Method for visual monitoring equipment based on neural network technology

Abstract

The present application relates to the technical field of power grids, and provides a method, device, equipment and storage medium for visual monitoring equipment based on neural network technology. It includes: obtaining historical operation data of power equipment and standard operation data corresponding to the power equipment set by the power industry standard; performing deep learning convolution calculation on the historical operation data and the standard operation data through a neural network to obtain a learning result; obtaining real-time operation data of the power equipment; performing equipment fault monitoring on the real-time operation data according to the learning result to obtain a monitoring result for the power equipment; performing visual processing according to the monitoring result and the power equipment to obtain visual monitoring information, which can accurately and real-timely monitor the information of the power equipment in the power grid and meet the existing power grid's work requirements for equipment monitoring.

Description

Technical Field

[0001] The present application relates to the field of power grid technology, and in particular to a method, device, computer equipment and storage medium for visual monitoring equipment based on neural network technology. Background Art

[0002] In the power system, in order to identify and control the real-time operation of the power grid, the power dispatching and control center will be equipped with a large number of data acquisition and monitoring systems, automatic control systems and management information systems. In power dispatching and control, power grid fault sets are needed. As the main technical method for simulating various emergencies in the operation of the power grid, power grid fault sets have long been manually written and maintained by power grid analysts. However, this method of manually writing and maintaining fault sets has the defect of real-time adaptability of manually written fault sets to the current real-time / quasi-real-time power grid structure, and cannot meet the existing power grid's work requirements for power equipment monitoring. Summary of the invention

[0003] Based on this, it is necessary to provide a method, device, computer equipment and storage medium for visual monitoring equipment based on neural network technology to address the above technical problems.

[0004] A method for visually monitoring equipment based on neural network technology, the method comprising:

[0005] Acquire historical operation data of the electric power equipment and standard operation data corresponding to the electric power equipment set by the electric power industry standard;

[0006] Performing deep learning convolution calculation on the historical operation data and the standard operation data through a neural network to obtain a learning result;

[0007] Acquiring real-time operating data of the electric power equipment;

[0008] Performing equipment fault monitoring on the real-time operation data according to the learning result to obtain a monitoring result for the power equipment;

[0009] Visualization processing is performed based on the monitoring results and the power equipment to obtain visualized monitoring information.

[0010] In one embodiment, the real-time operation data includes electrical data; and the performing of equipment fault monitoring on the real-time operation data according to the learning result to obtain a monitoring result for the electrical equipment includes:

[0011] Performing equipment fault monitoring on the electrical data according to the learning result;

[0012] When the electrical data is greater than an electrical data threshold corresponding to the electrical data in the learning result, a monitoring result for the electric equipment is generated; the monitoring result is a fault result of the electrical data of the electric equipment that is greater than the electrical data threshold.

[0013] In one embodiment, the visualizing the monitoring result and the power equipment to obtain the visual monitoring information includes:

[0014] A fault mark for representing visual monitoring information is generated on the icon of the electric power equipment.

[0015] In one of the embodiments, when the number of the electrical data greater than the electrical data threshold is one, the fault identifier is an image identifier corresponding to the electrical data.

[0016] In one of the embodiments, when the number of the electrical data greater than the electrical data threshold is greater than or equal to two, the fault identifier is a digital image identifier corresponding to the number.

[0017] In one of the embodiments, after generating a fault mark for representing the visual monitoring information on the icon of the power equipment, the method further includes:

[0018] Obtaining an operation instruction for the fault identifier;

[0019] According to the operation instruction, fault report information for displaying the monitoring result is generated.

[0020] In one embodiment, the real-time operation data includes interface data; and the performing equipment fault monitoring on the real-time operation data according to the learning result to obtain a monitoring result for the power equipment includes:

[0021] Performing equipment fault monitoring on the interface data according to the learning result;

[0022] When the interface data is inconsistent with the standard interface data in the learning result, a monitoring result for the electric device is generated; the monitoring result is a result of an interface connection error of the electric device.

[0023] A device for visual monitoring equipment based on neural network technology, the device comprising:

[0024] A first operation data acquisition module, used to acquire historical operation data of the power equipment and standard operation data corresponding to the power equipment set by the power industry standard;

[0025] A learning result acquisition module, used to perform deep learning convolution calculation on the historical operation data and the standard operation data through a neural network to obtain a learning result;

[0026] A second operation data acquisition module, used to acquire real-time operation data of the electric power equipment;

[0027] A monitoring result acquisition module, used to perform equipment fault monitoring on the real-time operation data according to the learning result to obtain a monitoring result for the power equipment;

[0028] The monitoring information acquisition module is used to perform visualization processing based on the monitoring results and the power equipment to obtain visualized monitoring information.

[0029] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the above method when executing the computer program.

[0030] A computer-readable storage medium stores a computer program, which implements the above method when executed by a processor.

[0031] The above-mentioned method, device, computer equipment and storage medium of the visualization monitoring equipment based on neural network technology obtain the historical operation data of the power equipment and the standard operation data corresponding to the power equipment set by the power industry standard; perform deep learning convolution calculation on the historical operation data and the standard operation data through the neural network to obtain the learning result; obtain the real-time operation data of the power equipment; perform equipment fault monitoring on the real-time operation data according to the learning result to obtain the monitoring result for the power equipment; perform visualization processing on the monitoring result and the power equipment to obtain visualization monitoring information. By obtaining the real-time operation data of the power equipment, and performing equipment fault monitoring on the acquired real-time operation data according to the learning result to obtain the monitoring result, and then displaying the monitoring result through visualization, the information of the power equipment in the power grid can be accurately and real-time monitored, meeting the existing power grid's work requirements for equipment monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A schematic diagram of a flow chart of a method for visually monitoring a device based on a neural network technology in one embodiment;

[0033] Figure 2 A schematic diagram of a flow chart of a method for visually monitoring a device based on a neural network technology in one embodiment;

[0034] Figure 3 A schematic diagram of a flow chart of a method for visually monitoring a device based on a neural network technology in one embodiment;

[0035] Figure 4 A structural block diagram of a device for visual monitoring equipment based on neural network technology in one embodiment;

[0036] Figure 5 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0038] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments.

[0039] The present application provides a method for visually monitoring equipment based on neural network technology, such as Figure 1 As shown, the method can be applied to a computer device, comprising the following steps:

[0040] Step S101, acquiring historical operation data of the power equipment and standard operation data corresponding to the power equipment set by the power industry standard;

[0041] Among them, the power equipment is a power equipment in the power grid. The monitoring of each power equipment in the power grid is equivalent to real-time monitoring of the operation and layout of the power grid. In the existing power system, the power grid topology can be established in real time in the power system, and the attribute parameters of the power equipment corresponding to the power equipment unit in the power grid topology can be obtained in real time.

[0042] It should be noted that the power equipment may be power equipment that is already under construction, power equipment that is planned to be built, power equipment that is ready to be put into use, or different combinations of the above three types of power equipment, which are not specifically limited in this embodiment.

[0043] It should be noted that the power equipment may include transformers, high-voltage cabinets, low-voltage cabinets, busbar bridges, DC panels, analog panels, high-voltage cables, etc., and this embodiment does not specifically limit them.

[0044] The operating data may include output power, power equipment load data, input voltage and other data.

[0045] Step S102, performing deep learning convolution calculation on the historical operation data and the standard operation data through a neural network to obtain a learning result;

[0046] Step S103, obtaining real-time operation data of the power equipment;

[0047] Step S104, performing equipment fault monitoring on the real-time operation data according to the learning result to obtain a monitoring result for the power equipment;

[0048] Step S105, performing visualization processing according to the monitoring result and the power equipment to obtain visualized monitoring information.

[0049] Among them, the power industry standard can be the IEC 61970 standard, and the specification information included in the IEC 61970 standard at least includes: core package information; domain package information; power generation package information; load model package information; measurement package information; deactivation package information; protection package information; topology package information; and wire package information.

[0050] It should be noted that the two pillars of the IEC61970 protocol are the Common Information Model (CIM) and the Component Interface Specification (CIS). The CIM Common Information Model defines the semantics of information exchange content, and the CIS Component Interface Specification specifies the syntax of information exchange.

[0051] Among them, CIM is the foundation and core of the entire IEC61970 protocol framework. The IEC61970 series of standards puts forward the concept of power system resources. It is used to describe the logical structure and relationship of all objects in the power system, and constitutes the main nodes of the power system network topology. Through the connection of these nodes, the basic grid structure of the power system is formed.

[0052] The CIM common information model provides a standardized method to describe power system resources (Power SystemResource) as object classes, attributes and the relationships between them. Since April 1998, CCAPI has released cimu07a.mdl and started to use the object-oriented unified modeling language UML to describe CIM. In UML, the type of real-world entity is defined as a "class", the properties of the entity type are defined as "class attributes", and the relationship between entity types is described as "relationships between classes", including inheritance, association, and aggregation.

[0053] In the IEC61970 protocol, the CIM model is the basis of the overall framework of the IEC61970 protocol. It is an information model that describes the logical structure and relationships of all objects in the power system, and provides a unified platform-independent logical description of the power system for various applications. It defines the standard object model for the power industry and provides a standard for representing power system objects, including their attributes and relationships. The objects in the CIM model are abstract in nature and can be applied in various systems. The CIM model helps integrate EMS systems developed by different suppliers, and helps integrate EMS systems and other application systems involved in different areas of power system operation.

[0054] CIM is divided into three parts. IEC61970-301 is the basic part of CIM, IEC61970-302 is the part of CIM used for energy planning, maintenance and finance, and IEC61970-303 is the part of CIM used for SCADA. For the convenience of management, the developers of CIM divide the classes in CIM into multiple packages. Packages are a method of artificially grouping related model elements. A package represents a combination of model elements related to specific content, often for certain specific application scopes. The establishment of packages has no specific meaning in semantics, mainly to make the entire model easier to design, understand and study.

[0055] IEC61970-301 is divided into 9 packages: Core, Topology, Wires, Outage, Protection, Meas, LoadModel, Generation and Domain. Here are some of them:

[0056] (1) Core package. The core package contains object classes such as Naming, PowerSystemResource, EquipmentContainer, ConductingEquipment, and Switch. The core package defines many application-common models such as the Substation class and the VoltageLevel class. The core package is shared by all application systems and does not depend on any other packages. However, most other packages have associations and generalizations that depend on the core package.

[0057] (2) Topology package. The topology package is an extension of the core package. It defines the physical connection relationship of the equipment by establishing an association relationship with the terminal class that describes the connection relationship. The topology package defines topological relationship models such as connectivity node and topological island. At the same time, the topology package also describes the topological structure, that is, the logical connection relationship of the equipment connected together through closed switches or circuit breakers. The topology definition is independent of other electrical data.

[0058] (3) Wires. The wires package is an extension of the core package and the topology package. It describes the electrical data information of the transmission network and distribution network equipment. This information will be used for applications such as state estimation, power flow calculation, safety analysis, and optimal power flow. The wires package defines models required for network analysis applications such as circuit breakers and disconnectors.

[0059] (4) Measurement Package (Meas): The measurement package contains entities that describe the dynamic measurement data exchanged between applications.

[0060] (5) SCADA package (SCADA). The SCADA package describes the model information used for data acquisition and control applications, involving devices such as measurement, TV, TA, RTU, scanning devices, and communication circuits. The control application supports control operations on devices, such as opening / closing circuit breakers; the data acquisition application collects telemetry data from multiple sources, and the subtypes of telemetry entities follow the definition of the IEC61850 standard.

[0061] Specifically, a neural network (NN) is a mathematical model or computational model that mimics the structure and function of a biological neural network. A neural network is calculated by connecting a large number of artificial neurons. In most cases, an artificial neural network can change its internal structure based on external information and is an adaptive system. Modern neural networks are a nonlinear statistical data modeling tool that is often used to model complex relationships between inputs and outputs, or to explore data patterns. A neural network is a computational model consisting of a large number of nodes and connections between them. Each node represents a specific output function, called an excitation function or activation function. The connection between each two nodes represents a weighted value for the signal passing through the connection, called a weight, which is equivalent to the memory of an artificial neural network. The output of the network varies depending on the network's connection method, weight value, and excitation function. The network itself is usually an approximation of a certain algorithm or function in nature, or it may be an expression of a logical strategy. Artificial neural networks are usually optimized through a learning method based on mathematical statistics, so artificial neural networks are also a practical application of mathematical statistics. Through standard mathematical methods, we can obtain a large number of local structure spaces that can be expressed by functions. On the other hand, in the field of artificial perception in artificial intelligence, we can use the application of mathematical statistics to make decisions in artificial perception (that is, through statistical methods, artificial neural networks can have simple decision-making and simple judgment abilities similar to humans). This method is more advantageous than formal logical reasoning calculations. The learning results obtained by deep learning convolution calculations on the IEC 61970 standard and the equipment parameters through neural network technology can improve the accuracy and efficiency of monitoring results.

[0062] Specifically, the monitoring results and the power equipment can be combined for visualization, and the visualized monitoring information corresponding to the power equipment can be obtained, so that the user can intuitively and quickly know the problematic power equipment unit and the problem of the failure of the power equipment unit.

[0063] In the above-mentioned method of visual monitoring equipment based on neural network technology, the real-time operation data of the power equipment is obtained, and the equipment fault monitoring is performed on the acquired real-time operation data according to the learning results to obtain the monitoring results, and then the monitoring results are displayed through visualization. It is possible to accurately monitor the information of the power equipment in the power grid in real time and meet the existing power grid's work requirements for equipment monitoring.

[0064] In one embodiment, when the real-time operating data includes electrical data, Figure 2As shown, when the computer device executes step S104, it can specifically execute the following steps: step S201, perform equipment fault monitoring on the electrical data according to the learning result; step S202, when the electrical data is greater than the electrical data threshold corresponding to the electrical data in the learning result, generate a monitoring result for the electrical equipment; the monitoring result is the fault result of the electrical data of the electrical equipment that is greater than the electrical data threshold.

[0065] Specifically, the electrical data of the power equipment can be monitored. When the electrical data is greater than the electrical data threshold corresponding to the electrical data in the learning results, a monitoring result of the fault result of the electrical data of the power equipment unit that is greater than the electrical data threshold is generated. This can obtain problems with one or more electrical data of the power equipment or obtain problems with the electrical data of one or several power equipment. That is, the user can quickly obtain problems with the electrical data and then analyze and process the problems.

[0066] In one of the embodiments, when the computer device performs visualization processing based on the monitoring result and the power equipment to obtain the visualized monitoring information, the computer device may specifically perform the following steps: generating a fault mark for representing the visualized monitoring information on the icon of the power equipment.

[0067] Specifically, when the problem is the electrical data of the power equipment, it can be considered that the problem occurs in the power equipment itself, and a fault mark for representing the visual monitoring information can be generated on the icon of the power equipment, which can directly reflect the power equipment with problems through the fault mark.

[0068] Furthermore, when the number of electrical data greater than the electrical data threshold is one, the fault identifier is an image identifier corresponding to the electrical data. The image identifier can directly reflect which electrical data of the power equipment has a problem, and the fault information can be obtained without clicking, which can improve the efficiency of obtaining fault information.

[0069] Furthermore, when the number of electrical data greater than the electrical data threshold is at least two, the fault identifier is a digital image identifier corresponding to the number, which can directly reflect the number of electrical data with problems in the power equipment through the image identifier. Subsequently, by clicking on the digital image identifier, you can get information such as what specific electrical data has problems, what the value is, what the deviation from the standard is, and what the level of the fault is.

[0070] In one of the embodiments, after the computer device generates a fault indicator for representing visual monitoring information on the icon of the power equipment, it can also perform the following steps: obtaining an operation instruction for the fault indicator; and generating fault report information for displaying the monitoring result according to the operation instruction.

[0071] Specifically, after the computer device generates a fault identification of visual monitoring information, the user can perform a first operation on the fault identification. After the computer device obtains the first operation instruction for the fault identification, it will generate fault report information for displaying the monitoring results according to the first operation instruction. The fault report information includes but is not limited to the following information: the properties of the electrical data where the fault occurs, the monitoring value of the electrical data, the difference between the monitoring value and the standard value, and the level of the fault.

[0072] It should be noted that the first operation instruction may be an operation of single-clicking the fault mark, an operation of double-clicking the fault mark, or an operation of staying on the fault mark for a certain period of time, which is not specifically limited in this embodiment.

[0073] It should be noted that the electrical data may be an operating power value or a voltage value, which is not limited in this embodiment.

[0074] It should be noted that the operating power value may be real-time or non-real-time, and this embodiment does not specifically limit it.

[0075] In one embodiment, when the real-time operation data includes interface data, such as Figure 3 As shown, when the computer device executes step S104, it can specifically perform the following steps: step S301, perform equipment fault monitoring on the interface data according to the learning result; step S302, when the interface data is inconsistent with the standard interface data in the learning result, generate a monitoring result for the power equipment; the monitoring result is the result of an interface connection error of the power equipment.

[0076] Specifically, the interface data of the power equipment can be monitored. When the interface data does not correspond to the standard interface data in the learning results, a monitoring result of the interface connection error is generated, which can reflect the specific connection error interface of the power equipment. The monitoring result is then visualized and a fault mark is generated at the specific interface of the power equipment. The fault mark can directly reflect the problematic interface.

[0077] It should be understood that although Figures 1 to 3The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figures 1 to 3 At least part of the steps may include multiple steps or multiple stages. These steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

[0078] In one embodiment, Figure 4 As shown, a device for visual monitoring equipment based on neural network technology is provided, comprising:

[0079] The first operation data acquisition module 401 is used to acquire historical operation data of the power equipment and standard operation data corresponding to the power equipment set by the power industry standard;

[0080] A learning result acquisition module 402 is used to perform deep learning convolution calculation on the historical operation data and the standard operation data through a neural network to obtain a learning result;

[0081] The second operation data acquisition module 403 is used to acquire the real-time operation data of the power equipment;

[0082] A monitoring result acquisition module 404 is used to perform equipment fault monitoring on the real-time operation data according to the learning result to obtain a monitoring result for the power equipment;

[0083] The monitoring information acquisition module 405 is used to perform visualization processing according to the monitoring results and the power equipment to obtain visualized monitoring information.

[0084] In one embodiment, the real-time operation data includes electrical data; the monitoring result acquisition module 404 is also used to perform equipment fault monitoring on the electrical data according to the learning result; when the electrical data is greater than an electrical data threshold corresponding to the electrical data in the learning result, a monitoring result for the electrical equipment is generated; the monitoring result is a fault result of the electrical data of the electrical equipment that is greater than the electrical data threshold.

[0085] In one of the embodiments, the monitoring information acquisition module 405 is further used to generate a fault mark for representing visual monitoring information on the icon of the power equipment.

[0086] In one of the embodiments, when the number of the electrical data greater than the electrical data threshold is one, the fault identifier is an image identifier corresponding to the electrical data.

[0087] In one of the embodiments, when the number of the electrical data greater than the electrical data threshold is greater than or equal to two, the fault identifier is a digital image identifier corresponding to the number.

[0088] In one of the embodiments, the device further includes a fault report information generating module, which is used to obtain an operation instruction for the fault identifier; and generate fault report information for displaying the monitoring result according to the operation instruction.

[0089] In one embodiment, the real-time operation data includes interface data; the monitoring result acquisition module 404 is also used to perform equipment fault monitoring on the interface data according to the learning result; when the interface data is inconsistent with the standard interface data in the learning result, a monitoring result for the power equipment is generated; the monitoring result is the result of an interface connection error of the power equipment.

[0090] For the specific definition of the apparatus of the visualization monitoring device based on neural network technology, please refer to the definition of the method of the visualization monitoring device based on neural network technology above, which will not be repeated here. Each module in the apparatus of the visualization monitoring device based on neural network technology can be implemented in whole or in part by software, hardware and a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0091] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data of a visualization monitoring device based on neural network technology. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for a visualization monitoring device based on neural network technology is implemented.

[0092] Those skilled in the art will understand that Figure 5The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0093] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps in the above-mentioned various method embodiments when executing the computer program.

[0094] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0095] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the above-mentioned computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0096] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0097] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for visual monitoring equipment based on neural network technology, characterized in that: The method comprises: Acquire historical operation data of the electric power equipment and standard operation data corresponding to the electric power equipment set by the electric power industry standard; Performing deep learning convolution calculation on the historical operation data and the standard operation data through a neural network to obtain a learning result; Acquiring real-time operating data of the electric power equipment; the real-time operating data includes electrical data; The real-time operation data is monitored for equipment faults according to the learning results to obtain monitoring results for the power equipment, including: monitoring the electrical data for equipment faults according to the learning results; when the electrical data is greater than an electrical data threshold value corresponding to the electrical data in the learning results, generating a monitoring result for the power equipment; the monitoring result is a fault result of the electrical data of the power equipment that is greater than the electrical data threshold value; Visualization processing is performed based on the monitoring results and the power equipment to obtain visualized monitoring information.

2. The method according to claim 1, characterized in that The visualizing process is performed according to the monitoring result and the electric power equipment to obtain the visual monitoring information, including: A fault mark for representing visual monitoring information is generated on the icon of the electric power equipment.

3. The method according to claim 2, characterized in that When the number of the electrical data greater than the electrical data threshold is one, the fault identifier is an image identifier corresponding to the electrical data.

4. The method according to claim 2, characterized in that: When the number of the electrical data greater than the electrical data threshold is greater than or equal to two, the fault identifier is a digital image identifier corresponding to the number.

5. The method according to claim 2, characterized in that: After generating a fault mark for representing the visual monitoring information on the icon of the electric power equipment, the method further includes: Obtaining an operation instruction for the fault identifier; According to the operation instruction, fault report information for displaying the monitoring result is generated.

6. The method according to claim 1, characterized in that The real-time operation data includes interface data; the real-time operation data is subjected to equipment fault monitoring according to the learning result to obtain a monitoring result for the power equipment, including: Performing equipment fault monitoring on the interface data according to the learning result; When the interface data is inconsistent with the standard interface data in the learning result, a monitoring result for the electric device is generated; the monitoring result is a result of an interface connection error of the electric device.

7. A device for visual monitoring equipment based on neural network technology, characterized in that: The device comprises: A first operation data acquisition module, used to acquire historical operation data of the power equipment and standard operation data corresponding to the power equipment set by the power industry standard; A learning result acquisition module, used to perform deep learning convolution calculation on the historical operation data and the standard operation data through a neural network to obtain a learning result; A second operation data acquisition module is used to acquire real-time operation data of the power equipment; the real-time operation data includes electrical data; A monitoring result acquisition module, used to perform equipment fault monitoring on the real-time operation data according to the learning result to obtain a monitoring result for the power equipment, including: performing equipment fault monitoring on the electrical data according to the learning result; when the electrical data is greater than an electrical data threshold value corresponding to the electrical data in the learning result, generating a monitoring result for the power equipment; the monitoring result is a fault result of the electrical data of the power equipment that is greater than the electrical data threshold value; The monitoring information acquisition module is used to perform visualization processing based on the monitoring results and the power equipment to obtain visualized monitoring information.

8. The device according to claim 7, characterized in that The monitoring information acquisition module is also used for: A fault mark for representing visual monitoring information is generated on the icon of the electric power equipment.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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