In this embodiment, by collecting operation response data generated during the execution of operation commands by the target energy meter in the business system, a response record set Rec is constructed. Based on this, a valid set of similar operations, Vrc, is formed. Furthermore, a discrete sequence Vrs is constructed. Continuous analysis of the consistency changes in responses to similar operation commands is performed to obtain a difference result Trd. This allows for the early identification of the gradually dispersive trend of response duration Rtm for similar operation commands, even when the operation command still returns normally and the execution result Res does not fail. This enables the business system to detect potential degradation in the energy meter's communication processing or internal response capabilities. For example, in actual operation, the energy meter corresponding to a certain target identifier Mid consistently achieves a successful execution result Res for parameter query commands, but its response duration Rtm shows a continuously increasing dispersion across different time periods. By analyzing the evolution of the discrete sequence Vrs to form the difference result Trd, a predictive result can be output before a complete communication anomaly or operation timeout occurs. This provides maintenance personnel with a basis for early intervention, thereby avoiding concentrated disconnections, batch timeouts, or business interruptions caused by the cumulative degradation of response capabilities. Thus, this solution achieves a shift from "discovering faults after the fact" to "predicting in advance based on changes in response consistency," improving the foresight and practical usability of energy meter operation status assessment. Example 2

[0022] Specifically: S1 includes S11; S11. During the process of the business system issuing operation commands to the target energy meter, the operation command interaction information is obtained by monitoring and recording the command interaction process between the business system and the target energy meter. Based on the acquired operation command interaction information, the system parses the command issuance records generated by the business system to obtain the command category Typ corresponding to the operation command and the issuance time Snd when the command was sent to the target energy meter; it parses the response message returned by the target energy meter to obtain the response time Ack generated by the target energy meter for the operation command and the corresponding execution result Res; it parses the session information recorded by the business system during the command interaction process to obtain the link identifier Lnk used for the operation command interaction; and it parses the device identifier information pre-established in the business system to obtain the target identifier Mid of the target energy meter corresponding to the operation command interaction. The acquired command category (Typ), issuance time (Snd), response time (Ack), execution result (Res), link identifier (Lnk), and target identifier (Mid) are structured and organized to form the raw operation response data.

[0023] S1 further includes S12; S12. Based on the command category Typ in the original operation response data, and under the constraints of the same target identifier Mid and the same link identifier Lnk, merge the operation command interaction information belonging to the same command category Typ to form a sequence of similar response samples. For each response sample in the same type of response sample sequence, the response duration Rtm of the response sample is calculated based on the issuance time Snd and the response time Ack corresponding to the response sample. The command category Typ, response duration Rtm, execution result Res, link identifier Lnk and target identifier Mid of the response sample are combined to form the response sample record Rcr. Multiple response sample records Rcr generated at different times under the same target identifier Mid are aggregated, and an indexable aggregation structure is established according to the command category Typ to obtain a set of response records Rec representing the operational response behavior of the target energy meter.

[0024] In this embodiment, a standardized data acquisition and structured modeling mechanism for the operation command interaction process is established, enabling the operation response behavior of the target energy meter in the business system to be completely and traceably recorded and managed. Specifically, during the process of the business system issuing operation commands to the target energy meter, the command category Typ, issuance time Snd, response time Ack, execution result Res, link identifier Lnk, and target identifier Mid are uniformly parsed and organized to form the raw operation response data. Furthermore, based on the command category Typ as the classification basis and under the constraints of the target identifier Mid and the link identifier Lnk, a response sample record Rcr is constructed, thereby obtaining the response record set Rec. This ensures that the response behavior formed by different types of operation commands at different times has a clear organizational structure. This approach avoids the problems of scattered operation logs, fragmented response information, and difficulty in horizontal comparison by command type found in existing technologies. For example, in actual operation, when maintenance personnel need to analyze the response of a target identifier Mid to a specific energy meter under parameter query commands, they can directly query and statistically analyze the response sample record Rcr of the corresponding command category Typ in the response record set Rec, without having to manually concatenate the issue time Snd and response time Ack from multiple source logs. Therefore, this solution achieves unified modeling and standardized storage of energy meter operation response behavior without changing any energy meter hardware structure, providing a stable and reusable data foundation for subsequent analysis, while significantly reducing the complexity of business systems in response behavior analysis, problem backtracking, and maintenance localization. Example 3

[0025] Specifically: S2 includes S21; S21. Extract multiple response sample records Rcr from the response record set Rec as the analysis object; Then, using the command category Typ contained in the response sample record Rcr as the basic criterion for class-based analysis, multiple response sample records Rcr with the same command category Typ are divided into the same analysis group; Within each analysis group, the analysis object is limited to the same target energy meter based on the target identifier Mid, and the analysis object is limited to the same communication link condition based on the link identifier Lnk. This ensures that the multiple response sample records Rcr within the same analysis group are consistent in terms of operating environment and communication conditions. This forms a set of multiple similar response sample groups that represent the operational response behavior characteristics of the target energy meter under specific command category Typ, target identifier Mid, and link identifier Lnk.

[0026] S2 further includes S22; S22. For each analysis group in the same type of response sample grouping set, perform validity screening processing to remove response sample records Rcr that do not meet the response consistency analysis conditions; The validity screening process includes: removing response sample records Rcr that failed to execute, did not return, or had incomplete status based on the execution result Res in the response sample record Rcr; removing response sample records Rcr with missing response duration based on the response duration Rtm in the response sample record Rcr; and verifying the consistency of communication links within the same type of response sample group based on the link identifier Lnk in the response sample record Rcr to avoid link differences interfering with subsequent consistency analysis results. After completing the validity screening, the multiple response sample records Rcr that were selected and retained were summarized to construct a similar valid set Vrc for response consistency quantification and evolution analysis.

[0027] In this embodiment, by performing targeted classification and validity screening of response sample records Rcr in the response record set Rec, the data entering the subsequent statistical analysis stage has a high degree of consistency in business semantics, operating environment, and communication conditions. Specifically, by using the command category Typ as the basic criterion for classification analysis, and simultaneously introducing the target identifier Mid and link identifier Lnk as constraints, response sample records Rcr from different operation types, different energy meters, or different communication links are effectively distinguished, avoiding the statistical distortion problem caused by mixing incomparable response data in the prior art. Furthermore, by removing response sample records Rcr with abnormal execution results Res or missing response duration Rtm from the same type of response sample group set, a valid set Vrc of the same type is constructed, ensuring that subsequent analysis is based only on valid samples that truly reflect the normal response capability of the energy meter. For example, in actual operation, the same target identifier Mid may correspond to both manual meter reading and automatic data acquisition links. When all response data are analyzed indiscriminately, link differences can easily be misjudged as equipment malfunctions. However, the valid set Vrc of the same type formed in stage S2 of this solution ensures that the data entering the analysis all come from the same link identifier Lnk and the same command category Typ, thus making the changes in response consistency truly reflect the changes in the state of the electricity meter itself. Therefore, this solution effectively improves the reliability and interpretability of response consistency analysis results without increasing additional hardware costs, providing a reliable data foundation for the subsequent construction of the discrete sequence Vrs and the determination of the difference result Trd. Example 4

[0028] Specifically: S3 includes S31; S31. For multiple response sample records Rcr belonging to the same command category Typ, target identifier Mid and link identifier Lnk constraint in the same valid set Vrc, extract the response duration Rtm corresponding to the response sample record Rcr as the basis for the discreteness calculation object. Based on the extracted multiple response durations Rtm, according to the preset dispersion calculation rules, the degree of difference in the values ​​of response duration Rtm within the statistical interval is statistically calculated, the deviation of the central tendency of response duration Rtm is quantified, and a dispersion index Var representing the consistency of responses to similar operation commands is constructed. The dispersion index Var is used to reflect the degree of dispersion between the response times of multiple operations under the same command category Typ. After the dispersion index Var is calculated, it is associated with the corresponding command category Typ, target identifier Mid, and link identifier Lnk to form a single dispersion calculation result.

[0029] S3 further includes S32; S32. For the multiple dispersion indices Var calculated in different time intervals, arrange and aggregate the dispersion indices Var in the time dimension according to the preset time organization rules. Among them, the time organization rule is used to determine the corresponding position of each dispersion index Var on the time axis, and the corresponding position is at least associated with the time window formed by the response sample record Rcr that constitutes the dispersion index Var. According to the time organization rules, multiple dispersion indicators Var are arranged in chronological order to form a dispersion sequence Vrs that reflects the trend of consistency of response to similar operation commands over time.

[0030] In this embodiment, the response duration Rtm, which is originally discrete and difficult to directly judge its changing trend, is transformed into an analytical index system with clear statistical semantics and temporal evolution characteristics. This allows the changes in the consistency of responses to similar operation commands to be continuously and objectively quantified. Specifically, by limiting the consistency conditions of command category Typ, target identifier Mid, and link identifier Lnk in the valid set Vrc of the same type, the response duration Rtm corresponding to multiple response sample records Rcr is centrally statistically analyzed to construct the dispersion index Var. This makes the results of a single analysis no longer dependent on the occasional fluctuations of individual response sample records Rcr, but reflects the degree of dispersion of the overall response characteristics of similar operation commands. Furthermore, by arranging and aggregating the dispersion index Var calculated in different time intervals according to time organization rules, a dispersion sequence Vrs is formed, allowing the process of change in response consistency to be presented continuously in the time dimension. For example, in actual operation, the electricity meter corresponding to a certain target identifier Mid may experience occasional abnormal response times (Rtm) due to network jitter within a short period of time. However, through statistical processing of the dispersion index Var, such sporadic anomalies will not be directly misjudged as equipment problems. When the dispersion sequence Vrs continuously increases within a continuous time window, it can clearly reflect that the response consistency of the electricity meter under the corresponding command category Typ is undergoing a systematic change. Thus, this solution achieves the transformation from "single-point response observation" to "trend consistency quantitative analysis" without introducing complex models and additional hardware, providing a stable and interpretable index basis for subsequent evolutionary analysis and Trd determination of difference results based on the dispersion sequence Vrs. Example 5

[0031] Specifically: S4 includes S41; S41. For the discrete sequence Vrs, according to the preset evolution analysis rules, the change relationship between adjacent time positions of the discrete sequence Vrs is compared and analyzed segment by segment to obtain the evolution characteristics of the discrete sequence Vrs. The evolutionary analysis rules are as follows: By comparing the values ​​of the dispersion index Var at adjacent time positions in the dispersion sequence Vrs, the direction of change of the dispersion sequence Vrs at continuous time positions is determined. Specifically, when the dispersion index Var value at a later time position is greater than that at a previous time position, the dispersion sequence Vrs is determined to show an increasing direction of the dispersion index Var, indicating a decreasing direction of change in the consistency of responses to similar operation commands. When the dispersion index Var value at a later time position is less than that at a previous time position, the dispersion sequence Vrs is determined to show a decreasing direction of the dispersion index Var, indicating an increasing direction of change in the consistency of responses to similar operation commands. When the dispersion index Var values ​​at adjacent time positions are within a preset allowable fluctuation range, the dispersion sequence Vrs is determined to be in a stable direction, indicating a state where the consistency of responses to similar operation commands has not changed. Meanwhile, by statistically analyzing the differences in the values ​​of the dispersion index Var corresponding to the dispersion sequence Vrs between adjacent time positions, the magnitude of the change in the dispersion sequence Vrs at continuous time positions is assessed, representing the strength of the change in the dispersion index Var value. By counting the number of consecutive time positions in the discrete sequence Vrs that satisfy the same direction of change, we can determine the time span during which the direction of change in the discrete sequence Vrs is maintained, and thus indicate the persistence of the trend of change in the discrete sequence Vrs. When the discreteness sequence Vrs simultaneously shows that the discreteness index Var increases in both direction and magnitude at multiple consecutive time points, and the direction of change remains consistent, it is determined that the consistency of the response to similar operation commands shows a deterioration trend, and this is recorded as a consistency degradation feature.

[0032] S4 also includes S42; S42. Based on the consistency degradation characteristics, the difference amplification judgment is made on the response consistency change process reflected by the discrete sequence Vrs, and the difference result Trd is generated as the output of the energy meter fault prediction result and displayed in the business system. The determination of the amplification of differences is as follows: Determination condition 1: Based on the change direction conclusion corresponding to the consistency degradation feature, determine whether the discreteness sequence Vrs has a continuous time period within the target time range in which the discreteness index Var increases; Determination condition 2: Based on the change magnitude conclusion corresponding to the consistency degradation feature, determine whether the degree of difference in the discreteness index Var value is in an enhancing state within the continuous time period in which the discreteness index Var increases; Determination condition 3: Based on the change persistence conclusion corresponding to the consistency degradation feature, determine whether the increase direction of the discreteness index Var meets the preset persistence requirement; When all three conditions are met, a difference result Trd representing the amplification of differences in responses to similar operation commands is generated, and the difference result Trd is determined as a valid difference amplification result. When any of the decision conditions is not met, a difference result Trd is generated that indicates that the difference in the response of the same type of operation command is not amplified. When generating the difference result Trd, the difference result Trd is associated with the corresponding command category Typ, target identifier Mid, link identifier Lnk, and the target time range of the discrete sequence Vrs, so that the difference result Trd can be used for subsequent fault risk assessment and support the tracing and location of the source of difference expansion.

[0033] In this embodiment, by introducing explicit evolutionary analysis rules and judgment conditions based on the obtained discrete sequence Vrs, the process of changing the consistency of responses to similar operation commands is transformed into a predictive conclusion with clear business semantics. Specifically, by performing segment-by-segment comparative analysis on the discrete index Var corresponding to adjacent time positions of the discrete sequence Vrs, the evolutionary state of response consistency is characterized from three dimensions: direction of change, magnitude of change, and duration of change. This ensures that consistency degradation no longer relies on manual experience judgment but is automatically identified by repeatable analysis rules. Furthermore, by setting a multi-condition joint judgment mechanism based on the consistency degradation characteristics, a difference result Trd is generated. This ensures that the predictive conclusion is only output when the discrete index Var continuously increases and the trend of change is stable, thereby avoiding misjudging short-term network jitter or occasional business fluctuations as electricity meter failures. For example, in actual operation, the energy meter corresponding to a target identifier Mid may experience a brief increase in the dispersion index Var due to temporary link congestion during a certain period. However, since this change does not form a sustained increasing trend in the dispersion sequence Vrs, the difference result Trd will be judged as a result of non-expansion of the response difference and will not trigger a fault prediction prompt. On the other hand, when the dispersion sequence Vrs continuously shows an increasing direction of the dispersion index Var and an increased magnitude of change in multiple consecutive time windows, the difference result Trd will be determined as a valid result of increased difference, and a prompt will be output in the business system. This allows maintenance personnel to conduct targeted checks on the energy meter's operating status before communication becomes completely abnormal or command timeouts occur frequently. Thus, this solution achieves a closed-loop transformation from "observing consistent changes" to "outputting directly usable prediction results," improving the accuracy, interpretability, and practical maintenance value of energy meter fault prediction conclusions. Example 6

[0034] A fault prediction system for electricity meters, please refer to Figure 2 Specifically, it includes an electricity meter data acquisition module, a data filtering module, a data trend analysis module, and a prediction output module; The electricity meter data acquisition module collects operation response data generated by the target electricity meter during the execution of operation commands in the business system, including command type Typ, issuance time Snd, response time Ack, execution result Res, link identifier Lnk, and target identifier Mid. Based on the issuance time Snd and response time Ack, it obtains the response duration Rtm and constructs a response record set Rec. The data filtering module merges the response record set Rec based on the command category Typ, and filters the merged response records to obtain a valid set Vrc representing the response characteristics of similar operation commands. The data trend analysis module performs statistical analysis on the same effective set Vrc, constructs a dispersion index of the consistency of response to the same operation command, and forms a dispersion sequence Vrs that reflects the changing trend of response characteristics in chronological order. The prediction output module performs evolutionary analysis on the discrete sequence Vrs, determines whether the response characteristics of similar operation commands show consistency degradation, generates a difference result Trd, and sends it out.

[0035] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended technical solutions and their equivalents.

Claims

1. A method for predicting faults in electricity meters, characterized in that: Includes the following steps: S1. Collect the operation response data generated by the target energy meter during the execution of operation commands in the business system, including command type Typ, issuance time Snd, response time Ack, execution result Res, link identifier Lnk and target identifier Mid, and obtain the response duration Rtm based on the issuance time Snd and response time Ack, and construct the response record set Rec. S2. Based on the command category Typ, merge the response record set Rec into similar categories, and filter the merged response records to obtain a valid set Vrc representing the response characteristics of similar operation commands. S3. Perform statistical analysis on the valid sets Vrc of the same type, construct a dispersion index for the consistency of responses to similar operation commands, and form a dispersion sequence Vrs that reflects the changing trend of response characteristics in chronological order. S4. Perform evolutionary analysis on the discrete sequence Vrs to determine whether the response characteristics of similar operation commands have degraded in consistency, generate the difference result Trd, and issue it.

2. The method for predicting electricity meter faults according to claim 1, characterized in that: S1 includes S11; S11. During the process of the business system issuing operation commands to the target energy meter, the operation command interaction information is obtained by monitoring and recording the command interaction process between the business system and the target energy meter. Based on the acquired operation command interaction information, the command category Typ corresponding to the operation command and the sending time Snd when the command was sent to the target energy meter are obtained by parsing the command issuance record generated by the business system. By parsing the response message returned by the target energy meter, the response time Ack generated by the target energy meter in response to the operation command and the corresponding execution result Res are obtained; by parsing the session information recorded by the business system during the command interaction, the link identifier Lnk used for the operation command interaction is obtained; by parsing the device identifier information pre-established in the business system, the target identifier Mid of the target energy meter corresponding to the operation command interaction is obtained. The acquired command category (Typ), issuance time (Snd), response time (Ack), execution result (Res), link identifier (Lnk), and target identifier (Mid) are structured and organized to form the raw operation response data.

3. The method for predicting electricity meter faults according to claim 2, characterized in that: S1 further includes S12; S12. Based on the command category Typ in the original operation response data, and under the constraints of the same target identifier Mid and the same link identifier Lnk, merge the operation command interaction information belonging to the same command category Typ to form a sequence of similar response samples. For each response sample in the same type of response sample sequence, the response duration Rtm of the response sample is calculated based on the issuance time Snd and the response time Ack corresponding to the response sample. The command category Typ, response duration Rtm, execution result Res, link identifier Lnk and target identifier Mid of the response sample are combined to form the response sample record Rcr. Multiple response sample records Rcr generated at different times under the same target identifier Mid are aggregated, and an indexable aggregation structure is established according to the command category Typ to obtain a set of response records Rec representing the operational response behavior of the target energy meter.

4. The method for predicting electricity meter faults according to claim 3, characterized in that: S2 includes S21; S21. Extract multiple response sample records Rcr from the response record set Rec as the analysis object; Then, using the command category Typ contained in the response sample record Rcr as the basic criterion for class-based analysis, multiple response sample records Rcr with the same command category Typ are divided into the same analysis group; Within each analysis group, the analysis object is limited to the same target energy meter based on the target identifier Mid, and the analysis object is limited to the same communication link condition based on the link identifier Lnk. This ensures that the multiple response sample records Rcr within the same analysis group are consistent in terms of operating environment and communication conditions. This forms a set of multiple similar response sample groups that represent the operational response behavior characteristics of the target energy meter under specific command category Typ, target identifier Mid, and link identifier Lnk.

5. The method for predicting electricity meter faults according to claim 4, characterized in that: S2 further includes S22; S22. For each analysis group in the same type of response sample grouping set, perform validity screening processing to remove response sample records Rcr that do not meet the response consistency analysis conditions; The validity screening process includes: removing response sample records Rcr that failed to execute, did not return, or had incomplete status based on the execution result Res in the response sample record Rcr; removing response sample records Rcr with missing response duration based on the response duration Rtm in the response sample record Rcr; and verifying the consistency of communication links within the same type of response sample group based on the link identifier Lnk in the response sample record Rcr. After completing the validity screening, the multiple response sample records Rcr that were selected and retained were summarized to construct a similar valid set Vrc for response consistency quantification and evolution analysis.

6. The method for predicting electricity meter faults according to claim 5, characterized in that: S3 includes S31; S31. For multiple response sample records Rcr belonging to the same command category Typ, target identifier Mid and link identifier Lnk constraint in the same valid set Vrc, extract the response duration Rtm corresponding to the response sample record Rcr as the basis for the discreteness calculation object. Based on the extracted multiple response durations Rtm, according to the preset dispersion calculation rules, the degree of difference in the values ​​of response duration Rtm within the statistical interval is statistically calculated, the deviation of the central tendency of response duration Rtm is quantified, and a dispersion index Var representing the consistency of responses to similar operation commands is constructed. The dispersion index Var is used to reflect the degree of dispersion between the response times of multiple operations under the same command category Typ. After the dispersion index Var is calculated, it is associated with the corresponding command category Typ, target identifier Mid, and link identifier Lnk to form a single dispersion calculation result.

7. The method for predicting electricity meter faults according to claim 6, characterized in that: S3 further includes S32; S32. For the multiple dispersion indices Var calculated in different time intervals, arrange and aggregate the dispersion indices Var in the time dimension according to the preset time organization rules. Among them, the time organization rule is used to determine the corresponding position of each dispersion index Var on the time axis, and the corresponding position is at least associated with the time window formed by the response sample record Rcr that constitutes the dispersion index Var. According to the time organization rules, multiple dispersion indicators Var are arranged in chronological order to form a dispersion sequence Vrs that reflects the trend of consistency of response to similar operation commands over time.

8. The method for predicting electricity meter faults according to claim 7, characterized in that: S4 includes S41; S41. For the discrete sequence Vrs, according to the preset evolution analysis rules, the change relationship between adjacent time positions of the discrete sequence Vrs is compared and analyzed segment by segment to obtain the evolution characteristics of the discrete sequence Vrs. The evolutionary analysis rules are as follows: By comparing the values ​​of the dispersion index Var at adjacent time positions in the dispersion sequence Vrs, the direction of change of the dispersion sequence Vrs at continuous time positions is determined. Specifically, when the value of the dispersion index Var at a later time position is greater than the value of the dispersion index Var at a previous time position, the dispersion sequence Vrs is determined to show an increasing direction of the dispersion index Var, indicating a decreasing direction of change in the consistency of responses to similar operation commands. When the value of the dispersion index Var at a later time position is less than the value of the dispersion index Var at a previous time position, the dispersion sequence Vrs is determined to show a decreasing direction of the dispersion index Var, indicating an increasing direction of change in the consistency of responses to similar operation commands. When the values ​​of the dispersion index Var at adjacent time positions are within a preset allowable fluctuation range, the dispersion sequence Vrs is determined to be in a stable direction, indicating a state where the consistency of responses to similar operation commands has not changed. Meanwhile, by statistically analyzing the differences in the values ​​of the dispersion index Var corresponding to the dispersion sequence Vrs between adjacent time positions, the magnitude of the change in the dispersion sequence Vrs at continuous time positions is assessed, representing the strength of the change in the dispersion index Var value. By counting the number of consecutive time positions in the discrete sequence Vrs that satisfy the same direction of change, we can determine the time span during which the direction of change in the discrete sequence Vrs is maintained, and thus indicate the persistence of the trend of change in the discrete sequence Vrs. When the discreteness sequence Vrs simultaneously shows that the discreteness index Var increases in both direction and magnitude at multiple consecutive time points, and the direction of change remains consistent, it is determined that the consistency of the response to similar operation commands shows a deterioration trend, and this is recorded as a consistency degradation feature.

9. The method for predicting electricity meter faults according to claim 8, characterized in that: S4 also includes S42; S42. Based on the consistency degradation characteristics, the difference amplification judgment is made on the response consistency change process reflected by the discrete sequence Vrs, and the difference result Trd is generated as the output of the energy meter fault prediction result and displayed in the business system. The determination of the amplification of differences is as follows: Determination condition 1: Based on the change direction conclusion corresponding to the consistency degradation feature, determine whether the discreteness sequence Vrs has a continuous time period within the target time range in which the discreteness index Var increases; Determination condition 2: Based on the change magnitude conclusion corresponding to the consistency degradation feature, determine whether the degree of difference in the discreteness index Var value is in an enhancing state within the continuous time period in which the discreteness index Var increases; Determination condition 3: Based on the change persistence conclusion corresponding to the consistency degradation feature, determine whether the increase direction of the discreteness index Var meets the preset persistence requirement; When all three conditions are met, a difference result Trd representing the amplification of differences in responses to similar operation commands is generated, and the difference result Trd is determined as a valid difference amplification result. When any of the decision conditions is not met, a difference result Trd is generated that represents the non-amplified difference between the responses to similar operation commands.

10. A power meter fault prediction system, applied to the power meter fault prediction method according to any one of claims 1 to 9, characterized in that: It includes an electricity meter data acquisition module, a data filtering module, a data trend analysis module, and a prediction output module; The electricity meter data acquisition module collects operation response data generated by the target electricity meter during the execution of operation commands in the business system, including command type Typ, issuance time Snd, response time Ack, execution result Res, link identifier Lnk, and target identifier Mid. Based on the issuance time Snd and response time Ack, it obtains the response duration Rtm and constructs a response record set Rec. The data filtering module merges the response record set Rec based on the command category Typ, and filters the merged response records to obtain a valid set Vrc representing the response characteristics of similar operation commands. The data trend analysis module performs statistical analysis on the same effective set Vrc, constructs a dispersion index of the consistency of response to the same operation command, and forms a dispersion sequence Vrs that reflects the changing trend of response characteristics in chronological order. The prediction output module performs evolutionary analysis on the discrete sequence Vrs, determines whether the response characteristics of similar operation commands show consistency degradation, generates a difference result Trd, and sends it out.

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