Electric power operation supervision method and system based on AI behavior recognition and semantic analysis

Abstract

The invention discloses an electric power operation supervision method and system based on AI behavior recognition and semantic analysis, and belongs to the technical field of electric power system safety, and the method comprises the steps: arranging a control architecture and collection equipment of an electric power system, and continuously collecting voiceprint data and video stream data; verifying the identity of an operator through voiceprint recognition, carrying out semantic analysis on a voice instruction based on an electric power standard term library, and judging the consistency between the instruction content and a task issued by a system; identifying the position of an operator through visual positioning, performing spatial matching with the interval number of the target equipment, and detecting the normalization of an operation behavior through video analysis; and generating a voice interaction prompt according to instruction fuzziness, triggering hierarchical alarm through multi-modal data fusion, and synchronously recording and storing violation operation data. The power system supervision efficiency is improved, authorization operation is ensured, human errors are reduced, safety is guaranteed through visual monitoring, alarm is triggered through multi-mode fusion, violation is responded in time, evidence is recorded, and traceable safety management is supported.

Description

Technical Field

[0001] The present invention relates to the field of power system safety technology, and specifically to a power operation supervision method and system based on AI behavior recognition and semantic analysis. Background Art

[0002] The 6KV switchroom in a thermal power plant is the core control area of the power system. Operators frequently perform high-risk operations such as equipment startup and shutdown, and switching. Traditional operational supervision relies on manual monitoring and a two-person confirmation system, which presents the following problems: human fatigue can easily lead to misjudgments or omissions; unclear voice commands or slips of the tongue are difficult to correct in real time; operators entering the wrong compartment (or inadvertently entering a non-operating area) are difficult to detect in time; and the lack of full digital record keeping makes accident tracking difficult.

[0003] Currently, in the relevant technologies in this field, especially in power plants, manual monitoring is relied upon to prevent wrong intervals and misoperation. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problems solved by the present invention are: how to solve the risks of misoperation, including ambiguous voice instructions (such as "disconnect section A" and "close section A") that can easily cause accidents; solve the problem of going into the wrong interval, such as the operator mistakenly entering the non-target equipment area; solve the problem of determining violations, the lack of automated recording and judgment of the entire operation process (voice + behavior), and the inability to control and warn violations in real time.

[0006] To solve the above technical problems, the present invention provides the following technical solution: a power operation supervision method based on AI behavior recognition and semantic analysis, which includes the following steps:

[0007] Arrange the control architecture and acquisition equipment of the power system to continuously collect voiceprint data and video stream data; verify the operator's identity through voiceprint recognition, and perform semantic analysis on voice commands based on the power standard terminology library to determine the consistency between the command content and the tasks issued by the system; identify the operator's position through visual positioning and spatially match it with the target equipment interval number, and detect the standardization of operating behavior through video analysis; generate voice interaction prompts based on the ambiguity of the command, and trigger graded alarms through multimodal data fusion, and simultaneously record and store illegal operation data.

[0008] As a preferred solution of the power operation supervision method based on AI behavior recognition and semantic analysis described in the present invention, the continuous collection of voiceprint data and video stream data includes an edge computing device deployed in the control room, integrating voiceprint recognition, semantic analysis and behavior recognition models;

[0009] The distributed deployment of high-precision microphone arrays and infrared camera groups forms a spatial voiceprint-vision collaborative acquisition network.

[0010] As a preferred solution of the power operation supervision method based on AI behavior recognition and semantic analysis described in the present invention, the voiceprint recognition includes matching the operator with the pre-authorized task list through the voiceprint features;

[0011] Combined with the electric power standard terminology library, multi-dimensional semantic analysis of command actions, equipment numbers and operating parameters is performed to identify logical conflicts between commands and tasks.

[0012] As a preferred solution of the power operation supervision method based on AI behavior recognition and semantic analysis described in the present invention, the semantic analysis includes dynamically updating the power standard terminology library to support the conversion between dialect terminology and standard terminology;

[0013] Generate interactive voice prompts for non-standard instructions to guide operators to repeat standard instructions.

[0014] As a preferred solution of the power operation supervision method based on AI behavior recognition and semantic analysis described in the present invention, the visual positioning includes establishing a spatial mapping relationship between the personnel position and the equipment spacing through infrared visual positioning;

[0015] Real-time analysis of electrical inspection procedures and compliance of wearing safety protection equipment based on video streaming data.

[0016] As a preferred solution of the power operation supervision method based on AI behavior recognition and semantic analysis described in the present invention, the hierarchical alarm includes triggering a hierarchical response of voice alarm, light warning and remote notification based on the multimodal fusion of voiceprint, semantic, spatial location and behavior data;

[0017] Automatically capture audio and video clips within the set time window before and after the illegal operation to generate a traceable chain of evidence.

[0018] As a preferred solution of the power operation supervision method based on AI behavior recognition and semantic analysis described in the present invention, the hierarchical alarm also includes establishing a task-driven closed-loop supervision logic to associate the verification results of the operator identity, instruction content, spatial location, and behavior norms with the same task number;

[0019] Generate behavioral profiles of personnel who have committed multiple violations and feed them back to the security management platform.

[0020] Another object of the present invention is to provide an electric power operation supervision system based on AI behavior recognition and semantic analysis.

[0021] To solve the above technical problems, the present invention provides the following technical solutions: an electric power operation supervision system based on AI behavior recognition and semantic analysis, comprising: a data acquisition module, a voiceprint recognition module, a semantic analysis module, a behavior recognition module, a decision-making and alarm module, and a data storage module;

[0022] The data acquisition module arranges the control architecture and acquisition equipment of the power system to continuously collect voiceprint data and video stream data;

[0023] The voiceprint recognition module verifies the operator's identity through voiceprint recognition;

[0024] The semantic analysis module performs semantic analysis on the voice command based on the electric power standard terminology library to determine the consistency between the command content and the task issued by the system;

[0025] The behavior recognition module identifies the operator's position through visual positioning and spatially matches it with the target equipment interval number, while detecting the standardization of the operating behavior through video analysis;

[0026] The decision-making and alarm module generates voice interaction prompts based on the ambiguity of the instructions and triggers graded alarms through multimodal data fusion;

[0027] The data storage module synchronously records and stores illegal operation data.

[0028] The present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the power operation supervision method based on AI behavior recognition and semantic analysis are implemented.

[0029] The present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the power operation supervision method based on AI behavior recognition and semantic analysis are implemented.

[0030] The beneficial effects of the present invention are as follows: by arranging the control architecture and acquisition equipment of the power system, continuous voiceprint and video stream data collection is achieved, an efficient monitoring network is established, and the efficiency and accuracy of power operation supervision are improved. Voiceprint recognition ensures that only authorized personnel perform operations, and combined with semantic analysis to determine the consistency of instructions and tasks, it reduces the risk of human error. Visual positioning and behavioral norm detection monitor the position and actions of operators in real time to ensure safety and compliance and reduce accidents caused by insufficient personal safety protection. The hierarchical alarm mechanism triggered by multimodal data fusion can respond to illegal operations in a timely manner and automatically record audio and video materials to provide a traceable chain of evidence for safety management, thereby achieving safe and efficient management of the power industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0032] Figure 1 This is an overall flow chart of a power operation supervision method based on AI behavior recognition and semantic analysis provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0033] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0034] Example 1, with reference to Figure 1 , which is the first embodiment of the present invention, provides a power operation supervision method based on AI behavior recognition and semantic analysis, including:

[0035] S1. Arrange the control architecture and acquisition equipment of the power system to continuously collect voiceprint data and video stream data.

[0036] It should be noted that the edge computing equipment deployed in the control room integrates voiceprint recognition, semantic analysis, and behavior recognition models;

[0037] The distributed deployment of high-precision microphone arrays and infrared camera groups forms a spatial voiceprint-vision collaborative acquisition network.

[0038] Among them, the edge computing device (host system) is deployed in the control room, and the duty officer dispatches tasks through it. At the same time, it serves as a server for data storage and real-time processing. The sound and video collected on site are all processed centrally by it.

[0039] A high-precision microphone array collects operating instructions and ambient sounds; a microphone is used to collect the operator's voice, with each switch cabinet corresponding to one microphone.

[0040] Infrared cameras support low-light environments and capture the position and movements of personnel; multiple cameras are used to conduct all-round monitoring of the site to determine the exact position of the operator (mainly whether the position of the personnel corresponds to the position of the corresponding switch cabinet) and the standardization of personnel behavior.

[0041] S2. Verify the operator's identity through voiceprint recognition, and perform semantic analysis on voice commands based on the power standard terminology library to determine the consistency between the command content and the tasks issued by the system.

[0042] It should be noted that the operator is matched with the pre-authorized task list through voiceprint features;

[0043] Specifically, the system verifies the operator's identity and extracts voiceprint features. The system also pre-enters the voiceprints of all operators into the system for identification. For example, if operators A and B are assigned to perform an operation, but operator C actually performs the operation, the system will detect the misidentification based on the operator's voice during the counting of votes and issue an alarm.

[0044] It should also be noted that the multi-dimensional semantic analysis of command actions, equipment numbers and operating parameters is carried out in combination with the power standard terminology library to identify logical conflicts between commands and tasks;

[0045] Dynamically update the electric power standard terminology database to support the conversion between dialect terminology and standard terminology;

[0046] Generate interactive voice prompts for non-standard instructions to guide operators to repeat standard instructions.

[0047] Specifically, in one embodiment of the present invention, power operation terms are entered into the system in advance. For example, A and B are arranged to jointly perform "closing the 6KV-A switch", but when B personnel calls out the votes, he outputs "disconnecting the 6KV-A switch" or "closing the 6KV-B switch". The system finds through semantic analysis that it is inconsistent with the task being performed and issues an alarm.

[0048] Or if the operator does not use standard terms in the system during the vote count, an alarm will be issued.

[0049] In an optional embodiment, multi-dimensional semantic parsing can be used to collect and organize a keyword library of power operation instructions, including common operation commands and equipment names, and use natural language processing (NLP) technology to extract keywords from the operator's voice instructions, identify keywords related to task parameters, and use context analysis methods to determine the relevance of keywords to the current power system status. For example, when an operator issues a "close" instruction, it is necessary to identify whether any equipment is currently in the disconnected state and determine the rationality of the operation. If the identified keyword does not match the instruction in the task list, the system will prompt the operator through a visual interface or audio feedback, such as "Please confirm whether you want to close the 6KV-A switch?"

[0050] In another optional embodiment, multi-dimensional semantic analysis can also integrate the operator's historical operation records into the power standard terminology library, update instructions and operation specifications in real time, form a dynamic terminology library, and update the non-standard expressions in the terminology library by analyzing the non-standard instructions generated in previous operations, and establish a mapping relationship between the non-standard expressions and the standard expressions. When the operator issues an instruction, a similarity analysis is performed with the historical operation records to determine whether the current instruction conforms to the previous operation mode; for example, if the operator often uses "switch 6KV-A switch" instead of the standard "close the switch", the system can automatically identify and prompt it as a non-standard instruction, and guide the operator to repeat the standard terminology.

[0051] S3. Identify the operator's position through visual positioning and spatially match it with the target equipment interval number. At the same time, detect the standardization of the operating behavior through video analysis.

[0052] It should be noted that the spatial mapping relationship between personnel positions and equipment intervals is established through infrared visual positioning;

[0053] Real-time analysis of electrical inspection procedures and compliance of wearing safety protection equipment based on video streaming data.

[0054] Specifically, in one embodiment of the present invention, electrical operations have standardized actions, and the standardized actions are entered into the system in advance. If the physical actions of the on-site personnel do not match the standard actions in the action library, it is judged as non-compliant. For the operation of the electrical high-voltage switch, before each operation, the supervisor will first repeat the action content, so that the compliance of the action can be judged based on voice and video.

[0055] Video analysis is used to check operator location (whether they are within the target equipment compartment) and compliance of their actions (e.g., whether electrical testing is performed correctly and whether they are wearing safety protective equipment). Each switchgear is pre-numbered and located using on-site cameras. For example, if A and B are assigned to "disconnect 6KV-A switch," but before the call or work, they are standing in front of 6KV-B switchgear instead of 6KV-A, the on-site camera captures their actions, detects the incorrect position, and issues an alarm with a voice message: "Be careful, you have entered the wrong compartment." The voice pickup microphone also detects the incorrect compartment and issues an alarm if the operator, standing in front of 6KV-A, issues a command to operate 6KV-B. This provides dual protection through video and voice.

[0056] At the same time, when certain labor protection equipment needs to be worn according to the task content, for example, if operators A and B do not wear labor protection equipment such as safety helmets or insulating gloves when operating the switch to stop power supply, an alarm will be issued and a voice message will be given to inform them to wear labor protection equipment correctly.

[0057] In an optional embodiment, video analysis can be used to detect the standardization of operating behavior by arranging accelerometers, pressure sensors, and temperature sensors around each electrical equipment to monitor the operator's environment and behavior; the accelerometer detects the operator's movement state (such as walking, stopping, bending, etc.), and the pressure sensor can sense the pressure applied by the operator on the equipment to determine whether to perform the operation. The data characteristics of standard electrical operating actions are pre-defined to form a feature library, and the sensor data collected in real time is compared with the standard action library to determine whether the actions in the operation comply with the specifications. If the detected non-compliance with the standards is detected, the system will alert the operator through voice and visual prompts.

[0058] In another optional embodiment, the standardization of operational behaviors detected by video analysis can also be achieved by labeling standard actions in different operating processes based on video stream data, and using the collected labeled video data to train a deep learning model so that it can automatically identify and classify different power operation actions. During actual operation, the trained model is used to analyze the video stream in real time to identify the operator's actions. The system compares the identification results with the standard operating actions. If any irregular behavior is found, an alarm will be immediately triggered and the operator will be prompted by voice.

[0059] S4. Generate voice interaction prompts based on the ambiguity of the instructions, trigger graded alarms through multimodal data fusion, and simultaneously record and store illegal operation data.

[0060] In one embodiment of the present invention, based on the multimodal fusion of voiceprint, semantics, spatial location and behavioral data, a hierarchical response of voice alarm, light warning and remote notification is triggered;

[0061] Automatically capture audio and video clips within a set time window before and after the illegal operation to generate a traceable chain of evidence;

[0062] Establish task-driven closed-loop supervision logic to associate operator identity, instruction content, spatial location, and behavioral norms verification results with the same task number;

[0063] Generate behavioral profiles of personnel who have committed multiple violations and feed them back to the security management platform.

[0064] In an optional embodiment, graded alarms can be established by integrating voiceprints, video streams, and environmental data to create a situational awareness model. The operator's voice, location, movements, and changes in the surrounding environment, such as temperature and humidity, are captured in real time. Based on normal operation records, a behavioral model is established to conduct standardized analysis of power operations, setting thresholds for normal and abnormal behaviors. During operation, artificial intelligence is used to analyze the current operating context and integrate environmental factors with operating behaviors. For example, if an operator is found not wearing protective equipment in a high-temperature environment, a corresponding alarm is triggered. Based on the results of the situational analysis, a multi-level alarm triggering strategy is set. Minor violations (such as improper clothing) are prompted by audio, while more serious violations (such as not wearing necessary safety equipment) trigger strong warning signals and notify relevant management personnel. After all alarms are generated, the system automatically records audio and video clips and synchronizes the corresponding alarm information to the security management platform to form a traceable chain of evidence.

[0065] In another optional embodiment, the graded alarm can also be used to collect historical operation data, including voiceprints, video streams, and corresponding environmental sounds, such as machine operation sounds, etc., to create a multimodal operation database, extract behavioral features from audio and video data, such as the frequency and amplitude changes of sounds and the movement trajectory of people in videos, and perform pre-processing and standardization. Machine learning technology is applied to train models using supervised learning algorithms (such as decision trees, random forests, etc.) to identify normal and abnormal operation behaviors. Normal operation data can be used as positive samples and non-standard behaviors as negative samples for learning. In actual operation, the behavior of operators is monitored in real time, and the collected new data is input into the trained model. The behavior is judged to be compliant based on the prediction results of the model. If the model determines that it is abnormal behavior, a graded alarm is set according to the degree of abnormality. Minor violations are notified through prompt sounds, and serious violations are issued with high-intensity alarms and notifications to safety supervisors.

[0066] Example 2 is the second embodiment of the present invention, which is different from the previous embodiment in that:

[0067] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0068] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0069] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0070] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art and a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0071] Example 3 is the third embodiment of the present invention, which provides an electric power operation supervision system based on AI behavior recognition and semantic analysis, including: a data acquisition module, a voiceprint recognition module, a semantic analysis module, a behavior recognition module, a decision and alarm module, and a data storage module;

[0072] The data acquisition module arranges the control architecture and acquisition equipment of the power system to continuously collect voiceprint data and video stream data;

[0073] Voiceprint recognition module, which verifies the operator's identity through voiceprint recognition;

[0074] The semantic analysis module performs semantic analysis on voice commands based on the power standard terminology library to determine the consistency between the command content and the tasks issued by the system;

[0075] The behavior recognition module uses visual positioning to identify the operator's position and spatially matches it with the target equipment interval number. It also uses video analysis to detect the standardization of operating behavior.

[0076] The decision-making and alarm module generates voice interaction prompts based on the ambiguity of instructions and triggers graded alarms through multimodal data fusion;

[0077] The data storage module synchronously records and stores illegal operation data.

[0078] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A power operation supervision method based on AI behavior recognition and semantic analysis, characterized by: This includes arranging the control architecture and acquisition equipment of the power system to continuously collect voiceprint data and video stream data; The operator's identity is verified through voiceprint recognition, and the voice command is semantically analyzed based on the power standard terminology library to determine the consistency of the command content with the task issued by the system; Identify the operator's position through visual positioning and spatially match it with the target equipment interval number. At the same time, use video analysis to detect the standardization of operating behavior. Generate voice interaction prompts based on the ambiguity of instructions, trigger graded alarms through multimodal data fusion, and simultaneously record and store illegal operation data.

2. The power operation supervision method based on AI behavior recognition and semantic analysis according to claim 1, characterized in that: The continuous collection of voiceprint data and video stream data includes edge computing equipment deployed in the control room, integrating voiceprint recognition, semantic analysis and behavior recognition models; The distributed deployment of high-precision microphone arrays and infrared camera groups forms a spatial voiceprint-vision collaborative acquisition network.

3. The power operation supervision method based on AI behavior recognition and semantic analysis according to claim 2, characterized in that: The voiceprint recognition includes matching the operator with the pre-authorized task list through the voiceprint characteristics; Combined with the electric power standard terminology library, multi-dimensional semantic analysis of command actions, equipment numbers and operating parameters is performed to identify logical conflicts between commands and tasks.

4. The power operation supervision method based on AI behavior recognition and semantic analysis according to claim 3, characterized in that: The semantic analysis includes dynamically updating the electric power standard terminology library to support the conversion between dialect terms and standard terms; Generate interactive voice prompts for non-standard instructions to guide operators to repeat standard instructions.

5. The power operation supervision method based on AI behavior recognition and semantic analysis according to claim 4, characterized in that: The visual positioning includes establishing a spatial mapping relationship between the personnel position and the equipment spacing through infrared visual positioning; Real-time analysis of electrical inspection procedures and compliance of wearing safety protection equipment based on video streaming data.

6. The power operation supervision method based on AI behavior recognition and semantic analysis according to claim 5, characterized in that: The graded alarm includes triggering graded responses such as voice alarm, light warning and remote notification based on multimodal fusion of voiceprint, semantic, spatial location and behavioral data; Automatically capture audio and video clips within the set time window before and after the illegal operation to generate a traceable chain of evidence.

7. The power operation supervision method based on AI behavior recognition and semantic analysis according to claim 6, characterized in that: The hierarchical alarm also includes establishing a task-driven closed-loop supervision logic to associate the verification results of the operator's identity, instruction content, spatial location, and behavioral norms with the same task number; Generate behavioral profiles of personnel who have committed multiple violations and feed them back to the security management platform.

8. An electric power operation supervision system based on AI behavior recognition and semantic analysis, applying the electric power operation supervision method based on AI behavior recognition and semantic analysis as described in any one of claims 1 to 7, characterized in that: include: Data acquisition module, voiceprint recognition module, semantic analysis module, behavior recognition module, decision-making and alarm module, and data storage module; The data acquisition module arranges the control architecture and acquisition equipment of the power system to continuously collect voiceprint data and video stream data; The voiceprint recognition module verifies the operator's identity through voiceprint recognition; The semantic analysis module performs semantic analysis on the voice command based on the electric power standard terminology library to determine the consistency between the command content and the task issued by the system; The behavior recognition module identifies the operator's position through visual positioning and spatially matches it with the target equipment interval number, while detecting the standardization of the operating behavior through video analysis; The decision-making and alarm module generates voice interaction prompts based on the ambiguity of the instructions and triggers graded alarms through multimodal data fusion; The data storage module synchronously records and stores illegal operation data.

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 steps of the power operation supervision method based on AI behavior recognition and semantic analysis are implemented as described in any one of claims 1 to 7.

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 steps of the power operation supervision method based on AI behavior recognition and semantic analysis according to any one of claims 1 to 7 are implemented.

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