A method and system for monitoring energy equipment data based on edge intelligent control

Abstract

The present application relates to the technical field of data monitoring, and in particular to a method and system for monitoring energy equipment data based on edge intelligent control, the method comprising: obtaining equipment structure information, real-time application data and real-time environment information, performing data format conversion on real-time application data to obtain application transcoding data, collecting parameter change data of different energy equipment, constructing equipment detection models of different energy equipment, judging whether there is at least one energy equipment application anomaly, and if there is at least one energy equipment application anomaly, performing an anomaly analysis on at least one energy equipment based on the equipment detection model and real-time environment information, obtaining an equipment anomaly set and a probability value corresponding to each equipment anomaly in the equipment anomaly set, and binding and marking the equipment anomaly set and the probability value with the equipment detection model according to the structural position. The present application improves the production quality control efficiency of different energy equipment.

Description

Technical Field

[0001] The present application relates to the technical field of data monitoring, and in particular to a method and system for monitoring energy equipment data based on edge intelligent control. Background Art

[0002] With the rapid development of Internet of Things technology, the amount of data generated by various devices and sensors has exploded, which has put forward higher requirements on the speed and efficiency of data processing. In this context, energy equipment data monitoring based on edge intelligent control has emerged as the times require, becoming one of the key technologies to solve the challenge of massive data processing.

[0003] Energy equipment data monitoring based on edge intelligent control is actually a combination of edge computing and intelligent control data collection. Edge computing is a technology that provides edge intelligent services nearby through a distributed open platform that integrates network, computing, storage, and application core capabilities at the edge of the network close to the data source. The core idea is to directly process and analyze data on the local or adjacent network nodes where the data is generated to reduce data transmission delays, improve data processing efficiency, and ensure data privacy and security. Intelligent control data collection is a specific application of edge computing in the field of data collection, which aims to achieve remote data collection, monitoring, and intelligent management of front-end mechanical equipment and intelligent devices through intelligent control and edge processing. This method can not only obtain the operating status and parameters of the equipment in real time, but also perform preliminary data cleaning, filtering, and preprocessing at the edge, providing a high-quality data foundation for subsequent data analysis and application.

[0004] At present, energy equipment data monitoring based on edge intelligent control for different energy equipment tends to be more inclined to the synchronous detection of multiple identical energy equipment. However, for the real-time application data collection of multiple heterogeneous energy equipment, it is necessary to detect the real-time application data of each energy equipment separately according to the different data formats or data interfaces, and infer the operating status of each energy equipment. Multiple energy equipment within the same detection range form information islands due to different data formats, and it is difficult to perform unified fault detection on heterogeneous energy equipment within the same detection range, thereby reducing the production quality control efficiency of different energy equipment. Summary of the invention

[0005] In order to solve at least one of the above-mentioned technical problems, the present application provides an energy equipment data monitoring method and system based on edge intelligent control.

[0006] In the first aspect, the present application provides an energy equipment data monitoring method based on edge intelligent control, which adopts the following technical solutions:

[0007] Obtain equipment construction information, real-time application data, and real-time environmental information of different energy equipment, wherein the equipment construction information is the production and assembly information of the energy equipment, the real-time application data is the real-time operation data generated by different energy equipment during operation obtained by edge intelligent control, and the real-time environmental information is the environmental parameter information of different energy equipment during operation;

[0008] Performing data format conversion on the real-time application data to obtain application transcoded data in a unified data format for different energy devices;

[0009] Collect parameter change data corresponding to different structures in the equipment construction information of different energy equipment under different environmental parameter information after different application durations;

[0010] Building a device detection model for different energy devices based on the device construction information, the application transcoding data, and the parameter change data;

[0011] Performing application detection on each energy device based on the application transcoded data to determine whether there is at least one energy device application anomaly; if there is at least one energy device application anomaly, performing an anomaly analysis on the at least one energy device based on the device detection model and the real-time environment information to obtain a device anomaly set and a probability value corresponding to each device anomaly in the device anomaly set;

[0012] The device anomaly set and the probability value are bound and marked with the device detection model according to the structural position, and the device detection model after the binding mark is controlled to be displayed.

[0013] By adopting the above technical solutions, the core information of different energy devices is comprehensively and accurately obtained, including their equipment construction information, real-time application data and real-time environmental information. This information constitutes the basis for accurate management and optimization of energy equipment. In particular, real-time application data, as a direct product of edge intelligent control, ensures the timeliness and accuracy of data, and provides a reliable basis for subsequent analysis. Next, the real-time application data is formatted uniformly to generate application transcoding data. This step eliminates the barriers between data, allowing data from different sources and formats to be seamlessly integrated, paving the way for building a unified detection model across devices. This not only simplifies the data processing process, but also improves the efficiency and accuracy of data processing. Furthermore, the parameter change data of the equipment under different environmental conditions is collected, thereby revealing the dynamic relationship between equipment performance and environmental factors, injecting the ability of "context perception" into the equipment detection model, enabling it to more accurately predict and identify the behavior patterns of equipment under different conditions. The equipment detection model built based on rich data not only covers the static characteristics of the equipment, but also incorporates its dynamic behavior, realizing comprehensive monitoring of the equipment status. This comprehensive model can more accurately identify abnormal conditions of energy equipment. Based on the application of transcoded data, each energy device is tested in real time. Once an abnormality is found, it is immediately analyzed in depth by combining the device detection model and real-time environmental information. This fast abnormal response mechanism can not only accurately locate the source of the abnormality, but also provide a probability assessment of the occurrence of the abnormality, providing powerful data for timely maintenance measures. Finally, the abnormal information is bound to the equipment detection model and presented in a visual way, which improves the intuitiveness and convenience of energy equipment management. Maintenance personnel can understand the equipment status at a glance, thereby improving the production quality control efficiency of different energy equipment.

[0014] In a possible implementation, the data format conversion of the real-time application data to obtain application transcoded data in a unified data format for different energy devices includes:

[0015] Performing filtering and amplification processing on the real-time application data to obtain operation processing data after filtering out abnormal band data, wherein the abnormal band data is used to represent abnormal real-time application data that is too high or too low due to overload operation of energy equipment;

[0016] The real-time application data is subjected to protocol conversion processing to obtain application transcoded data conforming to the same data transmission protocol.

[0017] In a possible implementation, the constructing of a device detection model for different energy devices based on the device configuration information, the application transcoding data, and the parameter change data includes:

[0018] Determine structural image information, structural material information and relative position information of different parts of different energy devices according to the device construction information;

[0019] Performing object detection on the structural image information to obtain a node coordinate sequence of different components of the energy device;

[0020] An equipment detection model for different energy equipment is constructed based on the node coordinate sequence, the relative position information, the application transcoding data and the parameter change data.

[0021] In a possible implementation, the constructing of the device detection model of different energy devices based on the node coordinate sequence, the relative position information, the application transcoding data and the parameter change data includes:

[0022] Determine a framework node coordinate sequence and an internal node coordinate sequence according to the node coordinate sequence;

[0023] The first node coordinate in the framework node coordinate sequence is used as the origin of the modeling three-dimensional coordinate system, the connecting line segment between the second node coordinate in the framework node coordinate sequence and the first node coordinate is used as the y-axis of the modeling three-dimensional coordinate system, the connecting line segment between the third node coordinate in the framework node coordinate sequence and the first node coordinate is used as the x-axis of the modeling three-dimensional coordinate system, and the connecting line segment between the fourth node coordinate in the framework node coordinate sequence and the first node coordinate is used as the z-axis of the modeling three-dimensional coordinate system;

[0024] Based on the relative position information, the remaining node coordinates in the frame node coordinate system sequence and the internal node coordinate sequence are mapped into the modeling three-dimensional coordinate system to obtain the frame model position corresponding to the frame node coordinate sequence and the internal model position corresponding to the internal node coordinate sequence;

[0025] Acquire a first component model corresponding to the structural image information in a preset model library;

[0026] Collecting material rendering effects corresponding to the structural material information in a preset standard material library, and rendering and updating the first component model based on the material rendering effects to obtain a second component model;

[0027] Binding the application transcoding data and the parameter change data to the second component model accordingly to obtain a third component model;

[0028] The third component model is assembled according to the position of the internal model to obtain equipment detection models of different energy equipment.

[0029] In a possible implementation, performing application detection on each energy device based on the application transcoding data to determine whether there is at least one energy device application abnormality includes:

[0030] Acquire the abnormal data standard of the energy device, and perform data security detection on the application transcoded data based on the abnormal data standard to obtain a data security detection result;

[0031] It is determined whether there is at least one energy device application abnormality according to the data security detection result.

[0032] In a possible implementation, performing an abnormality analysis on the at least one energy device based on the device detection model and the real-time environment information to obtain a device abnormality set and a probability value corresponding to each device abnormality in the device abnormality set includes:

[0033] Determine the real-time application duration corresponding to the energy device according to the application transcoding data;

[0034] According to the real-time environment information and the real-time application duration, the environmental parameter information and the application duration in the device detection model are matched to obtain characteristic data of different structures of the energy device;

[0035] Inputting the characteristic data into a preset abnormality monitoring model for detection to obtain a structural abnormality probability corresponding to the characteristic data;

[0036] Determine whether the structural abnormality probability exceeds a preset abnormality probability. If so, match the abnormal structure position corresponding to the structural abnormality probability with the associated structure position of the historical fault information in the equipment detection model to obtain abnormal data of the abnormal structure position when different abnormalities occur and a set of associated data of other structures without abnormalities when different abnormalities occur at the abnormal structure position;

[0037] Binding the abnormal data and the associated data set according to the abnormal type to obtain multiple sets of feature data sets;

[0038] Determine abnormal feature data and normal feature data existing in the real-time application data based on the data security detection result, and compare the abnormal feature data and the normal feature data with each set of feature data sets in the multiple sets of feature data sets for similarity, to obtain a sequence of feature data sets whose similarities meet a preset similarity;

[0039] Data simplification and binding are performed on the feature data set sequence and the similarity corresponding to each feature data set in the feature data set sequence to obtain a device anomaly set and a probability value corresponding to each device anomaly in the device anomaly set.

[0040] In the second aspect, the present application provides an energy equipment data monitoring system based on edge intelligent control, which adopts the following technical solutions:

[0041] An energy equipment data monitoring system based on edge intelligent control, comprising:

[0042] An information acquisition module is used to acquire device construction information, real-time application data, and real-time environmental information of different energy devices. The device construction information is the production and assembly information of the energy devices. The real-time application data is the real-time operation data generated by different energy devices during operation acquired by edge intelligent control. The real-time environmental information is the environmental parameter information of different energy devices during operation.

[0043] A data conversion module, used to convert the data format of the real-time application data to obtain application transcoded data in a unified data format for different energy devices;

[0044] A data acquisition module is used to collect parameter change data corresponding to different structures in the device construction information of different energy devices under different environmental parameter information after different application durations;

[0045] A model building module, used to build a device detection model for different energy devices based on the device construction information, the application transcoding data and the parameter change data;

[0046] an abnormality analysis module, configured to perform application detection on each energy device based on the application transcoded data, determine whether there is at least one energy device application abnormality, and if there is at least one energy device application abnormality, perform abnormality analysis on the at least one energy device based on the device detection model and the real-time environment information, and obtain a device abnormality set and a probability value corresponding to each device abnormality in the device abnormality set;

[0047] The data binding module is used to bind and mark the device anomaly set and the probability value with the device detection model according to the structural position, and control the display of the device detection model after the binding mark.

[0048] In a possible implementation, when the data conversion module performs data format conversion on the real-time application data to obtain application transcoded data in a unified data format for different energy devices, it is specifically used to:

[0049] Performing filtering and amplification processing on the real-time application data to obtain operation processing data after filtering out abnormal band data, wherein the abnormal band data is used to represent abnormal real-time application data that is too high or too low due to overload operation of energy equipment;

[0050] The real-time application data is subjected to protocol conversion processing to obtain application transcoded data conforming to the same data transmission protocol.

[0051] In another possible implementation, when the model building module builds the device detection model of different energy devices based on the device construction information, the application transcoding data, and the parameter change data, it is specifically used to:

[0052] Determine structural image information, structural material information and relative position information of different parts of different energy devices according to the device construction information;

[0053] Performing object detection on the structural image information to obtain a node coordinate sequence of different components of the energy device;

[0054] An equipment detection model for different energy equipment is constructed based on the node coordinate sequence, the relative position information, the application transcoding data and the parameter change data.

[0055] In another possible implementation, when the model building module builds the device detection model of different energy devices based on the node coordinate sequence, the relative position information, the application transcoding data, and the parameter change data, it is specifically used to:

[0056] Determine a framework node coordinate sequence and an internal node coordinate sequence according to the node coordinate sequence;

[0057] The first node coordinate in the framework node coordinate sequence is used as the origin of the modeling three-dimensional coordinate system, the connecting line segment between the second node coordinate in the framework node coordinate sequence and the first node coordinate is used as the y-axis of the modeling three-dimensional coordinate system, the connecting line segment between the third node coordinate in the framework node coordinate sequence and the first node coordinate is used as the x-axis of the modeling three-dimensional coordinate system, and the connecting line segment between the fourth node coordinate in the framework node coordinate sequence and the first node coordinate is used as the z-axis of the modeling three-dimensional coordinate system;

[0058] Based on the relative position information, the remaining node coordinates in the frame node coordinate system sequence and the internal node coordinate sequence are mapped into the modeling three-dimensional coordinate system to obtain the frame model position corresponding to the frame node coordinate sequence and the internal model position corresponding to the internal node coordinate sequence;

[0059] Acquire a first component model corresponding to the structural image information in a preset model library;

[0060] Collecting material rendering effects corresponding to the structural material information in a preset standard material library, and rendering and updating the first component model based on the material rendering effects to obtain a second component model;

[0061] Binding the application transcoding data and the parameter change data to the second component model accordingly to obtain a third component model;

[0062] The third component model is assembled according to the position of the internal model to obtain equipment detection models of different energy equipment.

[0063] In another possible implementation, when the abnormality analysis module performs application detection on each energy device based on the application transcoding data to determine whether there is at least one energy device application abnormality, it is specifically used to:

[0064] Acquire the abnormal data standard of the energy device, and perform data security detection on the application transcoded data based on the abnormal data standard to obtain a data security detection result;

[0065] It is determined whether there is at least one energy device application abnormality according to the data security detection result.

[0066] In another possible implementation, when the abnormality analysis module performs abnormality analysis on the at least one energy device based on the device detection model and the real-time environment information to obtain a device abnormality set and a probability value corresponding to each device abnormality in the device abnormality set, it is specifically used to:

[0067] Determine the real-time application duration corresponding to the energy device according to the application transcoding data;

[0068] According to the real-time environment information and the real-time application duration, the environmental parameter information and the application duration in the device detection model are matched to obtain characteristic data of different structures of the energy device;

[0069] Inputting the characteristic data into a preset abnormality monitoring model for detection to obtain a structural abnormality probability corresponding to the characteristic data;

[0070] Determine whether the structural abnormality probability exceeds a preset abnormality probability. If so, match the abnormal structure position corresponding to the structural abnormality probability with the associated structure position of the historical fault information in the equipment detection model to obtain abnormal data of the abnormal structure position when different abnormalities occur and a set of associated data of other structures without abnormalities when different abnormalities occur at the abnormal structure position;

[0071] Binding the abnormal data and the associated data set according to the abnormal type to obtain multiple sets of feature data sets;

[0072] Determine abnormal feature data and normal feature data existing in the real-time application data based on the data security detection result, and compare the abnormal feature data and the normal feature data with each set of feature data sets in the multiple sets of feature data sets for similarity, to obtain a sequence of feature data sets whose similarities meet a preset similarity;

[0073] Data simplification and binding are performed on the feature data set sequence and the similarity corresponding to each feature data set in the feature data set sequence to obtain a device anomaly set and a probability value corresponding to each device anomaly in the device anomaly set.

[0074] In a third aspect, the present application provides an electronic device, which adopts the following technical solution:

[0075] at least one processor;

[0076] Memory;

[0077] 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 an energy equipment data monitoring method based on edge intelligent control as described in any one of the first aspects.

[0078] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution:

[0079] A computer-readable storage medium having a computer program stored thereon. When the computer program is executed in a computer, the computer is caused to execute any of the energy equipment data monitoring methods based on edge intelligent control as described in the first aspect.

[0080] In summary, the present application includes at least one of the following beneficial technical effects:

[0081] When collecting operation data of different energy devices, the core information of different energy devices is comprehensively and accurately obtained, including their equipment construction information, real-time application data and real-time environmental information. This information forms the basis for accurate management and optimization of energy equipment. In particular, real-time application data, as a direct product of edge intelligent control, ensures the timeliness and accuracy of data and provides a reliable basis for subsequent analysis. Next, the real-time application data is formatted uniformly to generate application transcoding data. This step eliminates the barriers between data, allowing data from different sources and formats to be seamlessly integrated, paving the way for building a unified detection model across devices. This not only simplifies the data processing process, but also improves the efficiency and accuracy of data processing. Furthermore, the parameter change data of the equipment under different environmental conditions is collected, thereby revealing the dynamic relationship between equipment performance and environmental factors, injecting the ability of "context perception" into the equipment detection model, enabling it to more accurately predict and identify the behavior patterns of equipment under different conditions. The equipment detection model built based on rich data not only covers the static characteristics of the equipment, but also incorporates its dynamic behavior, realizing comprehensive monitoring of the equipment status. This comprehensive model can more accurately identify abnormal conditions of energy equipment. Based on the application of transcoded data, each energy device is tested in real time. Once an abnormality is found, it is immediately analyzed in depth by combining the device detection model and real-time environmental information. This fast abnormal response mechanism can not only accurately locate the source of the abnormality, but also provide a probability assessment of the occurrence of the abnormality, providing powerful data for timely maintenance measures. Finally, the abnormal information is bound to the equipment detection model and presented in a visual way, which improves the intuitiveness and convenience of energy equipment management. Maintenance personnel can understand the equipment status at a glance, thereby improving the production quality control efficiency of different energy equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 A flow chart of an energy equipment data monitoring method based on edge intelligent control provided in an embodiment of the present application.

[0083] Figure 2 A structural schematic diagram of an energy equipment data monitoring system based on edge intelligent control provided in an embodiment of the present application.

[0084] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0085] The following is combined with Figure 1-3 This application is described in further detail.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0090] The embodiment of the present application provides a method for monitoring energy equipment data based on edge intelligent control, 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:

[0091] Step S10: Acquire device construction information, real-time application data, and real-time environmental information of different energy devices.

[0092] Among them, the equipment construction information is the production and assembly information of the energy equipment, the real-time application data is the real-time operation data generated by different energy equipment during operation obtained by edge intelligent control, and the real-time environmental information is the environmental parameter information of different energy equipment during operation.

[0093] For the embodiments of the present application, the equipment construction information represents the production and assembly information of the energy equipment, which specifically covers the structural design, component configuration, assembly process and other detailed information of the equipment, and is the basis for understanding the internal structure and working principle of the equipment. Real-time application data refers to the operating data generated in real time by different energy equipment during operation using edge intelligent control technology, such as current, voltage, temperature, power, etc. These data reflect the immediate working status of the equipment. Real-time environmental information is used to represent the environmental parameter information of different energy equipment during operation, including external environmental conditions such as temperature, humidity, air pressure, vibration, etc., which have an important impact on the operating performance and life of the equipment. For example, in wind power generation equipment, the equipment construction information may include the blade design and gearbox structure of the wind turbine; the real-time application data may include the speed and power generation of the wind turbine; and the real-time environmental information may include wind speed, wind direction, temperature, etc.

[0094] Specifically, a dedicated data acquisition module is configured, which is directly connected to the energy equipment and is responsible for collecting and organizing equipment construction information, real-time application data, and real-time environmental information, and then uploading them to the management system. Optionally, edge computing nodes are used. These nodes are deployed near the equipment and can process the data generated by the equipment in real time, and upload the processed data together with the environmental information. Optionally, a wireless sensor network can also be used. The sensors are distributed around the equipment, responsible for monitoring environmental parameters, and sending data to the management system via a wireless network. It is understandable that other methods can also be used to implement this step, such as using existing Internet of Things platforms or cloud services, as long as they can meet the needs of data acquisition, which is not limited here.

[0095] Step S11: convert the data format of the real-time application data to obtain application transcoded data in a unified data format for different energy devices.

[0096] For the embodiment of the present application, the real-time application data is filtered and amplified to obtain the operation processing data after filtering out the abnormal band data. The abnormal band data is used to represent the abnormal real-time application data that is too high or too low caused by the overload operation of the energy equipment. The real-time application data is subjected to protocol conversion processing to obtain the application transcoded data that complies with the same data transmission protocol.

[0097] In the embodiments of the present application, existing data conversion tools or platforms are used. These tools usually provide a rich data format conversion function, and users only need to configure the corresponding conversion rules. Specifically, for the data of each device, it is necessary to first identify its data format, then convert it according to the predefined rules, and finally output the data in a unified format. It is understandable that other methods can also be used to implement this step, such as using a programming language to write a custom data conversion script, or using machine learning technology to automatically identify and convert data formats, which is not limited here.

[0098] Step S12: collecting parameter change data corresponding to different structures in the equipment construction information of different energy equipment under different environmental parameter information after different application durations.

[0099] Specifically, determine the type of parameters that need to be collected and the collection frequency, and then select appropriate sensors and detection instruments for arrangement and connection. Next, data collection is performed according to a predetermined plan, and the data is stored in a suitable data storage system. Optionally, existing data acquisition systems or platforms are used, which generally provide flexible data acquisition configurations and powerful data processing functions. For each device and environmental parameter, it is necessary to design a corresponding data collection plan and ensure that the collected data is accurate and reliable. After the data collection is completed, the data needs to be cleaned, calibrated and analyzed to obtain useful parameter change data. It is understandable that other methods can also be used to implement this step, such as using remote monitoring technology to achieve real-time data collection and transmission, or using artificial intelligence algorithms to perform intelligent analysis of the collected data, which is not limited here.

[0100] Step S13: constructing device detection models for different energy devices based on device construction information, application transcoding data, and parameter change data.

[0101] For the embodiments of the present application, the structural image information, structural material information and relative position information of different components of different energy devices are determined according to the device construction information, and object target detection is performed on the structural image information to obtain the node coordinate sequence of different components of the energy equipment. Based on the node coordinate sequence, relative position information, application transcoding data and parameter change data, equipment detection models for different energy devices are constructed.

[0102] Specifically, the frame node coordinate sequence and the internal node coordinate sequence are determined according to the node coordinate sequence, the first node coordinate in the frame node coordinate sequence is used as the origin of the modeling three-dimensional coordinate system, the connecting line segment between the second node coordinate and the first node coordinate in the frame node coordinate sequence is used as the y-axis of the modeling three-dimensional coordinate system, the connecting line segment between the third node coordinate in the frame node coordinate sequence and the first node coordinate sequence is used as the x-axis of the modeling three-dimensional coordinate system, the connecting line segment between the fourth node coordinate in the frame node coordinate sequence and the first node coordinate is used as the z-axis of the modeling three-dimensional coordinate system, and the remaining node coordinates in the frame node coordinate sequence and the internal node coordinate sequence are connected based on the relative position information. The node coordinate sequence is mapped to the modeling three-dimensional coordinate system to obtain the frame model position corresponding to the frame node coordinate sequence and the internal model position corresponding to the internal node coordinate sequence, the first component model corresponding to the structural image information in the preset model library is collected, the material rendering effect corresponding to the structural material information in the preset standard material library is collected, and the first component model is rendered and updated based on the material rendering effect to obtain the second component model, the application transcoding data and the parameter change data are correspondingly bound to the second component model to obtain the third component model, and the third component model is assembled according to the internal model position to obtain the equipment detection model of different energy equipment.

[0103] Step S14: Perform application detection on each energy device based on the application transcoding data to determine whether there is at least one energy device application anomaly. If there is at least one energy device application anomaly, perform an anomaly analysis on at least one energy device based on the device detection model and real-time environmental information to obtain a device anomaly set and a probability value corresponding to each device anomaly in the device anomaly set.

[0104] Specifically, the abnormal data standard of the energy device is obtained, and based on the abnormal data standard, data security detection is performed on the application transcoded data to obtain the data security detection result, and it is determined whether there is at least one energy device application abnormality according to the data security detection result.

[0105] For the embodiment of the present application, the real-time application duration corresponding to the energy device is determined according to the application transcoding data, and the real-time environment information and the real-time application duration are matched with the environment parameter information and the application duration in the equipment detection model to obtain the characteristic data of different structures of the energy device, and the characteristic data is input into the preset abnormality monitoring model for detection to obtain the structural abnormality probability corresponding to the characteristic data, and it is determined whether the structural abnormality probability exceeds the preset abnormality probability. If it exceeds, the abnormal structure position corresponding to the structural abnormality probability is matched with the associated structure position of the historical fault information in the equipment detection model to obtain the abnormal data of the abnormal structure position when different abnormalities occur and the abnormal structure position when different abnormalities occur. He has not found an associated data set with an abnormal structure, binds the abnormal data and the associated data set according to the abnormal type to obtain multiple sets of feature data sets, determines the abnormal feature data and normal feature data existing in the real-time application data based on the data security detection result, and compares the abnormal feature data and the normal feature data with each set of feature data sets in the multiple sets of feature data sets for similarity to obtain a feature data set sequence whose similarity meets the preset similarity, performs data simplification and binding on the feature data set sequence and the similarity corresponding to each feature data set in the feature data set sequence to obtain a device abnormality set and a probability value corresponding to each device abnormality in the device abnormality set.

[0106] Step S15: Bind and mark the device anomaly set and probability value with the device detection model according to the structural position, and control the display of the device detection model after the binding mark.

[0107] In the embodiment of the present application, when collecting operation data of different energy devices, the core information of different energy devices is comprehensively and accurately obtained, including their equipment construction information, real-time application data and real-time environment information. This information constitutes the basis for accurate management and optimization of energy equipment, especially real-time application data, as a direct product of edge intelligent control, which ensures the timeliness and accuracy of data and provides a reliable basis for subsequent analysis. Next, the real-time application data is formatted uniformly to generate application transcoding data. This step eliminates the barriers between data, allowing data from different sources and different formats to be seamlessly integrated, paving the way for building a unified detection model across devices. This not only simplifies the data processing process, but also improves the efficiency and accuracy of data processing. Further, the parameter change data of the equipment under different environmental conditions is collected, thereby revealing the dynamic relationship between equipment performance and environmental factors, and injecting the ability of "context perception" into the equipment detection model, so that it can more accurately predict and identify the behavior patterns of equipment under different conditions. The equipment detection model built based on rich data not only covers the static characteristics of the equipment, but also incorporates its dynamic behavior, realizing comprehensive monitoring of the equipment status. This comprehensive model can more accurately identify abnormal conditions of energy equipment. Based on the application of transcoded data, each energy device is tested in real time. Once an abnormality is found, it is immediately analyzed in depth by combining the device detection model and real-time environmental information. This fast abnormal response mechanism can not only accurately locate the source of the abnormality, but also provide a probability assessment of the occurrence of the abnormality, providing powerful data for timely maintenance measures. Finally, the abnormal information is bound to the equipment detection model and presented in a visual way, which improves the intuitiveness and convenience of energy equipment management. Maintenance personnel can understand the equipment status at a glance, thereby improving the production quality control efficiency of different energy equipment.

[0108] The following is an introduction to an energy device data monitoring system based on edge intelligent control provided in an embodiment of the present application. The energy device data monitoring system based on edge intelligent control described below and the energy device data monitoring method based on edge intelligent control described above can be referred to each other. Please refer to Figure 2 , Figure 2 : is a structural diagram of an energy equipment data monitoring system 20 based on edge intelligent control provided in an embodiment of the present application, including:

[0109] The information acquisition module 21 is used to obtain the equipment construction information, real-time application data and real-time environment information of different energy equipment. The equipment construction information is the production and assembly information of the energy equipment. The real-time application data is the real-time operation data generated by different energy equipment during operation obtained by edge intelligent control. The real-time environment information is the environment parameter information of different energy equipment during operation.

[0110] A data conversion module 22, used to convert the data format of the real-time application data to obtain application transcoded data in a unified data format for different energy devices;

[0111] The data collection module 23 is used to collect parameter change data corresponding to different structures in the equipment construction information of different energy equipment under different environmental parameter information after different application time;

[0112] A model building module 24, for building a device detection model for different energy devices based on device construction information, application transcoding data, and parameter change data;

[0113] The abnormality analysis module 25 is used to perform application detection on each energy device based on the application transcoding data to determine whether there is at least one energy device application abnormality. If there is at least one energy device application abnormality, then perform abnormality analysis on at least one energy device based on the device detection model and real-time environment information to obtain a device abnormality set and a probability value corresponding to each device abnormality in the device abnormality set;

[0114] The data binding module 26 is used to bind and mark the device anomaly set and probability value with the device detection model according to the structural position, and control the display of the device detection model after the binding mark.

[0115] In a possible implementation of the embodiment of the present application, the data conversion module 22 is specifically used to convert the data format of the real-time application data to obtain the application transcoded data in a unified data format for different energy devices:

[0116] Perform filtering and amplification processing on the real-time application data to obtain the operation processing data after filtering out the abnormal band data. The abnormal band data is used to indicate the abnormal real-time application data that is too high or too low caused by the overload operation of the energy equipment;

[0117] Perform protocol conversion on real-time application data to obtain application transcoded data that complies with the same data transmission protocol.

[0118] In another possible implementation of the embodiment of the present application, when the model building module 24 builds the device detection model of different energy devices based on the device construction information, the application transcoding data and the parameter change data, it is specifically used to:

[0119] Determine the structural image information, structural material information and relative position information of different parts of different energy equipment according to the equipment construction information;

[0120] Perform object detection on the structural image information to obtain the node coordinate sequence of different parts of the energy equipment;

[0121] Equipment detection models for different energy devices are constructed based on node coordinate sequences, relative position information, application transcoding data, and parameter change data.

[0122] In another possible implementation of the embodiment of the present application, when the model building module 24 builds the device detection model of different energy devices based on the node coordinate sequence, relative position information, application transcoding data and parameter change data, it is specifically used to:

[0123] Determine the frame node coordinate sequence and the internal node coordinate sequence according to the node coordinate sequence;

[0124] The first node coordinate in the framework node coordinate sequence is used as the origin of the modeling three-dimensional coordinate system, the connecting line segment between the second node coordinate and the first node coordinate in the framework node coordinate sequence is used as the y-axis of the modeling three-dimensional coordinate system, the connecting line segment between the third node coordinate in the framework node coordinate sequence and the first node coordinate sequence is used as the x-axis of the modeling three-dimensional coordinate system, and the connecting line segment between the fourth node coordinate in the framework node coordinate sequence and the first node coordinate is used as the z-axis of the modeling three-dimensional coordinate system;

[0125] Based on the relative position information, the remaining node coordinates in the frame node coordinate system sequence and the internal node coordinate sequence are mapped to the modeling three-dimensional coordinate system to obtain the frame model position corresponding to the frame node coordinate sequence and the internal model position corresponding to the internal node coordinate sequence;

[0126] Acquire a first component model corresponding to the structural image information in a preset model library;

[0127] Collecting material rendering effects corresponding to the structural material information in a preset standard material library, and rendering and updating the first component model based on the material rendering effects to obtain a second component model;

[0128] Binding the application transcoded data and the parameter change data to the second component model accordingly to obtain a third component model;

[0129] The third component model is assembled according to the position of the internal model to obtain equipment detection models of different energy equipment.

[0130] In another possible implementation of the embodiment of the present application, when the abnormality analysis module 25 performs application detection on each energy device based on the application transcoding data to determine whether there is at least one energy device application abnormality, it is specifically used to:

[0131] Acquire abnormal data standards of energy equipment, and perform data security detection on application transcoded data based on the abnormal data standards to obtain data security detection results;

[0132] Determine whether there is at least one energy device application abnormality based on the data security detection result.

[0133] In another possible implementation of the embodiment of the present application, the abnormality analysis module 25 performs abnormality analysis on at least one energy device based on the device detection model and the real-time environmental information to obtain a device abnormality set and a probability value corresponding to each device abnormality in the device abnormality set, specifically for:

[0134] Determine the real-time application duration corresponding to the energy device based on the application transcoding data;

[0135] According to the real-time environment information and real-time application duration, the environmental parameter information and application duration in the equipment detection model are matched to obtain the characteristic data of different structures of energy equipment;

[0136] The characteristic data is input into a preset abnormality monitoring model for detection to obtain the structural abnormality probability corresponding to the characteristic data;

[0137] Determine whether the structural abnormality probability exceeds the preset abnormality probability. If it exceeds, match the abnormal structure position corresponding to the structural abnormality probability with the associated structure position of the historical fault information in the equipment detection model to obtain the abnormal data of the abnormal structure position when different abnormalities occur and the associated data set of other structures without abnormalities when different abnormalities occur at the abnormal structure position;

[0138] Bind the abnormal data and the associated data sets according to the abnormal type to obtain multiple sets of feature data sets;

[0139] Based on the data security detection results, abnormal feature data and normal feature data existing in the real-time application data are determined, and the abnormal feature data and the normal feature data are respectively compared with each set of feature data sets in the multiple sets of feature data sets for similarity, so as to obtain a sequence of feature data sets whose similarity meets the preset similarity;

[0140] Data simplification and binding are performed on the feature data set sequence and the similarity corresponding to each feature data set in the feature data set sequence to obtain a device anomaly set and a probability value corresponding to each device anomaly in the device anomaly set.

[0141] 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 3The 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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 energy equipment data monitoring method based on edge intelligent control are implemented.

[0149] 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.

[0150] 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.

[0151] In actual application scenarios, the energy equipment data monitoring method based on edge intelligent control provided in this application can be integrated into a functional module in a software system, and this software system may also include other related functional modules.

[0152] 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 method for monitoring energy equipment data based on edge intelligent control, characterized in that: include: Obtain equipment construction information, real-time application data, and real-time environmental information of different energy equipment, wherein the equipment construction information is the production and assembly information of the energy equipment, the real-time application data is the real-time operation data generated by different energy equipment during operation obtained by edge intelligent control, and the real-time environmental information is the environmental parameter information of different energy equipment during operation; Performing data format conversion on the real-time application data to obtain application transcoded data in a unified data format for different energy devices; Collect parameter change data corresponding to different structures in the equipment construction information of different energy equipment under different environmental parameter information after different application durations; Building a device detection model for different energy devices based on the device construction information, the application transcoding data, and the parameter change data; Wherein, constructing a device detection model for different energy devices based on the device construction information, the application transcoding data and the parameter change data includes: Determine structural image information, structural material information and relative position information of different parts of different energy devices according to the device construction information; Performing object detection on the structural image information to obtain a node coordinate sequence of different components of the energy device; Building equipment detection models for different energy equipment based on the node coordinate sequence, the relative position information, the application transcoding data, and the parameter change data; Wherein, the device detection model of different energy devices is constructed based on the node coordinate sequence, the relative position information, the application transcoding data and the parameter change data, including: Determine a framework node coordinate sequence and an internal node coordinate sequence according to the node coordinate sequence; The first node coordinate in the framework node coordinate sequence is used as the origin of the modeling three-dimensional coordinate system, the connecting line segment between the second node coordinate in the framework node coordinate sequence and the first node coordinate is used as the y-axis of the modeling three-dimensional coordinate system, the connecting line segment between the third node coordinate in the framework node coordinate sequence and the first node coordinate is used as the x-axis of the modeling three-dimensional coordinate system, and the connecting line segment between the fourth node coordinate in the framework node coordinate sequence and the first node coordinate is used as the z-axis of the modeling three-dimensional coordinate system; Based on the relative position information, the remaining node coordinates in the frame node coordinate system sequence and the internal node coordinate sequence are mapped into the modeling three-dimensional coordinate system to obtain the frame model position corresponding to the frame node coordinate sequence and the internal model position corresponding to the internal node coordinate sequence; Acquire a first component model corresponding to the structural image information in a preset model library; Collecting material rendering effects corresponding to the structural material information in a preset standard material library, and rendering and updating the first component model based on the material rendering effects to obtain a second component model; Binding the application transcoding data and the parameter change data to the second component model accordingly to obtain a third component model; Assembling the third component model according to the position of the internal model to obtain equipment detection models of different energy equipment; Performing application detection on each energy device based on the application transcoded data to determine whether there is at least one energy device application anomaly; if there is at least one energy device application anomaly, performing an anomaly analysis on the at least one energy device based on the device detection model and the real-time environment information to obtain a device anomaly set and a probability value corresponding to each device anomaly in the device anomaly set; The device anomaly set and the probability value are bound and marked with the device detection model according to the structural position, and the device detection model after the binding mark is controlled to be displayed.

2. According to claim 1, a method for monitoring energy equipment data based on edge intelligent control is characterized in that: The data format conversion of the real-time application data to obtain application transcoded data in a unified data format for different energy devices includes: Performing filtering and amplification processing on the real-time application data to obtain operation processing data after filtering out abnormal band data, wherein the abnormal band data is used to represent abnormal real-time application data that is too high or too low due to overload operation of energy equipment; The real-time application data is subjected to protocol conversion processing to obtain application transcoded data conforming to the same data transmission protocol.

3. According to claim 1, a method for monitoring energy equipment data based on edge intelligent control is characterized in that: The performing application detection on each energy device based on the application transcoded data to determine whether at least one energy device application is abnormal includes: Acquire the abnormal data standard of the energy device, and perform data security detection on the application transcoded data based on the abnormal data standard to obtain a data security detection result; It is determined whether there is at least one energy device application abnormality according to the data security detection result.

4. According to claim 3, a method for monitoring energy equipment data based on edge intelligent control is characterized in that: The performing anomaly analysis on the at least one energy device based on the device detection model and the real-time environmental information to obtain a device anomaly set and a probability value corresponding to each device anomaly in the device anomaly set includes: Determine the real-time application duration corresponding to the energy device according to the application transcoding data; According to the real-time environment information and the real-time application duration, the environmental parameter information and the application duration in the device detection model are matched to obtain characteristic data of different structures of the energy device; Inputting the characteristic data into a preset abnormality monitoring model for detection to obtain a structural abnormality probability corresponding to the characteristic data; Determine whether the structural abnormality probability exceeds a preset abnormality probability. If so, match the abnormal structure position corresponding to the structural abnormality probability with the associated structure position of the historical fault information in the equipment detection model to obtain abnormal data of the abnormal structure position when different abnormalities occur and a set of associated data of other structures without abnormalities when different abnormalities occur at the abnormal structure position; Binding the abnormal data and the associated data set according to the abnormal type to obtain multiple sets of feature data sets; Determine abnormal feature data and normal feature data existing in the real-time application data based on the data security detection result, and compare the abnormal feature data and the normal feature data with each set of feature data sets in the multiple sets of feature data sets for similarity, to obtain a sequence of feature data sets whose similarity meets a preset similarity; Data simplification and binding are performed on the feature data set sequence and the similarity corresponding to each feature data set in the feature data set sequence to obtain a device anomaly set and a probability value corresponding to each device anomaly in the device anomaly set.

5. An energy equipment data monitoring system based on edge intelligent control, characterized in that: include: An information acquisition module is used to acquire device construction information, real-time application data, and real-time environmental information of different energy devices. The device construction information is the production and assembly information of the energy devices. The real-time application data is the real-time operation data generated by different energy devices during operation acquired by edge intelligent control. The real-time environmental information is the environmental parameter information of different energy devices during operation. A data conversion module, used to convert the data format of the real-time application data to obtain application transcoded data in a unified data format for different energy devices; A data acquisition module is used to collect parameter change data corresponding to different structures in the device construction information of different energy devices under different environmental parameter information after different application durations; A model building module, used to build a device detection model for different energy devices based on the device construction information, the application transcoding data and the parameter change data; Wherein, when the model building module builds the device detection model of different energy devices based on the device construction information, the application transcoding data and the parameter change data, it is specifically used to: Determine structural image information, structural material information and relative position information of different parts of different energy devices according to the device construction information; Performing object detection on the structural image information to obtain a node coordinate sequence of different components of the energy device; Building equipment detection models for different energy equipment based on the node coordinate sequence, the relative position information, the application transcoding data, and the parameter change data; Wherein, when the model building module builds the device detection model of different energy devices based on the node coordinate sequence, the relative position information, the application transcoding data and the parameter change data, it is specifically used to: Determine a framework node coordinate sequence and an internal node coordinate sequence according to the node coordinate sequence; The first node coordinate in the framework node coordinate sequence is used as the origin of the modeling three-dimensional coordinate system, the connecting line segment between the second node coordinate in the framework node coordinate sequence and the first node coordinate is used as the y-axis of the modeling three-dimensional coordinate system, the connecting line segment between the third node coordinate in the framework node coordinate sequence and the first node coordinate is used as the x-axis of the modeling three-dimensional coordinate system, and the connecting line segment between the fourth node coordinate in the framework node coordinate sequence and the first node coordinate is used as the z-axis of the modeling three-dimensional coordinate system; Based on the relative position information, the remaining node coordinates in the frame node coordinate system sequence and the internal node coordinate sequence are mapped into the modeling three-dimensional coordinate system to obtain the frame model position corresponding to the frame node coordinate sequence and the internal model position corresponding to the internal node coordinate sequence; Acquire a first component model corresponding to the structural image information in a preset model library; Collecting material rendering effects corresponding to the structural material information in a preset standard material library, and rendering and updating the first component model based on the material rendering effects to obtain a second component model; Binding the application transcoding data and the parameter change data to the second component model accordingly to obtain a third component model; Assembling the third component model according to the position of the internal model to obtain equipment detection models of different energy equipment; an abnormality analysis module, configured to perform application detection on each energy device based on the application transcoded data, determine whether there is at least one energy device application abnormality, and if there is at least one energy device application abnormality, perform abnormality analysis on the at least one energy device based on the device detection model and the real-time environment information, and obtain a device abnormality set and a probability value corresponding to each device abnormality in the device abnormality set; The data binding module is used to bind and mark the device anomaly set and the probability value with the device detection model according to the structural position, and control the display of the device detection model after the binding mark.

6. According to claim 5, an energy equipment data monitoring system based on edge intelligent control is characterized in that: When the data conversion module performs data format conversion on the real-time application data to obtain application transcoded data in a unified data format for different energy devices, it is specifically used to: Performing filtering and amplification processing on the real-time application data to obtain operation processing data after filtering out abnormal band data, wherein the abnormal band data is used to represent abnormal real-time application data that is too high or too low due to overload operation of energy equipment; The real-time application data is subjected to protocol conversion processing to obtain application transcoded data conforming to the same data transmission protocol.

7. 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 an energy equipment data monitoring method based on edge intelligent control as described in any one of claims 1-4.

8. A computer-readable storage medium, characterized in that: include: A computer program is stored which can be loaded by a processor and executes a method for monitoring energy equipment data based on edge intelligent control as described in any one of claims 1 to 4.

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