Data processing method and platform for electric energy meter assembly line

Abstract

The invention provides a data processing method and platform for an electric energy meter assembly line. According to the method, a detection data set of a to-be-detected electric energy meter uploaded by a target detection station on an electric energy meter assembly line is obtained, if it is determined that to-be-confirmed abnormal detection data exists in the detection data set, a detection data curve is generated according to the detection data set, and a detection data effective value is determined according to the detection data curve; and if the detection data effective value is within the preset monitoring data effective value range, determining a local abnormal characteristic value of the abnormal detection data to be confirmed by using a preset detection data evaluation model, thereby determining the performance state of the electric energy meter to be detected according to the local abnormal characteristic value. Therefore, automatic acquisition and processing of the detection data of the electric energy meter on the assembly line are realized, and the detection efficiency and accuracy of the electric energy meter on the assembly line are improved.

Description

Technical Field

[0001] The present application relates to data processing technology, and in particular to a data processing method and platform for an electric energy meter assembly line. Background Art

[0002] In the power industry, the accuracy and reliability of energy meters are crucial as they are an important basis for power measurement and settlement. With the development of smart grids, the automated testing and assembly line production of energy meters have become an industry trend. However, in the assembly line production process of energy meters, there are still many challenges in the need to conduct comprehensive and accurate performance testing of each energy meter.

[0003] Traditional energy meter detection methods mainly rely on manual operation and empirical judgment. This method is not only inefficient, but also easily affected by human factors, making it difficult to ensure the accuracy and consistency of the detection results. Especially in the energy meter assembly line environment, due to the fast production speed and large detection volume, the traditional manual detection method is even more difficult to meet actual needs. Summary of the invention

[0004] The present application provides a data processing method and platform for an electric energy meter assembly line, which are used to realize the automatic collection and processing of electric energy meter detection data on the assembly line, thereby improving the detection efficiency and accuracy of the electric energy meters on the assembly line.

[0005] In a first aspect, the present application provides a data processing method for an electric energy meter assembly line, which is applied to an electric energy meter assembly line detection management platform, wherein the electric energy meter assembly line detection management platform is used to communicate with the detection data ports of each detection station on the electric energy meter assembly line; the method comprises: Acquire a detection data set of the electric energy meter to be detected uploaded by the target detection station on the electric energy meter assembly line, wherein the detection data set includes a plurality of detection data, each of which is marked with a timestamp; If it is determined that there is abnormal detection data to be confirmed in the detection data set, a detection data curve is generated according to the detection data set, and a detection data valid value is determined according to the detection data curve, wherein the abnormal detection data to be confirmed is detection data greater than a preset detection data threshold; If the effective value of the detection data is within the preset effective value range of the monitoring data, the local abnormal characteristic value of the abnormal detection data to be confirmed is determined by using the preset detection data evaluation model; The performance status of the electric energy meter to be detected is determined according to the local abnormal characteristic value, wherein if the local abnormal characteristic value is greater than or equal to a preset abnormal characteristic threshold, the performance status is determined to be an abnormal status, and if the local abnormal characteristic value is less than the preset abnormal characteristic threshold, the performance status is determined to be a normal status.

[0006] In the above scheme, the electric energy meter assembly line detection management platform realizes seamless communication connection with the detection data port of each detection station on the electric energy meter assembly line, thereby realizing the automatic collection and processing of detection data. This automated process greatly reduces manual intervention, improves detection efficiency and accuracy, and reduces the risk of human error. In addition, the above scheme can quickly identify and process the abnormal detection data to be confirmed in the detection data set. By setting a preset detection data threshold, it is possible to quickly filter out possible abnormal data, and by generating a detection data curve and calculating the effective value of the detection data, further confirm the authenticity and impact of these data, thereby effectively improving the timeliness and accuracy of abnormal detection. Then, after confirming the abnormal data, the preset detection data evaluation model is used to calculate the local abnormal feature value, so as to accurately evaluate the performance status of the electric energy meter to be detected. By setting a preset abnormal feature threshold, it is possible to clearly distinguish the normal state and abnormal state of the electric energy meter, providing a reliable basis for subsequent decision-making and processing.

[0007] Optionally, generating a detection data curve according to the detection data set, and determining a detection data valid value according to the detection data curve, includes: Generate a detection data period curve of each preset detection period according to the detection data set and the preset detection period of the detection data, wherein the detection data set includes detection data of multiple preset detection periods, and each preset detection period corresponds to multiple detection data; The effective value of the detection data is determined according to the detection data period curve of each preset detection period.

[0008] In the above scheme, by dividing the detection data set according to the preset detection cycle and generating the detection data cycle curve of each cycle, the operation status of the electric energy meter in different time periods can be analyzed more finely. The effective value of the detection data is determined based on the detection data cycle curve of each preset detection cycle, which ensures the accuracy and representativeness of the effective value and provides a solid foundation for subsequent anomaly detection and performance evaluation. By generating the detection data curve and determining the effective value, the operation status of the electric energy meter can be more accurately reflected. Compared with relying only on a single data point or a simple average value, this method can better resist the influence of data noise and fluctuations, thereby improving the accuracy and reliability of detection. In addition, the generated detection data curve and the determined effective value can not only be used for current anomaly detection and performance evaluation, but also serve as an important reference for subsequent processing and analysis. For example, these curves and effective values ​​will play an important role when it is necessary to trace historical data or conduct long-term trend analysis.

[0009] Optionally, the determining the local abnormal feature value of the abnormal detection data to be confirmed by using a preset detection data evaluation model includes: Determine a neighboring detection data set from the detection data set according to the abnormal detection data to be confirmed and a preset time proximity range, and determine the number of feature detection data in the neighboring detection data set; Determine, according to the proximity detection data set, a proximity detection feature density corresponding to the abnormal detection data to be confirmed; Determine the abnormal detection data to be confirmed according to the proximity detection feature density and the proximity detection data set A corresponding first local abnormal feature value, the local abnormal feature value includes the first local abnormal feature value, and the preset abnormal feature threshold includes a first preset abnormal feature threshold corresponding to the first local abnormal feature value.

[0010] In the above scheme, by considering the abnormal detection data to be confirmed and its neighboring detection data set, the context of the abnormal data can be analyzed more finely. By determining the neighboring detection data set, not only a single data point is focused on, but also the change trend and distribution of the surrounding data are considered, thereby improving the accuracy and sensitivity of abnormal detection. The neighboring detection feature density corresponding to the abnormal detection data to be confirmed is calculated according to the neighboring detection data set. This process fully considers the relative relationship and density between the data. The introduction of feature density provides a quantitative indicator for evaluating the isolation degree of abnormal data, making abnormal detection more objective and scientific. Then, by calculating the first local abnormal feature value, the abnormal data is deeply evaluated from a local perspective. The first local abnormal feature value reflects the prominence of the abnormal data in its neighboring range, which provides an important basis for the subsequent performance status judgment. At the same time, the preset first preset abnormal feature threshold ensures the consistency and comparability of the evaluation results. Then, the preset detection data evaluation model is used for abnormal detection, so that the method has strong flexibility and adaptability. The model can be adjusted and optimized according to different application scenarios and needs to adapt to different electric energy meter detection standards and requirements. Based on the evaluation results of the local abnormal feature value, the performance status of the electric energy meter to be detected can be more accurately judged. This process not only reduces the possibility of misjudgment and omission, but also improves the scientific nature and reliability of decision-making.

[0011] Optionally, the determining the local abnormal feature value of the abnormal detection data to be confirmed by using a preset detection data evaluation model further includes: A second local abnormal feature value is determined according to the first local abnormal feature value and the proximity detection data set, the local abnormal feature value includes the second local abnormal feature value, and the preset abnormal feature threshold includes a second preset abnormal feature threshold corresponding to the second local abnormal feature value.

[0012] In the above scheme, by introducing the second local abnormal characteristic value, a comprehensive evaluation of the abnormal detection data to be confirmed from multiple angles is realized. The first local abnormal characteristic value focuses on the prominence of the data in the local range, while the second local abnormal characteristic value may be based on different algorithms or consider different factors (such as global distribution, time series characteristics, etc.), thereby providing more comprehensive abnormal information. This multi-dimensional evaluation method makes abnormal detection more comprehensive and accurate. Then, by combining the first local abnormal characteristic value and the second local abnormal characteristic value for comprehensive judgment, the real abnormal data can be more accurately identified. In some cases, a single characteristic value may be affected by noise or special working conditions and cause misjudgment, while the comprehensive evaluation of multiple characteristic values ​​can effectively reduce this risk and improve the accuracy of identification. It can be seen that by comprehensively considering multiple local abnormal characteristic values, more comprehensive and scientific support can be provided for subsequent decision-making. When judging the performance status of the electric energy meter to be detected, the evaluation results of multiple characteristic values ​​can be combined for comprehensive consideration, thereby improving the accuracy and reliability of the decision.

[0013] In addition, although the calculation of the second local abnormal eigenvalue is introduced, this process is carried out on the basis of existing data and analysis results, so it does not significantly increase the overall complexity of data processing. On the contrary, through parallel or step-by-step calculation, the calculation process can be optimized and the data processing efficiency can be improved.

[0014] Optionally, before determining the first local abnormal feature value corresponding to the abnormal detection data to be confirmed according to the proximity detection feature density and the proximity detection data set, the method further includes: Determine a feature difference value set according to the anomaly detection data and the proximity detection data set, wherein the feature difference values ​​in the feature difference value set are actual differences between each detection data and the anomaly detection data; Sorting the feature difference value set in ascending order to determine an ascending feature difference value set; Determine a reference feature difference value according to the feature difference value ascending set, wherein the reference feature difference value is a feature difference value arranged at a preset ranking in the feature difference value ascending set; Determine the reference detection data corresponding to the proximity detection data set according to the reference feature difference, determine the detection data in the proximity detection data set that is less than or equal to the reference detection data as the feature detection data, and determine the number of the feature detection data.

[0015] In the above scheme, by calculating the actual difference between the abnormal detection data and each data in its neighboring detection data set, a feature difference set is constructed, thereby providing an accurate data basis for subsequent sorting and analysis, and helping to more accurately capture the relationship between the abnormal data and its neighboring data. Then, the feature difference set is sorted in ascending order to form a feature difference ascending set. This process makes the relationship between the data clearer and facilitates subsequent analysis and decision-making. The sorted set can intuitively reflect the degree of proximity between each detection data and the abnormal detection data, providing a basis for determining the reference feature difference. Then, the reference feature difference is determined based on the feature difference ascending set, and this value is used as the standard for judging the feature detection data. The reference feature difference is selected by setting a preset ranking, which ensures the objectivity and rationality of the selection process. This step provides a scientific basis for determining the range and quantity of feature detection data, which helps to improve the accuracy of anomaly detection. According to the reference feature difference, the detection data less than or equal to the value is determined as feature detection data in the neighboring detection data set, and its number is counted. This step eliminates the interference of noise and irrelevant data by screening the data closely related to the abnormal detection data, making the subsequent calculation of feature density and local abnormal feature value more accurate. Through the above preprocessing steps, the method can more accurately determine the local environment of the anomaly detection data to be confirmed, thereby more scientifically evaluating its degree of anomaly. This process not only improves the accuracy of anomaly detection, but also enhances the robustness of the method, enabling it to better cope with various complex situations. Introducing these preprocessing steps in the anomaly detection process makes the entire process more systematic and standardized. Each step has a clear goal and operation method, which reduces the arbitrariness and uncertainty of human operation, thereby improving the efficiency and stability of the detection process.

[0016] Optionally, after determining the performance state of the electric energy meter to be detected according to the local abnormal characteristic value, the method further includes: If the performance status is an abnormal status, the target detection station is instructed to transfer the electric energy meter to be detected to a manual detection station through the electric energy meter assembly line; If the performance status is normal, the target detection station is instructed to transfer the electric energy meter to be detected to the next detection station through the electric energy meter assembly line.

[0017] In the above scheme, the flow direction of the energy meter to be tested is automatically indicated according to its performance status, so as to realize the combination of automation and intelligence of the detection process. This processing method not only improves the detection efficiency, but also reduces the error and uncertainty of manual operation, and improves the accuracy and reliability of the overall detection process. When the performance status of the energy meter to be tested is determined to be abnormal, the method can respond quickly and instruct the target detection station to transfer it to the manual detection station for further inspection and confirmation. This flexible exception handling mechanism ensures that the problematic energy meter can be handled in a timely and effective manner, and prevents the spread of potential quality problems. For energy meters with normal performance status, the scheme instructs them to continue to flow to the next detection station, avoiding unnecessary waste of resources and extension of detection time. This way of optimizing resource allocation improves the overall efficiency and capacity of the production line. By automatically indicating the flow direction of the energy meter according to the performance status, the method reduces manual intervention and waiting time, making the entire detection process smoother and more efficient. This is of great significance for improving the production efficiency of the production line and reducing production costs.

[0018] Optionally, the step of obtaining a detection data set of the electric energy meter to be detected uploaded by the target detection station on the electric energy meter assembly line includes: Detecting the detection voltage data of the electric energy meter to be detected by the voltage detection device on the target detection station to form a detection voltage data set; Detecting the detection current data of the electric energy meter to be detected by the current detection device on the target detection station to form a detection current data set; The detection voltage data set and the detection current data set are uploaded to the electric energy meter assembly line detection management platform, wherein the detection data set includes at least one of the detection voltage data set and the detection current data set.

[0019] In the above scheme, by collecting voltage and current data at the same time, this method provides a more comprehensive basis for evaluating the performance status of the energy meter. Voltage and current are key parameters for the operation of the energy meter. Their real-time data can reflect the working status of the energy meter under different working conditions, thereby providing rich and accurate information for subsequent performance analysis and anomaly detection.

[0020] Improve data accuracy: Use special voltage detection devices and current detection devices for data collection to ensure data accuracy and reliability. Integrate the detection voltage data set and the detection current data set and upload them to the electric energy meter assembly line detection management platform to achieve centralized management and unified analysis of data. This step simplifies the data processing process and improves the efficiency and convenience of data processing. At the same time, it is also convenient for managers to grasp the detection status of the electric energy meter in real time and discover and solve problems in a timely manner. Among them, the detection data set includes at least one of the detection voltage data set and the detection current data set. This design makes the method more flexible. In actual applications, you can choose to collect only voltage data, only current data, or both data at the same time as needed to meet different detection needs and scenarios.

[0021] Furthermore, since the detection stations on the electric energy meter assembly line may have different configurations and detection capabilities, this method allows the collection method of detection data to be flexibly adjusted according to actual conditions. This design enables the system to adapt to different production environments and detection requirements, improving the adaptability and scalability of the system.

[0022] Then, through the automated data collection and upload process, manual intervention and waiting time are reduced, and the overall detection efficiency is improved. At the same time, due to the improvement of data accuracy and the convenience of data analysis, the feedback of detection results is more timely and accurate, which helps to timely discover and deal with potential problems.

[0023] In a second aspect, the present application provides an electric energy meter assembly line detection management platform, which is used to communicate with the detection data ports of each detection station on the electric energy meter assembly line; the platform includes: An acquisition module, used for acquiring a detection data set of the electric energy meter to be detected uploaded by the target detection station on the electric energy meter assembly line, wherein the detection data set includes a plurality of detection data, each of which is marked with a timestamp; A processing module, used for determining that there is abnormal detection data to be confirmed in the detection data set, generating a detection data curve according to the detection data set, and determining a detection data valid value according to the detection data curve, wherein the abnormal detection data to be confirmed is detection data greater than a preset detection data threshold; A determination module, configured to determine the local abnormal characteristic value of the abnormal detection data to be confirmed by using a preset detection data evaluation model when it is determined that the detection data effective value is within the preset monitoring data effective value range; The determination module is also used to determine the performance status of the electric energy meter to be detected based on the local abnormal characteristic value, wherein if the local abnormal characteristic value is greater than or equal to a preset abnormal characteristic threshold, the performance status is determined to be an abnormal state; if the local abnormal characteristic value is less than the preset abnormal characteristic threshold, the performance status is determined to be a normal state.

[0024] Optionally, the processing module is specifically used to: Generate a detection data period curve of each preset detection period according to the detection data set and the preset detection period of the detection data, wherein the detection data set includes detection data of multiple preset detection periods, and each preset detection period corresponds to multiple detection data; The effective value of the detection data is determined according to the detection data period curve of each preset detection period.

[0025] Optionally, the determining module is specifically used to: Determine a neighboring detection data set from the detection data set according to the abnormal detection data to be confirmed and a preset time proximity range, and determine the number of feature detection data in the neighboring detection data set; Determine, according to the proximity detection data set, a proximity detection feature density corresponding to the abnormal detection data to be confirmed; A first local abnormal feature value corresponding to the abnormal detection data to be confirmed is determined according to the proximity detection feature density and the proximity detection data set, the local abnormal feature value includes the first local abnormal feature value, and the preset abnormal feature threshold includes a first preset abnormal feature threshold corresponding to the first local abnormal feature value.

[0026] Optionally, the determination module is also used to determine a second local abnormality feature value based on the first local abnormality feature value and the proximity detection data set, the local abnormality feature value includes the second local abnormality feature value, and the preset abnormality feature threshold includes a second preset abnormality feature threshold corresponding to the second local abnormality feature value.

[0027] Optionally, the determining module is further specifically configured to: Determine a feature difference value set according to the anomaly detection data and the proximity detection data set, wherein the feature difference values ​​in the feature difference value set are actual differences between each detection data and the anomaly detection data; Sorting the feature difference value set in ascending order to determine an ascending feature difference value set; Determine a reference feature difference value according to the feature difference value ascending set, wherein the reference feature difference value is a feature difference value arranged at a preset ranking in the feature difference value ascending set; Determine the reference detection data corresponding to the proximity detection data set according to the reference feature difference, determine the detection data in the proximity detection data set that is less than or equal to the reference detection data as the feature detection data, and determine the number of the feature detection data.

[0028] Optionally, the determining module is further specifically configured to: If the performance status is an abnormal status, the target detection station is instructed to transfer the electric energy meter to be detected to a manual detection station through the electric energy meter assembly line; If the performance status is normal, the target detection station is instructed to transfer the electric energy meter to be detected to the next detection station through the electric energy meter assembly line.

[0029] Optionally, the acquisition module is specifically used to: Detecting the detection voltage data of the electric energy meter to be detected by the voltage detection device on the target detection station to form a detection voltage data set; Detecting the detection current data of the electric energy meter to be detected by the current detection device on the target detection station to form a detection current data set; The detection voltage data set and the detection current data set are uploaded to the electric energy meter assembly line detection management platform, wherein the detection data set includes at least one of the detection voltage data set and the detection current data set.

[0030] In a third aspect, the present application provides an electronic device, including: processor; and, A memory, configured to store executable instructions of the processor; The processor is configured to perform any possible method described in the first aspect by executing the executable instructions.

[0031] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement any possible method described in the first aspect.

[0032] The data processing method and platform for an electric energy meter assembly line provided in the present application obtain a detection data set of an electric energy meter to be detected uploaded by a target detection station on the electric energy meter assembly line. If it is determined that there is abnormal detection data to be confirmed in the detection data set, a detection data curve is generated according to the detection data set, and the effective value of the detection data is determined according to the detection data curve. If the effective value of the detection data is within the effective value range of the preset monitoring data, the local abnormal characteristic value of the abnormal detection data to be confirmed is determined using a preset detection data evaluation model, so as to determine the performance status of the electric energy meter to be detected according to the local abnormal characteristic value, thereby realizing the automatic collection and processing of the detection data of the electric energy meters on the assembly line, and improving the detection efficiency and accuracy of the electric energy meters on the assembly line. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0034] Figure 1 is a flow chart of a data processing method for an electric energy meter assembly line according to an exemplary embodiment of the present application; Figure 2 is a flow chart of a data processing method for an electric energy meter assembly line according to another exemplary embodiment of the present application; Figure 3 is a structural schematic diagram of an electric energy meter assembly line detection management platform according to an exemplary embodiment of the present application; Figure 4 It is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present application.

[0035] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0036] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0037] In order to solve the above problems, the embodiments provided by the present application realize the automatic collection and processing of detection data by seamlessly communicating with the detection data ports of each detection station on the electric energy meter assembly line through the electric energy meter assembly line detection management platform. This process reduces manual intervention, improves detection efficiency and accuracy, and reduces the risk of human error. The voltage and current data of the electric energy meter are obtained in real time through the voltage detection device and the current detection device at the target detection station, and uploaded to the management platform, providing comprehensive data support for subsequent abnormality detection and performance evaluation. The specific inventive concepts of the embodiments provided by the present application are as follows: Efficient anomaly detection mechanism: The embodiment provided in this application sets a preset detection data threshold value for preliminarily screening out the abnormal detection data to be confirmed. Subsequently, a detection data curve is generated based on the detection data set, and the authenticity and impact of the abnormal data are further confirmed by calculating the effective value of the detection data. On this basis, the local abnormal feature value is calculated using the preset detection data evaluation model to accurately evaluate the performance status of the electric energy meter to be detected. Through a multi-step abnormal detection process, this method can quickly and accurately identify the abnormal state of the electric energy meter.

[0038] Multi-dimensional anomaly assessment: In order to more comprehensively assess the characteristics of the abnormal detection data to be confirmed, the embodiment provided in this application introduces a calculation method for multiple local abnormal feature values ​​(including a first local abnormal feature value and a second local abnormal feature value). These feature values ​​evaluate the abnormal data from multiple angles, improving the accuracy and reliability of anomaly detection. By setting a preset abnormal feature threshold, the normal state and abnormal state of the electric energy meter can be clearly distinguished, providing a reliable basis for subsequent processing.

[0039] Intelligent decision-making and transfer: After confirming the performance status of the electric energy meter to be tested, the embodiment provided by the present application can automatically instruct the target detection station to perform the corresponding transfer operation. For electric energy meters with abnormal performance status, they are transferred to the manual detection station for further inspection and confirmation; and for electric energy meters with normal performance status, they are instructed to continue to flow to the next detection station. This intelligent decision-making and transfer mechanism improves the efficiency of the detection process and ensures that problematic electric energy meters can be handled in a timely and effective manner.

[0040] Flexible data collection and processing strategy: Considering that different detection stations may have different configurations and detection capabilities, the embodiments provided in this application allow for flexible adjustment of the detection data collection method according to actual conditions. The detection data set may include a detection voltage data set, a detection current data set, or a combination of the two to adapt to different detection requirements and scenarios. This flexible data collection and processing strategy enhances the adaptability and scalability of the system.

[0041] To sum up, the concept of the embodiments provided in this application is to realize intelligent management of the electricity meter assembly line detection process through automated data collection and processing, efficient anomaly detection mechanism, multi-dimensional anomaly assessment, intelligent decision-making and circulation, and flexible data collection and processing strategies, thereby improving detection efficiency and accuracy and reducing the risk of human error.

[0042] Figure 1 FIG. 1 is a flow chart of a data processing method for an electric energy meter assembly line according to an exemplary embodiment of the present application. Figure 1 As shown, the data processing method for the electric energy meter assembly line provided in this embodiment includes: S101, obtaining a detection data set of the electric energy meter to be detected uploaded by the target detection station on the electric energy meter assembly line.

[0043] The data processing method for an electric energy meter assembly line provided in this embodiment may be applied to an electric energy meter assembly line detection management platform, and the electric energy meter assembly line detection management platform is used to communicate with the detection data ports of each detection station on the electric energy meter assembly line.

[0044] In this step, a detection data set of the electric energy meter to be detected uploaded by the target detection station on the electric energy meter assembly line is obtained, wherein the detection data set includes a plurality of detection data, and each detection data is marked with a timestamp.

[0045] Specifically, first, the electric energy meter assembly line detection management platform needs to establish a stable communication connection with the detection data port of each detection station on the electric energy meter assembly line. Then, the voltage and current data of the electric energy meter to be detected are detected in real time through the voltage detection device and the current detection device on the target detection station. These data will be automatically recorded and a detection data set containing a timestamp will be generated. The detection data set can contain only voltage data, current data, or a combination of the two, depending on actual needs. The collected detection data set (including voltage and / or current data) is then uploaded to the electric energy meter assembly line detection management platform for subsequent processing and analysis.

[0046] S102: Determine whether there is abnormal detection data to be confirmed in the detection data set.

[0047] If it is determined that there is abnormal detection data to be confirmed in the detection data set, a detection data curve is generated according to the detection data set, and the detection data valid value is determined according to the detection data curve. The abnormal detection data to be confirmed is the detection data greater than the preset detection data threshold.

[0048] Specifically, a detection data threshold is preset in the management platform to preliminarily screen out possible abnormal data. Then, each detection data in the detection data set is compared with the preset threshold. If the detection data is greater than the preset threshold, it is marked as abnormal detection data to be confirmed. Then, based on the detection data set and the preset detection cycle, a detection data cycle curve for each preset detection cycle is generated. These curves can intuitively show the changes in detection data over time. And use specific algorithms (such as weighted average, median, etc.) to calculate the effective value of the detection data based on the detection data cycle curve of each preset detection cycle. This effective value is used to further evaluate the authenticity and impact of the abnormal detection data to be confirmed.

[0049] In a possible implementation, a detection data period curve of each preset detection period may be generated according to a detection data set and a preset detection period of the detection data, wherein the detection data set includes detection data of multiple preset detection periods, and each preset detection period corresponds to multiple detection data.

[0050] Using formula 1, determine the effective value of the test data according to the test data cycle curve of each preset test cycle , Formula 1 is: ; in, is the number of preset detection cycles included in the detection data set, To preset the detection cycle, For the The detection data cycle curve within a preset detection cycle.

[0051] It is worth noting that by subdividing the detection data set according to the preset detection cycle and generating the detection data cycle curve of each cycle, a detailed analysis of the operation status of the electric energy meter in different time periods can be achieved. This method can more comprehensively reflect the real-time performance changes of the electric energy meter than relying on a single data point or a simple average value, and helps to discover potential problems or abnormal trends.

[0052] The above formula 1 calculates the time integral of the square of the detection data in each preset detection cycle, and then takes the average value of all cycles and then squares it to obtain a comprehensive detection data effective value. This process not only takes into account the average level of the data, but also fully incorporates the volatility information of the data. This makes the evaluation result more comprehensive. Reflect the overall operating status of the energy meter during the detection cycle, including any possible transient abnormalities or continuous instability.

[0053] In practical applications, the detection data of the electric energy meter may be interfered by various external factors, such as electromagnetic noise, measurement errors, etc. The design of formula 1 can effectively reduce the impact of these interference factors on the data processing results. Through square and integral operations, the formula can smooth the noise part in the data, making the processing results more robust and reliable.

[0054] In the anomaly detection process, simply relying on the absolute size of the data point is often not enough to accurately determine the operating status of the energy meter. Formula 1 comprehensively evaluates the volatility of the data, allowing the system to more sensitively capture those detection data that are not obviously over the limit in terms of value, but show abnormalities in the trend or fluctuation characteristics. This greatly improves the accuracy of anomaly detection and helps to detect potential problems in a timely manner.

[0055] The calculated effective value of the detection data not only provides an important basis for the current abnormal detection and performance evaluation, but also lays a solid foundation for subsequent processing and analysis. Managers can evaluate the operating stability of the electric energy meter, predict potential failure trends, and formulate reasonable maintenance plans and improvement measures based on this effective value. This scientific decision-making process helps to improve the overall operating efficiency and product quality of the electric energy meter assembly line.

[0056] The introduction of formula 1 makes the data processing flow of the electric energy meter assembly line more automated and intelligent. By automatically calculating the effective value of the detection data, the need for manual intervention is reduced, and the processing efficiency and accuracy are improved. At the same time, it also provides reliable data support for subsequent automated decision-making and flow operations, further promoting the optimization and upgrading of the production process.

[0057] In summary, by generating detection data curves and calculating effective values ​​based on detection data sets, the refinement of data processing in the electric energy meter assembly line, data validity and sensitivity of anomaly detection are significantly improved, which provides a strong guarantee for subsequent decision support and promotes the automation development of the detection process.

[0058] S103: Determine the local abnormal feature value of the abnormal detection data to be confirmed by using a preset detection data evaluation model.

[0059] If the effective value of the detection data is within the preset effective value range of the monitoring data, the preset detection data evaluation model is used to determine the local abnormal characteristic value of the abnormal detection data to be confirmed.

[0060] Specifically, the abnormal detection data to be confirmed can be taken as the center, and the adjacent detection data can be selected from the detection data set according to the preset time proximity range to form a proximity detection data set. Then, the proximity detection feature density is calculated based on the proximity detection data set and the abnormal detection data to be confirmed. Then, the first local abnormal feature value of the abnormal detection data to be confirmed is calculated by combining the proximity detection feature density and other related data using the preset detection data evaluation model. Optionally, the second local abnormal feature value can also be calculated to provide a multi-dimensional evaluation.

[0061] In a possible implementation, the abnormal detection data to be confirmed may be and a preset time proximity range to determine a neighboring detection data set from the detection data set , and determine the number of feature detection data in the proximity detection dataset; Using formula 2 and based on the proximity detection dataset Determine the anomaly detection data to be confirmed The corresponding proximity detection feature density , Formula 2 is: ; in, is the neighborhood detection feature density corresponding to the abnormal detection data to be confirmed, Detecting data for anomalies to be confirmed The proximity detection data set consists of detection data within a preset time proximity range. for The Test data, Detecting data for anomalies to be confirmed and The Test data The characteristic difference between Detecting data for anomalies to be confirmed and The Test data The actual difference between for Middle Test data With Test data The actual difference between for The number of feature detection data in ; Using formula 3, and based on the neighboring detection feature density And the proximity detection dataset Determine the anomaly detection data to be confirmed The corresponding first local abnormal eigenvalue , the local abnormal eigenvalues ​​include the first local abnormal eigenvalue The preset abnormal feature threshold includes the first local abnormal feature value The corresponding first preset abnormal feature threshold, formula 3 is: ; in, is the first local abnormal eigenvalue, for The Test data A collection of detection data within a preset time range. for The Test data The corresponding proximity detection feature density, for The number of feature detection data in .

[0062] It is worth noting that Formula 2 effectively quantifies the density of data in the area by calculating the feature difference between each data point in the neighboring detection data set and the anomaly detection data to be confirmed, and combining it with the number of feature detection data. The level of feature density directly reflects the aggregation of data in the area, providing an objective basis for evaluating the isolation of abnormal data. Through the calculation of feature density, the system can more accurately identify those points that appear isolated or abnormal compared with the surrounding data. This method can capture subtle changes in data better than simply comparing the numerical size, thereby improving the sensitivity of anomaly detection. Formula 2 takes into account the relative relationship and density between data, so that the detection process has a certain resistance to noise and fluctuations. Even in the presence of noise or interference, the abnormality of data can be judged more accurately. The introduction of feature density provides a new dimension for the evaluation of abnormal data, allowing the system to conduct more in-depth analysis and judgment from the perspective of data density. This helps to find points that may not be obvious in terms of value but are abnormal in data distribution.

[0063] Formula 3 quantifies the degree of local anomaly by comparing the feature density of the abnormal detection data to be confirmed with its neighboring data, avoiding the deviation of subjective judgment. By calculating the first local abnormality feature value, the system can more accurately identify those points that appear abnormal in the local range, not only considering the isolation of the data points, but also combining the environmental factors of the surrounding data, thereby improving the accuracy of anomaly identification. Formula 3 has strong flexibility and can be adjusted according to different application scenarios and needs. For example, different detection standards and requirements can be adapted by changing the calculation method of the feature density or adjusting the preset abnormal feature threshold. In addition, the first local abnormality feature value can be used as an important basis for comprehensive decision-making. Combined with other evaluation indicators and dimensions, the system can more comprehensively evaluate the performance status of the electric energy meter to be detected, and make reasonable decisions and processing accordingly.

[0064] In summary, the above formulas 2 and 3 improve the sensitivity, accuracy and robustness of anomaly detection in the electric energy meter assembly line data processing method by quantifying the data feature density and local abnormal feature values, and provide a reliable basis for subsequent decision-making and processing.

[0065] In order to determine the number of feature detection data mentioned above , we can first detect the data based on anomalies And the proximity detection dataset Determine the feature difference set, the feature difference in the feature difference set is the difference between each detection data and the abnormal detection data The actual difference between Sorting the feature difference value set in ascending order to determine the feature difference value ascending set; Determine a reference feature difference value according to the feature difference value ascending set, where the reference feature difference value is a feature difference value arranged at a preset ranking in the feature difference value ascending set; Determine the neighboring detection data set based on the reference feature difference The corresponding reference detection data in the adjacent detection data set is determined The detection data that is less than or equal to the reference detection data is the feature detection data, and the number of feature detection data is determined .

[0066] Furthermore, Formula 4 can be used, and according to the first local abnormal characteristic value And the proximity detection dataset Determine the second local anomaly eigenvalue , the local abnormal eigenvalue includes the second local abnormal eigenvalue The preset abnormal feature threshold includes the second local abnormal feature value The corresponding second preset abnormal feature threshold, formula 4 is: ; in, For the proximity detection dataset The total number of detection data in is the first A test data.

[0067] It is worth noting that Formula 4 calculates the second local abnormal eigenvalue by combining the first local abnormal eigenvalue and the average value of all detection data in the neighboring detection data set (obtained by summing and dividing by the total number). This calculation method not only considers the isolation and feature density of the abnormal data point in its local range, but also considers the overall level of the neighboring detection data set, thereby realizing a multi-dimensional evaluation of the abnormal data point. The introduction of the second local abnormal eigenvalue provides the system with more references when performing abnormality detection. When a single eigenvalue (such as the first local abnormal eigenvalue) may fluctuate due to noise or special working conditions, the second local abnormal eigenvalue can provide verification from another perspective, thereby enhancing the reliability of abnormal identification. Formula 4 considers the overall level of the neighboring detection data set, so that abnormality detection is not only limited to the local range, but also takes into account the global perspective. This comprehensive evaluation method helps to find those points that are not prominent in the local range but appear abnormal from a global perspective, thereby improving the comprehensiveness of detection. In addition, the introduction of the second local abnormal eigenvalue makes the detection method more flexible and adaptable. According to the specific application scenario and requirements, the system can flexibly adjust the weights or thresholds of the two eigenvalues ​​to achieve the best detection effect. By combining the first local abnormal characteristic value and the second local abnormal characteristic value for comprehensive evaluation, the system can more accurately determine the performance status of the electric energy meter to be tested. This comprehensive decision-making method reduces the possibility of misjudgment and missed judgment, and improves the scientificity and reliability of decision-making.

[0068] S104: Determine the performance status of the electric energy meter to be detected according to the local abnormal characteristic value.

[0069] In this step, the performance status of the electric energy meter to be tested is determined based on the local abnormal characteristic value, wherein if the local abnormal characteristic value is greater than or equal to the preset abnormal characteristic threshold, the performance status is determined to be an abnormal state, and if the local abnormal characteristic value is less than the preset abnormal characteristic threshold, the performance status is determined to be a normal state.

[0070] Specifically, the calculated local abnormal characteristic value is compared with a preset abnormal characteristic threshold. If the local abnormal characteristic value is greater than or equal to the preset abnormal characteristic threshold, the performance state of the electric energy meter to be detected is determined to be an abnormal state; otherwise, it is determined to be a normal state.

[0071] In this embodiment, by obtaining the detection data set of the electric energy meter to be detected uploaded by the target detection station on the electric energy meter assembly line, if it is determined that there is abnormal detection data to be confirmed in the detection data set, a detection data curve is generated according to the detection data set, and the effective value of the detection data is determined according to the detection data curve. If the effective value of the detection data is within the preset monitoring data effective value range, the preset detection data evaluation model is used to determine the local abnormal characteristic value of the abnormal detection data to be confirmed, so as to determine the performance status of the electric energy meter to be detected according to the local abnormal characteristic value, thereby realizing the automatic collection and processing of the detection data of the electric energy meters on the assembly line, thereby improving the detection efficiency and accuracy of the electric energy meters on the assembly line.

[0072] Figure 2 FIG. 1 is a flow chart of a data processing method for an electric energy meter assembly line according to another exemplary embodiment of the present application. Figure 2 As shown, the data processing method for the electric energy meter assembly line provided in this embodiment includes: S201, obtaining a detection data set of the electric energy meter to be detected uploaded by the target detection station on the electric energy meter assembly line.

[0073] In this step, a detection data set of the electric energy meter to be detected uploaded by the target detection station on the electric energy meter assembly line is obtained, wherein the detection data set includes a plurality of detection data, and each detection data is marked with a timestamp.

[0074] Specifically, first, the electric energy meter assembly line detection management platform needs to establish a stable communication connection with the detection data port of each detection station on the electric energy meter assembly line. Then, the voltage and current data of the electric energy meter to be detected are detected in real time through the voltage detection device and the current detection device on the target detection station. These data will be automatically recorded and a detection data set containing a timestamp will be generated. The detection data set can contain only voltage data, current data, or a combination of the two, depending on actual needs. The collected detection data set (including voltage and / or current data) is then uploaded to the electric energy meter assembly line detection management platform for subsequent processing and analysis.

[0075] In a possible implementation, the voltage detection device at the target detection station detects the detection voltage data of the electric energy meter to be detected to form a detection voltage data set; Detecting the detection current data of the electric energy meter to be detected by the current detection device at the target detection station to form a detection current data set; The detection voltage data set and the detection current data set are uploaded to the electric energy meter assembly line detection management platform, wherein the detection data set includes at least one of the detection voltage data set and the detection current data set.

[0076] S202: Delete the detection data of the first preset detection cycle and the last preset detection cycle in the detection data set to update the detection data set.

[0077] It is worth noting that after obtaining the test data set uploaded by the target test station, a data integrity check is first performed to ensure that the data set contains all necessary test data and that each test data is marked with a timestamp for subsequent processing.

[0078] Then, since the electric energy meter may have unstable factors in the initial operation stage, such as startup shock, preheating process, etc., the data in these stages may not accurately reflect the normal performance status of the electric energy meter. Therefore, this embodiment chooses to delete all data in the first preset detection cycle in the data set to reduce the impact of these unstable factors on subsequent analysis.

[0079] Similarly, considering that the energy meter may have a state change (such as shutdown preparation, cleaning process, etc.) before the end of operation or the next detection, the data in these stages may also contain unstable factors. In order to maintain the consistency and accuracy of data analysis, this embodiment also chooses to delete all data in the last preset detection cycle in the data set.

[0080] After completing the above data deletion operation, the detection data set is updated to ensure that the data set used for subsequent processing is complete and stable. The updated data set will only contain the detection data of the intermediate stable operation stage, which can more accurately reflect the normal performance status of the electric energy meter.

[0081] By deleting the data of the first and last preset detection cycles in the detection data set, this step effectively reduces the impact of unstable factors on data analysis and improves the accuracy and reliability of data processing. At the same time, this data update method also simplifies the data processing process, reduces the complexity of data processing, and improves the overall detection efficiency. In addition, the updated data set can more accurately reflect the normal performance status of the electric energy meter, providing a solid foundation for subsequent abnormal detection and performance evaluation.

[0082] S203: Determine whether there is abnormal detection data to be confirmed in the detection data set.

[0083] If it is determined that there is abnormal detection data to be confirmed in the detection data set, a detection data curve is generated according to the detection data set, and the detection data valid value is determined according to the detection data curve. The abnormal detection data to be confirmed is the detection data greater than the preset detection data threshold.

[0084] Specifically, a detection data threshold is preset in the management platform to preliminarily screen out possible abnormal data. Then, each detection data in the detection data set is compared with the preset threshold. If the detection data is greater than the preset threshold, it is marked as abnormal detection data to be confirmed. Then, based on the detection data set and the preset detection cycle, a detection data cycle curve for each preset detection cycle is generated. These curves can intuitively show the changes in detection data over time. And use specific algorithms (such as weighted average, median, etc.) to calculate the effective value of the detection data based on the detection data cycle curve of each preset detection cycle. This effective value is used to further evaluate the authenticity and impact of the abnormal detection data to be confirmed.

[0085] In a possible implementation, a detection data period curve of each preset detection period may be generated according to a detection data set and a preset detection period of the detection data, wherein the detection data set includes detection data of multiple preset detection periods, and each preset detection period corresponds to multiple detection data.

[0086] Using formula 1, determine the effective value of the test data according to the test data cycle curve of each preset test cycle , Formula 1 is: ; in, is the number of preset detection cycles included in the detection data set, To preset the detection cycle, For the The detection data cycle curve within a preset detection cycle.

[0087] S204: Determine the local abnormal feature value of the abnormal detection data to be confirmed by using a preset detection data evaluation model.

[0088] If the effective value of the detection data is within the preset effective value range of the monitoring data, the preset detection data evaluation model is used to determine the local abnormal characteristic value of the abnormal detection data to be confirmed.

[0089] Specifically, the abnormal detection data to be confirmed can be taken as the center, and the adjacent detection data can be selected from the detection data set according to the preset time proximity range to form a proximity detection data set. Then, the proximity detection feature density is calculated based on the proximity detection data set and the abnormal detection data to be confirmed. Then, the first local abnormal feature value of the abnormal detection data to be confirmed is calculated by combining the proximity detection feature density and other related data using the preset detection data evaluation model. Optionally, the second local abnormal feature value can also be calculated to provide a multi-dimensional evaluation.

[0090] In a possible implementation, the abnormal detection data to be confirmed may be and a preset time proximity range to determine a neighboring detection data set from the detection data set , and determine the number of feature detection data in the proximity detection dataset; Using formula 2 and based on the proximity detection dataset Determine the anomaly detection data to be confirmed The corresponding proximity detection feature density , Formula 2 is: ; in, is the neighborhood detection feature density corresponding to the abnormal detection data to be confirmed, Detecting data for anomalies to be confirmed The proximity detection data set consists of detection data within a preset time proximity range. for The Test data, Detecting data for anomalies to be confirmed and The Test data The characteristic difference between Detecting data for anomalies to be confirmed and The Test data The actual difference between for Middle Test data With Test data The actual difference between for The number of feature detection data in ; Using formula 3, and based on the neighboring detection feature density And the proximity detection dataset Determine the anomaly detection data to be confirmed The corresponding first local abnormal eigenvalue , the local abnormal eigenvalues ​​include the first local abnormal eigenvalue The preset abnormal feature threshold includes the first local abnormal feature value The corresponding first preset abnormal feature threshold, formula 3 is: ; in, is the first local abnormal eigenvalue, for The Test data A collection of detection data within a preset time range. for The Test data The corresponding proximity detection feature density, for The number of feature detection data in .

[0091] In order to determine the number of feature detection data mentioned above , we can first detect the data based on anomalies And the proximity detection dataset Determine the feature difference set, the feature difference in the feature difference set is the difference between each detection data and the abnormal detection data The actual difference between Sorting the feature difference value set in ascending order to determine the feature difference value ascending set; Determine a reference feature difference value according to the feature difference value ascending set, where the reference feature difference value is a feature difference value arranged at a preset ranking in the feature difference value ascending set; Determine the neighboring detection data set based on the reference feature difference The corresponding reference detection data in the adjacent detection data set is determined The detection data that is less than or equal to the reference detection data is the feature detection data, and the number of feature detection data is determined .

[0092] Furthermore, Formula 4 can be used, and according to the first local abnormal characteristic value And the proximity detection dataset Determine the second local anomaly eigenvalue , the local abnormal eigenvalue includes the second local abnormal eigenvalue The preset abnormal feature threshold includes the second local abnormal feature value The corresponding second preset abnormal feature threshold, formula 4 is: ; in, For the proximity detection dataset The total number of detection data in is the first A test data.

[0093] S205. Determine the performance status of the electric energy meter to be detected according to the local abnormal characteristic value.

[0094] In this step, the performance status of the electric energy meter to be tested is determined based on the local abnormal characteristic value, wherein if the local abnormal characteristic value is greater than or equal to the preset abnormal characteristic threshold, the performance status is determined to be an abnormal state, and if the local abnormal characteristic value is less than the preset abnormal characteristic threshold, the performance status is determined to be a normal state.

[0095] Specifically, the calculated local abnormal characteristic value is compared with a preset abnormal characteristic threshold. If the local abnormal characteristic value is greater than or equal to the preset abnormal characteristic threshold, the performance state of the electric energy meter to be detected is determined to be an abnormal state; otherwise, it is determined to be a normal state.

[0096] If the performance status is abnormal, the management platform automatically instructs the target detection station to transfer the electric energy meter to be detected through the electric energy meter assembly line to the manual detection station for further inspection and processing.

[0097] If the performance status is normal, the management platform instructs the target inspection station to transfer the electric energy meter to be inspected to the next inspection station to continue the subsequent inspection process.

[0098] S206. Determine, according to the performance status, how the target inspection station handles the electric energy meter to be inspected.

[0099] If the performance status is abnormal, the target detection station is instructed to transfer the electric energy meter to be detected through the electric energy meter assembly line to the manual detection station for further inspection and processing.

[0100] If the performance status is normal, the target inspection station is instructed to transfer the electric energy meter to be inspected through the electric energy meter assembly line to the next inspection station to continue the subsequent inspection process.

[0101] Specifically, after calculating the local abnormal characteristic value of the abnormal detection data to be confirmed through the preset detection data evaluation model, and determining the performance status of the electric energy meter to be detected based on the comparison result between the characteristic value and the preset abnormal characteristic threshold, the data processing method will further execute the flow indication logic.

[0102] Abnormal state processing: If the performance state of the energy meter to be tested is determined to be abnormal (i.e. the local abnormal characteristic value is greater than or equal to the preset abnormal characteristic threshold), the data processing method will automatically instruct the target detection station to transfer the energy meter to be tested to the manual detection station through the energy meter assembly line. This step ensures that the problematic energy meter can be manually re-inspected in a timely and effective manner, thereby avoiding the spread of potential quality problems.

[0103] Normal state processing: If the performance state of the energy meter to be tested is determined to be normal (i.e. the local abnormal characteristic value is less than the preset abnormal characteristic threshold), the data processing method will instruct the target detection station to continue to transfer the energy meter to be tested through the energy meter assembly line to the next detection station. This step avoids unnecessary waste of resources and extension of detection time, and improves the overall efficiency and capacity of the production line.

[0104] In order to realize the above-mentioned automatic flow indication, the electric energy meter assembly line detection management platform needs to establish a close communication connection with each detection station on the electric energy meter assembly line. When the data processing method completes the performance status judgment, it will send a flow indication signal to the target detection station through the preset communication protocol. After receiving the flow indication signal, the target detection station will control the transmission mechanism of the electric energy meter assembly line according to the signal content to realize the automatic flow of the electric energy meter.

[0105] By automatically indicating the flow direction of the energy meter to be tested according to its performance status, the automation and intelligence of the detection process are combined. This processing method not only improves the detection efficiency, reduces the errors and uncertainties of manual operation, but also improves the accuracy and reliability of the overall detection process. At the same time, for energy meters with abnormal performance status, it can respond quickly and instruct them to flow to the manual inspection station for further inspection and confirmation, ensuring that the problematic energy meters can be handled in a timely and effective manner. For energy meters with normal performance status, it can avoid unnecessary waste of resources and extension of detection time, and improve the overall efficiency and production capacity of the production line.

[0106] Figure 3 1 is a schematic diagram of the structure of an electric energy meter assembly line detection management platform according to an exemplary embodiment of the present application. Figure 3 As shown, the electric energy meter assembly line detection management platform 300 provided in this embodiment includes: An acquisition module 310 is used to acquire a detection data set of the electric energy meter to be detected uploaded by the target detection station on the electric energy meter assembly line, wherein the detection data set includes a plurality of detection data, each of which is marked with a timestamp; The processing module 320 is used to determine that there is abnormal detection data to be confirmed in the detection data set, generate a detection data curve according to the detection data set, and determine the detection data effective value according to the detection data curve, wherein the abnormal detection data to be confirmed is the detection data greater than a preset detection data threshold; A determination module 330 is used to determine the local abnormal feature value of the abnormal detection data to be confirmed by using a preset detection data evaluation model when it is determined that the detection data effective value is within the preset monitoring data effective value range; The determination module 330 is also used to determine the performance status of the electric energy meter to be detected based on the local abnormal characteristic value, wherein if the local abnormal characteristic value is greater than or equal to a preset abnormal characteristic threshold, the performance status is determined to be an abnormal state; if the local abnormal characteristic value is less than the preset abnormal characteristic threshold, the performance status is determined to be a normal state.

[0107] Optionally, the processing module 320 is specifically configured to: Generate a detection data period curve of each preset detection period according to the detection data set and the preset detection period of the detection data, wherein the detection data set includes detection data of multiple preset detection periods, and each preset detection period corresponds to multiple detection data; The effective value of the detection data is determined according to the detection data period curve of each preset detection period.

[0108] Optionally, the determining module 330 is specifically configured to: Determine a neighboring detection data set from the detection data set according to the abnormal detection data to be confirmed and a preset time proximity range, and determine the number of feature detection data in the neighboring detection data set; Determine, according to the proximity detection data set, a proximity detection feature density corresponding to the abnormal detection data to be confirmed; A first local abnormal feature value corresponding to the abnormal detection data to be confirmed is determined according to the proximity detection feature density and the proximity detection data set, the local abnormal feature value includes the first local abnormal feature value, and the preset abnormal feature threshold includes a first preset abnormal feature threshold corresponding to the first local abnormal feature value.

[0109] Optionally, the determination module 330 is also used to determine a second local abnormality feature value based on the first local abnormality feature value and the proximity detection data set, the local abnormality feature value includes the second local abnormality feature value, and the preset abnormality feature threshold includes a second preset abnormality feature threshold corresponding to the second local abnormality feature value.

[0110] Optionally, the determining module 330 is further specifically configured to: Determine a feature difference value set according to the anomaly detection data and the proximity detection data set, wherein the feature difference values ​​in the feature difference value set are actual differences between each detection data and the anomaly detection data; Sorting the feature difference value set in ascending order to determine an ascending feature difference value set; Determine a reference feature difference value according to the feature difference value ascending set, wherein the reference feature difference value is a feature difference value arranged at a preset ranking in the feature difference value ascending set; Determine the reference detection data corresponding to the proximity detection data set according to the reference feature difference, determine the detection data in the proximity detection data set that is less than or equal to the reference detection data as the feature detection data, and determine the number of the feature detection data.

[0111] Optionally, the determining module 330 is further specifically configured to: If the performance status is an abnormal status, the target detection station is instructed to transfer the electric energy meter to be detected to a manual detection station through the electric energy meter assembly line; If the performance status is normal, the target detection station is instructed to transfer the electric energy meter to be detected to the next detection station through the electric energy meter assembly line.

[0112] Optionally, the acquisition module 310 is specifically configured to: Detecting the detection voltage data of the electric energy meter to be detected by the voltage detection device on the target detection station to form a detection voltage data set; Detecting the detection current data of the electric energy meter to be detected by the current detection device on the target detection station to form a detection current data set; The detection voltage data set and the detection current data set are uploaded to the electric energy meter assembly line detection management platform, wherein the detection data set includes at least one of the detection voltage data set and the detection current data set.

[0113] Figure 4 is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present application. Figure 4 As shown, an electronic device 400 provided in this embodiment includes: a processor 401 and a memory 402; wherein: The memory 402 is used to store computer programs, and the memory may also be a flash memory.

[0114] The processor 401 is used to execute the execution instructions stored in the memory to implement each step in the above method. For details, please refer to the relevant description in the above method embodiment.

[0115] Optionally, the memory 402 may be independent or integrated with the processor 401 .

[0116] When the memory 402 is a device independent of the processor 401, the electronic device 400 may further include: The bus 403 is used to connect the memory 402 and the processor 401 .

[0117] This embodiment further provides a readable storage medium, in which a computer program is stored. When at least one processor of an electronic device executes the computer program, the electronic device executes the methods provided in the above-mentioned various implementation modes.

[0118] This embodiment also provides a program product, which includes a computer program stored in a readable storage medium. At least one processor of the electronic device can read the computer program from the readable storage medium, and at least one processor executes the computer program so that the electronic device implements the methods provided in the above various embodiments.

[0119] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the claims.

[0120] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A data processing method for an electric energy meter assembly line, characterized in that: Applied to an electric energy meter assembly line detection management platform, the electric energy meter assembly line detection management platform is used to communicate with the detection data ports of each detection station on the electric energy meter assembly line; the method includes: Acquire a detection data set of the electric energy meter to be detected uploaded by the target detection station on the electric energy meter assembly line, wherein the detection data set includes a plurality of detection data, each of which is marked with a timestamp; If it is determined that there is abnormal detection data to be confirmed in the detection data set, a detection data curve is generated according to the detection data set, and a detection data valid value is determined according to the detection data curve, wherein the abnormal detection data to be confirmed is detection data greater than a preset detection data threshold; If the effective value of the detection data is within the preset effective value range of the monitoring data, the local abnormal characteristic value of the abnormal detection data to be confirmed is determined by using the preset detection data evaluation model; The performance status of the electric energy meter to be detected is determined according to the local abnormal characteristic value, wherein if the local abnormal characteristic value is greater than or equal to a preset abnormal characteristic threshold, the performance status is determined to be an abnormal status, and if the local abnormal characteristic value is less than the preset abnormal characteristic threshold, the performance status is determined to be a normal status.

2. The data processing method for an electric energy meter assembly line according to claim 1, characterized in that: Generating a detection data curve according to the detection data set, and determining a detection data valid value according to the detection data curve, includes: Generate a detection data period curve of each preset detection period according to the detection data set and the preset detection period of the detection data, wherein the detection data set includes detection data of multiple preset detection periods, and each preset detection period corresponds to multiple detection data; The effective value of the detection data is determined according to the detection data period curve of each preset detection period.

3. The data processing method for an electric energy meter assembly line according to claim 1 or 2, characterized in that: The method of using a preset detection data evaluation model to determine the local abnormal feature value of the abnormal detection data to be confirmed includes: Determine a neighboring detection data set from the detection data set according to the abnormal detection data to be confirmed and a preset time proximity range, and determine the number of feature detection data in the neighboring detection data set; Determine, according to the proximity detection data set, a proximity detection feature density corresponding to the abnormal detection data to be confirmed; A first local abnormal feature value corresponding to the abnormal detection data to be confirmed is determined according to the proximity detection feature density and the proximity detection data set, the local abnormal feature value includes the first local abnormal feature value, and the preset abnormal feature threshold includes a first preset abnormal feature threshold corresponding to the first local abnormal feature value.

4. The data processing method for an electric energy meter assembly line according to claim 3, characterized in that: The method of using a preset detection data evaluation model to determine the local abnormal feature value of the abnormal detection data to be confirmed also includes: A second local abnormal feature value is determined according to the first local abnormal feature value and the proximity detection data set, the local abnormal feature value includes the second local abnormal feature value, and the preset abnormal feature threshold includes a second preset abnormal feature threshold corresponding to the second local abnormal feature value.

5. The data processing method for an electric energy meter assembly line according to claim 3, characterized in that: Before determining the first local abnormal feature value corresponding to the abnormal detection data to be confirmed according to the proximity detection feature density and the proximity detection data set, the method further includes: Determine a feature difference value set according to the anomaly detection data and the proximity detection data set, wherein the feature difference values ​​in the feature difference value set are actual differences between each detection data and the anomaly detection data; Sorting the feature difference value set in ascending order to determine an ascending feature difference value set; Determine a reference feature difference value according to the feature difference value ascending set, wherein the reference feature difference value is a feature difference value arranged at a preset ranking in the feature difference value ascending set; Determine the reference detection data corresponding to the proximity detection data set according to the reference feature difference, determine the detection data in the proximity detection data set that is less than or equal to the reference detection data as the feature detection data, and determine the number of the feature detection data.

6. The data processing method for an electric energy meter assembly line according to claim 1 or 2, characterized in that: After determining the performance state of the electric energy meter to be detected according to the local abnormal characteristic value, the method further includes: If the performance status is an abnormal status, the target detection station is instructed to transfer the electric energy meter to be detected to a manual detection station through the electric energy meter assembly line; If the performance status is normal, the target detection station is instructed to transfer the electric energy meter to be detected to the next detection station through the electric energy meter assembly line.

7. The data processing method for an electric energy meter assembly line according to claim 6, characterized in that: The step of obtaining a detection data set of the electric energy meter to be detected uploaded by the target detection station on the electric energy meter assembly line includes: Detecting the detection voltage data of the electric energy meter to be detected by the voltage detection device on the target detection station to form a detection voltage data set; Detecting the detection current data of the electric energy meter to be detected by the current detection device on the target detection station to form a detection current data set; The detection voltage data set and the detection current data set are uploaded to the electric energy meter assembly line detection management platform, wherein the detection data set includes at least one of the detection voltage data set and the detection current data set.

8. An electric energy meter assembly line detection management platform, characterized in that: Used to communicate with the detection data ports of each detection station on the electric energy meter assembly line; the platform includes: An acquisition module, used for acquiring a detection data set of the electric energy meter to be detected uploaded by the target detection station on the electric energy meter assembly line, wherein the detection data set includes a plurality of detection data, each of which is marked with a timestamp; A processing module, used for determining that there is abnormal detection data to be confirmed in the detection data set, generating a detection data curve according to the detection data set, and determining a detection data valid value according to the detection data curve, wherein the abnormal detection data to be confirmed is detection data greater than a preset detection data threshold; A determination module, configured to determine the local abnormal characteristic value of the abnormal detection data to be confirmed by using a preset detection data evaluation model when it is determined that the detection data effective value is within the preset monitoring data effective value range; The determination module is also used to determine the performance status of the electric energy meter to be detected based on the local abnormal characteristic value, wherein if the local abnormal characteristic value is greater than or equal to a preset abnormal characteristic threshold, the performance status is determined to be an abnormal state; if the local abnormal characteristic value is less than the preset abnormal characteristic threshold, the performance status is determined to be a normal state.

9. An electronic device, characterized in that: include: processor; as well as, A memory, configured to store executable instructions of the processor; The processor is configured to perform the method of any one of claims 1 to 7 by executing the executable instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

Images