High and low voltage power distribution cabinet remote monitoring method and system based on Internet of Things

Abstract

The invention relates to the technical field of high-low voltage power distribution cabinet monitoring, in particular to a high-low voltage power distribution cabinet remote monitoring method and system based on the Internet of Things, and the method comprises the steps: obtaining historical monitoring information, current monitoring data, a monitoring abnormal standard and historical operation data of a preset point location of a high-low voltage power distribution cabinet, creating a data recognition network model, and training the data identification network model based on the historical monitoring information, the monitoring abnormity standard and the historical operation data to obtain a trained data identification network model, judging whether the current monitoring data accords with the monitoring abnormity standard, and if so, executing the next step. If yes, time node features and detection parameter features, corresponding to the time node features, of the high-and-low-voltage power distribution cabinet are determined, the time node features and the detection parameter features are input into the trained data recognition network model for anomaly recognition, and an anomaly recognition result is obtained, and the monitoring efficiency of the high-and-low-voltage power distribution cabinet is improved.

Description

Technical Field

[0001] The present application relates to the technical field of high and low voltage distribution cabinet monitoring, and in particular to a method and system for remote monitoring of high and low voltage distribution cabinets based on the Internet of Things. Background Art

[0002] With the rapid development of my country's economy, the power industry is gradually realizing intelligence, digitization and automation. In the management of modern distribution rooms and computer rooms, unattended, comprehensive management and safety management of substations and distribution rooms have become the only way to modernize the power grid. As key equipment in the power system, the stability and safety of high and low voltage distribution cabinets directly affect the operation quality of the entire power grid. Therefore, the development of an efficient and reliable remote monitoring method is of great significance to improving the reliability and safety of the distribution system.

[0003] At present, the traditional monitoring method of distribution cabinets mainly relies on manual inspections and regular maintenance, which has many shortcomings. First, manual inspections require a lot of manpower and material resources, and the inspection cycle is long, making it difficult to timely discover and deal with potential equipment hazards. Secondly, although regular maintenance can guarantee the normal operation of equipment to a certain extent, it often lacks real-time and pertinence, and cannot achieve real-time monitoring and early warning of equipment status. In addition, traditional monitoring methods also have problems such as difficulty in data acquisition and inconvenient operation and management, which are difficult to meet the needs of modern power systems, thereby reducing the monitoring efficiency of high and low voltage distribution cabinets. Summary of the invention

[0004] In order to solve at least one of the above technical problems, the present application provides a remote monitoring method and system for high and low voltage distribution cabinets based on the Internet of Things.

[0005] In the first aspect, the present application provides a remote monitoring method for high and low voltage distribution cabinets based on the Internet of Things, which adopts the following technical solutions: Obtaining historical monitoring information of preset points of high and low voltage distribution cabinets within a historical period, current monitoring data within a current time, monitoring abnormality standards, and historical operation data within the historical period, wherein the monitoring abnormality standards are determination standards for determining whether the monitoring data is abnormal or not; Creating a data recognition network model, and training the data recognition network model based on the historical monitoring information, the monitoring abnormality standard, and the historical operation data to obtain a trained data recognition network model; Determine whether the current monitoring data meets the monitoring abnormality standard. If so, determine the time node characteristics and the detection parameter characteristics of the high and low voltage distribution cabinets corresponding to the time node characteristics based on the current monitoring data, and input the time node characteristics and the detection parameter characteristics into the trained data recognition network model for abnormality recognition to obtain an abnormality recognition result; The maintenance information is generated according to the abnormality identification result, and the maintenance information is sent to a target terminal, where the target terminal is a terminal used by maintenance personnel.

[0006] By adopting the above technical solutions, detailed monitoring information of the preset points of high and low voltage distribution cabinets in the historical period, real-time data in the current time, strict monitoring abnormality standards and historical operation data are fully obtained, providing a solid data foundation for subsequent analysis. These data work together to more comprehensively reflect the operating status of the distribution cabinet and provide a multi-dimensional reference for subsequent abnormality identification. By creating and training the data recognition network model, the model is deeply optimized using historical monitoring information, monitoring abnormality standards and historical operation data to obtain a highly accurate trained model. The model can accurately identify abnormalities and potential abnormalities of the distribution cabinet, improve the accuracy and efficiency of identification, and lay the foundation for subsequent rapid response. When the current monitoring data is judged to be abnormal, the trained data recognition network model is used to perform deep abnormality identification in combination with the time node characteristics and detection parameter characteristics. Not only does it improve the accuracy of abnormality identification, but it can also quickly locate the time and specific parameters of the abnormality, providing strong support for rapid troubleshooting. According to the abnormality identification results, the maintenance information is automatically generated and sent to the maintenance personnel terminal in real time. It greatly shortens the time from abnormality discovery to maintenance response, improves maintenance efficiency, and ensures the safe and stable operation of high and low voltage distribution cabinets.

[0007] In a possible implementation, the data recognition network model is trained based on the historical monitoring information, the monitoring abnormality standard, and the historical operation data to obtain the trained data recognition network model, including: Determine the association relationship between the historical monitoring information and the historical operation data according to the time node; Determine whether the historical monitoring information meets the monitoring abnormality standard. If so, determine the abnormal monitoring information and abnormal operation data according to the association relationship, and perform data decomposition on the abnormal monitoring information and the abnormal operation data to obtain the monitoring abnormality data of the high and low voltage distribution cabinets at different time nodes; Converting the monitoring abnormal data into a pixel grayscale image, and extracting abnormal time node features and abnormal parameter features in the pixel grayscale image; The abnormal time node characteristics, the abnormal parameter characteristics and the preset maintenance information are input into the data recognition network model for training to obtain a trained data recognition network model.

[0008] In a possible implementation, the data recognition network model is trained based on the historical monitoring information, the monitoring abnormality standard, and the historical operation data to obtain a trained data recognition network model, and the method further includes: Acquire transmission monitoring data, wherein the transmission monitoring data is flow monitoring data of the historical monitoring information, the current monitoring data, and the historical operation data during the acquisition and transmission process; Determine, according to the transmission monitoring data, a first flow distribution feature corresponding to the historical monitoring information, a second flow distribution feature corresponding to the current monitoring data, and a third flow distribution feature corresponding to the historical operation data; Arrange the first traffic distribution feature, the second traffic distribution feature, and the third traffic distribution feature according to transmission time nodes to obtain a comprehensive transmission fluctuation graph; The comprehensive transmission fluctuation graph and the abnormal transmission fluctuation graph are fitted according to the transmission time nodes, and it is determined whether there are abnormalities in the historical monitoring information, the current monitoring data and the historical operation data corresponding to the different transmission time nodes. If so, the first abnormal fluctuation segment corresponding to the first traffic distribution feature, the second abnormal fluctuation segment corresponding to the second traffic distribution feature, and the third abnormal fluctuation segment corresponding to the third traffic distribution feature are determined, and data transmission anomaly analysis is performed on the first abnormal fluctuation segment, the second abnormal fluctuation segment and the third abnormal fluctuation segment respectively to obtain the degree of abnormal transmission.

[0009] In a possible implementation, performing data transmission anomaly analysis on the first abnormal fluctuation segment, the second abnormal fluctuation segment, and the third abnormal fluctuation segment respectively to obtain the abnormal transmission degree includes: Check whether the first abnormal fluctuation segment, the second abnormal fluctuation segment and the third abnormal fluctuation segment are empty respectively; if they are empty, filter the corresponding empty fluctuation segments; if they are not empty, calculate the maximum flow transmission rate and the minimum flow transmission rate of each fluctuation segment respectively; Calculate the ratio of the highest flow transmission rate to the lowest flow transmission rate to obtain a transmission rate change value corresponding to each fluctuation segment; Arranging the transmission rate change values ​​in time sequence to obtain a data transmission sequence corresponding to each fluctuation segment; Segmenting the data transmission sequence to obtain two or more transmission data segments; Performing mean calculation on the transmission data segments corresponding to each fluctuation segment to obtain abnormality degree values ​​corresponding to the first abnormal fluctuation segment, the second abnormal fluctuation segment, and the third abnormal fluctuation segment; Comparing the abnormality degree value with a preset abnormality degree standard to obtain a first abnormality degree corresponding to the first abnormal fluctuation segment, a second abnormality degree corresponding to the second abnormal fluctuation segment, and a third abnormality degree corresponding to the third abnormal fluctuation segment; The first abnormality degree, the second abnormality degree and the third abnormality degree are calculated and summarized according to the proportional coefficient to obtain the abnormal transmission degree.

[0010] In a possible implementation manner, segmenting the data transmission sequence to obtain two or more transmission data segments includes: Determine all the split points in the transmission sequence according to the transmission rate change value of each production data transmission process in the data transmission sequence and each transmission rate value; The transmission sequence is segmented according to all segmentation points, the similarity of the change value sequences corresponding to each two adjacent transmission sequences is obtained, and whether each two adjacent transmission sequences need to be merged is determined according to the similarity to obtain two or more transmission data segments.

[0011] In a possible implementation, the data transmission anomaly analysis is performed on the first abnormal fluctuation segment, the second abnormal fluctuation segment, and the third abnormal fluctuation segment respectively to obtain the abnormal transmission degree, and then the following is further included: It is determined whether the abnormal transmission degree exceeds a preset degree range. If so, a data acquisition abnormality instruction is generated to control the abnormal warning device to output a response.

[0012] In the second aspect, the present application provides a remote monitoring system for high and low voltage distribution cabinets based on the Internet of Things, which adopts the following technical solutions: A remote monitoring system for high and low voltage distribution cabinets based on the Internet of Things, comprising: An information acquisition module is used to obtain historical monitoring information of preset points of high and low voltage distribution cabinets within a historical period, current monitoring data within a current time, monitoring abnormality standards, and historical operation data within the historical period, wherein the monitoring abnormality standards are determination standards for determining whether the monitoring data is abnormal or not; A model processing module, used for creating a data recognition network model, and training the data recognition network model based on the historical monitoring information, the monitoring abnormality standard and the historical operation data to obtain a trained data recognition network model; A data identification module is used to determine whether the current monitoring data meets the monitoring abnormality standard. If so, the time node characteristics and the detection parameter characteristics of the high and low voltage distribution cabinets corresponding to the time node characteristics are determined based on the current monitoring data, and the time node characteristics and the detection parameter characteristics are input into the trained data identification network model for abnormality identification to obtain an abnormality identification result; The equipment maintenance module is used to generate maintenance information according to the abnormality identification result, and send the maintenance information to a target terminal, where the target terminal is a terminal used by maintenance personnel.

[0013] In a possible implementation, when the model processing module trains the data recognition network model based on the historical monitoring information, the monitoring abnormality standard, and the historical operation data to obtain the trained data recognition network model, it is specifically used to: Determine the association relationship between the historical monitoring information and the historical operation data according to the time node; Determine whether the historical monitoring information meets the monitoring abnormality standard. If so, determine the abnormal monitoring information and abnormal operation data according to the association relationship, and perform data decomposition on the abnormal monitoring information and the abnormal operation data to obtain the monitoring abnormality data of the high and low voltage distribution cabinets at different time nodes; Converting the monitoring abnormal data into a pixel grayscale image, and extracting abnormal time node features and abnormal parameter features in the pixel grayscale image; The abnormal time node characteristics, the abnormal parameter characteristics and the preset maintenance information are input into the data recognition network model for training to obtain a trained data recognition network model.

[0014] In another possible implementation, the system further includes: a data acquisition module, a feature determination module, a feature sorting module, and an abnormality analysis module, wherein: The data acquisition module is used to acquire transmission monitoring data, wherein the transmission monitoring data is the flow monitoring data of the historical monitoring information, the current monitoring data and the historical operation data during the acquisition and transmission process; The feature determination module is used to determine, based on the transmission monitoring data, a first flow distribution feature corresponding to the historical monitoring information, a second flow distribution feature corresponding to the current monitoring data, and a third flow distribution feature corresponding to the historical operation data; The feature sorting module is used to sort the first traffic distribution feature, the second traffic distribution feature and the third traffic distribution feature according to transmission time nodes to obtain a comprehensive transmission fluctuation graph; The abnormal analysis module is used to fit the comprehensive transmission fluctuation graph and the abnormal transmission fluctuation graph according to the transmission time nodes, and determine whether there are abnormalities in the historical monitoring information, the current monitoring data and the historical operation data corresponding to the different transmission time nodes. If so, determine the first abnormal fluctuation segment corresponding to the first traffic distribution feature, the second abnormal fluctuation segment corresponding to the second traffic distribution feature, and the third abnormal fluctuation segment corresponding to the third traffic distribution feature, and perform data transmission abnormality analysis on the first abnormal fluctuation segment, the second abnormal fluctuation segment and the third abnormal fluctuation segment respectively to obtain the degree of abnormal transmission.

[0015] In another possible implementation, when the abnormal analysis module performs data transmission abnormality analysis on the first abnormal fluctuation segment, the second abnormal fluctuation segment, and the third abnormal fluctuation segment respectively to obtain the abnormal transmission degree, it is specifically used to: Check whether the first abnormal fluctuation segment, the second abnormal fluctuation segment and the third abnormal fluctuation segment are empty respectively; if they are empty, filter the corresponding empty fluctuation segments; if they are not empty, calculate the maximum flow transmission rate and the minimum flow transmission rate of each fluctuation segment respectively; Calculate the ratio of the highest flow transmission rate to the lowest flow transmission rate to obtain a transmission rate change value corresponding to each fluctuation segment; Arranging the transmission rate change values ​​in time sequence to obtain a data transmission sequence corresponding to each fluctuation segment; Segmenting the data transmission sequence to obtain two or more transmission data segments; Performing mean calculation on the transmission data segments corresponding to each fluctuation segment to obtain abnormality degree values ​​corresponding to the first abnormal fluctuation segment, the second abnormal fluctuation segment, and the third abnormal fluctuation segment; Comparing the abnormality degree value with a preset abnormality degree standard to obtain a first abnormality degree corresponding to the first abnormal fluctuation segment, a second abnormality degree corresponding to the second abnormal fluctuation segment, and a third abnormality degree corresponding to the third abnormal fluctuation segment; The first abnormality degree, the second abnormality degree and the third abnormality degree are calculated and summarized according to the proportional coefficient to obtain the abnormal transmission degree.

[0016] In another possible implementation, when the abnormality analysis module performs segment processing on the data transmission sequence to obtain two or more transmission data segments, the abnormality analysis module is specifically configured to: Determine all the split points in the transmission sequence according to the transmission rate change value of each production data transmission process in the data transmission sequence and each transmission rate value; The transmission sequence is segmented according to all segmentation points, the similarity of the change value sequences corresponding to each two adjacent transmission sequences is obtained, and whether each two adjacent transmission sequences need to be merged is determined according to the similarity to obtain two or more transmission data segments.

[0017] In another possible implementation, the system further includes: an alarm response module, wherein: The alarm response module is used to determine whether the abnormal transmission degree exceeds a preset degree range. If it exceeds, it generates a data acquisition abnormality instruction and controls the abnormal warning device to output a response.

[0018] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: at least one processor; Memory; At least one application, wherein at least one application is stored in a memory and configured to be executed by at least one processor, and at least one application is configured to: execute a remote monitoring method for high and low voltage distribution cabinets based on the Internet of Things as described in any one of the first aspects.

[0019] 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, which, when executed in a computer, causes the computer to execute any of the methods for remote monitoring of high and low voltage distribution cabinets based on the Internet of Things as described in the first aspect.

[0020] In summary, the present application includes at least one of the following beneficial technical effects: By adopting the above technical solutions, detailed monitoring information of the preset points of high and low voltage distribution cabinets in the historical period, real-time data in the current time, strict monitoring abnormality standards and historical operation data are fully obtained, providing a solid data foundation for subsequent analysis. These data work together to more comprehensively reflect the operating status of the distribution cabinet and provide a multi-dimensional reference for subsequent abnormality identification. By creating and training the data recognition network model, the model is deeply optimized using historical monitoring information, monitoring abnormality standards and historical operation data to obtain a highly accurate trained model. The model can accurately identify abnormalities and potential abnormalities of the distribution cabinet, improve the accuracy and efficiency of identification, and lay the foundation for subsequent rapid response. When the current monitoring data is judged to be abnormal, the trained data recognition network model is used to perform deep abnormality identification in combination with the time node characteristics and detection parameter characteristics. Not only does it improve the accuracy of abnormality identification, but it can also quickly locate the time and specific parameters of the abnormality, providing strong support for rapid troubleshooting. According to the abnormality identification results, the maintenance information is automatically generated and sent to the maintenance personnel terminal in real time. It greatly shortens the time from abnormality discovery to maintenance response, improves maintenance efficiency, and ensures the safe and stable operation of high and low voltage distribution cabinets. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flowchart of a remote monitoring method for high and low voltage distribution cabinets based on the Internet of Things is provided in an embodiment of the present application.

[0022] Figure 2 A schematic diagram of the structure of a remote monitoring system for high and low voltage distribution cabinets based on the Internet of Things provided in an embodiment of the present application.

[0023] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] The following is combined with Figure 1-3 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, a person skilled in the art may make non-creative modifications to the present embodiment as needed, but such modifications are protected by the patent law as long as they are within the scope 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] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0029] The embodiment of the present application provides a method for remote monitoring of high and low voltage distribution cabinets based on the Internet of Things, which is executed by an electronic device, wherein the electronic device can be an independent physical electronic device, or an electronic device cluster or distributed system composed of multiple physical electronic devices, or a cloud electronic device that provides cloud computing services. The embodiment of the present application is not limited here, such as Figure 1 As shown, the method includes: Step S10, obtaining historical monitoring information of preset points of high and low voltage distribution cabinets within a historical period, current monitoring data within a current time, monitoring abnormality standards and historical operation data within a historical period.

[0030] Among them, the monitoring abnormality standard is a definite standard for determining whether the monitoring data is abnormal or not.

[0031] Specifically, high and low voltage distribution cabinets refer to equipment used in power systems to distribute, control and protect electric energy to ensure the stable operation of the power system. "Preset points" refer to key locations or parameter points that are pre-set on high and low voltage distribution cabinets and need to be monitored. Historical monitoring information refers to the monitoring data records of these preset points in a certain period of time in the past (i.e., historical cycle), which are used to analyze the operating status of the equipment. Current monitoring data refers to the real-time data of these preset points at the current time point. The monitoring abnormality standard is a determination standard for determining whether the monitoring data exceeds the normal range or whether an abnormality occurs, and is an important basis for evaluating the health status of the equipment. Historical operation data refers to the overall operation data of the high and low voltage distribution cabinets in the historical cycle, including but not limited to load changes, fault records, etc., which are used to comprehensively evaluate the performance of the equipment.

[0032] In an embodiment of the present application, the preset point information of the high and low voltage distribution cabinets is set by manual input to ensure the accuracy of data collection, and then sensors or detection devices are used to monitor the current data of the preset points in real time and automatically store historical data.

[0033] Step S11: create a data recognition network model, and train the data recognition network model based on historical monitoring information, monitoring abnormality standards and historical operation data to obtain a trained data recognition network model.

[0034] For the embodiments of the present application, the correlation relationship between historical monitoring information and historical operation data is determined according to the time nodes, and it is judged whether the historical monitoring information meets the monitoring abnormality standard. If it does, the abnormal monitoring information and abnormal operation data are determined according to the correlation relationship, and the abnormal monitoring information and abnormal operation data are decomposed to obtain the monitoring abnormality data of the high and low voltage distribution cabinets at different time nodes, the monitoring abnormality data is converted into a pixel grayscale image, and the abnormal time node features and abnormal parameter features in the pixel grayscale image are extracted, and the abnormal time node features, abnormal parameter features and preset maintenance information are input into the data recognition network model for training to obtain the trained data recognition network model.

[0035] Step S12, determine whether the current monitoring data meets the monitoring abnormality standard. If so, determine the time node characteristics and the detection parameter characteristics of the high and low voltage distribution cabinets corresponding to the time node characteristics based on the current monitoring data, and input the time node characteristics and the detection parameter characteristics into the trained data recognition network model for abnormality recognition to obtain the abnormality recognition result.

[0036] Step S13: Generate maintenance information according to the abnormality identification result, and send the maintenance information to the target terminal.

[0037] Among them, the target terminal is the terminal used by the maintenance personnel.

[0038] For the embodiment of the present application, the detailed monitoring information of the preset points of the high and low voltage distribution cabinets in the historical period, the real-time data in the current time, the strict monitoring abnormality standards and the historical operation data are fully obtained, which provides a solid data foundation for subsequent analysis. These data work together to more comprehensively reflect the operating status of the distribution cabinet and provide a multi-dimensional reference for subsequent abnormal identification. By creating and training the data recognition network model, the model is deeply optimized using historical monitoring information, monitoring abnormality standards and historical operation data to obtain a highly accurate trained model. The model is enabled to accurately identify the abnormalities and potential abnormalities of the distribution cabinet, improve the accuracy and efficiency of identification, and lay the foundation for subsequent rapid response. When the current monitoring data is judged to be abnormal, the trained data recognition network model is used to perform deep abnormality identification in combination with the time node characteristics and the detection parameter characteristics. Not only the accuracy of abnormal identification is improved, but also the time and specific parameters of the abnormality can be quickly located, providing strong support for rapid troubleshooting. The maintenance information is automatically generated according to the abnormal identification results and sent to the maintenance personnel terminal in real time. The time from abnormal discovery to maintenance response is greatly shortened, the maintenance efficiency is improved, and the safe and stable operation of the high and low voltage distribution cabinets is ensured.

[0039] Furthermore, the data identification network model is trained based on historical monitoring information, monitoring anomaly standards and historical operation data to obtain a trained data identification network model, which also includes: obtaining transmission monitoring data, the transmission monitoring data is the flow monitoring data of historical monitoring information, current monitoring data and historical operation data in the process of obtaining transmission, determining the first flow distribution feature corresponding to the historical monitoring information, the second flow distribution feature corresponding to the current monitoring data and the third flow distribution feature corresponding to the historical operation data according to the transmission monitoring data, sorting the first flow distribution feature, the second flow distribution feature and the third flow distribution feature according to the transmission time node to obtain a comprehensive transmission fluctuation graph, fitting the comprehensive transmission fluctuation graph with the abnormal transmission fluctuation graph according to the transmission time node, judging whether the historical monitoring information, current monitoring data and historical operation data corresponding to different transmission time nodes are abnormal, if so, determining the first abnormal fluctuation segment corresponding to the first flow distribution feature, the second abnormal fluctuation segment corresponding to the second flow distribution feature and the third abnormal fluctuation segment corresponding to the third flow distribution feature, and performing data transmission anomaly analysis on the first abnormal fluctuation segment, the second abnormal fluctuation segment and the third abnormal fluctuation segment respectively to obtain the degree of abnormal transmission.

[0040] Specifically, check whether the first abnormal fluctuation segment, the second abnormal fluctuation segment and the third abnormal fluctuation segment are empty respectively. If they are empty, filter the corresponding empty fluctuation segments. If they are not empty, calculate the maximum flow transmission rate and the minimum flow transmission rate of each fluctuation segment respectively, calculate the ratio of the maximum flow transmission rate to the minimum flow transmission rate, obtain the transmission rate change value corresponding to each fluctuation segment, arrange the transmission rate change values ​​in time sequence to obtain the data transmission sequence corresponding to each fluctuation segment, segment the data transmission sequence to obtain two or more transmission data segments, and average the transmission data segments corresponding to each fluctuation segment. The value is calculated to obtain the abnormality degree values ​​corresponding to the first abnormal fluctuation segment, the second abnormal fluctuation segment and the third abnormal fluctuation segment, the abnormality degree values ​​are compared with the preset abnormality degree standard to obtain the first abnormality degree corresponding to the first abnormal fluctuation segment, the second abnormality degree corresponding to the second abnormal fluctuation segment and the third abnormality degree corresponding to the third abnormal fluctuation segment, the first abnormality degree, the second abnormality degree and the third abnormality degree are summarized and calculated according to the proportional coefficient to obtain the abnormal transmission degree, and it is determined whether the abnormal transmission degree exceeds the preset degree range. If exceeded, a data acquisition abnormal instruction is generated to control the abnormal warning device to output a response.

[0041] Specifically, all segmentation points in the transmission sequence are determined according to the transmission rate change value of each production data transmission process in the data transmission sequence and each transmission rate value, the transmission sequence is segmented according to all the segmentation points, and the similarity of the change value sequences corresponding to each two adjacent transmission sequences is obtained. According to the similarity, it is determined whether each two adjacent transmission sequences need to be merged, and two or more transmission data segments are obtained.

[0042] The following is an introduction to a remote monitoring system for high and low voltage distribution cabinets based on the Internet of Things provided in an embodiment of the present application. The remote monitoring system for high and low voltage distribution cabinets based on the Internet of Things described below and the remote monitoring method for high and low voltage distribution cabinets based on the Internet of Things described above can be referred to each other. Please refer to Figure 2 , Figure 2 1 is a schematic diagram of a high and low voltage distribution cabinet remote monitoring system 20 based on the Internet of Things provided in an embodiment of the present application, including: The information acquisition module 21 is used to obtain the historical monitoring information of the preset points of the high and low voltage distribution cabinets in the historical period, the current monitoring data in the current time, the monitoring abnormality standard and the historical operation data in the historical period, and the monitoring abnormality standard is the determination standard for determining whether the monitoring data is abnormal or not; The model processing module 22 is used to create a data recognition network model and train the data recognition network model based on historical monitoring information, monitoring abnormality standards and historical operation data to obtain a trained data recognition network model; The data identification module 23 is used to determine whether the current monitoring data meets the monitoring abnormality standard. If it does, the time node characteristics and the detection parameter characteristics of the high and low voltage distribution cabinets corresponding to the time node characteristics are determined based on the current monitoring data, and the time node characteristics and the detection parameter characteristics are input into the trained data identification network model for abnormality identification to obtain abnormality identification results; The equipment maintenance module 24 is used to generate maintenance information according to the abnormality identification result, and send the maintenance information to a target terminal, which is a terminal used by maintenance personnel.

[0043] In one possible implementation of the embodiment of the present application, the model processing module 22 trains the data recognition network model based on historical monitoring information, monitoring abnormality standards and historical operation data to obtain the trained data recognition network model, specifically for: Determine the correlation between historical monitoring information and historical operation data according to time nodes; Determine whether the historical monitoring information meets the monitoring abnormality standard. If it does, determine the abnormal monitoring information and abnormal operation data based on the association relationship, and decompose the abnormal monitoring information and abnormal operation data to obtain the monitoring abnormality data of the high and low voltage distribution cabinets at different time nodes; Convert the monitored abnormal data into a pixel grayscale image, and extract the abnormal time node features and abnormal parameter features in the pixel grayscale image; The abnormal time node characteristics, abnormal parameter characteristics and preset maintenance information are input into the data recognition network model for training to obtain the trained data recognition network model.

[0044] In another possible implementation of the embodiment of the present application, the system 20 further includes: a data acquisition module, a feature determination module, a feature sorting module and an abnormality analysis module, wherein: A data acquisition module is used to acquire transmission monitoring data, where the transmission monitoring data is historical monitoring information, current monitoring data, and flow monitoring data of historical operation data during the acquisition and transmission process; a feature determination module, for determining, based on the transmission monitoring data, a first flow distribution feature corresponding to the historical monitoring information, a second flow distribution feature corresponding to the current monitoring data, and a third flow distribution feature corresponding to the historical operation data; A feature sorting module, used to sort the first flow distribution feature, the second flow distribution feature and the third flow distribution feature according to the transmission time node to obtain a comprehensive transmission fluctuation graph; The abnormal analysis module is used to fit the comprehensive transmission fluctuation diagram and the abnormal transmission fluctuation diagram according to the transmission time nodes, and determine whether there are abnormalities in the historical monitoring information, current monitoring data and historical operation data corresponding to different transmission time nodes. If so, the first abnormal fluctuation segment corresponding to the first traffic distribution feature, the second abnormal fluctuation segment corresponding to the second traffic distribution feature, and the third abnormal fluctuation segment corresponding to the third traffic distribution feature are determined, and data transmission abnormality analysis is performed on the first abnormal fluctuation segment, the second abnormal fluctuation segment and the third abnormal fluctuation segment respectively to obtain the degree of abnormal transmission.

[0045] In another possible implementation of the embodiment of the present application, when the abnormal analysis module performs data transmission abnormality analysis on the first abnormal fluctuation segment, the second abnormal fluctuation segment, and the third abnormal fluctuation segment respectively to obtain the abnormal transmission degree, it is specifically used to: Check whether the first abnormal fluctuation segment, the second abnormal fluctuation segment and the third abnormal fluctuation segment are empty respectively. If they are empty, filter the corresponding empty fluctuation segments. If they are not empty, calculate the maximum flow transmission rate and the minimum flow transmission rate of each fluctuation segment respectively. The ratio of the highest flow transmission rate to the lowest flow transmission rate is calculated to obtain the transmission rate change value corresponding to each fluctuation segment; Arrange the transmission rate change values ​​in time sequence to obtain the data transmission sequence corresponding to each fluctuation segment; Segmenting the data transmission sequence to obtain two or more transmission data segments; Performing mean calculation on the transmission data segments corresponding to each fluctuation segment to obtain abnormality degree values ​​corresponding to the first abnormal fluctuation segment, the second abnormal fluctuation segment, and the third abnormal fluctuation segment; Comparing the abnormality degree value with a preset abnormality degree standard to obtain a first abnormality degree corresponding to the first abnormal fluctuation segment, a second abnormality degree corresponding to the second abnormal fluctuation segment, and a third abnormality degree corresponding to the third abnormal fluctuation segment; The first abnormality degree, the second abnormality degree and the third abnormality degree are calculated and summarized according to the proportional coefficient to obtain the abnormal transmission degree.

[0046] In another possible implementation of the embodiment of the present application, when the abnormal analysis module performs segmentation processing on the data transmission sequence to obtain two or more transmission data segments, it is specifically used to: Determine all the split points in the transmission sequence according to the transmission rate change value of each production data transmission process in the data transmission sequence and each transmission rate value; The transmission sequence is segmented according to all the segmentation points, and the similarity of the change value sequence corresponding to each two adjacent transmission sequences is obtained. According to the similarity, it is determined whether each two adjacent transmission sequences need to be merged to obtain two or more transmission data segments.

[0047] In another possible implementation of the embodiment of the present application, the system 20 further includes: an alarm response module, wherein: The alarm response module is used to determine whether the abnormal transmission degree exceeds the preset range. If it exceeds, it generates a data acquisition abnormal instruction and controls the abnormal warning device to output a response.

[0048] The present application embodiment provides an electronic device, such as Figure 3 As shown, Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application, 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.

[0049] 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 the embodiments of the present 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, and the like.

[0050] 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 this does not mean that there is only one bus or only one type of bus.

[0051] 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.

[0052] The memory 303 is used to store application code for executing the solution of the embodiment 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 content shown in the above method embodiment.

[0053] Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. 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.

[0054] A computer-readable storage medium provided in an embodiment of the present application is introduced below. The computer-readable storage medium described below and the method described above can be referenced to each other.

[0055] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned method for remote monitoring of high and low voltage distribution cabinets based on the Internet of Things are implemented.

[0056] Since the embodiments of the computer-readable storage medium part and the embodiments of the method part correspond to each other, the embodiments of the computer-readable storage medium part refer to the description of the embodiments of the method part.

[0057] 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.

[0058] 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 remote monitoring method for high and low voltage distribution cabinets based on the Internet of Things, characterized in that: include: Obtaining historical monitoring information of preset points of high and low voltage distribution cabinets within a historical period, current monitoring data within the current time, monitoring abnormality standards, and historical operation data within the historical period, wherein the monitoring abnormality standards are the determination standards for determining whether the monitoring data is abnormal or not; Creating a data recognition network model, and training the data recognition network model based on the historical monitoring information, the monitoring abnormality standard, and the historical operation data to obtain a trained data recognition network model; Determine whether the current monitoring data meets the monitoring abnormality standard. If so, determine the time node characteristics and the detection parameter characteristics of the high and low voltage distribution cabinets corresponding to the time node characteristics based on the current monitoring data, and input the time node characteristics and the detection parameter characteristics into the trained data recognition network model for abnormality recognition to obtain an abnormality recognition result; The maintenance information is generated according to the abnormality identification result, and the maintenance information is sent to a target terminal, where the target terminal is a terminal used by maintenance personnel.

2. According to a method for remote monitoring of high and low voltage distribution cabinets based on the Internet of Things according to claim 1, it is characterized in that: The data recognition network model is trained based on the historical monitoring information, the monitoring abnormality standard and the historical operation data to obtain the trained data recognition network model, including: Determine the association relationship between the historical monitoring information and the historical operation data according to the time node; Determine whether the historical monitoring information meets the monitoring abnormality standard. If so, determine the abnormal monitoring information and abnormal operation data according to the association relationship, and perform data decomposition on the abnormal monitoring information and the abnormal operation data to obtain the monitoring abnormality data of the high and low voltage distribution cabinets at different time nodes; Converting the monitoring abnormal data into a pixel grayscale image, and extracting abnormal time node features and abnormal parameter features in the pixel grayscale image; The abnormal time node characteristics, the abnormal parameter characteristics and the preset maintenance information are input into the data recognition network model for training to obtain a trained data recognition network model.

3. According to a method for remote monitoring of high and low voltage distribution cabinets based on the Internet of Things according to claim 1, it is characterized in that: The data recognition network model is trained based on the historical monitoring information, the monitoring abnormality standard and the historical operation data to obtain a trained data recognition network model, and the method further includes: Acquire transmission monitoring data, wherein the transmission monitoring data is flow monitoring data of the historical monitoring information, the current monitoring data, and the historical operation data during the acquisition and transmission process; Determine, according to the transmission monitoring data, a first flow distribution feature corresponding to the historical monitoring information, a second flow distribution feature corresponding to the current monitoring data, and a third flow distribution feature corresponding to the historical operation data; Arrange the first traffic distribution feature, the second traffic distribution feature, and the third traffic distribution feature according to transmission time nodes to obtain a comprehensive transmission fluctuation graph; The comprehensive transmission fluctuation graph and the abnormal transmission fluctuation graph are fitted according to the transmission time nodes, and it is determined whether there are abnormalities in the historical monitoring information, the current monitoring data and the historical operation data corresponding to the different transmission time nodes. If so, the first abnormal fluctuation segment corresponding to the first traffic distribution feature, the second abnormal fluctuation segment corresponding to the second traffic distribution feature, and the third abnormal fluctuation segment corresponding to the third traffic distribution feature are determined, and data transmission anomaly analysis is performed on the first abnormal fluctuation segment, the second abnormal fluctuation segment and the third abnormal fluctuation segment respectively to obtain the degree of abnormal transmission.

4. According to a method for remote monitoring of high and low voltage distribution cabinets based on the Internet of Things according to claim 3, it is characterized in that: The performing data transmission anomaly analysis on the first abnormal fluctuation segment, the second abnormal fluctuation segment and the third abnormal fluctuation segment respectively to obtain the abnormal transmission degree includes: Check whether the first abnormal fluctuation segment, the second abnormal fluctuation segment and the third abnormal fluctuation segment are empty respectively; if they are empty, filter the corresponding empty fluctuation segments; if they are not empty, calculate the maximum flow transmission rate and the minimum flow transmission rate of each fluctuation segment respectively; Calculate the ratio of the highest flow transmission rate to the lowest flow transmission rate to obtain a transmission rate change value corresponding to each fluctuation segment; Arranging the transmission rate change values ​​in time sequence to obtain a data transmission sequence corresponding to each fluctuation segment; Segmenting the data transmission sequence to obtain two or more transmission data segments; Performing mean calculation on the transmission data segments corresponding to each fluctuation segment to obtain abnormality degree values ​​corresponding to the first abnormal fluctuation segment, the second abnormal fluctuation segment, and the third abnormal fluctuation segment; Comparing the abnormality degree value with a preset abnormality degree standard to obtain a first abnormality degree corresponding to the first abnormal fluctuation segment, a second abnormality degree corresponding to the second abnormal fluctuation segment, and a third abnormality degree corresponding to the third abnormal fluctuation segment; The first abnormality degree, the second abnormality degree and the third abnormality degree are calculated and summarized according to the proportional coefficient to obtain the abnormal transmission degree.

5. The remote monitoring method of high and low voltage distribution cabinets based on the Internet of Things according to claim 4 is characterized in that: The segmenting of the data transmission sequence to obtain two or more transmission data segments includes: Determine all the split points in the transmission sequence according to the transmission rate change value of each production data transmission process in the data transmission sequence and each transmission rate value; The transmission sequence is segmented according to all segmentation points, the similarity of the change value sequences corresponding to each two adjacent transmission sequences is obtained, and whether each two adjacent transmission sequences need to be merged is determined according to the similarity to obtain two or more transmission data segments.

6. The remote monitoring method of high and low voltage distribution cabinets based on the Internet of Things according to claim 3 is characterized in that: The step of performing data transmission anomaly analysis on the first abnormal fluctuation segment, the second abnormal fluctuation segment, and the third abnormal fluctuation segment to obtain an abnormal transmission degree further includes: It is determined whether the abnormal transmission degree exceeds a preset degree range. If so, a data acquisition abnormality instruction is generated to control the abnormal warning device to output a response.

7. A remote monitoring system for high and low voltage distribution cabinets based on the Internet of Things, characterized in that: include: An information acquisition module is used to obtain historical monitoring information of preset points of high and low voltage distribution cabinets within a historical period, current monitoring data within a current time, monitoring abnormality standards, and historical operation data within the historical period, wherein the monitoring abnormality standards are determination standards for determining whether the monitoring data is abnormal or not; A model processing module, used for creating a data recognition network model, and training the data recognition network model based on the historical monitoring information, the monitoring abnormality standard and the historical operation data to obtain a trained data recognition network model; A data identification module is used to determine whether the current monitoring data meets the monitoring abnormality standard. If so, the time node characteristics and the detection parameter characteristics of the high and low voltage distribution cabinets corresponding to the time node characteristics are determined based on the current monitoring data, and the time node characteristics and the detection parameter characteristics are input into the trained data identification network model for abnormality identification to obtain an abnormality identification result; The equipment maintenance module is used to generate maintenance information according to the abnormality identification result, and send the maintenance information to a target terminal, where the target terminal is a terminal used by maintenance personnel.

8. The remote monitoring system for high and low voltage distribution cabinets based on the Internet of Things according to claim 7 is characterized in that: When the model processing module trains the data recognition network model based on the historical monitoring information, the monitoring abnormality standard and the historical operation data to obtain the trained data recognition network model, it is specifically used to: Determine the association relationship between the historical monitoring information and the historical operation data according to the time node; Determine whether the historical monitoring information meets the monitoring abnormality standard. If so, determine the abnormal monitoring information and abnormal operation data according to the association relationship, and perform data decomposition on the abnormal monitoring information and the abnormal operation data to obtain the monitoring abnormality data of the high and low voltage distribution cabinets at different time nodes; Converting the monitoring abnormal data into a pixel grayscale image, and extracting abnormal time node features and abnormal parameter features in the pixel grayscale image; The abnormal time node characteristics, the abnormal parameter characteristics and the preset maintenance information are input into the data recognition network model for training to obtain a trained data recognition network model.

9. An electronic device, characterized in that: The electronic device includes: 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 a remote monitoring method for high and low voltage distribution cabinets based on the Internet of Things as described in any one of claims 1-6.

10. A computer-readable storage medium, characterized in that: include: A computer program is stored which can be loaded by a processor and execute a remote monitoring method for high and low voltage distribution cabinets based on the Internet of Things as described in any one of claims 1 to 6.

Images