Substation operation risk analysis method

Abstract

The invention provides a substation operation risk analysis method, and relates to the technical field of information, and the method comprises the steps: recognizing whether a person correctly wears a protection device or not in real time through a radio frequency identification reader on the protection device, triggering an alarm if the person does not wear the protection device, and synchronizing the alarm information to a block chain node; the block chain is used for carrying out whole-process recording on the operation behavior of personnel, the operation state of equipment and the disposal condition of a security event to form auditing evidence of security operation, and meanwhile, according to the behavior of the personnel and the state of the equipment, a block chain node dynamically optimizes a security management strategy and an emergency plan to form closed-loop management, so that the security accident is effectively prevented, and the security management efficiency is improved. And the overall safety management level of the transformer substation is improved.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a method for analyzing risks of substation operations. Background Art

[0002] There are risks in personnel construction during substation operation, which are mainly reflected in the need for personnel qualification review, personnel behavior control, and personnel location tracking. At present, substations mainly rely on manual review for personnel qualification review, which has problems such as lax review and identity fraud, and the control of personnel behavior mainly relies on video surveillance, which has problems such as blind spots in monitoring and difficulty in post-event tracing. These will cause many risks and hidden dangers in substation operations, such as improper operation leading to electric shock, improper equipment detection leading to more serious accidents such as burning and explosion. Blockchain technology can be used for substation personnel construction risk control. To apply blockchain technology to substation personnel construction risk control, it is necessary to deeply analyze the substation business scenarios, abstract general technical requirements, and carry out targeted technical research. For example, study lightweight blockchain node deployment solutions, simplify identity authentication and authority management processes, optimize smart contract writing methods, and improve blockchain data storage and computing mechanisms. Summary of the invention

[0003] The present invention provides a substation operation risk analysis method, which mainly includes: The facial recognition equipment deployed at the entrance and exit of the substation and the work site collects the facial images of people entering and leaving and extracts feature vectors. The identity of the person is authenticated through the distributed consensus method of the blockchain node, and the authentication result is written into the blockchain to manage the entry and exit rights and work qualifications of the personnel. If the identity authentication fails or the protective equipment is not worn as required, the person will be denied entry or participation in the work; the real-time location data of the person after entering the substation is uploaded to the blockchain node, and an electronic fence is set up using a smart contract to determine whether the person has entered the high-voltage equipment area. If the person has entered the high-voltage equipment area, an alarm is triggered and the location information and alarm information are written into the block At the same time, according to the location information and operation type of the personnel, it is determined whether they are in the dangerous area of ​​equipment maintenance and switching operation. If they are in the dangerous area, an early warning prompt will be triggered; video surveillance equipment is installed in the high-voltage equipment area and the workplace to collect the operation behavior of the personnel in real time, and the data encryption transmission of the blockchain node is used to upload the video data to the blockchain system. The pre-trained deep learning model is called through the smart contract to analyze the behavior of the personnel and determine whether it is a violation. If a violation is identified, an alarm is triggered and the judgment result is written into the blockchain; the operation process of the substation is encoded using the smart contract of the blockchain, The required personnel authority and safety regulations information are stored in the contract in advance. During the operation, the blockchain node determines whether the operation process meets the requirements based on the up-chain data of personnel identity, location, behavior and equipment operation status. If there is any illegal operation, including violation of operation steps and unauthorized change of operation plan, an alarm is triggered and the process execution status is written into the blockchain; the operating parameters of the high-voltage equipment in the substation are collected in real time, and the operating parameters include voltage, current and temperature. The collected data is uploaded to the down-chain system of the blockchain node through an encrypted transmission channel, and the equipment data is stored and analyzed using a time series database. Safety thresholds are set through smart contracts. If equipment parameters exceed the safety threshold range, a safety event warning is triggered to avoid the risk of shutdown, short circuit and combustion caused by abnormal equipment operation. The radio frequency identification reader on the protective device can be used to identify in real time whether the personnel are wearing the protective device correctly. If not, an alarm is triggered and the alarm information is synchronized to the blockchain node. The blockchain is used to record the entire process of personnel's work behavior, equipment operating status, and the handling of safety incidents to form audit evidence of safe operations. At the same time, based on the behavior of personnel and the status of equipment, the blockchain node dynamically optimizes the safety management strategy and emergency plan to form a closed-loop management.

[0004] The technical solution provided by the embodiment of the present invention may have the following beneficial effects: The present invention discloses a method for analyzing the risks of substation operations. By deploying face recognition, positioning and video monitoring equipment in the substation, personnel identity, location and behavior data are collected, and blockchain nodes are used for identity authentication, electronic fences and behavior analysis. At the same time, the operation process is encoded into a smart contract, and whether it meets the requirements is determined based on the on-chain data. The operating parameters of the high-voltage equipment are monitored in real time, and an early warning is triggered if the threshold is exceeded. In addition, the wearing status of the protective device is identified by RFID, and the operation is prohibited if it is not worn. The present invention realizes the intelligent management of the entire process of personnel, equipment and operations in the substation, forms credible safety audit evidence, effectively prevents safety accidents, and improves the overall safety management level of the substation. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] Figure 1 The present invention is a flow chart of the substation operation risk analysis method. DETAILED DESCRIPTION

[0006] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be described clearly and completely below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0007] like Figure 1 The substation operation risk analysis method of this embodiment may specifically include: Step S101, the facial recognition equipment deployed at the entrance and exit of the substation and the work site collects the facial images of the people entering and leaving and extracts the feature vectors, authenticates the identity of the people through the distributed consensus method of the blockchain nodes, and writes the authentication results into the blockchain to manage the entry and exit rights and work qualifications of the people. If the identity authentication fails or the protective equipment is not worn as required, the person will be denied entry or participation in the work.

[0008] A face recognition device is used to collect face images of personnel entering and leaving the substation. The face recognition device extracts feature vectors from the face images through a local binary pattern algorithm. The feature vectors are compared and matched with feature vectors stored in a preset personnel information database to obtain an identity authentication data packet containing personnel ID, timestamp, and location information. The identity authentication data packet is transmitted to multiple nodes in the blockchain network through an encrypted communication protocol, and the blockchain nodes use a proof-of-stake consensus mechanism to verify the identity authentication data. If a preset number of nodes reach an agreement, a new block containing the identity authentication result, timestamp, and node signature is generated on the blockchain. The permission verification logic corresponding to the smart contract is triggered through the new block. The smart contract verifies the entry and exit permissions and work qualifications of the authenticated personnel based on the personnel permission data stored on the blockchain. If the permission verification and work qualification verification pass, an authorization token containing permission information is generated. The authorization token is obtained, and the personnel are tested to see if they are wearing protective devices correctly. The validity and permission information of the authorization token are verified, and combined with the detection results of the protective devices, it is determined whether the personnel are allowed to pass. The access control system is opened or closed according to the judgment result to achieve control over personnel entry and exit and work participation.

[0009] Exemplarily, a face recognition device is used to collect face images of personnel entering and leaving the substation, and feature vectors are extracted from the face images by a local binary pattern algorithm, and the feature vectors are compared and matched with feature vectors stored in a preset personnel information database. The identity of the personnel is preliminarily authenticated according to the matching results, and an identity authentication data packet containing the personnel ID, timestamp, and location information is generated. The identity authentication data packet is transmitted to multiple nodes in the blockchain network through an encrypted communication protocol, and the blockchain nodes use a proof-of-stake consensus mechanism to verify the identity authentication data. If at least 2 / 3 of the nodes reach an agreement, a new block containing the identity authentication result, timestamp, and node signature is generated on the blockchain. The corresponding permission verification logic is triggered by a smart contract. The smart contract verifies the entry and exit permissions and work qualifications of the authenticated personnel based on the personnel permission data stored on the blockchain. If the permission verification and work qualification verification pass, an authorization token containing permission information is generated. The smart terminal obtains the authorization token and detects whether the personnel are wearing protective equipment correctly through near-field communication technology. The smart terminal transmits the authorization token to the access control system through Bluetooth low energy technology. The access control system verifies the validity and permission information of the token, and decides whether to allow personnel to pass through in combination with the detection results of the protective device. The intelligent terminal comprehensively analyzes the authorization token and the detection results of the protective device, and issues control instructions to the access control system of the substation entrance and exit or the working area. After receiving the instructions, the access control system performs the corresponding opening or closing operations to achieve accurate control of personnel entry and exit and operation participation. The intelligent terminal uploads the access control operation records to the blockchain to form a complete trajectory of personnel entry and exit. The face recognition cameras installed at the entrance and exit of the substation collect high-definition face images of people entering and leaving, with a resolution of 1920x1080 pixels. The collected images are preprocessed, including grayscale, histogram equalization and other steps to enhance the image quality. Subsequently, the local binary pattern algorithm is used to extract the face feature vector, which divides the face image into a 7x7 grid, calculates the LBP feature in each grid, and finally obtains a 2048-dimensional feature vector. The feature vector is compared with the personnel information pre-stored in the database, and the cosine similarity is used to calculate the similarity, and the threshold is set to 0.85. When the similarity exceeds the threshold, the system generates an authentication data packet containing personnel ID, timestamp, and location information. The data packet size is about 2KB. After the data packet is encrypted by the AES-256 encryption algorithm, it is transmitted to at least 10 nodes in the blockchain network. The blockchain network adopts a proof-of-stake consensus mechanism, which requires at least 2 / 3 of the nodes (i.e. 7 nodes) to reach an agreement to generate a new block. The new block contains the authentication result, a 32-bit timestamp, and digital signatures of at least 7 nodes. The block size is about 4KB. The smart contract is automatically triggered and verified according to the permission data stored on the blockchain. The permission data includes entry and exit permission levels 1-5 and the type of operation qualification, such as high-voltage operation, low-voltage operation, etc.After verification, an authorization token containing permission information is generated, and the token is valid for 8 hours. The smart terminal reads the electronic tag on the helmet or safety vest through NFC technology, with a detection frequency of once per second and an effective reading distance of less than 10 cm. The smart terminal transmits the authorization token to the access control system through Bluetooth low energy technology, with a transmission distance of up to 30 meters. The access control system verifies the digital signature and validity period of the token, and decides whether to allow personnel to pass in combination with the detection results of the protective device. The response time of the access control system does not exceed 100 milliseconds. The smart terminal records the access control operation, including personnel ID, time, location, operation type and other information, forming a record of about 1KB in size, which is uploaded to the blockchain in batches every 10 minutes to ensure the integrity and non-tamperability of personnel entry and exit trajectories.

[0010] Step S102, upload the real-time location data of the personnel after entering the substation to the blockchain node, use the smart contract to set up an electronic fence, determine whether the personnel have entered the high-voltage equipment area, if they have entered the high-voltage equipment area, trigger an alarm, and write the location information and alarm information into the blockchain. At the same time, according to the personnel's location information and operation type, determine whether they are in the dangerous area of ​​equipment maintenance and switching operation, and if they are in the dangerous area, trigger an early warning prompt.

[0011] Receive the fused location data of the global positioning system and Bluetooth beacon sent by the positioning device, the fused location data is collected and generated by the positioning device according to a preset sampling frequency; generate location information in a standard format according to the fused location data, the location information includes a personnel ID and location coordinates; upload the location information to the blockchain node, the blockchain node calls the electronic fence logic preset in the smart contract according to the location information, the electronic fence logic is formulated according to the high-voltage equipment distribution map and the safety area of ​​the substation, and a polygonal electronic fence is set based on the electronic fence logic; determine whether the location coordinates are within the preset polygonal electronic fence, if the location coordinates are outside the polygonal electronic fence, trigger an out-of-bounds alarm mechanism; obtain pre-stored operation type data, and use the K nearest neighbor algorithm to determine whether the person is in a dangerous area according to the location coordinates and the operation type data; if it is determined that the person is in a dangerous area, generate an early warning message, the early warning message includes the personnel ID and location coordinates; package the early warning message into a transaction and write it into the blockchain, and push it to the terminal device and the monitoring center through the message queue.

[0012] For example, the positioning device worn by the personnel of the substation collects real-time location data through the fusion technology of the global positioning system and Bluetooth beacon, with a positioning accuracy of sub-meter level and a sampling frequency of once per second. In the outdoor area, it mainly relies on the global positioning system for positioning, and automatically switches to Bluetooth beacon positioning after entering the indoor area. The two positioning data are fused through the Kalman filter algorithm to improve the positioning accuracy and continuity. The IoT gateway receives the encrypted location data packet sent by the positioning device, decrypts and preprocesses the data, and generates location information in a standard format. The IoT gateway uploads the processed location data to the blockchain node through a secure communication protocol to ensure the integrity and security of data transmission. After receiving the location data, the blockchain node calls the preset electronic fence logic in the smart contract. The electronic fence logic in the smart contract sets a polygonal electronic fence according to the high-voltage equipment distribution map and safety area division of the substation. The smart contract uses the ray method to determine whether the personnel position is within the electronic fence. The specific implementation process is to make a ray from the point to be determined in any direction and calculate the number of intersections between the ray and the polygon boundary. If the number of intersections is an odd number, the point is inside the polygon; if it is an even number, the point is outside the polygon. If the judgment result is out of bounds, the alarm mechanism is triggered. The alarm information contains key data such as personnel ID, out-of-bounds time, and out-of-bounds location coordinates. The smart contract packages the location information and alarm information into a transaction and writes it into a new block through a consensus mechanism. At the same time, the smart contract uses the K nearest neighbor algorithm to determine whether the personnel are in the dangerous area of ​​equipment maintenance or switching operation based on the personnel's real-time location information and pre-stored operation type data. The determination of the dangerous area is based on the equipment operation status, maintenance plan, and historical accident data. The specific application of the K nearest neighbor algorithm is to pre-mark known dangerous points. For the current location, find the 5 nearest known points. If more than half of these 5 points are dangerous points, the current location is determined to be a dangerous area. The smart contract packages the dangerous area judgment result into a transaction, writes it into the blockchain, and pushes it to the relevant terminal equipment and monitoring center through the message queue. If the judgment result shows that the person is in a dangerous area, the smart contract triggers the early warning prompt mechanism. The early warning information is transmitted to the on-site smart terminal and security monitoring center through the blockchain network. After receiving the early warning information, the smart terminal issues a warning to the on-site personnel through the sound and light alarm. The security monitoring center receives warning information, marks dangerous areas and personnel locations on electronic maps, and initiates emergency response plans, including notifying relevant persons in charge, adjusting work arrangements, sending evacuation orders and other specific measures. The positioning equipment worn by substation staff uses a dual-mode positioning chip that supports the global positioning system and Bluetooth 5.0 technology. In outdoor areas, the global positioning system provides location data with an accuracy of 3 meters; after entering the room, it automatically switches to Bluetooth beacon positioning, and the accuracy is increased to 0.5 meters. The positioning device collects location data once a second, and fuses the two types of positioning data through the Kalman filter algorithm, with the final positioning accuracy reaching 0.3 meters.The IoT gateway uses the AES-256 encryption algorithm to decrypt the received location data. The decrypted data contains latitude and longitude coordinates, timestamps, and device IDs. The gateway converts the data into JSON format and uploads it to the blockchain node through the MQTT protocol with a transmission speed of 10Mbps. After receiving the data, the blockchain node calls the electronic fence function in the smart contract. The electronic fence is represented by a polygon and defined by 20 vertex coordinates. During the ray method judgment process, a ray is drawn from the point to be judged in the east direction to calculate the number of intersections with the polygon boundary. If the number of intersections is 7 (odd number), the judgment point is inside the polygon; if it is 8 (even number), the judgment point is outside the polygon. The out-of-bounds judgment threshold is set to 1 meter, that is, when the person's position is less than 1 meter from the electronic fence boundary, an alarm is triggered. The alarm information includes the person ID, out-of-bounds time (accurate to milliseconds), out-of-bounds location coordinates, and out-of-bounds distance. The smart contract uses the K nearest neighbor algorithm to determine the dangerous area, and the K value is set to 5. 100 known dangerous points are marked on the substation map in advance, and each point contains coordinates and a danger level (level 1-5). For the current location, the algorithm calculates the Euclidean distance to find the five nearest points. If the danger level of three or more of these points is greater than level 3, the current location is determined to be a dangerous area. The judgment result is pushed through the RabbitMQ message queue, and the message size is about 2KB. The electronic map resolution of the security monitoring center is 3840x2160 pixels. The dangerous area is marked in red, and the location of the personnel is represented by flashing blue dots. The emergency response plan contains five levels, which are automatically selected according to the area of ​​the dangerous area and the number of people involved. The execution of the plan is automated through the workflow engine, including sending SMS notifications with a delay of less than 3 seconds, adjusting the task priority in the job scheduling system, and sending voice evacuation instructions to the broadcasting system of the designated area with a volume of 80 decibels.

[0013] Step S103: Install video surveillance equipment in the high-voltage equipment area and the workplace to collect real-time data on personnel's work behaviors. Utilize data encryption transmission of blockchain nodes to upload video data to the blockchain system. Use smart contracts to call pre-trained deep learning models to analyze personnel's behaviors and determine whether they are violations. If violations are identified, an alarm is triggered and the judgment result is written into the blockchain.

[0014] Collect video data and compress the video data to obtain compressed video data; perform preliminary processing on the compressed video data, including removing noise, identifying personnel and extracting key frames, to obtain processed video data; encrypt the processed video data to generate an encrypted data packet; receive the encrypted data packet, and if the integrity and authenticity of the encrypted data packet are verified, decrypt and store the encrypted data packet; use a three-dimensional convolutional neural network model to analyze and store the processed video data in the decrypted data packet, and extract spatiotemporal features, wherein the three-dimensional convolutional neural network model is a pre-trained deep learning model; identify personnel behavior based on spatiotemporal features, and generate alarm information if violations are identified; obtain the alarm information, and push the alarm information to the terminal device through a preset alarm mechanism.

[0015] For example, high-definition network cameras are installed in high-voltage equipment areas and workplaces, and the collected video data is compressed using the H.265 encoding standard. The compression ratio is dynamically adjusted according to the video content, usually between 1:20 and 1:40. The compressed video data is transmitted to the edge computing node using the real-time streaming protocol. The edge computing node performs preliminary processing on the video data, including using Gaussian filtering to remove noise, then applying the target detection algorithm for personnel recognition, and extracting image blocks containing personnel as key frames. The edge computing node encrypts the processed video data using the AES-256 encryption algorithm to generate an encrypted data packet. The encrypted data packet is transmitted to the blockchain node via the secure socket layer protocol. The blockchain node uses a digital signature and timestamp to verify the integrity and authenticity of the data packet, and uses zero-knowledge proof technology to ensure data privacy. After verification, the data packet is decrypted and stored in a distributed file storage system. The blockchain node generates a transaction containing the video data hash value, timestamp, and camera ID, and submits the transaction to the blockchain network. The consensus node in the blockchain network verifies the validity of the transaction and packages the valid transaction into blocks. The newly generated block triggers the execution of the smart contract, which retrieves the video data from the storage system through the distributed file transfer protocol, and then calls the pre-trained 3D convolutional neural network model for analysis. The model uses the C3D architecture, including 5 3D convolutional layers and 2 fully connected layers, and is initialized with weights pre-trained using a large-scale video dataset, and then fine-tuned on a specific behavioral dataset. The 3D convolutional neural network model extracts spatiotemporal features in the video and identifies specific behaviors of personnel, such as wearing a helmet, operating a switch, and working at height. The smart contract determines whether there is an illegal operation based on the recognition results and preset rules. If an illegal behavior is identified, the smart contract generates an alarm message containing the type, time, location, and severity of the violation. The alarm information is broadcast to all nodes through the blockchain network and written into a new block. At the same time, the smart contract triggers the preset alarm mechanism, uses the message queue to push the alarm information to the terminal devices of the security monitoring center and the on-site management personnel, and pushes it to the Web interface and mobile application in real time through the WebSocket protocol, achieving real-time early warning and rapid response. 4K ultra-high-definition network cameras with a resolution of 3840x2160 are installed in the high-voltage equipment area and work site of the substation. The H.265 encoding standard is used to compress video data, with an average compression ratio of 1:30. Video data is transmitted to the edge computing node via the RTSP protocol at a rate of 30 frames per second, and NVIDIA Jetson Xavier NX is used as the edge computing platform. The edge node first uses a 5x5 core Gaussian filter to remove noise, and the signal-to-noise ratio is improved by about 3dB. Then the YOLOv5 target detection algorithm is applied to identify people in the picture, with a detection speed of 60FPS and an accuracy of 98%.The extracted keyframes are encrypted by AES-256 with a key length of 256 bits. The encrypted data packet size is about 500KB. The data packet is transmitted to the blockchain node via the TLS1.3 protocol, and the ED25519 digital signature algorithm is used to verify the integrity, with a verification time of less than 10 milliseconds. The Bulletproofs zero-knowledge proof protocol is used to protect data privacy, and the proof generation time is about 50 milliseconds. The verified data is stored in the IPFS distributed file system with a storage capacity of 10TB. The blockchain adopts the HyperledgerFabric framework, the consensus algorithm is PBFT, and the transaction throughput reaches 20,000TPS. The 3D convolutional neural network model called by the smart contract adopts the C3D architecture, which includes 5 3D convolutional layers (kernel size is 3x3x3) and 2 fully connected layers (4096 neurons each), with a model size of 321MB. The model is pre-trained on the Kinetics-400 dataset and then fine-tuned on a specific behavior dataset containing 10,000 annotated videos, with a behavior recognition accuracy of 95%. The judgment of illegal operations is based on 100 preset rules, and the judgment time is less than 5 milliseconds. The alarm information is pushed through the RabbitMQ message queue, and the delay is less than 50 milliseconds. The WebSocket protocol realizes real-time push on the Web and mobile terminals, with a refresh rate of 10 times / second.

[0016] Step S104, using the blockchain's smart contract, encode the substation's operating process, and pre-store the required personnel authority and safety regulations information in the contract. During the operation, the blockchain node determines whether the operation process meets the requirements based on the on-chain data of personnel identity, location, behavior and equipment status. If there is any illegal operation, including violation of the operation steps and unauthorized change of the operation plan, an alarm is triggered and the process execution status is written to the blockchain.

[0017] According to the preset substation operation process, the smart contract is encoded, and the smart contract contains the execution order, required permissions, parameter range of substation high-voltage equipment and safety regulations; relevant data in the personnel management system, equipment management system and safety management system are obtained, and the relevant data include personnel authority level, substation high-voltage equipment operating parameter threshold and safety operating procedures; personnel identity is identified, location coordinates are obtained, images are collected and behavior is analyzed using convolutional neural network algorithm, and the operating status of substation high-voltage equipment is collected from IoT sensors; the operating status of substation high-voltage equipment is encrypted and the encrypted data is transmitted to the blockchain node, and the blockchain node performs format verification and signature verification on the data; if the format verification and signature verification pass, the personnel identity, location, behavior and status of substation high-voltage equipment are logically judged according to the preset operation process and rules in the smart contract; if the logical judgment result does not meet the operation requirements, an alarm information containing the violation type, time and location is generated; the process execution state variable in the smart contract is updated, and the updated status is packaged into a transaction and written into the blockchain.

[0018] For example, the Solidity language is used to encode the substation operation process into a smart contract, including operation steps, execution order, required permissions, equipment parameter ranges, and safety procedures. The contract structure contains state variables for storing operation status, functions for executing operation logic, and events for recording important operations. Smart contracts are deployed through the blockchain network to generate a unique contract address. The contract content is hashed using the SHA-256 hash function to ensure the integrity and immutability of the contract content. Relevant data is extracted from the personnel management system, equipment management system, and safety management system, including personnel authority levels, equipment operating parameter thresholds, and safety operating procedures. The extracted data is converted into a format recognizable by the smart contract, and merkle trees for personnel permissions, equipment parameters, and safety procedures are constructed respectively. The root hash value of the merkle tree is written into the storage space of the smart contract through a blockchain transaction for subsequent rapid verification of the integrity and validity of the data. During the operation, RFID technology is used to identify personnel every 10 seconds, GPS and Bluetooth beacon fusion technology is used to obtain location coordinates once a second, high-definition cameras are used to collect 30 frames of images per second and use convolutional neural network algorithms to analyze behaviors, and IoT sensors are used to collect equipment operation status every 0.1 seconds. The collected data is encrypted by the AES-256 encryption algorithm and transmitted to the blockchain node, and the node verifies the format and ECDSA signature of the data. After verification, the data is packaged into transactions and written to the blockchain through the PBFT consensus algorithm. The smart contract uses an event listener to subscribe to new block events of the blockchain network. When a new block is received, the transaction data in it is parsed to extract information related to the operation. The contract makes logical judgments on personnel identity, location, behavior, and equipment status according to the preset operation process and rules. Merkle proofs are used to verify the validity of the data during the judgment process. If the judgment result does not meet the operation requirements, or if violations of the operation steps or unauthorized changes to the operation plan are detected, an alarm message containing the violation type, time, and location is generated. At the same time, the process execution state variable in the contract is updated, and the updated state is packaged into a transaction and written to the blockchain. In the substation operation process management, Solidity 0.8.0 version is used to write smart contracts. The contract contains 10 state variables to record the operation status, 15 functions to execute the operation logic, and 5 events to record key operations. When deploying the contract, about 2 million gas is consumed to generate a 64-byte unique contract address. The SHA-256 algorithm is used to hash the contract content and generate a 32-byte hash value. 1,000 personnel authority records, 500 equipment parameter thresholds, and 200 safety regulations are extracted from the management system to build a merkle tree with a depth of 10. The root hash value occupies 32 bytes of storage space.At the work site, RFID technology with 134.2kHz is used to identify personnel every 10 seconds, with a reading distance of 10 cm; the positioning accuracy of GPS and Bluetooth beacons is 0.5 meters, and the coordinates are updated once a second; the 4K resolution camera collects 30 frames of images per second, and uses the YOLOv5 algorithm for behavior analysis, with a detection speed of 60FPS; the sampling rate of the IoT sensor is 10Hz, and the measurement accuracy is 0.1%. The collected data is encrypted by AES-256, with a key length of 256 bits, and the encrypted data packet size is about 1KB. The blockchain node uses the ECDSA algorithm for signature verification, and the signature length is 64 bytes. The PBFT consensus algorithm requires at least 2f+1 nodes to reach a consensus, where f is the number of malicious nodes, and the consensus time is controlled within 3 seconds. The event listener checks the new block every 0.1 seconds, and the block size is limited to 1MB, containing about 1,000 transactions. When the smart contract executes logical judgment, a 16-layer merkle proof is used to verify the validity of the data, and the verification time does not exceed 10 milliseconds. If a violation is detected, an alarm message containing a 64-byte violation type code, an 8-byte timestamp, and a 16-byte geographic coordinate is generated. The updated process execution state variable occupies 256 bytes of storage space, is packaged into a transaction and written to the blockchain, and the confirmation time is about 15 seconds.

[0019] Step S105, collect the operating parameters of the high-voltage equipment in the substation in real time, including voltage, current and temperature, upload the collected data to the downlink system of the blockchain node through an encrypted transmission channel, use the time series database to store and analyze the equipment data, and set the safety threshold through the smart contract. If the equipment parameters exceed the threshold range, an early warning is triggered to avoid the risk of shutdown, short circuit and combustion caused by abnormal equipment operation.

[0020] Obtain operating parameter data of high-voltage equipment in the substation, the operating parameter data including voltage, current and temperature; generate an encrypted data packet according to the operating parameter data, the encrypted data packet is obtained by encrypting the preprocessed data by the on-site data concentrator using an encryption algorithm; receive the encrypted data packet and decrypt it to obtain the decrypted original data, the original data is obtained by decrypting the encrypted data packet by the downlink system using a preset key; write the decrypted original data into a time series database and perform real-time analysis, the real-time analysis includes data smoothing by sliding average method, trend analysis by exponential smoothing method and anomaly detection on the original data; determine whether the equipment parameters in the original data exceed the preset multi-level safety threshold range, if the equipment parameters exceed the multi-level safety threshold range, generate warning information including equipment ID, abnormal parameters, current value, threshold, timestamp and warning level.

[0021] For example, high-precision IoT sensors are installed on high-voltage equipment in substations, and a distributed data acquisition architecture is used to collect operating parameters such as voltage, current, and temperature of the equipment in real time. Different sampling frequencies are set according to the type of equipment, with the voltage and current sampling frequencies being 1,000 times per second and the temperature sampling frequency being 1 time per second. The collected raw data is pre-processed locally for preliminary screening and noise reduction to remove outliers and redundant data. The pre-processed data is transmitted to the on-site data concentrator via industrial Ethernet, and the data concentrator uses the AES-256 encryption algorithm to encrypt the data and generate an encrypted data packet. The encrypted data packet is preferentially transmitted to the blockchain node's downlink system via a dedicated fiber optic channel, and a 4G / 5G wireless network is equipped as a backup transmission method to ensure the reliability of data transmission. The transmission process uses the TLS1.3 protocol to ensure the security of data transmission. After receiving the encrypted data packet, the downlink system uses the preset key to decrypt it and restore the original data. The decrypted data is written into the time series database, and a column storage structure is used to optimize the storage and query efficiency of large-scale time series data. The time series database performs real-time data analysis, including data smoothing using the sliding average method, trend analysis using the exponential smoothing method, and anomaly detection based on statistical process control. The analysis results trigger the execution of the smart contract. The trigger mechanism is based on the preset rule engine, and the smart contract is automatically called when an anomaly is detected or the trend change exceeds the threshold. The smart contract judges the equipment parameters according to the pre-set multi-level safety threshold rules. The setting of the safety threshold is based on the historical operation data of the equipment, taking into account factors such as equipment type, operating environment and load conditions. If the smart contract determines that the equipment parameters exceed the threshold range, an early warning message is generated. The early warning message contains key information such as equipment ID, abnormal parameters, current value, threshold, timestamp and early warning level. The early warning information is broadcast to all nodes through the blockchain network and written into a new block. At the same time, the smart contract triggers the preset early warning response mechanism and executes different response strategies according to the early warning level. For low-level early warnings, the alarm information is pushed to the control center; for medium-level early warnings, the equipment association model is started, the graph theory algorithm is used to analyze the association relationship between equipment, and the fuzzy control algorithm is used to adaptively adjust the parameters of related equipment; for high-level early warnings, the emergency disposal process is automatically started, including emergency load reduction or shutdown of equipment. Smart sensors are installed on the main transformer of the 500kV substation to collect voltage, current and temperature data. The voltage and current sampling frequencies are set to 1000Hz, the temperature sampling frequency is 1Hz, and the sampling accuracy is 0.1%, 0.1% and 0.1℃ respectively. The local preprocessing unit uses the median filter algorithm to remove outliers, and the filter window size is 5. The data is transmitted to the data concentrator via 100Mbps industrial Ethernet and encrypted using AES-256 with a key length of 256 bits. The main fiber channel bandwidth is 1Gbps, and the backup 5G network peak rate is 1Gbps.The TLS1.3 protocol uses the ECDHE-ECDSA-AES256-GCM-SHA384 encryption suite. The time series database uses InfluxDB with a compression ratio of 10:1. Data analysis uses a 20-point moving average smoothing process, exponential smoothing with α=0.2 to predict trends, and the 3σ principle for anomaly detection. The rule engine sets 5 trigger conditions, including parameter overruns and trend anomalies. Smart contracts are written in Solidity language, with 3 levels of security thresholds: warning 90%, alarm 95%, and emergency 98%. The device association model uses the PageRank algorithm to calculate the importance of devices, and the association threshold is set to 0.6. Fuzzy control uses 3 input variables, including deviation, rate of change, integral, and 7 fuzzy sets, with 75 inference rules. Low-level warnings are pushed through WebSocket with a delay of <100ms; intermediate warnings call related device APIs for parameter adjustment with a response time of <1s; high-level warnings activate PLC emergency control logic with an action time of <10ms. The end-to-end delay of the entire process is controlled within 3s, meeting the needs of real-time monitoring and rapid response.

[0022] Step S106, the RFID reader on the protective device is used to identify in real time whether the person is wearing the protective device correctly. If not, an alarm is triggered and the alarm information is synchronized to the blockchain node.

[0023] Obtain the UHF RFID tag information embedded in the protective devices of the safety helmet and insulating gloves, and the RFID tag is wrapped with protective material; according to the RFID tag information, use multiple RFID readers installed in the substation operation area to read the protective device tag data using phased array antenna technology; analyze the signal strength to determine the precise location coordinates of the protective device; determine whether the distance between the precise location coordinates of the protective device and the real-time location data of the operator is less than the preset distance threshold; if the distance is greater than or equal to the preset distance threshold, it is determined that the person is not wearing the protective device correctly; if it is determined that the person is not wearing the protective device correctly, generate an alarm message, which includes the person ID, timestamp, location coordinates and the type of protective device not worn; receive the alarm message, package the alarm message into a transaction, and write it into a new block.

[0024] For example, ultra-high frequency radio frequency identification tags are embedded in protective devices such as safety helmets and insulating gloves. The tags are wrapped with special protective materials, with a frequency of 915MHz and a storage capacity of 96 bits. The unique number and type of protective device are written inside. Multiple radio frequency identification readers are installed in the substation operation area. The readers use phased array antenna technology, with a reading distance of up to 10 meters, a reading accuracy of 99.9%, and can simultaneously identify 1,000 tags per second. The reader uses frequency hopping technology to improve anti-interference capabilities. The radio frequency identification reader scans the operation area every 5 seconds to collect protective device tag information and signal strength data. The reader packages the collected data and transmits it to the edge computing node via industrial Ethernet. The edge computing node uses a triangulation positioning algorithm combined with signal strength analysis to calculate the precise location coordinates of the protective device, with a positioning accuracy of up to 0.1 meters. At the same time, the edge node obtains the real-time location data of the operator from the personnel positioning system and matches and analyzes the two sets of location data. The matching threshold distance is set to 0.5 meters. When the distance between the personnel position and the protective device position is less than the threshold, it is determined to be a successful match. The edge computing node determines whether the personnel are wearing the protective device correctly based on the matching results. If it is detected that the personnel position does not match the protective device position, or the necessary protective device tag information is missing, the alarm mechanism is triggered. A 30-second fault tolerance time is set to allow the operator to make temporary wearing adjustments. If the correct wearing is still not detected after the fault tolerance time, an alarm message is generated. The alarm message contains the personnel ID, timestamp, location coordinates, and the type of protective device not worn. The edge node transmits the alarm message to the blockchain node through an encrypted channel, using the elliptic curve encryption algorithm to ensure data transmission security, using the P-256 curve, and the key length is 256 bits. After receiving the alarm message, the blockchain node packages the information into a transaction and writes it into a new block through the consensus algorithm. Block generation triggers the execution of the smart contract, and the contract determines the violation level according to the preset rules. The preset rules can include that if the operator does not wear the necessary protective device in the working area for more than a set threshold (such as several minutes or longer), this can be considered a serious violation. The time threshold can be adjusted for different types of work areas or work tasks. If an operator frequently fails to wear protective gear (for example, multiple failures are detected within a certain period of time), the system can mark his behavior as a serious violation. This indicates that the operator may not realize the importance of wearing protective gear or has habitual violations. The safety risk level in some work areas is high, and failure to wear protective gear may lead to serious safety accidents. If the behavior of not wearing protective gear is found in a high-risk area, even if it is for a short time, it can be directly judged as a serious violation. Different protective gear corresponds to different safety needs. For example, hard hats and insulating gloves are essential to prevent head injuries and electric shock accidents.If it is detected that the operator is not wearing these key protective devices, even if other aspects (such as time and frequency) meet the standards, it can still be considered a serious violation. A comprehensive violation scoring system can be established to combine the above factors for evaluation. For example, the duration, frequency and regional hazard level of the violation can be assigned a certain score respectively. When the total score exceeds a preset value, it is judged as a serious violation. If it is judged as a serious violation, the permission management interface is automatically called to update the operating permission status of the person in the distributed permission database. The permission database is synchronized with all access points in real time to ensure that the permission change can take effect immediately on site. At the same time, instructions are sent to the site control system through smart contracts to activate the safety interlock mechanism of the corresponding area and cut off the power supply of the operating equipment. The contract execution results are written into the blockchain as a new transaction to form a complete safety management record. In the working area of ​​500kV substation, ultra-high frequency RFID tags with AlienHiggs-4 chips are embedded in safety helmets and insulating gloves. The operating frequency is 915MHz, the EPC capacity is 96 bits, and the user storage is 448 bits. 10 ImpinjR700RFID readers are installed in the operation area, using BeamSteering phased array antenna, with a reading distance of 12 meters, a reading accuracy of 99.9%, and 1,200 tags can be identified per second. The reader uses FHSS technology with a frequency hopping range of 902-928MHz to resist interference. The system scans once every 5 seconds and transmits data to the edge computing node via 1Gbps industrial Ethernet. The edge node uses the LANDMARC algorithm combined with RSSI signal strength to achieve a positioning accuracy of 0.1 meters. At the same time, the location of the operator is obtained from the UWB personnel positioning system with an accuracy of 0.3 meters. The matching threshold is set to 0.5 meters, allowing 30 seconds of fault tolerance. The alarm information is encrypted by ECIES (using the secp256r1 curve) and transmitted to the blockchain node via the TLS1.3 protocol. Using the HyperledgerFabric framework and the PBFT consensus algorithm, the transaction delay is <1 second. The smart contract is written in Go language and contains 5 levels of violation judgment logic. Serious violations automatically call the REST API to update the Couchdb distributed permission database, which maintains bidirectional synchronization with 100 access points with a delay of <100ms. The interlocking mechanism controls the Siemens S7-1500 PLC through the OPC UA protocol with an action time of <10ms.

[0025] Step S107, using blockchain to record the entire process of personnel's work behavior, equipment's operating status, and the handling of safety incidents, to form audit evidence of safe operations. At the same time, based on personnel's behavior and equipment's status, blockchain nodes dynamically optimize safety management strategies and emergency plans to form a closed-loop management.

[0026] Collect data including personnel operation behavior, equipment operation status and security incident handling situation; according to the personnel operation behavior, equipment operation status and security incident handling situation data, the edge computing node performs preprocessing operations and uses encryption algorithms to encrypt the preprocessed data; receive encrypted data, the blockchain node decrypts and verifies the received encrypted data, and if the verification is passed, the data is packaged into transactions; obtain the packaged transactions, and use the practical Byzantine fault tolerance algorithm for consensus, and the consensus includes three stages: pre-preparation, preparation and submission. In the pre-preparation stage, the master node broadcasts the pre-preparation message, the preparation stage broadcasts the preparation message, and the submission stage broadcasts the submission message by the node; determine whether a preset number of nodes reach an agreement in each stage, and if an agreement is reached, generate a new block; obtain the historical data and audit evidence accumulated on the chain, run the Q learning algorithm to optimize the security management strategy and emergency plan, and convert the security management strategy into state space and action space. The historical data includes the historical behavior data of personnel and the historical status data of equipment, wherein the state space includes the current security level, equipment status, and personnel behavior, and the action space includes modifying the operation process and updating the emergency plan.

[0027] For example, data on personnel operation behavior, equipment operation status, and security incident handling are collected in real time through IoT devices, and the collection frequency is dynamically adjusted according to the data type: personnel location coordinates are collected once per second, equipment operation parameters are collected once per millisecond, and operation actions and alarm information are triggered to collect in real time. The collected data includes personnel location coordinates, operation actions, equipment operation parameters, alarm information, and handling actions. Edge computing nodes are used to preprocess and encrypt the original data, and the AES-256 encryption algorithm is used to ensure data transmission security. The encrypted data is transmitted to the blockchain node through a dedicated network. After receiving the encrypted data, the blockchain node decrypts and verifies it, and the verified data is packaged into transactions. The transaction content includes data hash value, timestamp, data source and type, etc. The practical Byzantine fault tolerance algorithm is used for consensus, and the PBFT algorithm is specifically implemented, including three stages: pre-preparation, preparation, and submission. In the pre-preparation stage, the master node broadcasts the pre-preparation message; in the preparation stage, the node broadcasts the preparation message; in the submission stage, the node broadcasts the submission message. At least 2 / 3 of the nodes must reach consensus in each stage to generate a new block. The new block contains multiple transactions to form a complete operation record chain. Each block is associated with the previous block through a cryptographic hash function to ensure that the data cannot be tampered with. Smart contracts automatically perform audit and analysis functions and process data on the chain in real time. The contract determines whether the operation behavior is compliant, whether the equipment status is abnormal, and whether the security incident is handled in a timely and effective manner according to the preset rules. The judgment result is written into the blockchain as a new transaction to form audit evidence. The audit evidence contains information such as event description, relevant personnel, equipment status, handling process, and responsibility determination. The threshold signature scheme is used to implement the multi-signature mechanism, requiring at least k of the n relevant parties to sign to generate valid audit evidence to improve the credibility of the evidence. Based on the historical data and audit evidence accumulated on the chain, the Q-learning algorithm is run to dynamically optimize the security management strategy and emergency plan. The historical data includes the historical behavior data of personnel and the historical status data of equipment. The security management strategy is converted into a state space S and an action space A, where S includes the current security level, equipment status, personnel behavior, etc., and A includes adjusting security parameters, modifying operation procedures, updating emergency plans, etc. The inverse of the frequency of security incidents and the handling effect are used as the reward function R, and the strategy is optimized by iteratively updating the Q value table. The optimized strategies and plans are automatically updated to the relevant systems through smart contracts, and the update records are written into the blockchain as transactions. After the new strategy is implemented, the changes in safety indicators are continuously monitored, and the actual effects are fed back to the Q-learning algorithm to form a closed-loop management process of strategy optimization, execution, evaluation, and re-optimization. The safety management blockchain system is implemented in the 500kV substation, and 100 IoT sensor nodes are deployed. The collection frequency is: 1Hz for personnel positioning data, 1kHz for equipment operating parameters, and real-time triggering of operation actions and alarm information.The edge computing node uses NVIDIA Jetson AGXXavier, with 1.5 trillion floating-point operations / second and a data preprocessing capacity of 10GB / s. AES-256 encryption with a key length of 256 bits. The blockchain network consists of 21 nodes and uses the Hyperledger Fabric framework. In the PBFT consensus, the broadcast delay in the pre-preparation phase is <50ms, and the preparation and submission phases each require 2 rounds of communication, with a total consensus time of <500ms. The block size is 1MB, containing about 1,000 transactions, and the block time is 10 seconds. The smart contract is written in Go language and contains 50 audit rules. The threshold signature scheme uses a (7, 10) threshold, that is, at least 7 signatures out of 10 parties are valid. The Q-learning algorithm sets the learning rate α=0.1, the discount factor γ=0.9, and ε=0.1 in the ε-greedy strategy. The state space S contains 100 features and the action space A contains 20 operations. The reward function R is defined as: R=1 / (safety incident frequency+0.01)*disposal efficiency, and the disposal efficiency range is [0,1]. The size of the Q value table is 100x20, and the initial value is 0. The strategy is updated every 100 blocks (about 16 minutes). After the strategy is executed, the system automatically collects 15 key safety indicators, such as misoperation rate, equipment failure rate, emergency response time, etc., as feedback input to the Q-learning algorithm. The closed-loop process optimizes the safety strategy once an hour on average to continuously improve the management effect. After the system was running for 3 months, the incidence of safety incidents decreased by 30% and the emergency response speed increased by 50%.

[0028] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A substation operation risk analysis method, characterized in that: The method comprises: The facial recognition equipment deployed at the entrance and exit of the substation and the workplace collects the facial images of the people entering and leaving and extracts the feature vectors. The identity of the people is authenticated through the distributed consensus method of the blockchain node, and the authentication results are written into the blockchain to manage the entry and exit rights and work qualifications of the people. If the identity authentication fails or the protective equipment is not worn as required, the person will be denied entry or participation in the work; the real-time location data of the person after entering the substation is uploaded to the blockchain node, and the electronic fence is set up using the smart contract to determine whether the person has entered the high-voltage equipment area. If the person has entered the high-voltage equipment area, an alarm is triggered, and the location information and alarm information are written into the blockchain. At the same time, according to the location information and the type of work of the person, it is determined whether the person is in the dangerous area of ​​equipment maintenance and switching operation. If the person is in the dangerous area, an early warning prompt is triggered; video surveillance equipment is installed in the high-voltage equipment area and the workplace to collect the work behavior of the personnel in real time, and the data encryption transmission of the blockchain node is used to upload the video data to the blockchain system. The pre-trained deep learning model is called through the smart contract to analyze the behavior of the personnel and determine whether it is a violation. If a violation is identified, an alarm is triggered and the judgment result is written into the blockchain; the operation process of the substation is carried out using the smart contract of the blockchain. The required personnel authority and safety regulations are stored in the contract in advance. During the operation, the blockchain node determines whether the operation process meets the requirements based on the up-chain data of personnel identity, location, behavior and equipment operation status. If there is any illegal operation, including violation of operation steps and unauthorized change of operation plan, an alarm is triggered and the process execution status is written into the blockchain. The operating parameters of the high-voltage equipment in the substation are collected in real time, including voltage, current and temperature. The collected data is uploaded to the down-chain system of the blockchain node through an encrypted transmission channel, and the equipment data is stored and analyzed using the time series database. The safety threshold is set through the smart contract. If the equipment parameters exceed the safety threshold range, a safety event warning is triggered to avoid the risk of shutdown, short circuit and combustion caused by abnormal equipment operation; the radio frequency identification reader on the protective device is used to identify in real time whether the personnel are wearing the protective device correctly. If not, an alarm is triggered and the alarm information is synchronized to the blockchain node; the blockchain is used to record the entire process of personnel's work behavior, equipment operating status, and the handling of safety incidents to form audit evidence of safe operations. At the same time, according to the behavior of personnel and the status of equipment, the blockchain node dynamically optimizes the safety management strategy and emergency plan to form a closed-loop management.

2. The method according to claim 1, characterized in that The facial recognition equipment deployed at the entrance and exit of the substation and the work site collects the facial images of the people entering and leaving and extracts the feature vectors, authenticates the identity of the people through the distributed consensus method of the blockchain nodes, and writes the authentication results into the blockchain to manage the entry and exit rights and work qualifications of the people; if the identity authentication fails or the protective equipment is not worn as required, the person is denied entry or participation in the work, including: The facial recognition equipment is used to collect facial images of people entering and leaving the substation. The facial recognition equipment extracts feature vectors from the facial images through the local binary pattern algorithm. Compare and match the feature vector with the feature vector stored in the preset personnel information database to obtain an identity authentication data packet containing the personnel ID, timestamp and location information; The identity authentication data packet is transmitted to multiple nodes in the blockchain network through an encrypted communication protocol, and the blockchain nodes use a proof-of-stake consensus mechanism to verify the identity authentication data; If a preset number of nodes reach a consensus, a new block containing the identity authentication result, timestamp and node signature is generated on the blockchain; The new block triggers the permission verification logic corresponding to the smart contract. The smart contract verifies the access and operation qualifications of the authenticated personnel based on the personnel permission data stored on the blockchain. If the permission verification and job qualification check are passed, an authorization token containing permission information is generated; Obtain authorization tokens and detect whether personnel are wearing protective equipment correctly; Verify the validity and permission information of the authorization token, and determine whether to allow personnel to pass based on the detection results of the protective device; The access control system is opened or closed based on the judgment results to achieve control over personnel entry and exit and work participation.

3. The method according to claim 1, characterized in that The real-time location data of personnel after entering the substation is uploaded to the blockchain node, and an electronic fence is set up using a smart contract to determine whether the personnel has entered the high-voltage equipment area. If so, an alarm is triggered, and the location information and alarm information are written into the blockchain; at the same time, according to the personnel's location information and operation type, it is determined whether they are in the dangerous area of ​​equipment maintenance and switching operation. If they are in the dangerous area, an early warning prompt is triggered, including: Receive the global positioning system and Bluetooth beacon fusion position data sent by the positioning device, the fusion position data is collected and generated by the positioning device according to a preset sampling frequency; Generate location information in a standard format based on the fused location data, the location information including a person ID and location coordinates; The location information is uploaded to the blockchain node, and the blockchain node calls the electronic fence logic preset in the smart contract according to the location information. The electronic fence logic is formulated according to the high-voltage equipment distribution map and the safety area of ​​the substation, and a polygonal electronic fence is set based on the electronic fence logic; Determine whether the location coordinates are within the polygonal electronic fence. If the location coordinates are outside the polygonal electronic fence, the out-of-bounds alarm mechanism is triggered; Obtain pre-stored operation type data, and use the K nearest neighbor algorithm to determine whether the person is in a dangerous area based on the location coordinates and the operation type data; If it is determined that a person is in a dangerous area, a warning message is generated, which includes the person's ID and location coordinates; The warning information is packaged into transactions and written into the blockchain, and pushed to the terminal devices and monitoring center through the message queue.

4. The method according to claim 1, characterized in that: The video surveillance equipment is installed in the high-voltage equipment area and the workplace to collect the work behavior of the personnel in real time, and the video data is uploaded to the blockchain system by using the data encryption transmission of the blockchain node. The pre-trained deep learning model is called through the smart contract to analyze the behavior of the personnel and determine whether it is a violation. If a violation is identified, an alarm is triggered and the judgment result is written into the blockchain, including: Collecting video data and compressing the video data to obtain compressed video data; Performing preliminary processing on the compressed video data, including removing noise, identifying people, and extracting key frames, to obtain processed video data; Encrypting the processed video data to generate an encrypted data packet; Receive the encrypted data packet, and if the integrity and authenticity of the encrypted data packet are verified, decrypt and store the encrypted data packet; A three-dimensional convolutional neural network model is used to analyze the processed video data stored in the decrypted data packet to extract spatiotemporal features, wherein the three-dimensional convolutional neural network model is a pre-trained deep learning model; Identify human behavior based on spatiotemporal characteristics, and generate alarm information if any violation is identified; Obtain alarm information and push the alarm information to the terminal device through the preset alarm mechanism.

5. The method according to claim 1, characterized in that The smart contract using blockchain is used to encode the substation operation process, and the required personnel authority and safety regulations are pre-stored in the contract; during the operation process, the blockchain node determines whether the operation process meets the requirements based on the on-chain data of personnel identity, location, behavior and equipment operation status. If there is any illegal operation, including violation of operation steps and unauthorized change of operation plan, an alarm is triggered and the process execution status is written into the blockchain, including: Encode smart contracts according to the preset substation operation process, which include the execution order, required permissions, substation high-voltage equipment parameter ranges and safety regulations; Obtain relevant data from the personnel management system, equipment management system and safety management system, including personnel authority levels, operating parameter thresholds of substation high-voltage equipment and safety operating procedures; Identify people, obtain location coordinates, collect images and use convolutional neural network algorithms to analyze behavior, and collect the operating status of substation high-voltage equipment from IoT sensors; The operating status of the substation high-voltage equipment is encrypted and transmitted to the blockchain node, which verifies the format and signature of the data. If the format verification and signature verification pass, the personnel identity, location, behavior and status of the substation high-voltage equipment will be logically judged according to the preset operation process and rules in the smart contract; If the logical judgment result does not meet the operation requirements, an alarm message containing the violation type, time and location is generated; Update the process execution state variables in the smart contract, package the updated state into a transaction and write it to the blockchain.

6. The method according to claim 1, characterized in that The real-time collection of operating parameters of high-voltage equipment in the substation, including voltage, current and temperature, uploads the collected data to the downlink system of the blockchain node through an encrypted transmission channel, uses a time series database to store and analyze the equipment data, and sets security thresholds through smart contracts; If the equipment parameters exceed the safety threshold range, a safety event warning will be triggered to avoid the risk of shutdown, short circuit and combustion caused by abnormal equipment operation, including: Acquire operating parameter data of high-voltage equipment in a substation, wherein the operating parameter data includes voltage, current and temperature operating parameter data; Generate an encrypted data packet according to the operating parameter data, the encrypted data packet is obtained by encrypting the pre-processed data by the on-site data concentrator using an encryption algorithm; Receive the encrypted data packet and decrypt it to obtain the decrypted original data. The original data is obtained by the downlink system by decrypting the encrypted data packet using a preset key; The decrypted raw data is written into the time series database and analyzed in real time. The real-time analysis includes data smoothing by sliding average method and trend analysis and anomaly detection by exponential smoothing method on the raw data. Determine whether the device parameters in the original data exceed the pre-set multi-level safety threshold range. If the device parameters exceed the multi-level safety threshold range, generate warning information including device ID, abnormal parameters, current value, threshold, timestamp and warning level.

7. The method according to claim 1, characterized in that The RFID reader on the protective device can be used to identify in real time whether the person is wearing the protective device correctly. If not, an alarm is triggered and the alarm information is synchronized to the blockchain node, including: Obtain information about UHF radio frequency identification tags embedded inside protective helmets and insulating gloves, where the radio frequency identification tags are wrapped with protective materials; Based on the RFID tag information, multiple RFID readers installed in the substation operation area use phased array antenna technology to read the tag data of the protective device; Analyze signal strength to determine the precise location coordinates of the protective device; Determine whether the distance between the precise position coordinates of the protective device and the real-time position data of the operator is less than a preset distance threshold. If the distance is greater than or equal to the preset distance threshold, it is determined that the operator is not wearing the protective device correctly. If it is determined that the person is not wearing the protective device correctly, an alarm message is generated, which includes the person ID, timestamp, location coordinates and the type of protective device not worn; Receive alarm information, package the alarm information into transactions, and write them into new blocks.

8. The method according to claim 1, characterized in that The blockchain is used to record the entire process of personnel's work behavior, equipment operation status, and the handling of safety incidents to form audit evidence of safe operations. At the same time, according to the behavior of personnel and the status of equipment, the blockchain node dynamically optimizes the safety management strategy and emergency plan to form a closed-loop management, including: Collect data including personnel operation behavior, equipment operation status and safety incident handling; According to the data of personnel operation behavior, equipment operation status and security incident handling, the edge computing node performs preprocessing operations and uses an encryption algorithm to encrypt the preprocessed data; After receiving the encrypted data, the blockchain node decrypts and verifies the received encrypted data. If the verification passes, the data is packaged into a transaction. Get the packaged transactions and use the Practical Byzantine Fault Tolerance algorithm for consensus, which includes three stages: pre-preparation, preparation, and submission; Determine whether a preset number of nodes reach consensus at each stage. If consensus is reached, a new block is generated; Obtain historical data and audit evidence accumulated on the chain, run the Q learning algorithm to optimize the security management strategy and emergency plan, and transform the security management strategy into state space and action space. The historical data includes the historical behavior data of personnel and the historical status data of equipment.

9. The method according to claim 8, characterized in that In the pre-preparation phase, the master node broadcasts a pre-preparation message, in the preparation phase, the node broadcasts a preparation message, and in the submission phase, the node broadcasts a submission message.

10. The method according to claim 8, characterized in that The state space includes the current safety level, equipment status and personnel behavior, and the action space includes modifying the operation process and updating the emergency plan.

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