Concentrator data acquisition method, device, equipment and medium

Abstract

The invention relates to the technical field of data monitoring, in particular to a concentrator data acquisition method and device, equipment and a medium. The method comprises the following steps: collecting monitoring data received by a concentrator; classifying the monitoring data into a plurality of groups of data according to the acquisition equipment, wherein each group of data corresponds to one acquisition equipment; determining a device type corresponding to the target data set, and inputting the target data set into a preset recognition model corresponding to the device type to obtain a recognition result about whether the target data set output by the preset recognition model is abnormal, the target data set being any one of multiple groups of data; if the identification result is that the target data group is abnormal, analyzing an abnormal reason; and generating a warning signal containing the abnormal reason, and sending the warning signal to terminal equipment corresponding to the related personnel. According to the invention, the data exception of the concentrator can be identified in time.

Description

Technical Field

[0001] The present application relates to the field of data monitoring technology, and in particular to a concentrator data collection method, device, equipment and medium. Background Art

[0002] In modern power systems, concentrators, as the core components of remote centralized meter reading systems, carry out data collection and management tasks for large-scale smart meters, such as timed reading of smart meter data, system command transmission, data communication, network management, event recording and data transmission, etc., which are crucial to ensure the stable operation of power systems. However, since concentrators usually need to manage hundreds or even thousands of smart meters, the data collected by concentrators often faces unusual challenges due to the diversity of devices and the complexity of communications.

[0003] The relevant technology relies on manual on-site inspection and data analysis, which is inefficient and costly. Summary of the invention

[0004] In order to solve the problem of low efficiency in identifying abnormal data of a concentrator in the prior art, the present application provides a concentrator data collection method, device, equipment and medium.

[0005] In a first aspect, the present application provides a concentrator data collection method, which adopts the following technical solution: A concentrator data collection method, comprising: Collect monitoring data received by the concentrator; Classifying the monitoring data into multiple groups of data according to the collection devices, each group of data corresponds to one collection device; Determine a device type corresponding to a target data group, and input the target data group into a preset recognition model corresponding to the device type, to obtain a recognition result of whether the target data group is abnormal output by the preset recognition model, wherein the target data group is any one of the multiple data groups; If the recognition result is that the target data group is abnormal, analyzing the cause of the abnormality; Generate a warning signal containing the cause of the abnormality, and send the warning signal to the terminal device corresponding to the relevant personnel.

[0006] By adopting the above technical solution, the monitoring data received by the collection concentrator ensures that all monitoring data from different collection devices can be collected comprehensively and timely, and the monitoring data are classified into multiple groups of data according to the collection devices. The data of each collection device is processed separately, which helps to more accurately identify possible problems with specific equipment, determine the device type corresponding to the target data group, and input the target data group into the preset recognition model corresponding to the device type. Through the judgment of the recognition model, the abnormal situation in the data can be quickly and accurately identified. After confirming the data abnormality, the cause of the abnormality is further analyzed, which helps to have a deeper understanding of the problem, and can provide guidance for subsequent repair work. It can quickly notify the abnormal situation to relevant personnel, which helps relevant personnel to respond in time and take measures to solve the problem, thereby ensuring the stable operation of the system.

[0007] In a preferred example, the present application may be further configured as follows: if the recognition result is that the target data group is abnormal, analyzing the cause of the abnormality includes: Dividing the data in the target data group into normal data and abnormal data according to the recognition result; Obtaining recognition results corresponding to each of the plurality of data groups, and searching for associated data of target abnormal data from the recognition results corresponding to each of the plurality of data groups, wherein the target abnormal data is any abnormal data in the target data group, and the associated data is data of the same data type as the target abnormal data and in the same data collection area; The cause of the abnormality is determined according to the target abnormality data and the associated data.

[0008] By adopting the above technical solution, the data in the target data group is subdivided into normal data and abnormal data using the recognition results, so that the data with possible problems can be directly studied in depth. By obtaining the recognition results corresponding to multiple data groups and finding the data associated with the target abnormal data, it is helpful to reveal the complex relationship behind the abnormality, so as to locate the problem more accurately. The associated data is limited to data with the same data type as the target abnormal data and in the same data collection area, which further narrows the search scope and improves the analysis efficiency. By comparing the recognition results and associated data between different data groups, the commonalities and differences between the data can be found, providing more abundant information support for determining the cause of the abnormality.

[0009] In a preferred example, the present application may be further configured as follows: determining the cause of the abnormality according to the target abnormal data and the associated data includes: Determining whether there is abnormal data in the associated data; If there is no abnormal data in the associated data, the cause of the abnormality is determined to be a device abnormality, and the collection device corresponding to the target abnormal data is marked as an abnormal device; If there is abnormal data in the associated data, the cause of the abnormality is determined to be a data abnormality, and the data collection area corresponding to the target abnormal data is marked as a data abnormality.

[0010] By adopting the above technical solution, it is possible to automatically judge and determine the cause of the abnormality based on the analysis of the target abnormal data and related data, thereby greatly improving the accuracy and efficiency of problem diagnosis.

[0011] In a preferred example, the present application can be further configured as follows: the method further includes: Collecting historical monitoring data of a target collection device, wherein the target collection device is any collection device of the same device type as that corresponding to the target data group; Marking the historical monitoring data as normal or abnormal; The model is trained based on the annotated historical monitoring data to obtain a preset recognition model corresponding to the device type of the target acquisition device.

[0012] By adopting the above technical solution and using a large amount of labeled historical monitoring data to train the model, the accuracy of the preset recognition model can be significantly improved. The data covers various situations of the equipment under different operating conditions, allowing the model to learn more comprehensive features and thus more accurately identify the normal and abnormal states of the data.

[0013] In a preferred example, the present application can be further configured as follows: the method further includes: Acquire current weather data, where the current weather data includes weather type; Determining a collection frequency for each device type according to the weather type; Control each collection device corresponding to the concentrator to collect monitoring data according to the determined collection frequency.

[0014] By adopting the above technical solutions, different weather conditions may have different effects on the stability and accuracy of monitoring data. By adjusting the collection frequency according to the weather type, it is possible to reduce unnecessary data collection while ensuring data validity, thereby optimizing the utilization of system resources.

[0015] In a preferred example, the present application may be further configured as follows: the controlling each collection device corresponding to the concentrator to collect monitoring data according to a determined collection frequency includes: Obtaining a device type of each collection device corresponding to the concentrator; Determine the time interval between two collection moments according to the collection frequency of the target device type, wherein the target device type is any device type corresponding to the concentrator; Determine the number of devices of the target device type, and determine the initial collection time of each collection device corresponding to the target device type according to the time interval and the number of devices; Each collection device corresponding to the concentrator is controlled to collect monitoring data according to the determined collection frequency starting from the initial collection time.

[0016] By adopting the above technical scheme, the time interval between two collection moments is accurately calculated according to the collection frequency of the target device type, ensuring that each collection device can collect data at a predetermined frequency, which helps to improve the consistency and accuracy of the data and avoids data deviation caused by inconsistent collection frequencies. On the basis of determining the time interval, the initial collection moment of each collection device is further determined according to the number of devices of the target device type, thereby realizing refined scheduling of collection tasks, ensuring that each collection device completes the data collection task within a reasonable time period, avoiding conflicts and overlaps in collection tasks, and improving collection efficiency.

[0017] In a preferred example, the present application can be further configured as follows: the method further includes: Obtaining historical fault data and historical operation time of each collection device corresponding to the concentrator; Determine, based on the historical fault data and the historical operation time, a device replacement strategy for each collection device corresponding to the concentrator; The device replacement strategy is sent to the terminal devices corresponding to the relevant personnel.

[0018] By adopting the above technical solutions and analyzing the historical failure data and historical operating hours of the collection equipment, it is possible to predict possible failures of the equipment in the future, thereby formulating preventive maintenance plans. The data-based maintenance strategy can detect and solve problems in advance, avoid equipment failures at critical moments, and ensure the continuity and accuracy of data collection.

[0019] In a second aspect, the present application provides a concentrator data collection device, which adopts the following technical solution: A concentrator data acquisition device, comprising: A collection module, used to collect monitoring data received by the concentrator; A classification module, used for classifying the monitoring data into multiple groups of data according to the collection devices, each group of data corresponding to one collection device; an identification module, used to determine the device type corresponding to the target data group, and input the target data group into a preset identification model corresponding to the device type, and obtain an identification result of whether the target data group is abnormal output by the preset identification model, wherein the target data group is any one of the multiple data groups; An analysis module, configured to analyze the cause of the abnormality if the recognition result indicates that the target data group is abnormal; The generating module is used to generate a warning signal including the cause of the abnormality, and send the warning signal to the terminal device corresponding to the relevant personnel.

[0020] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: one or more processors; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the concentrator data collection method as described in any one of the first aspects.

[0021] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium stores a computer program thereon, which, when executed in a computer, causes the computer to execute the concentrator data collection method as described in any one of the first aspects.

[0022] In summary, this application includes the following beneficial technical effects: The present application collects the monitoring data received by the concentrator, thereby ensuring that all monitoring data from different collection devices can be collected comprehensively and timely, and classifies the monitoring data into multiple groups of data according to the collection device. The data of each collection device is processed separately, which helps to more accurately identify possible problems with specific equipment, determine the device type corresponding to the target data group, and input the target data group into a preset recognition model corresponding to the device type. Through the judgment of the recognition model, anomalies in the data can be quickly and accurately identified. After confirming the data anomaly, further analysis of the cause of the anomaly helps to have a deeper understanding of the problem, provide guidance for subsequent repair work, and quickly notify relevant personnel of the anomaly, which helps relevant personnel to respond in a timely manner and take measures to solve the problem, thereby ensuring the stable operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flow chart of a concentrator data collection method provided in an embodiment of the present application; Figure 2 It is a structural schematic diagram of a concentrator data acquisition device provided in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] The following is combined with Figure 1 -Attached Figure 3 This application is described in further detail.

[0025] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, those skilled in the art may make modifications to the present embodiment without any creative contribution as needed, but such modifications are protected by the patent law as long as they are within the scope of the claims of the present application.

[0026] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0027] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0028] It should be noted that in the optional embodiments of the present application, the object information and other related data involved, when the embodiments in the present application are applied to specific products or technologies, need to obtain the permission or consent of the object, and the collection, use and processing of the relevant data need to comply with the relevant laws, regulations and standards of the relevant countries and regions. In other words, if the embodiments of the present application involve data related to the object, it needs to be obtained with the authorization and consent of the object, the authorization and consent of the relevant departments, and in compliance with the relevant laws, regulations and standards of the country and region. If personal information is involved in the embodiments, the acquisition of all personal information needs to obtain the consent of the individual. If sensitive information is involved, the separate consent of the information subject needs to be obtained. The embodiments also need to be implemented with the authorization and consent of the object.

[0029] The present application embodiment provides a concentrator data collection method, such as Figure 1As shown, the method provided in the embodiment of the present application is performed by an electronic device, which can be a server or a terminal device, wherein the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in the embodiment of the present application. The method includes steps S101 to S105, wherein: S101. Collect monitoring data received by a concentrator.

[0030] The collection module included in the electronic device is used to collect the monitoring data received by the concentrator, and the concentrator receives the monitoring data sent by multiple collection devices. Specifically, the collection module includes a real-time monitoring unit, a mounting unit, a detection unit, a data collection unit and a data storage unit. The real-time monitoring unit starts working after the concentrator is started, and continuously detects whether there is a removable storage medium connected to the concentrator; when there is a removable storage medium connection, the mounting unit will mount the disk of the storage medium on the file system of the concentrator; the detection unit checks whether the mounted disk contains a data collection program, and if so, the data collection unit runs the data collection program and starts to obtain monitoring data from the concentrator; the data storage unit saves the acquired monitoring data to a specified file of the removable storage medium.

[0031] Among them, the monitoring data includes heartbeat packets and data packets. The heartbeat packet contains the device ID, timestamp and device status of the collection device corresponding to each data, and the data packet contains the data collected by the collection device, such as voltage, current, power consumption, water consumption, etc.

[0032] S102: Classify the monitoring data into multiple groups of data according to the collection devices, each group of data corresponds to one collection device.

[0033] Specifically, according to the device ID of the monitoring data center packet jump, the monitoring data is divided into multiple groups of data, each group of data corresponds to a collection device, and the data contained in each group of data can be of multiple data types. The data corresponding to one data type in the data collected by the collection device at one time is taken as one data. For example, the distribution transformer is a collection device, and the data collected at one time include voltage, current, power, electric energy, etc., and the voltage, current, power, and electric energy are taken as one data respectively.

[0034] S103, determining the device type corresponding to the target data group, and inputting the target data group into a preset recognition model corresponding to the device type to obtain a recognition result of whether the target data group output by the preset recognition model is abnormal, where the target data group is any one of the multiple groups of data.

[0035] Specifically, the acquisition device of the same device type as the target data group is used as the target acquisition device, the historical monitoring data of the target acquisition device is obtained, and the historical monitoring data is annotated, the annotated content is normal data or data abnormal data, and the model is trained using the annotated historical monitoring data to obtain the preset recognition model corresponding to the device type of the target acquisition device. Through the above model training process, the preset recognition model corresponding to each device type can be trained.

[0036] S104: If the recognition result is that the target data group is abnormal, analyze the cause of the abnormality.

[0037] Specifically, the data in the target data group is divided into normal data and abnormal data according to the recognition result, and any abnormal data in the target data group is used as the target abnormal data. The recognition results corresponding to each of the multiple data groups are obtained, and the associated data of the target abnormal data is searched therefrom. Then, the cause of the abnormality is determined according to the target abnormal data and the associated data, and the cause of the abnormality can be data abnormality or device abnormality.

[0038] S105: Generate a warning signal including the cause of the abnormality, and send the warning signal to a terminal device corresponding to a relevant person.

[0039] Among them, the warning signal can be sent to the corresponding terminal device of the relevant personnel via email, text message or in-application notification.

[0040] The embodiment of the present application collects the monitoring data received by the concentrator, thereby ensuring that all monitoring data from different collection devices can be collected comprehensively and timely, and classifies the monitoring data into multiple groups of data according to the collection device. The data of each collection device is processed separately, which helps to more accurately identify possible problems with specific equipment, determine the device type corresponding to the target data group, and input the target data group into a preset recognition model corresponding to the device type. Through the judgment of the recognition model, anomalies in the data can be quickly and accurately identified. After confirming the data anomaly, further analyzing the cause of the anomaly helps to have a deeper understanding of the problem, and can provide guidance for subsequent repair work. It can quickly notify the relevant personnel of the anomaly, which helps the relevant personnel to respond in time and take measures to solve the problem, thereby ensuring the stable operation of the system.

[0041] In a possible implementation of the embodiment of the present application, if the recognition result is that the target data group is abnormal, analyzing the cause of the abnormality includes: Dividing the data in the target data group into normal data and abnormal data according to the recognition result; Obtaining recognition results corresponding to each of the multiple data groups, and searching for associated data of target abnormal data from the recognition results corresponding to each of the multiple data groups, where the target abnormal data is any abnormal data in the target data group, and the associated data is data of the same data type as the target abnormal data and in the same data collection area; Determine the cause of the anomaly based on the target anomaly data and associated data.

[0042] In this embodiment, the data collection area where each collection device is located can be pre-set, and the collection devices on the same line can be divided into the same data collection area, such as multiple collection devices on the same cable; the collection devices that collect data in the same area can also be divided into the same data collection area, such as multiple collection devices that collect data from the same household, including the gas meter, electricity meter, water meter, etc. in the household.

[0043] The embodiment of the present application subdivides the data in the target data group into normal data and abnormal data through the recognition results, and can directly conduct in-depth research on data that may have problems. By obtaining the recognition results corresponding to each of the multiple data groups and searching for data associated with the target abnormal data, it helps to reveal the complex relationship behind the abnormality, thereby locating the problem more accurately. The associated data is limited to data with the same data type as the target abnormal data and in the same data collection area, which further narrows the search scope and improves analysis efficiency. By comparing the recognition results and associated data between different data groups, the commonalities and differences between the data can be discovered, providing richer information support for determining the cause of the abnormality.

[0044] A possible implementation of the embodiment of the present application is to determine the cause of the abnormality according to the target abnormal data and the associated data, including: Determine whether there is abnormal data in the associated data; If there is no abnormal data in the associated data, the cause of the abnormality is determined to be a device abnormality, and the collection device corresponding to the target abnormal data is marked as an abnormal device; If there is abnormal data in the associated data, the cause of the abnormality is determined to be data abnormality, and the data collection area corresponding to the target abnormal data is marked as data abnormality.

[0045] In one possible scenario, there is no abnormal data in the associated data, and it is inferred that the cause of the abnormality may come from the collection device itself, rather than external factors or data transmission problems. The collection device corresponding to the target abnormal data is determined as an abnormal device, and the warning signal contains the device ID of the abnormal device. In another possible scenario, there is abnormal data in the associated data, that is, other collection devices also report data anomalies in the same data collection area, then it is inferred that the cause of the abnormality is not a problem with a single device, but that the data in the data collection area is abnormal, and the warning signal contains the regional location information of the data collection area with abnormal data.

[0046] The embodiment of the present application can automatically judge and determine the cause of the abnormality based on the analysis of the target abnormal data and the associated data, thereby greatly improving the accuracy and efficiency of problem diagnosis.

[0047] In a possible implementation manner of the embodiment of the present application, the method further includes: Collect historical monitoring data of a target collection device, where the target collection device is any collection device of the same type as the device corresponding to the target data group; Mark historical monitoring data as normal or abnormal; The model is trained based on the annotated historical monitoring data to obtain a preset recognition model corresponding to the device type of the target acquisition device.

[0048] In this embodiment, the acquisition device of the same type as the device corresponding to the target data group is used as the target acquisition device. The duration of the historical monitoring data can be set according to actual needs. Optionally, the historical monitoring data is the monitoring data within one year. The historical monitoring data is labeled according to business needs and domain knowledge. Then, a suitable machine learning model is selected according to business needs. The optional machine learning models include decision trees, random forests, support vector machines, and neural networks.

[0049] Furthermore, the model is trained using the annotated historical monitoring data. The training process includes: dividing the annotated historical monitoring data into a training set and a test set, using the training set to train the selected model, and after the training is completed, using the test set to evaluate the trained model. The evaluation indicators may include accuracy, recall rate, F1 score, etc., to comprehensively evaluate the performance of the model. Finally, the model is tuned according to the evaluation results to further improve the performance and accuracy of the model. The trained and tuned model is deployed to actual applications for real-time anomaly detection of monitoring data.

[0050] The embodiment of the present application uses a large amount of labeled historical monitoring data to train the model, which can significantly improve the accuracy of the preset recognition model. The data covers various situations of the equipment under different operating conditions, allowing the model to learn more comprehensive features and thus more accurately identify the normal and abnormal states of the data.

[0051] In a possible implementation manner of the embodiment of the present application, the method further includes: Get the current weather data, including the weather type; Determine the collection frequency for each device type based on the weather type; Each collection device corresponding to the control concentrator collects monitoring data according to a determined collection frequency.

[0052] In this embodiment, the source of obtaining the current weather data may be a public weather API, a local weather station, etc. Weather types include sunny, rainy, snowy, windy, etc. For each weather type, a label may be set, and the label may be used to characterize the weather condition. Optionally, for the two weather types of sunny and cloudy, the label set may be good weather, and for the weather types of rainy, snowy, and windy, the label set may be bad weather.

[0053] Furthermore, an initial collection frequency can be determined for each device type based on data collection requirements. When the weather type indicates good weather, the initial collection frequency corresponding to each device type is used as the collection frequency. When the weather type indicates bad weather, for any device type, the product of the initial collection frequency of the device type and the preset ratio is used as the collection frequency of the device type, wherein the preset ratio is greater than 1, which means that when the weather is bad, the collection frequency is increased to detect abnormal conditions in a timely manner.

[0054] Different weather conditions in the embodiments of the present application may have different effects on the stability and accuracy of the monitoring data. By adjusting the collection frequency according to the weather type, it is possible to reduce unnecessary data collection while ensuring data validity, thereby optimizing the utilization of system resources.

[0055] A possible implementation of the embodiment of the present application is to control each collection device corresponding to the concentrator to collect monitoring data according to a determined collection frequency, including: Obtain the device type of each collection device corresponding to the concentrator; Determine the time interval between two collection moments according to the collection frequency of the target device type, where the target device type is any device type corresponding to the concentrator; Determine the number of devices of the target device type, and determine the initial collection time of each collection device corresponding to the target device type according to the time interval and the number of devices; Each collection device corresponding to the control concentrator collects monitoring data according to the determined collection frequency starting from the initial collection time.

[0056] In this embodiment, the time interval H is determined according to the collection frequency, such as if the collection frequency is 1 time / hour, the time interval H is 1 hour. The ratio of the time interval H corresponding to the target device type and the number of devices M is calculated as the time difference h. The zero o'clock of a certain day can be set as the starting time, and within the time interval H after the starting time, every time difference h is used as the initial collection time of a collection device corresponding to the target device type. For example, the time interval H corresponding to the target device type is 1h, the time difference h is 10min, and the number of devices is 6, then 00:00, 00:10, 00:20, 00:30, 00:40, and 00:50 are the initial collection times corresponding to the 6 collection devices respectively. For any collection device, starting from its corresponding initial collection time, monitoring data collection is performed according to the determined collection frequency.

[0057] The embodiment of the present application accurately calculates the time interval between two collection moments according to the collection frequency of the target device type, ensuring that each collection device can collect data at a predetermined frequency, helping to improve the consistency and accuracy of the data, and avoiding data deviations caused by inconsistent collection frequencies. On the basis of determining the time interval, the initial collection moment of each collection device is further determined according to the number of devices of the target device type, thereby achieving refined scheduling of collection tasks, ensuring that each collection device completes the data collection task within a reasonable time period, avoiding conflicts and overlaps in collection tasks, and improving collection efficiency.

[0058] In a possible implementation manner of the embodiment of the present application, the method further includes: Obtain the historical fault data and historical operation time of each collection device corresponding to the concentrator; Determine the equipment replacement strategy for each collection device corresponding to the concentrator based on historical fault data and historical operating hours; The device replacement strategy is sent to the corresponding terminal devices of relevant personnel.

[0059] In this embodiment, the historical fault data and historical operation data of the collection device can be obtained through the log recording function of the collection device, and the historical fault data includes the number of historical faults. For multiple collection devices under any device type, the number of faults that occurred from the start of operation to the elimination of the device is obtained, and the average number of faults of multiple collection devices under the device type is calculated.

[0060] For each collection device that is currently running and corresponds to the concentrator, for any of the collection devices, the ratio of the historical operation time of the collection device to the service life is calculated to determine whether the historical failure times of the collection device have reached the average failure times corresponding to the collection type of the collection device, and whether the obtained ratio has reached the preset life ratio. If the historical failure times reach the average failure times or the obtained ratio has reached the preset life ratio, it is determined that the collection device has reached the replacement condition. The device replacement strategy contains the device ID and device location information that have reached the replacement condition.

[0061] The embodiments of the present application can predict possible future failures of the equipment by analyzing and collecting the historical failure data and historical operating hours of the equipment, thereby formulating a preventive maintenance plan. The data-based maintenance strategy can discover and solve problems in advance, avoid equipment failures at critical moments, and ensure the continuity and accuracy of data collection.

[0062] The above embodiment introduces a concentrator data acquisition method from the perspective of method flow, and the following embodiment introduces a concentrator data acquisition device from the perspective of a virtual module or a virtual unit. Please refer to the following embodiment for details.

[0063] The present application embodiment provides a concentrator data collection device, such as Figure 2 As shown, the device may include: The collection module 201 is used to collect the monitoring data received by the concentrator; A classification module 202 is used to classify the monitoring data into multiple groups of data according to the collection devices, each group of data corresponds to one collection device; The identification module 203 is used to determine the device type corresponding to the target data group, and input the target data group into a preset identification model corresponding to the device type to obtain an identification result of whether the target data group output by the preset identification model is abnormal, where the target data group is any one of the multiple data groups; The analysis module 204 is used to analyze the cause of the abnormality if the recognition result shows that the target data group is abnormal; The generating module 205 is used to generate a warning signal including the cause of the abnormality, and send the warning signal to the terminal device corresponding to the relevant personnel.

[0064] In a preferred example, the present application may be further configured as follows: when the analysis module 204 performs the analysis of the abnormality cause if the recognition result is that the target data group is abnormal, it is specifically used to: Dividing the data in the target data group into normal data and abnormal data according to the recognition result; Obtaining recognition results corresponding to each of the multiple data groups, and searching for associated data of target abnormal data from the recognition results corresponding to each of the multiple data groups, where the target abnormal data is any abnormal data in the target data group, and the associated data is data of the same data type as the target abnormal data and in the same data collection area; Determine the cause of the anomaly based on the target anomaly data and associated data.

[0065] In a preferred example, the present application may be further configured as follows: when the analysis module 204 determines the cause of the abnormality according to the target abnormal data and the associated data, it is specifically used to: Determine whether there is abnormal data in the associated data; If there is no abnormal data in the associated data, the cause of the abnormality is determined to be a device abnormality, and the collection device corresponding to the target abnormal data is marked as an abnormal device; If there is abnormal data in the associated data, the cause of the abnormality is determined to be data abnormality, and the data collection area corresponding to the target abnormal data is marked as data abnormality.

[0066] In a preferred example, the present application can be further configured as follows: the device also includes a training module, specifically used for: Collect historical monitoring data of a target collection device, where the target collection device is any collection device of the same type as the device corresponding to the target data group; Mark historical monitoring data as normal or abnormal; The model is trained based on the annotated historical monitoring data to obtain a preset recognition model corresponding to the device type of the target acquisition device.

[0067] In a preferred example, the present application can be further configured as follows: the device also includes a control module, which is specifically used to: Get the current weather data, including the weather type; Determine the collection frequency for each device type based on the weather type; Each collection device corresponding to the control concentrator collects monitoring data according to a determined collection frequency.

[0068] In a preferred example, the present application can be further configured as follows: when the control module executes the control concentrator to collect monitoring data at a determined collection frequency for each collection device, it is specifically used to: Obtain the device type of each collection device corresponding to the concentrator; Determine the time interval between two collection moments according to the collection frequency of the target device type, where the target device type is any device type corresponding to the concentrator; Determine the number of devices of the target device type, and determine the initial collection time of each collection device corresponding to the target device type according to the time interval and the number of devices; Each collection device corresponding to the control concentrator collects monitoring data according to the determined collection frequency starting from the initial collection time.

[0069] In a preferred example, the present application can be further configured as follows: the device also includes a replacement module, specifically used for: Obtain the historical fault data and historical operation time of each collection device corresponding to the concentrator; Determine the equipment replacement strategy for each collection device corresponding to the concentrator based on historical fault data and historical operating hours; The device replacement strategy is sent to the corresponding terminal devices of relevant personnel.

[0070] A concentrator data acquisition device provided in an embodiment of the present application is applicable to the above method embodiment and will not be described in detail here.

[0071] An electronic device is provided in an embodiment of the present application, such as Figure 3 As shown, Figure 3 The electronic device 300 shown includes: a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, such as through a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in actual applications, the transceiver 304 is not limited to one, and the structure of the electronic device 300 does not constitute a limitation on the embodiments of the present application.

[0072] The processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. The processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0073] The bus 302 may include a path to transmit information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but it does not mean that there is only one bus or only one type of bus.

[0074] The memory 303 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0075] The memory 303 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the contents shown in the above-mentioned embodiment of the concentrator data collection method.

[0076] Figure 3 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0077] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer can execute the contents shown in the aforementioned embodiment of the concentrator data collection method.

[0078] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0079] The above are only some implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A concentrator data collection method, characterized in that: include: Collect monitoring data received by the concentrator; Classifying the monitoring data into multiple groups of data according to the collection devices, each group of data corresponds to one collection device; Determine a device type corresponding to a target data group, and input the target data group into a preset recognition model corresponding to the device type, to obtain a recognition result of whether the target data group is abnormal output by the preset recognition model, wherein the target data group is any one of the multiple data groups; If the recognition result is that the target data group is abnormal, analyzing the cause of the abnormality; Generate a warning signal containing the cause of the abnormality, and send the warning signal to the terminal device corresponding to the relevant personnel.

2. The concentrator data collection method according to claim 1, characterized in that: If the recognition result is that the target data group is abnormal, analyzing the cause of the abnormality includes: Dividing the data in the target data group into normal data and abnormal data according to the recognition result; Obtaining recognition results corresponding to each of the plurality of data groups, and searching for associated data of target abnormal data from the recognition results corresponding to each of the plurality of data groups, wherein the target abnormal data is any abnormal data in the target data group, and the associated data is data of the same data type as the target abnormal data and in the same data collection area; The cause of the abnormality is determined according to the target abnormality data and the associated data.

3. The concentrator data collection method according to claim 2, characterized in that: The determining the abnormality cause according to the target abnormal data and the associated data includes: Determining whether there is abnormal data in the associated data; If there is no abnormal data in the associated data, the cause of the abnormality is determined to be a device abnormality, and the collection device corresponding to the target abnormal data is marked as an abnormal device; If there is abnormal data in the associated data, the cause of the abnormality is determined to be a data abnormality, and the data collection area corresponding to the target abnormal data is marked as a data abnormality.

4. The concentrator data collection method according to claim 1, characterized in that: The method further comprises: Collecting historical monitoring data of a target collection device, wherein the target collection device is any collection device of the same device type as that corresponding to the target data group; Marking the historical monitoring data as normal or abnormal; The model is trained based on the annotated historical monitoring data to obtain a preset recognition model corresponding to the device type of the target acquisition device.

5. The concentrator data collection method according to claim 1, characterized in that: The method further comprises: Acquire current weather data, where the current weather data includes weather type; Determining a collection frequency for each device type according to the weather type; Control each collection device corresponding to the concentrator to collect monitoring data according to the determined collection frequency.

6. The concentrator data collection method according to claim 5, characterized in that: The controlling each collection device corresponding to the concentrator to collect monitoring data according to the determined collection frequency includes: Obtaining a device type of each collection device corresponding to the concentrator; Determine the time interval between two collection moments according to the collection frequency of the target device type, wherein the target device type is any device type corresponding to the concentrator; Determine the number of devices of the target device type, and determine the initial collection time of each collection device corresponding to the target device type according to the time interval and the number of devices; Each collection device corresponding to the concentrator is controlled to collect monitoring data according to the determined collection frequency starting from the initial collection time.

7. The concentrator data collection method according to claim 1, characterized in that: The method further comprises: Obtaining historical fault data and historical operation time of each collection device corresponding to the concentrator; Determine, based on the historical fault data and the historical operation time, a device replacement strategy for each collection device corresponding to the concentrator; The device replacement strategy is sent to the terminal devices corresponding to the relevant personnel.

8. A concentrator data acquisition device, characterized in that: include: A collection module, used to collect monitoring data received by the concentrator; A classification module, used to classify the monitoring data into multiple groups of data according to the collection devices, each group of data corresponds to one collection device; an identification module, used to determine the device type corresponding to the target data group, and input the target data group into a preset identification model corresponding to the device type, and obtain an identification result of whether the target data group is abnormal output by the preset identification model, wherein the target data group is any one of the multiple data groups; An analysis module, configured to analyze the cause of the abnormality if the recognition result indicates that the target data group is abnormal; The generating module is used to generate a warning signal including the cause of the abnormality, and send the warning signal to the terminal device corresponding to the relevant personnel.

9. An electronic device, characterized in that: include: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the concentrator data collection method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed in a computer, the computer is caused to execute the concentrator data collection method according to any one of claims 1 to 7.

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