Substation monitoring sharing system and method, device, electronic equipment and storage medium

Abstract

The present disclosure relates to a substation monitoring sharing system, method, device, electronic equipment and storage medium, and relates to the technical field of cloud computing and smart substation system. The system comprises a cloud node and an edge node, wherein: the cloud node is deployed in a central console, is configured to construct a monitoring point table, and is configured to send corresponding content of the monitoring point table to the edge node; the cloud node is configured to send a control command to the edge node, receive monitoring videos and images uploaded by each edge node, and store the monitoring videos and images in a classified manner for calling by a cloud external system; and the edge node is deployed in each substation, is configured to acquire monitoring videos, acquire monitoring images and perform image processing, receive a control command sent by the edge node and perform corresponding operations, upload the monitoring videos and images to the cloud node, and store the monitoring videos and images in a classified manner for calling by an edge external system. In the present disclosure, the monitoring videos and images of the substations are shared by each system, and the repeated transmission and storage of the monitoring videos and images are reduced.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of cloud computing and substation control, and particularly relates to a substation monitoring sharing system and method, device, electronic equipment and storage medium. BACKGROUND

[0002] In recent years, video monitoring technology has been more and more widely applied in the operation and maintenance work of substations. By installing monitoring cameras in substations, various devices, areas and personnel states in the substations can be monitored all day long. Through intelligent analysis and processing of monitoring videos and images by artificial intelligence algorithms, the purpose of remotely monitoring and intelligently analyzing and alarming the states of devices and personnel in the substations can be achieved.

[0003] However, with the in-depth application of digital technology in the power grid, especially the wide application of video images, the power internal network bandwidth has become one of the main bottlenecks restricting the digital transformation of the power grid. In order to meet the needs of different operation and maintenance work, the video of the same monitoring camera usually needs to be used by multiple different systems in the substation and deployed in the city company or provincial end, so that a large amount of monitoring videos and images are repeatedly transmitted and stored, which aggravates the impact of limited communication bandwidth.

[0004] In order to solve the above problems, the present disclosure provides a substation monitoring sharing system and method, device, electronic equipment and storage medium. SUMMARY

[0005] The present disclosure provides a substation monitoring sharing system and method, device, electronic equipment and storage medium to at least solve the problem that a large amount of monitoring videos and images are repeatedly transmitted and stored in the related art. The technical solutions of the present disclosure are as follows:

[0006] According to a first aspect of the embodiments of the present disclosure, a substation monitoring sharing system is provided, comprising a cloud node and an edge node:

[0007] The cloud node is deployed in a central control console, configured to construct a monitoring point table for each substation, and send the corresponding content of the monitoring point table to the edge node deployed in each substation according to the position of each monitoring point; send control commands to the edge node, and receive monitoring videos and monitoring images uploaded by each edge node; and classify and store the monitoring videos and monitoring images for calling by external systems of the cloud.

[0008] Edge nodes: Deployed in various substations, they are used to obtain monitoring videos based on the corresponding content of the received monitoring point list; obtain monitoring images from the monitoring videos and perform image processing on the monitoring images; receive control commands sent by edge nodes and execute corresponding operations, as well as upload monitoring videos and monitoring images to cloud nodes; and classify and store monitoring videos and video images for external edge systems to access.

[0009] In one exemplary embodiment, the cloud node includes a cloud communication module, a cloud image processing module, a cloud storage module, a cloud external service interface module, and a cloud task scheduling module, wherein:

[0010] The aforementioned cloud communication module is used to receive monitoring videos and images of the emergency power station uploaded by the edge nodes, and to send control commands to the edge nodes.

[0011] The aforementioned cloud-based image processing module is used to perform advanced image processing and analysis on the surveillance videos and images uploaded by each edge node.

[0012] The aforementioned cloud storage module is used to classify and store surveillance videos and images processed by the cloud image processing module;

[0013] The aforementioned cloud-based external service interface module is used to provide an interface to external cloud systems, enabling these systems to request the required surveillance videos and images from the cloud storage module.

[0014] The aforementioned cloud-based task scheduling module is used to implement the functions of task distribution, scheduling, and control.

[0015] In one exemplary embodiment, the aforementioned edge node includes a camera access module, an edge image processing module, an edge storage module, an edge host computer communication module, an edge external service interface module, and an edge task scheduling module, wherein:

[0016] The aforementioned camera access module is connected to each camera in the substation and is used to acquire monitoring video from the cameras;

[0017] The aforementioned edge image processing module is used to acquire surveillance video from the camera access module, extract surveillance images from the surveillance video, perform image processing on the surveillance images, and add type labels to the surveillance images based on relevant information, including the category information and alarm information of the surveillance images.

[0018] The aforementioned edge storage module is used to acquire monitoring images and videos from the edge image processing module, and to classify and store the monitoring images and videos according to type tags.

[0019] The aforementioned edge-end host computer communication module is used to receive control commands sent by the cloud communication module and execute corresponding operations, as well as to upload monitoring videos and monitoring images to the cloud communication module;

[0020] The aforementioned edge external service interface module is used to provide an interface to the edge external system, so that the edge external system can request the required monitoring video and monitoring images from the edge storage module;

[0021] The aforementioned edge task scheduling module is used to implement the functions of task distribution, scheduling, and control.

[0022] According to a second aspect of the present disclosure, a substation monitoring sharing method is provided, comprising:

[0023] The cloud node builds a monitoring point table for each substation and sets the sampling frequency and reporting strategy for each monitoring point in the monitoring point table. Based on the location of each monitoring point, the corresponding content of the monitoring point table, the sampling frequency and the reporting strategy are sent to the edge nodes deployed in each substation.

[0024] Edge nodes collect monitoring videos of monitoring points in the substation according to the sampling frequency, perform image processing on the monitoring, classify and store the monitoring videos and images according to the processing results, so that external edge systems can obtain monitoring videos and images from the edge nodes; and upload monitoring videos and images to cloud nodes according to the above strategy.

[0025] The cloud node receives surveillance videos and images, categorizes and stores them, so that external systems can retrieve the surveillance videos and images from the cloud node.

[0026] In one exemplary embodiment, the above-described substation monitoring sharing method further includes:

[0027] The cloud nodes determine the identification algorithm for each monitoring point and send it to the corresponding edge nodes;

[0028] After the edge node collects the monitoring video of the monitoring point, it obtains the monitoring image from the monitoring video and identifies the monitoring image through the recognition algorithm corresponding to the monitoring point to obtain the image recognition result.

[0029] In one exemplary embodiment, the monitoring points include remote signaling points, and the reporting strategy includes:

[0030] When image recognition results show a change in the status of the remote signaling point, the monitoring image and image recognition results are uploaded to the cloud node; and

[0031] When the image recognition result shows that the status of the remote signaling point remains unchanged, the confidence level of the monitoring image is calculated according to the preset formula. When the confidence level is lower than the preset threshold, the monitoring image and the image recognition result are uploaded to the cloud node.

[0032] In one exemplary embodiment, the monitoring points include telemetry points, and the reporting strategy includes:

[0033] The difference between the image recognition results of the telemetry points and the historical image recognition results is determined;

[0034] When the difference is greater than the preset threshold, the image recognition result and the monitoring image are uploaded to the cloud node. The above-mentioned historical image recognition result is the image recognition result of the previous remote sensing point before the current image recognition result.

[0035] Otherwise, the confidence level of the monitoring image is calculated according to a preset formula. When the confidence level is lower than a preset threshold, the monitoring image and image recognition results are uploaded to the cloud node.

[0036] In one exemplary embodiment, the aforementioned external edge system acquires surveillance video and surveillance images from edge nodes, including:

[0037] The external edge system matches corresponding monitoring points based on the desired surveillance video and images, and retrieves and obtains the surveillance video and images from the edge nodes; and

[0038] The aforementioned external cloud system obtains surveillance video and images from cloud nodes, including:

[0039] The external cloud system matches the corresponding monitoring points based on the monitoring videos and images to be acquired, and retrieves the monitoring videos and images from the cloud nodes.

[0040] In one exemplary embodiment, after retrieving and acquiring monitoring videos and images from the cloud node as described above, the substation monitoring sharing method further includes:

[0041] Determine whether the surveillance video and images meet the sampling requirements. If not, retrieve new surveillance video and images from the corresponding edge nodes through the cloud nodes.

[0042] According to a third aspect of the present disclosure, a substation monitoring sharing device is provided, comprising:

[0043] The cloud node setting module is configured to execute the cloud node to build a monitoring point table for each substation, and set the sampling frequency and reporting strategy for each monitoring point in the monitoring point table. Based on the location of each monitoring point, the corresponding content of the monitoring point table, the sampling frequency and the reporting strategy are sent to the edge nodes deployed in each substation.

[0044] The edge monitoring sharing module is configured to execute the edge node to collect monitoring videos of monitoring points in the substation according to the sampling frequency, perform image processing on the monitoring, classify and store the monitoring videos and monitoring images according to the processing results, so that external edge systems can obtain the monitoring videos and monitoring images from the edge node; and upload the monitoring videos and monitoring images to the cloud node according to the above reporting strategy.

[0045] The cloud monitoring and sharing module is configured to receive monitoring videos and images from cloud nodes, classify and store the monitoring videos and images, so that external cloud systems can obtain the monitoring videos and images from the cloud nodes.

[0046] According to a fourth aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the substation monitoring sharing method described in any of the preceding embodiments.

[0047] According to a fifth aspect of the present disclosure, a storage medium is provided that, when instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to perform the substation monitoring sharing method described in any of the preceding claims.

[0048] According to a sixth aspect of the present disclosure, a computer program product is provided, which, when executed by a processor, implements the substation monitoring and sharing method described in any of the preceding claims.

[0049] The technical solutions provided by the embodiments of this disclosure have at least the following beneficial effects:

[0050] The substation monitoring and sharing system provided in this embodiment includes cloud nodes deployed at a central control console and edge nodes deployed at each substation. The cloud nodes are used to construct a monitoring point table for each substation and send the corresponding content of the monitoring point table to the edge nodes deployed at each substation based on the location of each monitoring point; send control commands to the edge nodes and receive monitoring videos and images uploaded by each edge node; classify and store the monitoring videos and images for use by external cloud systems; the edge nodes are used to obtain monitoring videos based on the corresponding content of the received monitoring point table; obtain monitoring images from the monitoring videos and perform image processing on the monitoring images; receive control commands sent by the edge nodes and execute corresponding operations, and upload monitoring videos and images to the cloud nodes; classify and store the monitoring videos and images for use by external edge systems. This disclosure adopts a cloud-edge coordination approach to uniformly store the monitoring videos and images of each substation to cloud nodes and edge nodes. When any external system within the substation or deployed at the municipal or provincial level needs to obtain the monitoring videos and images of the substation, it can obtain them from the cloud nodes or edge nodes, thereby avoiding the repeated transmission and storage of a large number of monitoring videos and images and alleviating the pressure on communication bandwidth.

[0051] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0052] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure, and are not intended to unduly limit this disclosure.

[0053] Figure 1 This is a schematic diagram illustrating the system architecture of a substation monitoring and sharing system according to an exemplary embodiment;

[0054] Figure 2 This is a schematic diagram of the system architecture of a substation monitoring and sharing system according to a specific embodiment of an exemplary model;

[0055] Figure 3 This is a flowchart illustrating a substation monitoring sharing method according to an exemplary embodiment;

[0056] Figure 4 This is a block diagram illustrating a substation monitoring sharing device according to an exemplary embodiment;

[0057] Figure 5 This is a schematic diagram of the structure of a computer system for an electronic device according to an exemplary embodiment. Detailed Implementation

[0058] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this disclosure more comprehensive and complete, and to fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced with one or more of these specific details omitted, or other methods, components, apparatus, steps, etc., can be employed. In other instances, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0059] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0060] Figure 1 A schematic diagram of the system architecture of a substation monitoring and sharing system according to an embodiment of this disclosure is shown. Figure 1 As shown, the substation monitoring and sharing system includes cloud nodes deployed at the central control console and edge nodes deployed at each substation.

[0061] The aforementioned cloud nodes are used to build a monitoring point table for each substation, and send the corresponding content of the monitoring point table to the edge nodes deployed in each substation according to the location of each monitoring point; send control commands to the edge nodes, and receive monitoring videos and images uploaded by each edge node; classify and store the monitoring videos and images for external cloud systems to access.

[0062] The aforementioned edge nodes are used to obtain monitoring videos based on the corresponding content of the received monitoring point list; obtain monitoring images from the monitoring videos and perform image processing on the monitoring images; receive control commands sent by the edge nodes and execute corresponding operations, as well as upload monitoring videos and monitoring images to cloud nodes; and classify and store monitoring videos and video images for external edge systems to access.

[0063] For example, the aforementioned cloud nodes and edge nodes can be deployed on servers; it is understood that... Figure 1The number of edge nodes can be determined based on the actual situation of substations in the power grid, and this example implementation does not impose any special limitations on this.

[0064] In one exemplary embodiment, the structure of the above-mentioned substation monitoring sharing system is as follows: Figure 2 As shown below, in conjunction with Figure 2 The above-mentioned substation monitoring sharing system will be described in detail:

[0065] Figure 2 The substation monitoring sharing system shown consists of a cloud node deployed at the central control terminal and N edge nodes deployed at each substation. The cloud node includes a cloud communication module, a cloud image processing module, a cloud storage module, a cloud external service interface module, and a cloud task scheduling module; the edge nodes include a camera access module, an edge image processing module, an edge storage module, an edge host computer communication module, an edge external service interface module, and an edge task scheduling module.

[0066] The cloud communication module deployed in the cloud node and the edge host computer communication module deployed in each edge node have a communication link, providing a channel for communication between the cloud node and the edge node.

[0067] Furthermore, within the cloud node, the cloud task scheduling module maintains communication links with the cloud communication module, cloud image processing module, and cloud storage module. This allows the cloud task scheduling module to distribute tasks and perform unified scheduling and control over other modules within the cloud node, thereby enabling the cloud node's functionality. In addition to communication links with the edge-end host computer communication module and the cloud task scheduling module, the aforementioned cloud communication module also maintains communication links with the cloud image processing module and the cloud storage module. This allows the cloud image processing module to retrieve monitoring videos and images uploaded by the edge node from the cloud communication module, perform advanced image processing, and store the monitoring videos and images in the cloud storage module. The cloud storage module also maintains communication links with the cloud external service interface module and external systems, enabling external systems to request or store necessary monitoring videos and images from the cloud storage module through the cloud external service interface module.

[0068] The functions of each module in the aforementioned cloud node are explained in detail below:

[0069] The aforementioned cloud communication module is used to receive monitoring videos and images of the substation uploaded by edge nodes, and to send control commands to the edge nodes. For example, the control commands can be commands to upload monitoring videos and images. For instance, when a cloud node needs to obtain a monitoring image of a specific monitoring point in substation A, it can send a command to the edge node's host computer communication module to upload the monitoring video and image, so that the edge node's host computer communication module can upload the corresponding monitoring video and image.

[0070] The aforementioned cloud-based image processing module is used to perform advanced image processing and analysis on the monitoring videos and images uploaded by each edge node. For example, this advanced image processing and analysis can be used for reviewing or comparing images uploaded by edge nodes. Reviewing refers to manually examining the uploaded monitoring videos and images. Horizontal comparison is used in scenarios where it is necessary to compare certain content in monitoring videos or images from multiple substations. It is understood that the advanced image processing and analysis mentioned in this application may also be other image processing methods depending on the actual business requirements.

[0071] The aforementioned cloud storage module is used to classify and store surveillance videos and images processed by the cloud image processing module for use by external cloud systems. After each edge node acquires surveillance video, it performs image processing on the acquired video to add category tags to the surveillance images, and then uploads the surveillance video and images to the cloud node. The cloud node can then use this cloud storage module to classify and store the surveillance videos and images according to the category tags.

[0072] The aforementioned cloud-based external service interface module provides an interface to external cloud systems, enabling these systems to request necessary surveillance videos and images from the cloud storage module. For example, under the unified scheduling of the cloud task scheduling module, this cloud-based external service interface module can provide surveillance video and image retrieval services to the external monitoring systems at the centralized control terminal.

[0073] The aforementioned cloud-based task scheduling module is used to realize the overall task distribution, scheduling, and control functions of the entire system, and is the core driving module of the entire image sharing system.

[0074] Within the edge nodes, the edge task scheduling module maintains communication links with the edge image processing module, edge storage module, and edge host computer communication module. These links facilitate task distribution, unified scheduling, and control of other modules within the edge nodes, enabling the implementation of various functions within the edge nodes. The edge image processing module also maintains a communication link with the camera access module, allowing it to control cameras deployed in various substations to acquire monitoring video under the control of the edge task scheduling module. Furthermore, the edge external service interface module maintains communication links with the edge storage module and external systems, enabling external systems to request monitoring video and images from the edge storage module through the edge external service interface module.

[0075] The functions of each module in the aforementioned cloud node are explained in detail below:

[0076] The aforementioned camera access module is connected to each camera within the substation and is used to acquire monitoring video from the cameras. For example, by connecting to each video camera in the substation, the camera access module can send control commands such as turning, zooming, and taking pictures to the video cameras, and acquire real-time monitoring video streams from the video cameras.

[0077] The aforementioned edge image processing module is used to acquire surveillance video from the camera access module, extract surveillance images from the surveillance video, perform image processing on the surveillance images, and add type tags to the surveillance images based on relevant information, including the category information and alarm information of the surveillance images.

[0078] For example, the edge image processing module can obtain the real-time monitoring video stream from the camera access module, process the monitoring video stream according to the task issued by the edge task scheduling module, obtain the monitoring image, calculate the recognition result of the image and whether an alarm is triggered according to the specified algorithm of the monitoring point corresponding to the monitoring image and the type of result to be identified and the alarm threshold, and label the image with a type according to the type of the monitoring point and whether an alarm is triggered.

[0079] The aforementioned edge storage module is used to acquire surveillance images and videos from the edge image processing module, and classify and store the surveillance images and videos according to type tags. For example, this edge storage module can acquire video surveillance and images with type tags added from the edge image processing module, and classify and store the surveillance videos and images according to the type tags for retrieval by other modules.

[0080] The aforementioned edge-end host computer communication module is used to receive control commands sent by the cloud communication module and execute corresponding operations, as well as to upload monitoring videos and images to the cloud communication module. Specifically, this edge-end host computer communication module communicates with the cloud communication module in the aforementioned cloud node, receives various control information from the cloud, and uploads corresponding monitoring videos and images.

[0081] The aforementioned edge-side external service interface module provides an interface to edge-side external systems, enabling these systems to request necessary monitoring videos and images from the edge-side storage module. For example, under the unified scheduling of the edge-side task scheduling module, this edge-side external service interface module can provide monitoring video and image retrieval services to external monitoring systems within the substation.

[0082] The aforementioned edge task scheduling module is used to implement task distribution, scheduling, and control functions, and is the core control module of the edge node. For example, this edge task scheduling module can, through unified scheduling of other modules, realize tasks such as monitoring image slicing, analysis, processing, and retrieval in the edge node.

[0083] In summary, the embodiments provided by this disclosure... Figure 2 The substation monitoring sharing system shown in the figure achieves the sharing of monitoring videos and images between various monitoring systems within the substation and between various monitoring systems within the substation and the central control terminal through cloud-edge node access and collaborative control deployed in the power grid business middleware. This avoids the repeated transmission and storage of monitoring videos and images, and alleviates the bandwidth pressure on the power grid.

[0084] This disclosure also provides a substation monitoring sharing method, applied to the aforementioned substation monitoring sharing system. For example... Figure 3 As shown, the substation monitoring sharing method includes the following steps:

[0085] In step S310, the cloud node constructs a monitoring point table for each substation and sets the sampling frequency and reporting strategy for each monitoring point in the monitoring point table. Based on the location of each monitoring point, the corresponding content of the monitoring point table, the sampling frequency and the reporting strategy are sent to the edge nodes deployed in each substation.

[0086] The aforementioned monitoring point table is a list of monitoring points in each substation established by the cloud node. This table includes all monitoring points in all substations. Optionally, the monitoring points in the table are not limited by the monitoring scope and content of a specific monitoring system, and can include monitoring of primary equipment, secondary equipment, auxiliary control equipment, and security aspects in the substation. Items in the table may include information such as the monitoring point name, the substation to which it belongs, the region to which it belongs, the interval to which it belongs, the equipment name, the meter type, the storage type, and the identification type. Table 1 shows an example of a portion of the content in the monitoring point table.

[0087] Table 1:

[0088]

[0089] In this example implementation, the cloud node can establish the aforementioned monitoring point table through the cloud task scheduling module. After establishing the monitoring point table, the corresponding content in the monitoring point table is sent to the edge nodes of each substation according to the location of each monitoring point in the table. For example, assuming that there are 5 monitoring points in the monitoring point table located in substation A, the cloud node will send the content related to these 5 monitoring points in the monitoring point table to the edge node deployed in substation A.

[0090] For example, the algorithm used for intelligent identification, the results to be identified, alarm thresholds, etc., can also be set for each monitoring point in the cloud task scheduling module mentioned above, and then sent to the edge nodes of the emergency response power station, so that after the edge nodes collect the monitoring video of the monitoring point, they can obtain the monitoring image from the monitoring video and identify the monitoring image through the identification algorithm corresponding to the monitoring point to obtain the image recognition result.

[0091] Furthermore, the aforementioned cloud-based task scheduling module can also set sampling frequencies and reporting strategies for each monitoring point and distribute these settings to the corresponding edge nodes. The sampling frequency determines the sampling time interval for the edge nodes to sample the monitoring videos of each monitoring point in the aforementioned monitoring point table. The reporting strategy refers to the strategy by which each monitoring point uploads its monitoring videos to the cloud node.

[0092] For example, when the above-mentioned monitoring point is a remote signaling point, the reporting strategy can be as follows: when the image recognition result shows that the status of the remote signaling point has changed, the monitoring image and the image recognition result are uploaded to the cloud node; and when the image recognition result shows that the status of the remote signaling point remains unchanged, the confidence level of the monitoring image is calculated according to a preset formula, and when the confidence level is lower than a preset threshold, the monitoring image and the image recognition result are uploaded to the cloud node.

[0093] Taking the aforementioned remote signaling points as switches or lights as an example, the corresponding image recognition results are switch on or off, light on or off, etc., and the corresponding switch or light status is 1 or 0.

[0094] When the image recognition result changes state, such as a light changing from on to off or from off to on, or a switch changing from on to off or from off to on, the edge node must immediately report the monitoring video, image, and recognition result.

[0095] When the image recognition result undergoes a state change, the preset formula for calculating the confidence level is as follows: The confidence level of the monitoring image is calculated according to the following formula. When the confidence level is lower than the preset threshold, the edge node must immediately report the monitoring video, image, and recognition result:

[0096]

[0097] in, The maximum image sampling interval is T(t), where T(t0) is the current time, T(t0) is the last reported time, σ is the mean square oscillation factor, and n is the number of samplings between the current time and the last reported time.

[0098] For example, when the monitoring point is a telemetry point, the reporting strategy can be as follows: determine the difference between the image recognition result of the telemetry point and the historical image recognition result; when the difference is greater than a preset threshold, upload the image recognition result and the monitoring image to the cloud node, wherein the historical image recognition result is the previous image recognition result of the remote sensing point; otherwise, calculate the confidence level of the monitoring image according to a preset formula, and when the confidence level is lower than a preset threshold, upload the monitoring image and the image recognition result to the cloud node.

[0099] Taking the aforementioned telemetry points as examples such as pointer meters and temperature sensors, the specific reporting strategy can be as follows:

[0100] When the image recognition result x(t) is greater than the previously reported result x(t0), the threshold value f is greater than the preset threshold value f. v When this happens, edge nodes must immediately report the monitoring video, images, and recognition results. The difference between the current recognition result and the previous recognition result can be calculated using the following formula:

[0101]

[0102] Where t0 is the time of the last report, x max The maximum value of the telemetry point is k, which is a scaling factor that can be selected based on experience. In this example implementation, it is taken as 0.1 to 0.3.

[0103] Otherwise, the confidence level θ(t) of the image is calculated according to the following preset formula. When the confidence level is lower than the preset threshold, the edge node must immediately report the monitoring video, image, and recognition result:

[0104]

[0105] in, The maximum image sampling interval is T(t), where T(t0) is the current time, T(t0) is the last reported time, σ is the mean square oscillation factor, and n is the number of samplings between the current time and the last reported time.

[0106] In step S320, the edge node collects monitoring videos of the monitoring points in the substation according to the sampling frequency, performs image processing on the monitoring, classifies and stores the monitoring videos and images according to the processing results, so that external systems at the edge can obtain the monitoring videos and images from the edge node; and uploads the monitoring videos and images to the cloud node according to the above strategy.

[0107] For example, the above process can be implemented as follows: After receiving the monitoring points located at the emergency response power station in the monitoring point table, the edge node automatically acquires and analyzes video images at the corresponding sampling points (monitoring points) according to the sampling frequency set by the cloud node. The acquired video images and results are then classified and stored in the edge storage module, and the corresponding video images and recognition results are uploaded to the cloud node according to the set proactive reporting strategy. The processing of monitoring videos and images by the edge node, as well as the reporting strategy, have already been explained in the corresponding sections above and will not be repeated here.

[0108] Furthermore, the process by which the aforementioned external edge system acquires monitoring videos and images from edge nodes can be implemented as follows: the external edge system matches the corresponding monitoring points based on the monitoring videos and images to be acquired, and retrieves and acquires the monitoring videos and images from the edge nodes. For example, when various external monitoring systems within the substation need to retrieve corresponding monitoring videos, images, and recognition results, they can retrieve the latest monitoring videos and images from the edge storage module of the corresponding substation according to the matched monitoring points.

[0109] In step S330, the cloud node receives the monitoring video and monitoring images, classifies and stores the monitoring video and monitoring images, so that external cloud systems can obtain the monitoring video and monitoring images from the cloud node.

[0110] For example, the above process can be as follows: after receiving the monitoring video and images uploaded by the edge node, the cloud node performs advanced image processing and analysis on them, such as performing secondary analysis and review of the monitoring video and images, and then storing them in the cloud storage module. The process of image processing and classification storage by the cloud node has already been described in detail in the corresponding locations above, and therefore will not be repeated here.

[0111] The process by which the external cloud system acquires surveillance video and images from cloud nodes can be implemented as follows: the external cloud system matches the corresponding monitoring points based on the surveillance video and images to be acquired, and retrieves the surveillance video and images from the cloud nodes. Preferably, after retrieving and acquiring the surveillance video and images from the cloud nodes, the external system can also determine whether the surveillance video and images meet the sampling requirements. If not, it retrieves new surveillance video and images from the corresponding edge nodes through the cloud nodes.

[0112] Specifically, the above process can be as follows: When external monitoring systems at the central control terminal or provincial terminal need to retrieve corresponding monitoring videos, images, and results, they can retrieve the latest video images from the cloud storage module according to the matched stations and monitoring points, and determine whether the external system's sampling time requirements are met. Preferably, this determination can be achieved through the aforementioned confidence level. If the requirements are not met, the latest monitoring videos and images for that monitoring point can be obtained from the edge storage module through the cloud task scheduling module.

[0113] Correspondingly, this disclosure also provides a block diagram of a substation monitoring sharing device. For example... Figure 4 As shown, the device includes a cloud node setting module 410, an edge monitoring and sharing module 420, and a cloud monitoring and sharing module 430. Wherein:

[0114] The cloud node setting module 410 is configured to execute the cloud node to build a monitoring point table for each substation, and set the sampling frequency and reporting strategy for each monitoring point in the monitoring point table. Based on the location of each monitoring point, the corresponding content of the monitoring point table, the sampling frequency and the reporting strategy are sent to the edge nodes deployed in each substation.

[0115] The edge monitoring sharing module 420 is configured to perform edge node acquisition of monitoring video of monitoring points in the substation according to the sampling frequency, perform image processing on the monitoring, classify and store the monitoring video and monitoring image according to the processing results, so that the edge external system can obtain the monitoring video and monitoring image from the edge node; and upload the monitoring video and monitoring image to the cloud node according to the above reporting strategy.

[0116] The cloud monitoring sharing module 430 is configured to receive monitoring videos and images from the cloud node, classify and store the monitoring videos and images, so that external cloud systems can obtain the monitoring videos and images from the cloud node.

[0117] Regarding the apparatus in the above embodiments, the specific manner in which each unit or module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0118] Figure 5 A schematic diagram of the structure of a computer system suitable for implementing the embodiments of the present disclosure is shown.

[0119] It should be noted that, Figure 5 The computer system 500 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0120] like Figure 5 As shown, the computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 502 or programs loaded from storage section 508 into random access memory (RAM) 503. The RAM 503 also stores various programs and data required for system operation. The CPU 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0121] The following components are connected to I / O interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to I / O interface 505 as needed. A removable medium 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 510 as needed so that computer programs read from it can be installed into storage section 508 as needed.

[0122] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0123] In another aspect, this application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to perform the methods described in the following embodiments.

[0124] It should be noted that the computer-readable medium disclosed herein may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0125] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0126] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A substation monitoring and sharing system, characterized in that, include: Cloud Nodes: Deployed at the central control console, these nodes are used to build monitoring point tables for each substation. The monitoring point table includes the monitoring point name, the substation it belongs to, the region it belongs to, the equipment name, the storage type, and the identification type. The system determines the identification algorithm for each monitoring point in the table and sets the sampling frequency and reporting strategy for each monitoring point. Based on the location of each monitoring point, the system sends the corresponding content of the monitoring point table, the identification algorithm, the sampling frequency, and the reporting strategy to the edge nodes deployed at each substation. It also sends control commands to the edge nodes and receives monitoring videos and images uploaded by each edge node. Finally, it categorizes and stores the monitoring videos and images for use by external cloud systems. Edge nodes: Deployed in each substation, used to acquire the monitoring video based on the corresponding content of the received monitoring point table, the identification algorithm, and the sampling frequency; The system acquires the monitoring image from the monitoring video and identifies the monitoring image using the recognition algorithm to obtain the image recognition result; it receives control commands sent by the edge node and executes corresponding operations, and uploads the monitoring video and the monitoring image to the cloud node according to the reporting strategy; The surveillance videos and images are categorized and stored for use by external edge systems. The reporting strategy includes: When the monitoring point includes a remote signaling point, if the image recognition result shows that the status of the remote signaling point has changed, the monitoring image and the image recognition result are uploaded to the cloud node; and if the image recognition result shows that the status of the remote signaling point remains unchanged, the confidence level of the monitoring image is calculated according to a preset formula, and if the confidence level is lower than a preset threshold, the monitoring image and the image recognition result are uploaded to the cloud node. When the monitoring point includes a telemetry point, the difference between the image recognition result of the telemetry point and the historical image recognition result is determined; when the difference is greater than a preset threshold, the image recognition result and the monitoring image are uploaded to the cloud node, wherein the historical image recognition result is the previous image recognition result of the telemetry point; Otherwise, the confidence level of the monitoring image is calculated according to a preset formula. When the confidence level is lower than a preset threshold, the monitoring image and the image recognition result are uploaded to the cloud node.

2. The substation monitoring and sharing system according to claim 1, characterized in that, The cloud node includes a cloud communication module, a cloud image processing module, a cloud storage module, a cloud external service interface module, and a cloud task scheduling module, wherein: The cloud communication module is used to receive the monitoring video and monitoring image of the substation uploaded by the edge node, and to send the control command to the edge node; The cloud-based image processing module is used to perform high-level image processing and analysis on the surveillance videos and surveillance images uploaded by each of the edge nodes; The cloud storage module is used to classify and store the surveillance video and surveillance images processed by the cloud image processing module. The cloud external service interface module is used to provide an interface to the cloud external system so that the cloud external system can request the required monitoring video and monitoring images from the cloud storage module; The cloud-based task scheduling module is used to implement the functions of task distribution, scheduling, and control.

3. The substation monitoring and sharing system according to claim 2, characterized in that, The edge node includes a camera access module, an edge image processing module, an edge storage module, an edge host computer communication module, an edge external service interface module, and an edge task scheduling module, wherein: The camera access module is connected to each camera in the substation and is used to acquire the monitoring video from the cameras; The edge image processing module is used to acquire the monitoring video from the camera access module, extract the monitoring image from the monitoring video, perform image processing on the monitoring image, and add type tags to the monitoring image based on the relevant information of the monitoring image, including the category information and alarm information of the monitoring image; The edge storage module is used to obtain the monitoring image and the monitoring video from the edge image processing module, and classify and store the monitoring image and the monitoring video according to the type label; The edge-end host computer communication module is used to receive the control commands sent by the cloud communication module and execute the corresponding operations, as well as upload the monitoring video and the monitoring image to the cloud communication module; The edge external service interface module is used to provide an interface to the edge external system so that the edge external system can request the required monitoring video and monitoring images from the edge storage module; The edge task scheduling module is used to implement the functions of task distribution, scheduling and control.

4. A method for sharing substation monitoring data, characterized in that, include: The cloud node constructs a monitoring point table for each substation. The monitoring point table includes the monitoring point name, the substation to which it belongs, the region to which it belongs, the equipment name, the storage type, and the identification type. It determines an identification algorithm for each monitoring point in the monitoring point table, sets a sampling frequency and a reporting strategy for each monitoring point in the monitoring point table, and sends the corresponding content of the monitoring point table, the identification algorithm, the sampling frequency, and the reporting strategy to the edge nodes deployed in each of the substations according to the location of each monitoring point. The edge node collects monitoring videos of the monitoring points in the substation according to the sampling frequency, obtains monitoring images from the monitoring videos, and identifies the monitoring images using the recognition algorithm to obtain image recognition results. Based on the processing results, the monitoring videos and monitoring images are classified and stored so that external edge systems can obtain the monitoring videos and monitoring images from the edge node. And upload the surveillance video and the surveillance image to the cloud node according to the reporting strategy; The cloud node receives the surveillance video and the surveillance image, and classifies and stores the surveillance video and the surveillance image so that external cloud systems can obtain the surveillance video and the surveillance image from the cloud node; The reporting strategy includes: When the monitoring point includes a remote signaling point, if the image recognition result shows that the status of the remote signaling point has changed, the monitoring image and the image recognition result are uploaded to the cloud node; and if the image recognition result shows that the status of the remote signaling point remains unchanged, the confidence level of the monitoring image is calculated according to a preset formula, and if the confidence level is lower than a preset threshold, the monitoring image and the image recognition result are uploaded to the cloud node. When the monitoring point includes a telemetry point, the difference between the image recognition result of the telemetry point and the historical image recognition result is determined; when the difference is greater than a preset threshold, the image recognition result and the monitoring image are uploaded to the cloud node, wherein the historical image recognition result is the previous image recognition result of the telemetry point; Otherwise, the confidence level of the monitoring image is calculated according to a preset formula. When the confidence level is lower than a preset threshold, the monitoring image and the image recognition result are uploaded to the cloud node.

5. The substation monitoring sharing method according to claim 4, characterized in that, The external edge system acquires the surveillance video and surveillance images from the edge node, including: The external edge system matches the corresponding monitoring points based on the desired monitoring video and monitoring images, and retrieves and obtains the monitoring video and monitoring images from the edge nodes; and The external cloud system obtains the surveillance video and surveillance images from the cloud node, including: The cloud-based external system matches the corresponding monitoring points based on the monitoring video and monitoring images to be acquired, and retrieves and acquires the monitoring video and monitoring images from the cloud nodes.

6. The substation monitoring sharing method according to claim 5, characterized in that, After retrieving and obtaining the surveillance video and the surveillance image from the cloud node, the method further includes: Determine whether the monitoring video and the monitoring image meet the sampling requirements. If not, retrieve new monitoring video and the monitoring image from the corresponding edge node through the cloud node.

7. A substation monitoring and sharing device, characterized in that, The device is used to implement the substation monitoring sharing method as described in claim 4, and the device includes: The cloud node setting module is configured to execute the cloud node to build a monitoring point table for each substation, and set the sampling frequency and reporting strategy for each monitoring point in the monitoring point table. Based on the location of each monitoring point, the corresponding content of the monitoring point table, the sampling frequency and the reporting strategy are sent to the edge nodes deployed in each substation. The edge monitoring sharing module is configured to execute the edge node to collect monitoring videos of the monitoring points in the substation according to the sampling frequency, perform image processing on the monitoring, classify and store the monitoring videos and monitoring images according to the processing results, so that the edge external system can obtain the monitoring videos and monitoring images from the edge node; and upload the monitoring videos and monitoring images to the cloud node according to the reporting strategy. The cloud monitoring and sharing module is configured to receive the monitoring video and the monitoring image from the cloud node, classify and store the monitoring video and the monitoring image so that external cloud systems can obtain the monitoring video and the monitoring image from the cloud node.

8. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the substation monitoring sharing method as described in any one of claims 4 to 6.

9. A storage medium, wherein instructions in the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the substation monitoring sharing method as described in any one of claims 4 to 6.

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