Power equipment fingerprint coding method

Abstract

The invention relates to the technical field of power equipment abnormity monitoring, in particular to a power equipment fingerprint coding method. According to the method, quantum sensors are deployed at key parts of equipment to collect electric power parameters, and quantum-state electric power fingerprint data are generated, encrypted and stored. A block chain is utilized to construct a distributed account book to record equipment information, and dynamic calibration is realized through an intelligent contract. And feature extraction and modeling are carried out by means of quantum calculation, and an equipment fingerprint coding model is constructed. The model is combined with a quantum state matching algorithm to monitor the equipment state, and early warning and information broadcasting are carried out in time when abnormity occurs. And calling quantum computing resources through a block chain smart contract to position abnormity, judging types, generating processing measures, and automatically distributing the processing measures to execution nodes. The method realizes comprehensive monitoring and efficient fault diagnosis and processing of power equipment, guarantees data safety, improves the intelligent level of equipment management, reduces the operation and maintenance cost, and provides powerful support for stable and reliable operation of a power system.

Description

Technical Field

[0001] The present invention relates to the technical field of abnormal monitoring of electric power equipment, and in particular to a fingerprint coding method for electric power equipment. Background Art

[0002] In the field of power equipment monitoring and management, traditional technologies face many challenges as the scale of power systems continues to expand and their complexity continues to increase.

[0003] Data collection and processing problems. Traditional power equipment monitoring mostly relies on limited conventional sensors. The data collected is of a single dimension and it is difficult to fully reflect the actual operating status of the equipment. Moreover, the accuracy of these sensors is limited and they are easily disturbed in complex environments, so the accuracy of the data cannot be guaranteed. At the same time, the data processing efficiency is low and it is impossible to process a large amount of monitoring data in real time, which makes it difficult to detect potential equipment failures in a timely manner. For example, in large substations, traditional sensors can only collect basic voltage and current parameters, and cannot capture subtle changes inside the equipment. Some early signs of failure are easily overlooked. Data security and storage problems. Power equipment data contains a large amount of sensitive information. In the face of growing hacker attacks, traditional data encryption methods are gradually becoming less secure and have a high risk of data leakage. In addition, traditional data storage methods are mostly centralized storage, which has a single point of failure problem. Once the storage center fails, a large amount of data may be lost. In addition, data sharing is difficult, and it is difficult to achieve efficient interaction between data from different departments, which limits the collaborative management of equipment. Equipment status monitoring and fault diagnosis dilemma. Traditional equipment status monitoring methods are mainly based on threshold judgment, which cannot accurately distinguish between normal fluctuations and potential failures of equipment, and have high false alarm and missed alarm rates. In terms of fault diagnosis, the lack of effective models and algorithms makes it difficult to quickly and accurately locate the fault point and determine the fault type, resulting in a long fault handling time and affecting the normal operation of the power system. For example, when a transformer has a minor fault, traditional monitoring methods may not be able to detect it in time, or misjudge it as a normal fluctuation, delaying the maintenance time. Information updates and management lag, the operating status and connection relationship of power equipment will change over time, and traditional information management methods are difficult to update equipment information in real time, resulting in equipment records not matching the actual situation, which brings great inconvenience to equipment management and maintenance. When performing equipment maintenance or upgrades, due to inaccurate information, problems such as unreasonable maintenance plans and inadequate spare parts preparation may occur.

[0004] In view of the defects of the above-mentioned traditional technology, the present invention proposes a fingerprint coding method for power equipment. Summary of the invention

[0005] In view of the above deficiencies in the prior art, the purpose of the present invention is to quickly locate and handle anomalies, realize intelligent management of equipment, improve the reliability of power system operation, and solve the problems of delayed status judgment of existing equipment and low fault handling efficiency.

[0006] In order to achieve the above objectives, the present invention adopts the following technical solutions: A fingerprint coding method for electric power equipment comprises the following steps: S1. Power fingerprint collection: quantum sensors are deployed at key locations of various power equipment to collect power parameters of equipment in real time. The collected power parameters are used to generate quantum power fingerprint data. The collected data is encrypted using quantum encryption technology and stored in the central processor. S2. Blockchain distributed ledger and dynamic information calibration. Use blockchain technology to build a distributed ledger to record relevant information and device fingerprint data of power equipment. Each device fingerprint collection device acts as a node to upload encrypted data to the blockchain ledger in real time. Then, through smart contracts, the device-related information is automatically calibrated to ensure that the information in the ledger is consistent with the actual device status. S3. Extraction and modeling of device-related features: Using the parallel processing capability of quantum computing, we extract features of device fingerprint data in the blockchain ledger, extract features related to the device operating status, and establish a quantum device fingerprint coding model; S4. Equipment status monitoring and abnormal warning, use the quantum state device fingerprint coding model to analyze the real-time collected device fingerprint data, and use the quantum state matching algorithm to quickly determine whether the current equipment status is abnormal; once an abnormality is detected, the warning signal is immediately triggered, and the warning information is broadcast to all relevant nodes in real time through the blockchain ledger; the quantum state matching algorithm is used to achieve rapid monitoring of equipment status and abnormal warning.

[0007] Furthermore, the key parts described in step S1 include: deployed near the windings and core of the transformer to monitor voltage and current changes; deployed at the contacts of the circuit breaker to monitor the current on-off status in the open and closed states and related parameters when the arc is generated; deployed near the plates of the capacitor to obtain capacitance value and voltage change data.

[0008] Furthermore, in said S1, the step of generating quantum state power fingerprint data using the collected power parameters includes: S11. The power parameters are quantized and encoded. For power parameters such as voltage, current, power, harmonics, etc., the measurement range of the power parameters is determined. The measurement range is expressed as: , Indicates power parameters; divides the measurement range of power parameters into intervals; among them, is a positive integer. The higher the precision requirement, The larger the value, the longer the interval of each parameter. The calculation formula is: ; and They are the upper and lower limits of the power parameter measurement range respectively; For the collected discrete power parameter values, determine whether the power parameter values ​​fall within the measurement range According to the linear coding rule, the serial number in is mapped into the quantum bit state: ; ; ; in, is the collected discrete power parameter value; It is the serial number of the power parameter value falling within the measurement range; To discrete power parameters The quantum bit state obtained after mapping according to the linear encoding rule; is the quantum bit state In ground state The probability amplitude coefficient when is ; is the quantum bit state In ground state The probability amplitude coefficient when is ;and ; By using the above method, the voltage, current, power, harmonics and other parameters are converted into corresponding quantum bit states, so that the classical power parameter data has the expression form of quantum state; S12. Select the entanglement gate operation and use the CNOT gate to perform quantum entanglement operation. The CNOT gate acts on two quantum bits. When the control bit is When the control bit is When , the target bit state remains unchanged; its matrix form is: ; S13. Perform entanglement operations to construct composite quantum states: The entanglement operation uses the voltage quantum bit state as the control bit and the current quantum bit state as the target bit. After the CNOT gate operation, the entangled state is obtained. The entangled state formula is expressed as: ; get After that, continue to entangle with the power quantum bit state to A bit in (such as the first bit) is used as the control bit, and the power quantum bit state As the target bit, the entangled state containing voltage, current and power information is obtained after the operation. , then and harmonic qubit states It is entangled with other quantum bit states to finally construct a composite quantum state, namely the power fingerprint data; in, represents quantum entanglement operation; It represents the entangled state obtained after gate operation with the voltage quantum bit state as the control bit and the current quantum bit state as the target bit; The quantum bit state representing the voltage; The quantum bit state representing the electric current; and These are the two basic states of a quantum bit; and is the voltage qubit state The probability amplitude coefficients under the calculation basis determine The probability of being in different ground states; and is the current qubit state The probability amplitude coefficients under the calculation basis determine The probability of being in different ground states; It is an XOR operation.

[0009] Furthermore, the quantum encryption method in S1 is: using an encryption method based on quantum key distribution (QKD), using the BB84 protocol to generate a one-time quantum key, and randomly selecting horizontal / vertical ( , ) or 45˚ / 135˚ ( , ) quantum state sends photons, and the BB84 protocol receiver randomly selects a measurement basis for measurement; if the measurement basis matches the sending basis, the measurement result is recorded as the key bit, and if it does not match, it is discarded; the matching bits are selected to form the key , the collected power fingerprint data Encryption is performed, and the encryption formula is: ( is an XOR operation), and is encrypted and stored in the quantum storage module.

[0010] Furthermore, in S2, the step of calibrating the blockchain distributed ledger with dynamic information includes: S21. Construct a blockchain network and build a distributed ledger based on the alliance chain. Use the improved Byzantine Fault Tolerant (PBFT) consensus algorithm. The quantum random number generator is introduced into the improved Byzantine Fault Tolerant (PBFT) consensus algorithm. The uncertainty principle in quantum mechanics is used to generate true random numbers. The true random numbers are processed through hash functions to obtain the intermediate conversion parameters from random numbers to master node selection, and then the master node is obtained through calculation. The communication protocol of quantum key encryption is used for communication between nodes to ensure the security of information during transmission and prevent it from being stolen or tampered with.

[0011] S22. Data uploading and storage: Each power fingerprint collection device acts as a blockchain node, encapsulates the encrypted quantum power fingerprint data and device-related information into transactions according to the blockchain transaction format, performs hash calculation on the transaction content using SHA-256, and then uses the Merkle tree structure to store the transaction. The transaction hash values ​​are combined layer by layer to calculate the root hash value, which is stored in the blockchain block; S23. Dynamic information calibration: Write smart contracts to realize automatic calibration of device-related information. The smart contract periodically obtains the device information and real-time power data uploaded by each node, and compares and analyzes the power transmission relationship and connection status between adjacent devices based on Kirchhoff's current law and Kirchhoff's voltage law. If power transmission abnormalities or connection status changes are found, the smart contract starts the information update process, and broadcasts the updated device information through the blockchain to ensure that the ledger information is consistent with the actual device status.

[0012] Furthermore, in S21, the calculation step of the master node includes: In a quantum system, the state of a particle is in a superposition state before measurement, and the measurement result is completely random and unpredictable. The quantum random number generator uses this property to generate true random numbers , and then through the hash function For random numbers Processing is performed to obtain the intermediate conversion parameters from random numbers to master node selection , the formula is: ; Random Numbers It is a truly random number generated by a quantum random number generator based on the uncertainty principle in quantum mechanics. In a quantum system, the state of a particle is in a superposition state before measurement, and the measurement result is random and unpredictable. The quantum random number generator uses this feature to generate random numbers. ; It is the initial input parameter for the subsequent master node calculation process. Its randomness is the key to ensure that the master node selection is fair and difficult to be predicted by malicious nodes; In the process, due to the hash function It has one-way and uniqueness, that is, given an input random number The only intermediate conversion parameter can be obtained , but it is difficult to convert parameters through the middle Deducing random numbers ; Processing random numbers through hash functions further increases the unpredictability and security of master node selection.

[0013] Master Node It is used to determine which node serves as the master node in each round of consensus and is responsible for sorting and distributing client requests. The calculation formula becomes: ; Represents the number of the master node, Indicates the maximum value in the node number range plus 1, that is, the total number of nodes, when calculating the master node hour, Used to determine the value range of the master node number. The formula is: , so that the master node number falls within the legal node number range.

[0014] Furthermore, in S22, performing hash calculation on the transaction content using SHA-256 includes: S221. Message padding: For each transaction, the message padding operation must be performed first. The padding rule is to add a "1" bit at the end of the transaction content, and then continue to add "0" bits until the message length modulo 512 is equal to 448. Finally, a 64-bit block is added. The block records the length of the original message in bits. The length of the padded message is an integer multiple of 512 bits. The participating nodes of the alliance chain are authorized. In the power system scenario, the participating nodes include power companies, operation and maintenance departments, regulatory agencies and other relevant parties; S222. Message grouping: divide the padded transaction content into several 512-bit message blocks. If the padded length is exactly 512 bits, there will be only one message block; if the length exceeds 512 bits, it will be divided into multiple message blocks; S223. Initialization operation, setting a set of initial state values, the state values ​​will be continuously updated in each subsequent round of calculation, and the initial state value is the starting point of the entire hash calculation; S224. Cyclic compression, expanding each 512-bit message block into 64 32-bit words by message block expansion, and then calculating the intermediate value for each round by round-by-round calculation; S225. Update the state value, and update the state value according to the calculated intermediate value. The specific update rules are as follows: a part of the original state value is updated to another part of the state value, and the remaining part of the state value is updated by operating with other state values ​​and intermediate values; S226. Result generation: for each 512-bit message block, after 64 rounds of cyclic calculation, a set of updated state values ​​will be obtained. The updated state values ​​will be selected and combined in sequence according to the order of the state values ​​in the cyclic calculation process to obtain the partial hash result corresponding to the message block; if the input message is divided into multiple 512-bit message blocks, then for subsequent message blocks, the final state value obtained by the cyclic compression calculation of the previous message block is used as the initial state value of the cyclic compression calculation of the current message block, and the above cyclic compression steps are repeated; when all message blocks are processed, the final set of state values ​​are combined to form a 256-bit string, which is the SHA-256 hash value of the input message.

[0015] Further, in S224, the cyclic compression process includes: S2241. Message block expansion, expand each 512-bit message block into 64 32-bit words , the specific operations are as follows: The first 16 32-bit words arrive Extract directly from the message block in sequence; for Word , calculated using the following formula: ; ; ; in, Indicates input Circular right shift Bit operations; It is an XOR operation; Represents an extended 32-bit word; and Indicates input Perform circular right shift and XOR bitwise operations; S2242. Round robin calculation, for each round , The round that represents the number of loops does the following: calculate Median value: ; in, is the selection function, and its calculation formula is: ; is a logical AND operation, is the logical NOT operation, Indicates that according to the status value The value from and Select output in; is a predefined constant, which is the same as the number of loops. Correspondingly, different rounds have different constant values ​​to increase the complexity and security of the algorithm; It is The extended 32-bit word corresponding to the round is obtained through the message block expansion step; Indicates that the state value 2 is subjected to a similar right-shift and XOR bitwise operation; calculate Median value: ; in, is a majority function, and its calculation formula is: ; Used for judgment The majority of values.

[0016] Furthermore, the device-related feature extraction method in S3 is: S311. Prepare quantum computing resources, build a quantum computing environment, build quantum computing hardware facilities by yourself, and select the matching number of quantum bits and computing resources according to the device fingerprint data volume and computing complexity; at the same time, install the supporting quantum computing programming framework Qiskit to write and run quantum computing programs; S312. Feature extraction algorithm implementation; The quantum principal component analysis (QPCA) algorithm is used to extract the features of the device fingerprint data in the blockchain ledger. The device fingerprint data is read from the blockchain ledger and converted into a quantum state form and input into the quantum computing environment. The linear transformation of the data is then achieved through the Hadamard gate and quantum rotation gate operations. Among them, Hadamard Gate is expressed as: ; The quantum rotating gate is represented as: ; ; ; By adjusting the rotation angle, the state of the quantum bit can be precisely controlled and transformed, which is used in the quantum principal component analysis (QPCA) algorithm to achieve linear transformation of data to extract data features; In the formula, For the gate of Hatama, is a quantum gate operation, Transform the quantum bit from or state into superposition state or , used to realize the initialization and transformation of quantum states in quantum computing; , , Respectively represent the axis, axis, A quantum revolving door with axis rotation, is the rotation angle; represents an imaginary unit, satisfying ; Calculate the quantum state covariance matrix : ; For the Quantum state data, is the average quantum state; is the number of data samples; Then, the quantum singular value decomposition (QSVD) algorithm is used to obtain the principal components of the data. These principal components contain features related to the operating status of the equipment, such as power fluctuation features, harmonic change features, etc. Among them, the power fluctuation characteristics for: ; Harmonic change characteristics for: ; for Subharmonic current effective value; is the effective value of fundamental current; yes The power value at a moment is the power measurement value of the device at a specific point in time; express The power value at the moment, that is, the power measurement value at the next time point immediately following the moment; To determine a maximum harmonic order based on the equipment detection capability and actual conditions.

[0017] Furthermore, in S3, the device fingerprint coding model construction method is: Construct a quantum state device fingerprint coding model using a quantum neural network (QNN). The network structure of the quantum neural network (QNN) includes an input layer, a quantum hidden layer, and an output layer. The number of neurons in the input layer is determined according to the number of extracted features. Each neuron corresponds to one feature and is used to receive the extracted device feature data and convert it into a quantum state and input it into the network. The quantum hidden layer uses the superposition and entanglement characteristics of quantum bits to perform nonlinear transformations and enhance the expressive power of the model. In the quantum hidden layer, the quantum perceptron model is used: ; in, Represents the output quantum state of the quantum perceptron model after computational processing; is the weight used to measure each input quantum state the importance of It is the input quantum state, that is, the quantum state data transmitted from the input layer; For biasing, additional flexibility is introduced to the quantum perceptron model; is the symbolic function, the symbolic function Used to judge and output the calculation results; When the value of is greater than 0, The function returns 1; when When the value is less than 0, -1 is returned. In the device status monitoring scenario, if the quantum perceptron model is used to determine whether the device status is normal, the output result of the symbolic function can directly correspond to the device status. The number of neurons in the output layer is determined according to application requirements. If you only need to determine whether the device status is normal, you can set up 1 neuron, and the output result can be set as 1 for normal and -1 for abnormal. If you want to predict the type of equipment failure, for example, there are 5 types of failures, then the output layer will have 5 neurons, and the specific failure type will be determined by the activation of the neurons.

[0018] Further, the S4 includes: S41. Real-time data collection and processing, the quantum sensor continuously collects the power parameters of the power equipment in real time, and converts them into quantum state device fingerprint data according to the method in S1; S42. Implement the quantum state matching algorithm, use the quantum state device fingerprint coding model constructed in S3 to analyze the transformer fingerprint data collected in real time, use the matching algorithm based on quantum entanglement entropy to calculate the quantum entanglement entropy difference between the real-time data and the normal state data, and compare the quantum entanglement entropy difference with the threshold set according to the historical data and operating experience of the transformer to determine whether the transformer state is abnormal; Quantum entanglement entropy The calculation formula is: ; in, is the density matrix of the quantum state, which is used to describe the state of the quantum system; represents the trace of a matrix, which is the sum of the elements on the main diagonal of the matrix; is the density matrix Perform base 2 logarithm operation on each element in; Indicates that the logarithmic operation is completed to obtain a new matrix Perform matrix multiplication operations; Entanglement entropy in normal state The calculation formula is: ; in, is the density matrix corresponding to the transformer quantum state data in the normal state; the entanglement entropy in the normal state Used to measure the degree of entanglement of quantum states when the device is in normal operation; Entanglement entropy of real-time state The calculation formula is: ; in, is the density matrix of real-time collected data; The entanglement entropy of the real-time state is obtained based on density matrix calculation to reflect the quantum state entanglement of the device under the current operating state; Entanglement entropy difference The calculation formula is: ; The entanglement entropy difference is obtained by taking the real-time state entanglement entropy Entanglement entropy with normal state The absolute value of the difference quantifies the degree of difference between the two. In the monitoring of the operating status of power equipment, it is used to determine whether the operating status of the equipment is abnormal; When the quantum entanglement entropy difference calculated in real time When compared with the threshold, if If the value is greater than the threshold, the device status is considered abnormal, and the monitoring system immediately triggers an early warning signal. The early warning information is broadcast to the operation and maintenance personnel's mobile phone APP, the monitoring center's server and other related nodes through the blockchain account book; the early warning information includes the time when the abnormality occurred, the device identification, the preliminary judgment of the abnormality type, etc. At the same time, the system ensures that the operation and maintenance personnel can know the early warning information in time through SMS notifications, APP push notifications, etc. If the value is less than the threshold, the device is considered to be within the normal fluctuation range and regular monitoring continues.

[0019] In summary, due to the adoption of the above technical solution, the beneficial technical effects of the invention are: A fingerprint coding method for power equipment has innovated the power equipment management system as a whole, showing significant advantages in many aspects. In terms of ensuring the reliability of the power system, by deploying quantum sensors at key parts of the equipment, the equipment operation data can be collected in real time and comprehensively, and the equipment status can be accurately monitored with the help of the quantum state device fingerprint coding model. Once an abnormality occurs in the equipment, the early warning mechanism can be quickly triggered, combined with an efficient abnormality location and processing measure generation system to quickly eliminate hidden faults, greatly reducing the occurrence rate of equipment failures, and effectively reducing power outages caused by equipment failures, providing a solid guarantee for the stable and continuous power supply of the power system.

[0020] In terms of optimizing operation and maintenance costs and resource allocation, this technology, with the help of the synergy of quantum computing and blockchain technology, can accurately locate equipment failure points and generate scientific and reasonable processing strategies, avoiding the waste of resources caused by blind troubleshooting and excessive maintenance in traditional operation and maintenance methods. At the same time, the real-time calibration function of equipment information based on blockchain smart contracts and distributed ledgers enables operation and maintenance personnel to obtain accurate equipment information, and then scientifically plan maintenance plans and reasonably reserve spare parts, which significantly improves resource utilization efficiency and effectively reduces operation and maintenance costs.

[0021] Data security and management efficiency improvement are also important advantages of this technology. Quantum encryption technology is combined with the distributed ledger and encrypted communication protocol of blockchain to build a comprehensive data security protection system to ensure the security of power equipment data during collection, transmission and storage, and effectively prevent data leakage and tampering risks. In addition, the function of blockchain smart contracts to automatically calibrate equipment information has achieved efficient and accurate equipment information management. Different departments can share accurate data in real time, which has effectively promoted the development of collaborative work and greatly improved data management efficiency.

[0022] From the perspective of industry development, this invention deeply integrates cutting-edge technologies such as quantum technology and blockchain technology and applies them to the field of power equipment management, providing innovative solutions for the industry, opening up new paths for subsequent technological research and application expansion, and promoting the deep integration and innovative development of cross-domain technologies. It helps the power industry transform towards intelligence and digitalization, and improves the overall technical level and competitiveness of the power industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flow chart of a fingerprint coding method for electric power equipment; Figure 2 A flow chart of a method for generating quantum state power fingerprint data for power parameters; Figure 3 A flowchart of a blockchain distributed ledger and dynamic information calibration method; Figure 4is a flow chart of a method for extracting device-related features; Figure 5 The figure is a flow chart of the method for equipment status monitoring and abnormal warning. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the invention more clear, the invention is further described in detail below in combination with the embodiments. It should be understood that the specific embodiments described here are only used to explain the invention and are not used to limit the invention.

[0025] like Figure 1 As shown: A fingerprint coding method for electric power equipment comprises the following steps: S1. Power fingerprint collection: quantum sensors are deployed at key locations of various power equipment (including transformers, circuit breakers, capacitors, etc.) to collect power parameters of equipment operation in real time, and use the collected power parameters to generate quantum power fingerprint data. The collected data is encrypted using quantum encryption technology and stored in the central processor. This step uses quantum sensors to obtain multi-parameter data of power equipment during operation, providing a basis for subsequent analysis. Quantum encryption technology ensures data security. S2. Blockchain distributed ledger and dynamic information calibration. Use blockchain technology to build a distributed ledger to record relevant information and device fingerprint data of power equipment. Each device fingerprint collection device acts as a node to upload encrypted data to the blockchain ledger in real time. Then, through smart contracts, automatically calibrate the device-related information (such as device connection relationship, operating status, etc.) to ensure that the information in the ledger is consistent with the actual device status. Record device information and fingerprint data through the distributed ledger of the blockchain, and realize dynamic calibration through smart contracts to ensure the accuracy and real-time nature of the information. S3. Extraction and modeling of device-related features: Using the parallel processing capability of quantum computing, feature extraction is performed on the device fingerprint data in the blockchain ledger, and features related to the device operating status (such as power fluctuation features, harmonic change features, etc.) are extracted to establish a quantum state device fingerprint coding model; the device fingerprint coding model can effectively encode the operating status of the device, providing a basis for subsequent status monitoring and fault diagnosis; S4. Equipment status monitoring and abnormal warning, using the quantum state device fingerprint coding model to analyze the real-time collected device fingerprint data, and quickly determine whether the current device status is abnormal through the quantum state matching algorithm; once an abnormality is detected, the warning signal is immediately triggered, and the warning information is broadcast to all relevant nodes (such as operation and maintenance personnel, monitoring center, etc.) in real time through the blockchain account book; the quantum state matching algorithm is used to achieve rapid monitoring of the equipment status and abnormal warning, which helps to timely discover equipment problems and improve the reliability of equipment operation; S5. Abnormal location and processing measures generation. When an abnormal device status is detected, quantum computing resources are automatically called through the blockchain smart contract to locate the abnormality. Combining the quantum state device fingerprint coding model and the device-related information in the blockchain ledger, the smart contract can quickly determine the type of abnormality (such as equipment component failure, parameter abnormality, etc.) and generate corresponding processing measures (such as maintenance suggestions, replacement component notifications, etc.), and automatically distribute the processing measures to relevant execution nodes through the blockchain ledger. Blockchain smart contracts and quantum computing resources are used to achieve rapid location of abnormalities and automatic generation and distribution of processing measures, thereby improving the efficiency of equipment fault handling.

[0026] The key parts described in step S1 include: deployed near the windings and core of the transformer to monitor voltage and current changes; deployed at the contacts of the circuit breaker to monitor the current on-off status in the open and closed states and related parameters when the arc is generated; deployed near the plates of the capacitor to obtain capacitance value and voltage change data.

[0027] like Figure 2 As shown: In S1, the step of generating quantum state power fingerprint data using the collected power parameters includes: S11. The power parameters are quantized and encoded. For power parameters such as voltage, current, power, and harmonics, the measurement range of the power parameters is determined. The measurement range is expressed as: , Indicates power parameters; divides the measurement range of power parameters into intervals ( is a positive integer. The higher the precision requirement, The larger the value), the longer the interval of each parameter The calculation formula is: ; and They are the upper and lower limits of the power parameter measurement range respectively; For the collected discrete power parameter values, determine whether the power parameter values ​​fall within the measurement range According to the linear coding rule, the serial number in is mapped into the quantum bit state: ; ; ; in, is the collected discrete power parameter value; It is the serial number of the power parameter value falling within the measurement range; To discrete power parameters The quantum bit state obtained after mapping according to the linear encoding rule; is the quantum bit state In ground state The probability amplitude coefficient when is ; is the quantum bit state In ground state The probability amplitude coefficient when is ;and ; By using the above method, the voltage, current, power, harmonics and other parameters are converted into corresponding quantum bit states, so that the classical power parameter data has the expression form of quantum state; S12. Select the entanglement gate operation and use the controlled NOT gate (CNOT gate) to perform quantum entanglement operation. The CNOT gate acts on two quantum bits. When the control bit is When the control bit is When , the target bit state remains unchanged; its matrix form is: ; S13. Perform entanglement operations to construct composite quantum states: Entanglement operation, using the voltage quantum bit state as the control bit and the current quantum bit state as the target bit. After the CNOT gate operation, the entangled state is obtained. , the entangled state formula is expressed as: ; get After that, continue to entangle with the power quantum bit state to A bit in (such as the first bit) is used as the control bit, and the power quantum bit state As the target bit, the entangled state containing voltage, current and power information is obtained after the operation. , then and harmonic qubit states It is entangled with other quantum bit states to finally construct a composite quantum state, namely the power fingerprint data; in, represents quantum entanglement operation; Indicates passing After the gate operation, the entangled state is obtained with the voltage quantum bit state as the control bit and the current quantum bit state as the target bit; The quantum bit state representing the voltage; The quantum bit state representing the electric current; and These are the two basic states of a quantum bit; and is the voltage qubit state The probability amplitude coefficients under the calculation basis determine The probability of being in different ground states; and is the current qubit state The probability amplitude coefficients under the calculation basis determine The probability of being in different ground states; It is an XOR operation.

[0028] The quantum encryption method in S1 is: using an encryption method based on quantum key distribution (QKD), using the BB84 protocol to generate a one-time quantum key, and randomly selecting horizontal / vertical ( , ) or 45˚ / 135˚ ( , ) quantum state sends photons, and the BB84 protocol receiver randomly selects a measurement basis for measurement; if the measurement basis matches the sending basis, the measurement result is recorded as the key bit, and if it does not match, it is discarded; the matching bits are selected to form the key , the collected power fingerprint data Encryption is performed, and the encryption formula is: ( is an XOR operation), and is encrypted and stored in the quantum storage module.

[0029] like Figure 3 As shown, in S2, the steps of calibrating the blockchain distributed ledger with dynamic information include: S21. Blockchain network construction, building a distributed ledger based on the alliance chain. In the traditional PBFT algorithm, the selection of the master node is easily manipulated by malicious nodes. Therefore, the improved Byzantine Fault Tolerant (PBFT) consensus algorithm is adopted. The quantum random number generator is introduced into the improved Byzantine Fault Tolerant (PBFT) consensus algorithm, and the uncertainty principle in quantum mechanics is used to generate true random numbers. The true random numbers are processed by hash functions to obtain the intermediate conversion parameters from random numbers to master node selection, and then the master node is obtained by calculation; and the communication between nodes adopts the communication protocol of quantum key encryption to ensure the security of information during transmission and prevent it from being stolen or tampered with; The calculation steps of the master node include: In a quantum system, the state of a particle is in a superposition state before measurement, and the measurement result is completely random and unpredictable. The quantum random number generator uses this property to generate true random numbers , and then through the hash function For random numbers Processing is performed to obtain the intermediate conversion parameters from random numbers to master node selection , the formula is: ; Random Numbers It is a truly random number generated by a quantum random number generator based on the uncertainty principle in quantum mechanics. In a quantum system, the state of a particle is in a superposition state before measurement, and the measurement result is random and unpredictable. The quantum random number generator uses this feature to generate random numbers. ; It is the initial input parameter for the subsequent master node calculation process. Its randomness is the key to ensure that the master node selection is fair and difficult to be predicted by malicious nodes; In the process, due to the hash function It has one-way and uniqueness, that is, given an input random number The only intermediate conversion parameter can be obtained , but it is difficult to convert parameters through the middle Deducing random numbers ; Processing random numbers through hash functions further increases the unpredictability and security of master node selection.

[0030] Master Node It is used to determine which node serves as the master node in each round of consensus and is responsible for sorting and distributing client requests. The calculation formula becomes: ; Represents the number of the master node, Indicates the maximum value in the node number range plus 1, that is, the total number of nodes (assuming that the node number starts from 0). hour, Used to determine the value range of the master node number. The formula is: , so that the master node number falls within the legal node number range; Algorithm process: At the beginning of each round of consensus, a quantum random number generator generates a random number. , after being processed by the hash function, the intermediate conversion parameters are obtained , and then determine the master node ; Then enter the normal process of PBFT algorithm, including pre-preparation phase, preparation phase, confirmation phase, etc.; master node Responsible for sorting and distributing client requests. Other backup nodes verify and respond to the operations of the primary node. S22. Data upload and storage: Each power fingerprint collection device acts as a blockchain node, encapsulates the encrypted quantum power fingerprint data and device-related information (such as device type, connection relationship, etc.) into transactions according to the blockchain transaction format, performs hash calculation on the transaction content using SHA-256, and then uses the Merkle tree structure to store the transaction, and calculates the root hash value by combining the transaction hash values ​​layer by layer, which is stored in the blockchain block; S23. Dynamic information calibration: Write smart contracts to realize automatic calibration of device-related information. The smart contract periodically obtains the device information and real-time power data uploaded by each node, and compares and analyzes the power transmission relationship and connection status between adjacent devices based on Kirchhoff's current law and Kirchhoff's voltage law. If power transmission abnormalities or connection status changes are found, the smart contract starts the information update process, and broadcasts the updated device information through the blockchain to ensure that the ledger information is consistent with the actual device status.

[0031] In S22, SHA-256 is a commonly used cryptographic hash function that can convert input data of any length into a hash value of a fixed length (256 bits, i.e., 32 bytes). Its calculation process mainly includes the following steps: S221. Message padding. For each transaction, the message padding operation must be performed first. The padding rule is to add a "1" bit at the end of the transaction content, and then continue to add "0" bits until the message length modulo 512 is equal to 448. Finally, a 64-bit block is added, which records the length of the original message (in bits). In this way, the length of the padded message is an integer multiple of 512 bits. S222. Message grouping: divide the padded transaction content into several 512-bit message blocks. If the padded length is exactly 512 bits, there will be only one message block; if the length exceeds 512 bits, it will be divided into multiple message blocks; S223. Initialization operation, setting a set of initial state values, the state values ​​will be continuously updated in each subsequent round of calculation, and the initial state value is the starting point of the entire hash calculation; S224. Cyclic compression process: S2241. Message block expansion, expand each 512-bit message block into 64 32-bit words , the specific operations are as follows: The first 16 32-bit words arrive Extract directly from the message block in sequence; for Word , calculated using the following formula: ; ; ; in, Indicates input Circular right shift Bit operations; It is an XOR operation; Represents an extended 32-bit word; and Indicates input Perform circular right shift and XOR bitwise operations; S2242. Round robin calculation, for each round , The round that represents the number of loops does the following: calculate Median value: ; in, is the selection function, and its calculation formula is: ; is a logical AND operation, is the logical NOT operation, Indicates that according to the status value The value from and Select output in; is a predefined constant, which is the same as the number of loops. Correspondingly, different rounds have different constant values ​​to increase the complexity and security of the algorithm; It is The extended 32-bit word corresponding to the round is obtained through the message block expansion step; Indicates that the state value 2 is subjected to a similar right-shift and XOR bitwise operation; calculate Median value: ; in, is a majority function, and its calculation formula is: ; Used for judgment The majority of values; S225. Update status value: According to the calculated The median and The intermediate value updates the state value. The specific update rules are as follows: part of the original state value is updated to another state value (similar to pointer movement or assignment operation), and the remaining state value is updated by adding other state values ​​and or Perform operations; S226. Result generation: for each 512-bit message block, after 64 rounds of cyclic calculation, a set of updated state values ​​will be obtained. The updated state values ​​will be selected and combined in sequence according to the order of the state values ​​in the cyclic calculation process to obtain the partial hash result corresponding to the message block; if the input message is divided into multiple 512-bit message blocks, then for subsequent message blocks, the final state value obtained by the cyclic compression calculation of the previous message block is used as the initial state value of the cyclic compression calculation of the current message block, and the above cyclic compression steps are repeated; when all message blocks are processed, the final set of state values ​​are combined to form a 256-bit string, which is the SHA-256 hash value of the input message.

[0032] In S21, the participating nodes of the alliance chain are authorized. In the power system scenario, the participating nodes include power companies, operation and maintenance departments, regulatory agencies and other relevant parties.

[0033] like Figure 4 As shown, the device-related feature extraction method in S3 is: S311. Prepare quantum computing resources, build a quantum computing environment, build quantum computing hardware facilities by yourself, and select the matching number of quantum bits and computing resources according to the device fingerprint data volume and computing complexity; at the same time, install the supporting quantum computing programming framework Qiskit to write and run quantum computing programs; S312. Feature extraction algorithm implementation; The quantum principal component analysis (QPCA) algorithm is used to extract the features of the device fingerprint data in the blockchain ledger. The device fingerprint data is read from the blockchain ledger and converted into a quantum state form and input into the quantum computing environment. The linear transformation of the data is then achieved through the Hadamard gate and quantum rotation gate operations. Among them, Hadamard Gate is expressed as: ; The quantum rotating gate is represented as: ; ; ; By adjusting the rotation angle, the state of the quantum bit can be precisely controlled and transformed, which is used in the quantum principal component analysis (QPCA) algorithm to achieve linear transformation of data to extract data features; In the formula, For the gate of Hatama, is a quantum gate operation, Transform the quantum bit from or state into superposition state or , used to realize the initialization and transformation of quantum states in quantum computing; , , Respectively represent the axis, axis, A quantum revolving door with axis rotation, is the rotation angle; represents an imaginary unit, satisfying ; Calculate the quantum state covariance matrix : ; For the Quantum state data, is the average quantum state; is the number of data samples; Then, the quantum singular value decomposition (QSVD) algorithm is used to obtain the principal components of the data. These principal components contain features related to the operating status of the equipment, such as power fluctuation features, harmonic change features, etc. Among them, the power fluctuation characteristics for: ; Harmonic change characteristics for: ; for Subharmonic current effective value; is the effective value of fundamental current; yes The power value at a moment is the power measurement value of the device at a specific point in time; express The power value at the moment, that is, the power measurement value at the next time point immediately following the moment; To determine a maximum harmonic order based on the equipment detection capability and actual conditions; The device fingerprint encoding model construction method in S3: Construct a quantum state device fingerprint coding model using a quantum neural network (QNN). The network structure of the quantum neural network (QNN) includes an input layer, a quantum hidden layer, and an output layer. The number of neurons in the input layer is determined by the number of extracted features. Each neuron corresponds to one feature and is used to receive the extracted device feature data and convert it into a quantum state and input it into the network. For example, if 10 features are extracted from the device fingerprint data, then the input layer has 10 neurons. The quantum hidden layer uses the superposition and entanglement characteristics of quantum bits to perform nonlinear transformations and enhance the expressive power of the model. In the quantum hidden layer, the quantum perceptron model is used: ; in, Represents the output quantum state of the quantum perceptron model after computational processing; is the weight used to measure each input quantum state the importance of It is the input quantum state, that is, the quantum state data transmitted from the input layer; For biasing, additional flexibility is introduced to the quantum perceptron model; is the symbolic function, the symbolic function Used to judge and output the calculation results; When the value of is greater than 0, The function returns 1; when When the value is less than 0, -1 is returned. In the device status monitoring scenario, if the quantum perceptron model is used to determine whether the device status is normal, the output result of the symbolic function can directly correspond to the device status. The number of neurons in the output layer is determined according to application requirements. If you only need to determine whether the device status is normal, you can set up 1 neuron, and the output result can be set as 1 for normal and -1 for abnormal. If you want to predict the type of equipment failure, for example, there are 5 types of failures, then the output layer will have 5 neurons, and the specific failure type will be determined by the activation of the neurons.

[0034] like Figure 5 As shown, S4 equipment status monitoring and abnormal warning: S41. Real-time data collection and processing, the quantum sensor continuously collects the power parameters of the power equipment in real time, and converts them into quantum state device fingerprint data according to the method in S1; S42. Implement the quantum state matching algorithm, use the quantum state device fingerprint coding model constructed in S3 to analyze the transformer fingerprint data collected in real time, use the matching algorithm based on quantum entanglement entropy to calculate the quantum entanglement entropy difference between the real-time data and the normal state data, and compare the quantum entanglement entropy difference with the threshold set according to the historical data and operating experience of the transformer to determine whether the transformer state is abnormal; Quantum entanglement entropy The calculation formula is: ; in, is the density matrix of the quantum state, which is used to describe the state of the quantum system; represents the trace of a matrix, which is the sum of the elements on the main diagonal of the matrix; is the density matrix Perform base 2 logarithm operation on each element in ; Indicates that the logarithmic operation is completed to obtain a new matrix Perform matrix multiplication operations; Entanglement entropy in normal state The calculation formula is: ; in, is the density matrix corresponding to the transformer quantum state data in the normal state; the entanglement entropy in the normal state Used to measure the degree of entanglement of quantum states when the device is in normal operation; Entanglement entropy of real-time state The calculation formula is: ; in, is the density matrix of real-time collected data; The entanglement entropy of the real-time state is obtained based on density matrix calculation to reflect the quantum state entanglement of the device under the current operating state; Entanglement entropy difference The calculation formula is: ; The entanglement entropy difference is obtained by taking the real-time state entanglement entropy Entanglement entropy with normal state The absolute value of the difference quantifies the degree of difference between the two. In the monitoring of the operating status of power equipment, it is used to determine whether the operating status of the equipment is abnormal; When the quantum entanglement entropy difference calculated in real time When compared with the threshold, if If the value is greater than the threshold, the equipment status is judged to be abnormal, and the monitoring system immediately triggers an early warning signal, broadcasting the early warning information to the operation and maintenance personnel's mobile phone APP, the monitoring center's server and other related nodes through the blockchain account book; the early warning information includes the time when the abnormality occurred (such as 10:10 on October 10, 2024), the equipment identification (transformer number: 001), the preliminary judgment of the abnormality type (suspected power fluctuation abnormality), etc.; at the same time, the system ensures that the operation and maintenance personnel can know the early warning information in time through SMS notifications, APP push notifications, etc.; if If the value is less than the threshold, the device is considered to be within the normal fluctuation range and regular monitoring continues.

[0035] The above description is a preferred embodiment of the invention and is not intended to limit the invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the invention should be included in the protection scope of the invention.

Claims

1. A fingerprint coding method for electric power equipment, characterized in that: The following steps are involved: S1. Power fingerprint collection: quantum sensors are deployed at key locations of various power equipment to collect power parameters of equipment in real time. The collected power parameters are used to generate quantum power fingerprint data. The collected data is encrypted using quantum encryption technology and stored in the central processor. S2. Blockchain distributed ledger and dynamic information calibration, using blockchain technology to build a distributed ledger to record relevant information and device fingerprint data of power equipment. Each device fingerprint collection device acts as a node to upload the encrypted data to the blockchain ledger in real time; Then, the smart contract is used to automatically calibrate the relevant information of the equipment to ensure that the information in the ledger is consistent with the actual equipment status; S3. Extraction and modeling of device-related features: Using the parallel processing capability of quantum computing, we extract features of device fingerprint data in the blockchain ledger, extract features related to the device operating status, and establish a quantum device fingerprint coding model; S4. Equipment status monitoring and abnormal warning, use the quantum state device fingerprint coding model to analyze the real-time collected device fingerprint data, and use the quantum state matching algorithm to quickly determine whether the current equipment status is abnormal; once an abnormality is detected, the warning signal is immediately triggered, and the warning information is broadcast to all relevant nodes in real time through the blockchain ledger; the quantum state matching algorithm is used to achieve rapid monitoring of equipment status and abnormal warning.

2. A power equipment fingerprint coding method according to claim 1, characterized in that: The key locations include: deployment near the windings and core of the transformer to monitor voltage and current changes.

3. A fingerprint coding method for electric power equipment according to claim 1, characterized in that: In S1, the step of generating quantum state power fingerprint data using the collected power parameters includes: S11. The power parameters are quantized and encoded. For voltage, current, power, and harmonic power parameters, the measurement range of the power parameters is determined. The measurement range is expressed as: , Indicates power parameters; divides the measurement range of power parameters into intervals; among them, is a positive integer. The higher the precision requirement, The larger the value, the longer the interval of each parameter. The calculation formula is: ; and They are the upper and lower limits of the power parameter measurement range respectively; For the collected discrete power parameter values, determine whether the power parameter values ​​fall within the measurement range According to the linear coding rule, the serial number in is mapped into the quantum bit state: ； ； ； in, is the collected discrete power parameter value; It is the serial number of the power parameter value falling within the measurement range; To discrete power parameters The quantum bit state obtained after mapping according to the linear encoding rule; is the quantum bit state In the ground state The probability amplitude coefficient when ; is the quantum bit state In ground state The probability amplitude coefficient when ; and ; By converting voltage, current, power and harmonic parameters into corresponding quantum bit states, the classical power parameter data can be expressed in quantum state. S12. Select the entanglement gate operation and use the CNOT gate to perform quantum entanglement operation. The CNOT gate acts on two quantum bits. When the control bit is When the control bit is When , the target bit state remains unchanged; its matrix form is: ； S13. Perform entanglement operations to construct composite quantum states: The voltage quantum bit state is used as the control bit, and the current quantum bit state is used as the target bit. After the CNOT gate operation, the entangled state is obtained. The entangled state formula is expressed as: ； get After that, continue to entangle with the power quantum bit state to One bit in is used as the control bit, and the power quantum bit state As the target bit, the entangled state containing voltage, current and power information is obtained after the operation. , then and harmonic qubit states It is entangled with other quantum bit states to finally construct a composite quantum state, namely the power fingerprint data; in, represents quantum entanglement operation; It represents the entangled state obtained after the CNOT gate operation with the voltage quantum bit state as the control bit and the current quantum bit state as the target bit; The quantum bit state representing the voltage; The quantum bit state representing the electric current; and These are the two basic states of a quantum bit; and is the voltage qubit state Probability amplitude coefficient in the calculation basis; and is the current qubit state Probability amplitude coefficient in the calculation basis; It is an XOR operation.

4. A fingerprint coding method for electric power equipment according to claim 1, characterized in that: The quantum encryption method in S1 is: using an encryption method based on quantum key distribution (QKD), using the BB84 protocol to generate a one-time quantum key in the process, randomly selecting horizontal / vertical or 45˚ / 135˚ quantum states to send photons at the BB84 protocol sending end, and randomly selecting a measurement basis for measurement at the BB84 protocol receiving end; if the measurement basis matches the sending basis, the measurement result is recorded as the key bit, and if it does not match, it is discarded; the matching bits are screened out to form the key , the collected power fingerprint data Encryption is performed, and the encryption formula is: , It is an XOR operation and is encrypted and stored in the quantum storage module.

5. A fingerprint coding method for electric power equipment according to claim 1, characterized in that: In S2, the steps of calibrating the blockchain distributed ledger with dynamic information include: S21. Blockchain network construction, building a distributed ledger based on the alliance chain, using the improved Byzantine fault-tolerant consensus algorithm, using the uncertainty principle in quantum mechanics to generate true random numbers, processing the true random numbers through hash functions, obtaining the intermediate conversion parameters from random numbers to master node selection, and then obtaining the master node through calculation; and the communication between nodes uses the communication protocol of quantum key encryption to ensure the security of information during transmission and prevent it from being stolen or tampered with; among them, the quantum random number generator is introduced in the improved Byzantine fault-tolerant consensus algorithm; and the participating nodes of the alliance chain are authorized. In the power system scenario, the participating nodes include power companies, operation and maintenance departments, and regulatory agencies. S22. Data upload and storage: Each power fingerprint collection device acts as a blockchain node, encapsulates the encrypted quantum power fingerprint data and device-related information into transactions according to the blockchain transaction format, performs hash calculation on the transaction content using SHA-256, and then uses the Merkle tree structure to store the transaction. The transaction hash values ​​are combined layer by layer to calculate the root hash value, which is stored in the blockchain block; S23. Dynamic information calibration: Write smart contracts to realize automatic calibration of device-related information. The smart contract periodically obtains the device information and real-time power data uploaded by each node, and compares and analyzes the power transmission relationship and connection status between adjacent devices based on Kirchhoff's current law and Kirchhoff's voltage law. If power transmission abnormalities or connection status changes are found, the smart contract starts the information update process, and broadcasts the updated device information through the blockchain to ensure that the ledger information is consistent with the actual device status.

6. A fingerprint coding method for electric power equipment according to claim 5, characterized in that: In S21, the calculation step of the master node includes: In a quantum system, the state of a particle is in a superposition state before measurement, and the measurement result is completely random and unpredictable. The quantum random number generator uses this property to generate true random numbers , and then through the hash function For random numbers Processing is performed to obtain the intermediate conversion parameters from random numbers to master node selection , the formula is: ； Random Numbers It is a truly random number generated by a quantum random number generator based on the uncertainty principle in quantum mechanics. In a quantum system, the state of a particle is in a superposition state before measurement, and the measurement result is random and unpredictable. The quantum random number generator uses this feature to generate random numbers. ; It is the initial input parameter for the subsequent master node calculation process. Its randomness is the key to ensuring that the master node selection is fair and difficult to be predicted by malicious nodes; In the process, due to the hash function It has one-way and uniqueness, that is, given an input random number Get the only intermediate conversion parameter , but it is difficult to convert parameters through the middle Deducing random numbers ; Processing random numbers through hash functions further increases the unpredictability and security of master node selection; Master Node It is used to determine which node serves as the master node in each round of consensus and is responsible for sorting and distributing client requests. The calculation formula becomes: ； Represents the number of the master node, Indicates the maximum value in the node number range plus 1, that is, the total number of nodes, when calculating the master node hour, Used to determine the value range of the master node number. The formula is: , so that the master node number falls within the legal node number range.

7. A fingerprint coding method for electric power equipment according to claim 5, characterized in that: In S22, the hash calculation of the transaction content using SHA-256 includes: S221. Message padding: For each transaction, the message padding operation must be performed first. The padding rule is to add a "1" bit at the end of the transaction content, and then continue to add "0" bits until the message length modulo 512 is equal to 448. Finally, a 64-bit block is added. The block records the length of the original message in bits. The length of the padded message is an integer multiple of 512 bits. S222. Message grouping: divide the padded transaction content into several 512-bit message blocks. If the padded length is exactly 512 bits, there will be only one message block; if the length exceeds 512 bits, it will be divided into multiple message blocks; S223. Initialization operation, setting a set of initial state values, the state values ​​will be continuously updated in each subsequent round of calculation, and the initial state value is the starting point of the entire hash calculation; S224 loop compression; each 512-bit message block is expanded into 64 32-bit words by message block expansion, and then the intermediate value is calculated for each round by round-robin calculation; S225. Update the state value, and update the state value according to the calculated intermediate value. The specific update rules are as follows: a part of the original state value is updated to another part of the state value, and the remaining part of the state value is updated by operating with other state values ​​and intermediate values; S226. Result generation: for each 512-bit message block, after 64 rounds of cyclic calculation, a set of updated state values ​​will be obtained. The updated state values ​​will be selected and combined in sequence according to the order of the state values ​​in the cyclic calculation process to obtain the partial hash result corresponding to the message block; if the input message is divided into multiple 512-bit message blocks, then for subsequent message blocks, the final state value obtained by the cyclic compression calculation of the previous message block is used as the initial state value of the cyclic compression calculation of the current message block, and the cyclic compression step is repeated; when all message blocks are processed, the final set of state values ​​are combined to form a 256-bit string, which is the SHA-256 hash value of the input message.

8. A fingerprint coding method for electric power equipment according to claim 7, characterized in that: In the S22 and S224, the cyclic compression process includes: S2241. Message block expansion, expand each 512-bit message block into 64 32-bit words , the specific operations are as follows: The first 16 32-bit words arrive Extract directly from the message block in sequence; for Word , calculated using the following formula: ； ; ； in, Indicates input Circular right shift Bit operations; It is an XOR operation; Represents an extended 32-bit word; and Indicates input Perform circular right shift and XOR bitwise operations; S2242. Round cyclic calculation, for each round , The round that represents the number of loops does the following: calculate Median value: ; in, is the selection function, and its calculation formula is: ； is a logical AND operation, is the logical NOT operation, Indicates that according to the status value The value from and Select output in; is a predefined constant, which is the same as the number of loops. Correspondingly, different rounds have different constant values ​​to increase the complexity and security of the algorithm; It is The extended 32-bit word corresponding to the round is obtained through the message block expansion step; Indicates a circular right shift and XOR bitwise operation on the state value 2; calculate Median value: ; in, is a majority function, and its calculation formula is: ； Used for judgment The majority of values.

9. A fingerprint coding method for electric power equipment according to claim 1, characterized in that: In said S22, the device-related feature extraction method in said S3: S311. Prepare quantum computing resources, build a quantum computing environment, build quantum computing hardware facilities by yourself, and select the matching number of quantum bits and computing resources according to the device fingerprint data volume and computing complexity; at the same time, install the supporting quantum computing programming framework Qiskit to write and run quantum computing programs; S312. Feature extraction algorithm implementation; The quantum principal component analysis (QPCA) algorithm is used to extract the features of the device fingerprint data in the blockchain ledger. The device fingerprint data is read from the blockchain ledger and converted into a quantum state form and input into the quantum computing environment. The linear transformation of the data is then achieved through the Hadamard gate and quantum rotation gate operations. Among them, Hadamard Gate is expressed as: ; The quantum rotating gate is represented as: ； ； ； By adjusting the rotation angle, the state of the quantum bit is precisely controlled and transformed, which is used in the quantum principal component analysis (QPCA) algorithm to achieve linear transformation of data to extract data features. In the formula, For the gate of Hatama, is a quantum gate operation, Transform the quantum bit from or state into superposition state or , used to realize the initialization and transformation of quantum states in quantum computing; , , Respectively represent the axis, axis, A quantum revolving door with axis rotation, is the rotation angle; represents an imaginary unit, satisfying ; Calculate the quantum state covariance matrix : ； For the Quantum state data, is the average quantum state; is the number of data samples; Then, the quantum singular value decomposition (QSVD) algorithm is used to obtain the principal components of the data. These principal components contain features related to the operating status of the equipment, such as power fluctuation features and harmonic change features. Among them, the power fluctuation characteristics for: ; Harmonic change characteristics for: ; for Subharmonic current effective value; is the effective value of fundamental current; yes Power value at the moment; express The power value at the moment, that is, the power measurement value at the next time point immediately following the moment; To determine a maximum harmonic order based on the equipment detection capability and actual conditions; Device fingerprint encoding model construction method: Construct a quantum state device fingerprint coding model using a quantum neural network (QNN). The network structure of the quantum neural network (QNN) includes an input layer, a quantum hidden layer, and an output layer. The number of neurons in the input layer is determined according to the number of extracted features. Each neuron corresponds to one feature and is used to receive the extracted device feature data and convert it into a quantum state and input it into the network. The quantum hidden layer uses the superposition and entanglement characteristics of quantum bits to perform nonlinear transformations and enhance the expressive power of the model. In the quantum hidden layer, the quantum perceptron model is used: ； in, Represents the output quantum state of the quantum perceptron model after computational processing; is the weight used to measure each input quantum state the importance of It is the input quantum state, that is, the quantum state data transmitted from the input layer; is bias; is a symbolic function.

10. A power equipment fingerprint coding method according to claim 1, characterized in that: In the S22, the S4 includes: S41. Real-time data collection and processing, the quantum sensor continuously collects the power parameters of the power equipment in real time, and converts them into quantum state device fingerprint data according to the method in S1; S42. Implement the quantum state matching algorithm, use the quantum state device fingerprint coding model constructed in S3 to analyze the transformer fingerprint data collected in real time, use the matching algorithm based on quantum entanglement entropy to calculate the quantum entanglement entropy difference between the real-time data and the normal state data, and compare the quantum entanglement entropy difference with the threshold set according to the historical data and operating experience of the transformer to determine whether the transformer state is abnormal; The calculation formula of quantum entanglement entropy is: ； in, is the density matrix of the quantum state, which is used to describe the state of the quantum system; represents the trace of a matrix, which is the sum of the elements on the main diagonal of the matrix; is the density matrix Perform base 2 logarithm operation on each element in; Indicates that the logarithmic operation is completed to obtain a new matrix Perform matrix multiplication operations; By operating the density matrix, the entanglement entropy value of the quantum system is obtained, which reflects the degree of entanglement between the quantum states of the system; Entanglement entropy in normal state The calculation formula is: ； in, is the density matrix corresponding to the quantum state data of the transformer in the normal state; the entanglement entropy in the normal state Used to measure the degree of entanglement of quantum states when the device is in normal operation; Entanglement entropy of real-time state The calculation formula is: ； in, is the density matrix of real-time collected data; The entanglement entropy of the real-time state is obtained based on density matrix calculation to reflect the quantum state entanglement of the device under the current operating state; Entanglement entropy difference The calculation formula is: ； is the entanglement entropy difference; by taking the real-time state entanglement entropy Entanglement entropy with normal state The absolute value of the difference quantifies the degree of difference between the two. In the monitoring of the operating status of power equipment, it is used to determine whether the operating status of the equipment is abnormal; When the quantum entanglement entropy difference calculated in real time When compared with the threshold, if If the value is greater than the threshold, the device status is considered abnormal, and the monitoring system immediately triggers an early warning signal. The early warning information is broadcast to the operation and maintenance personnel's mobile phone APP and the server-related nodes of the monitoring center through the blockchain account book; the early warning information includes the time when the abnormality occurred, the device identification, and the preliminary judgment of the abnormality type; at the same time, the system ensures that the operation and maintenance personnel can know the early warning information in time through SMS notification and APP push notification; if If the value is less than the threshold, the device is considered to be within the normal fluctuation range and regular monitoring continues.

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