High-voltage special transformer remote cost control real-time optimization method based on quantum computing

Abstract

The invention discloses a high-voltage special transformer remote cost control real-time optimization method based on quantum computing. The method comprises the following steps: S1, acquiring voltage, current, load and temperature data of a high-voltage special transformer; s2, transmitting the data to a quantum computing optimization engine, and generating a preliminary optimization path and a key data mode through a quantum fractal mapping algorithm; s3, performing global optimization on the path by applying a topological quantum field algorithm, and determining an optimal control path and a cost control strategy; s4, adjusting equipment operation parameters in real time by adopting a quantum entanglement distributed computing architecture; s5, transmitting the optimization strategy to a control module through a quantum adaptive chaotic coding technology; s6, dynamically optimizing the path by using a quantum annealing algorithm in combination with real-time feedback; s7, continuously monitoring and optimizing the system, and maintaining stable operation; and S8, adaptively adjusting and optimizing engine parameters through a quantum multi-task evolutionary learning algorithm. According to the invention, the cost control efficiency and the system optimization capability are improved.

Description

Technical Field

[0001] The present invention relates to the field of quantum technology, and in particular to a real-time optimization method for remote fee control of a high-voltage special transformer based on quantum computing. Background Art

[0002] As the power system continues to develop, high-voltage dedicated transformers play a key role in modern power transmission and distribution. These transformers need to operate in a stable and efficient environment to ensure the continuity and safety of power. However, with the fluctuation of power load and changes in user demand, traditional transformer cost control management methods face many challenges.

[0003] First, most traditional cost control management methods are based on offline analysis and static optimization, which are difficult to adapt to the real-time changing power system environment. These methods rely on preset control strategies and cannot be adjusted in real time according to dynamic changes in the power system. This leads to slow system response in the face of complex power load changes and emergencies, which easily leads to waste of resources and unstable power supply.

[0004] Secondly, most of the existing optimization algorithms are based on classical computing methods, such as linear programming and genetic algorithms. These algorithms show obvious limitations when dealing with large-scale data and complex multivariable optimization problems. As the complexity of the power system increases, the performance of traditional algorithms in global optimization problems is not satisfactory, and they can often only find local optimal solutions, making it difficult to ensure the overall optimality of the system.

[0005] Third, traditional remote monitoring and control technologies also have deficiencies in accuracy and response speed. Due to data transmission and processing delays, the real-time performance of the control system is difficult to guarantee. At the same time, when facing large-scale distributed power systems, remote monitoring systems are prone to bottlenecks when processing massive amounts of data, and are unable to monitor and control the system in real time and accurately, resulting in reduced operating efficiency and safety of the power system.

[0006] In addition, the existing fee control system lacks effective encryption and security measures and is vulnerable to eavesdropping and tampering during data transmission. Traditional encryption methods are less secure when faced with attacks from high-performance computing technologies such as quantum computing, and cannot effectively protect data security in the power system.

[0007] In summary, the existing high-voltage dedicated transformer fee control system has problems such as low fee control efficiency, insufficient real-time optimization capability, limited data processing capability, low remote monitoring and control accuracy, and lack of effective safety assurance. With the expansion of the scale and complexity of the power system, these problems will become more prominent. Therefore, how to provide a real-time optimization method for remote fee control of high-voltage dedicated transformers based on quantum computing is an urgent problem that technicians in this field need to solve. Summary of the invention

[0008] One purpose of the present invention is to propose a real-time optimization method for remote cost control of high-voltage special transformers based on quantum computing. The present invention makes full use of quantum computing technology, combines quantum fractal mapping algorithm, topological quantum field algorithm, quantum entanglement distributed computing architecture and quantum adaptive chaos coding technology, and describes in detail how to achieve real-time optimization and precise control in complex power systems, with the advantages of high cost control efficiency, fast real-time response, strong security and stable system operation.

[0009] According to an embodiment of the present invention, a real-time optimization method for remote cost control of a high-voltage dedicated transformer based on quantum computing includes the following steps:

[0010] S1. Collecting real-time operating data of a high-voltage dedicated transformer, the data including voltage, current, load and temperature parameters;

[0011] S2. The collected multi-dimensional data is transmitted to the quantum computing optimization engine through the remote monitoring system, and the data of the power system is mapped to the multi-level fractal quantum state space using the quantum fractal mapping algorithm, from which the preliminary optimization path and key data patterns are extracted;

[0012] S3. Based on the preliminary optimization paths, the topological quantum field algorithm is applied to further analyze these paths, identify and utilize the topological invariance characteristics, perform global optimization on the preliminary paths, generate global optimal control paths with higher stability and adaptability, and determine the corresponding cost control strategy;

[0013] S4. Based on the generated global optimal path, the quantum entanglement distributed computing architecture is used to adjust the operating parameters of multi-node devices in real time and implement an optimized cost control strategy;

[0014] S5. Through quantum adaptive chaotic coding technology, the optimized fee control strategy is sent to the control module of the high-voltage dedicated transformer;

[0015] S6. Based on the preliminary optimization, the real-time data feedback mechanism is used to locally adjust and dynamically optimize the global optimal path through the quantum annealing algorithm to cope with sudden changes in the power system;

[0016] S7. Combined with the path optimized by the quantum annealing algorithm, the multi-node equipment in the power system is continuously monitored, and the comprehensive optimization of global and local paths is implemented through the quantum entanglement distributed computing architecture;

[0017] S8. Use the quantum multi-task evolutionary learning algorithm to adaptively learn and adjust the parameters in the quantum computing optimization engine to continuously improve the optimization capability and adaptability of the system.

[0018] Optionally, S2 includes the following steps:

[0019] S21, standardize the collected multidimensional data, normalize different types of data, and generate a standardized matrix N, where each matrix N i Represents time series data of a data type;

[0020] S22. Map the standardized data matrix N to the high-dimensional quantum state space through quantum state encoding:

[0021] S221, define each row of data in the standardized matrix N as the input vector v i , where v i The feature vector representing the i-th data type;

[0022] S222. Use the quantum state mapping function to map the vector into the corresponding high-dimensional quantum state:

[0023]

[0024] Among them, α ij is the amplitude coefficient, θ ij is the phase angle, γ is the nonlinear modulation factor, c j is the reference vector in the quantum state space;

[0025] S23. Using the quantum fractal mapping algorithm, perform multi-level fractal mapping on the generated high-dimensional quantum state. The specific operations include:

[0026] S231. Quantum state fractal mapping is defined as:

[0027]

[0028] Among them, δ p is the fractal level weight, θ kp is the quantum state phase adjustment parameter, ψ p (N) is the fractal quantum state after mapping;

[0029] S232, by adjusting δ p and θ kp , achieving multi-level fractal decoupling to reveal the self-similarity and correlation structure between quantum states;

[0030] S24. Analysis of the coherence and fractal dimension characteristics of quantum states:

[0031] S241. Coherence calculation: For each fractal quantum state ψ p (N), calculate its coherence with other levels of quantum states, the formula is:

[0032]

[0033] in, is the coherence modulation phase, ψ r (N) is the reference quantum state;

[0034] S242, generate a coherence matrix Ξ, where each element ξ pq Reflects the degree of coherence between fractal levels p and q;

[0035] S243, Fractal dimension calculation: For each fractal quantum state ψ p (N), and its complexity is analyzed by the fractal dimension formula, which is:

[0036]

[0037] Among them, var(ψ p (N)) is the variance of the quantum state;

[0038] S244, summarize the fractal dimensions of all levels and generate a dimensional feature matrix D, where each element D p Indicates the fractal dimension of the corresponding level;

[0039] S25. Extract key data patterns and generate preliminary optimization paths in multi-level fractal quantum state space:

[0040] S251. Based on the local stability of the quantum state, its stability at adjacent time points is evaluated. The formula is:

[0041]

[0042] Among them, Δt is the time step, S i is the stability coefficient of the i-th path point;

[0043] S252, select the path point π with the highest local stability i , these waypoints will be used as preliminary waypoint candidates;

[0044] S253. Based on the global correlation of quantum states, the global optimal path of quantum states is constructed. The path construction is calculated based on the correlation between path points:

[0045]

[0046] Among them, ξ ij is the coherence between path points, S i and S j is the stability of the path point, P * is the global optimal path set;

[0047] S26: Storing the extracted key data patterns and preliminary optimization paths in a storage module of the quantum computing optimization engine.

[0048] Optionally, S3 includes the following steps:

[0049] S31. Initialize the quantum field of the preliminary optimization path, map each path point in the path to the topological quantum field through the quantum field state, and generate the initial quantum field state Ψ(P), which is defined as:

[0050]

[0051] Among them, λ i is the weight coefficient of the path point, is the phase information of the path point, ψ(π i ) is the state function of the path point in the quantum field, is a nonlinear modulation function, defined as:

[0052]

[0053] Where α is the modulation intensity coefficient, which is used to optimize the nonlinear interaction between the weights and phases of the path points to adapt to the dynamic environment in complex power systems;

[0054] S32. Based on the initial quantum field state, a topological quantum field algorithm is applied to perform invariance analysis, which specifically includes the following steps:

[0055] S321. Define the topological structure tensor F(Ψ), whose elements are composed of the quantum coherence and phase difference between the path points, combined with the energy distribution ε(π i ) for dynamic adjustment, specifically:

[0056]

[0057] Among them, ξ ij is the path point π i and π j The correlation between is the phase difference between the path points, ε(π i ) is the energy distribution of the path points, which is used to describe the global correlation between the path points;

[0058] S322. Generate topological invariants by integral calculation It is defined as:

[0059]

[0060] in, is the closed loop of the quantum field, Tr is the trace operation, dr is the differential element on the integral path, and K(Ψ) is the topological constraint condition;

[0061] S33, through topological invariance analysis, select the most stable path point for global optimization, and reconstruct the optimized path P * :

[0062]

[0063] in, is the global correlation function between path points, used to combine the global topological relationship between path points;

[0064] S34. Generate a cost control strategy function C(P * ), combining the energy distribution of the path points, the coherence of the quantum state and the phase information:

[0065]

[0066] Among them, ν k is the path point π k The priority coefficient of is the energy factor corresponding to the path point, is the dynamic modulation function:

[0067]

[0068] Where β is the dynamic modulation coefficient, is the path point π k The expected value of the quantum state is adjusted nonlinearly according to the priority of the path points and the characteristics of the quantum state through a dynamic modulation function;

[0069] S35. Store the optimized global path and the corresponding cost control strategy in the quantum computing optimization engine, ready to be transmitted to subsequent steps to execute the optimization control strategy.

[0070] Optionally, S4 includes the following steps:

[0071] S41, the global optimal path P * ={π1,π2,...,π l Each path point π in i Initialize the quantum entangled state and generate the quantum entangled state Ψ E (P * ):

[0072]

[0073] Among them, ψ(π i ) represents the path point π i The quantum state of Represents entanglement operations of quantum states;

[0074] S42, the entangled state ΨE (P * ) is transmitted to multiple node devices through the quantum state transmission channel to maintain the quantum entanglement between nodes. The coherence of the quantum state is maintained through the following phase adjustment formula:

[0075] ψ(π i )→ψ(π i )exp(iθ i );

[0076] Among them, θ i is the path point π during the transmission process i The phase adjustment parameter is used to correct the phase error of the quantum state during transmission;

[0077] S43, each node device receives the entangled state ψ(π i ) adjusts its operating parameters, and the adjustment process is calculated through the following correlation formula:

[0078]

[0079] Among them, ΔP i Indicates the adjustment amount of the device parameters, is the Hamiltonian operator, is the expectation value of the quantum state under the Hamiltonian, represents the derivative operation with respect to time;

[0080] S44, combined with the optimized cost control strategy C (P * ), implement specific control in each node device, and the fee control strategy is based on the quantum state component ψ(π i ) is constructed by:

[0081]

[0082] Among them, γ i is the strategy weight, is the operator related to the cost control operation, is the expected value of the fee control operation in the quantum state;

[0083] S45. Real-time monitoring of the feedback information of each node device, and dynamic correction of the quantum state. During the feedback process, the maintenance of the entangled state is achieved through the following feedback correction formula:

[0084]

[0085] Among them, δ j The feedback correction phase is used to correct the quantum state according to real-time feedback and maintain the coherence and synchronization of the entangled state.

[0086] Optionally, S5 includes the following steps:

[0087] S51, optimize the cost control strategy C (P * ) to perform quantum state encoding processing and decompose it into a quantum state control instruction sequence {C1, C2, ..., C n}, where each control instruction C i and the quantum state ψ(C i ), the quantum state encoding is represented by:

[0088]

[0089] Among them, α k is the amplitude coefficient, θ k is the phase angle, f(C i ) is the control instruction C i Related nonlinear functions, |k is the quantum ground state;

[0090] S52, the quantum state ψ(C i ) generates the corresponding chaotic quantum state χ(C i ), which is achieved through the following formula:

[0091]

[0092] Among them, β j is the weight coefficient, λ j is the chaotic sequence parameter, ξ is the chaos control parameter, χ(C i ) is the generated chaotic quantum state;

[0093] S53, by real-time analysis of the noise characteristics of the quantum communication channel, dynamically modulating the chaotic quantum state χ(C i ), generate the encrypted quantum control instruction Φ(C i ), the formula is as follows:

[0094]

[0095] Among them, γ l is the modulation coefficient, Δ l is a phase correction parameter related to the noise characteristics of the communication channel;

[0096] S54, the encrypted quantum control instruction Φ(C i ) is transmitted to the control module of the high-voltage dedicated transformer through the quantum communication channel to ensure that it is not interfered with or eavesdropped by the outside world during the transmission process;

[0097] S55, in the control module of the high-voltage dedicated transformer, the received quantum control instruction Φ(C i) is decoded, and the original quantum state ψ(C i ), and then restore to control instruction C i And perform fee control operations. The decoding process is implemented through the following formula:

[0098]

[0099] S56, decoded control instruction C i Used to guide the actual operation of high-voltage special transformers.

[0100] Optionally, the S6 includes the following steps:

[0101] S61, collect real-time data during system operation, including parameters such as voltage fluctuation V(t), current anomaly I(t), load change L(t), temperature fluctuation T(t), etc., and use data mining algorithms to analyze these data to identify potential local unstable areas U k , where k represents different local areas;

[0102] S62: for the identified local unstable region U k , the quantum annealing algorithm is applied to perform local path perturbation optimization, which specifically includes the following steps:

[0103] S621, initialize the global optimal path P * The path point π in i and its corresponding quantum state Where P * =π1,π2,...,π n} represents a set of path points, each path point π i There is a quantum state associated with

[0104] S622. Define the objective function To measure the path point π i The stability after local disturbance is based on the coherence of the path points, the local energy distribution and the fractal dimension characteristics. The specific expression is:

[0105]

[0106] Among them, ξ ij is the path point π i and adjacent path points π j The coherence between ij is the phase difference, is the quantum state of the adjacent path points, is the local energy distribution of the path point, is the fractal dimension feature of the path point;

[0107] S623, during quantum annealing, by adjusting the phase of the path point and amplitude Use the path perturbation formula to optimize the path points:

[0108]

[0109] in, is the quantum state of the next time step, k B is the Boltzmann constant, T is the annealing temperature, is the objective function value of the current quantum state;

[0110] S624, by gradually reducing the annealing temperature T, find the local optimal solution and generate an updated global path P # ;

[0111] S625: Calculate the updated path point coherence matrix for the local optimal solution after the path disturbance and path energy distribution matrix Save the optimization results to the quantum computing optimization engine;

[0112] S63, according to the real-time feedback data of the system, the path P # Perform dynamic adjustments. The specific steps include:

[0113] S631. Monitor the operating status of the power system in real time, and identify any dynamic changes that may cause global or local instability in combination with data feedback from distributed monitoring nodes;

[0114] S632. Use the quantum entanglement distributed computing architecture to continuously adjust the global path so that the system can maintain a stable control strategy when sudden changes occur. The specific adjustment method is:

[0115]

[0116] Among them, γ opt is the optimized path coherence parameter, P new is the new path after adjustment;

[0117] S64, finally generating the adjusted global optimal path P new And use it to guide the real-time cost control strategy of high-voltage dedicated transformers to achieve dynamic optimization and stable control of the power system.

[0118] Optionally, the S7 includes the following steps:

[0119] S71, combined with the global path P after quantum annealing algorithm optimization #, continuously monitor multi-node devices in the power system, the monitoring includes the following:

[0120] S711, collect the operating status data of each node device in real time through the distributed sensor network, including the node voltage V i (t), node current I i (t), node load L i (t) and node temperature T i (t), where i represents the i-th monitoring node;

[0121] S712, combined with the quantum entanglement distributed computing architecture, transmit the monitoring data to the central control system in real time and analyze the quantum entanglement state between each node The coherence changes of the system are used to evaluate the overall stability of the system;

[0122] S72. Based on real-time monitoring data, comprehensive optimization of global and local paths is implemented through a quantum entanglement distributed computing architecture, specifically including the following steps:

[0123] S721, coherence matrix between node devices Perform dynamic analysis to identify areas in the system where coherence decreases, using the following formula:

[0124]

[0125] in, and Represent the path point π i and π j The quantum state of is the coherence value before optimization, Δξ ij is the coherence change value;

[0126] S722, according to the coherence change value Δξ ij Adjust the optimization strategy of global and local paths. The specific optimization process includes:

[0127] S7221. Optimize and adjust the coherence reduction area using the path perturbation algorithm. The optimized path coherence matrix The following conditions are met:

[0128]

[0129] in, is the adjusted coherence matrix;

[0130] S7222. Update the operating parameters of each node device to make them consistent with the optimized path;

[0131] S73. Dynamically adjust and optimize cost control strategies based on real-time feedback from the system To make the system run optimally in a dynamic environment, the specific steps include:

[0132] S731, based on feedback data, real-time update of the cost control strategy function of the route point Optimize the energy distribution and load scheduling of the path points. The formula is:

[0133]

[0134] Among them, ν k is the path point priority coefficient, E k is the waypoint energy, is a dynamic modulation function used to adjust the control strategy of the path points;

[0135] S732. Send the adjusted fee control strategy to the control module of each node device to execute the optimized fee control operation in real time.

[0136] Optionally, the S8 includes the following steps:

[0137] S81. Adaptively learn and adjust the parameters in the quantum computing optimization engine using a quantum multi-task evolutionary learning algorithm, specifically including the following steps:

[0138] S811, initialize the multi-task set T in the power system = {T1, T2, ..., T n}, each task T i Corresponding to an optimization target in the power system, including voltage stability V stability , Load Balancing balance 、Energy efficiency E efficiency and temperature control T control wait;

[0139] S812. For each task T i Initialize quantum state And define the fitness function Used to evaluate the optimization effect of the task, the fitness function combines the coherence of the quantum state, the task energy distribution and the task priority, and the formula is:

[0140]

[0141] Among them, ξ ij is the coherence coefficient between tasks, θ ij is the phase adjustment factor, For task T j The energy distribution of i is the task priority, is the reduced Planck constant, ω is the frequency of the quantum state;

[0142] S813. Through the quantum genetic operation and quantum state mutation operation in the quantum multi-task evolutionary learning algorithm, the multi-task quantum state is collaboratively optimized, specifically including:

[0143] S8131. Perform quantum genetic operations, select quantum states with higher fitness for crossover and recombination, and generate next generation task quantum states

[0144] S8132. Perform quantum state mutation operation to explore the diversity of optimization solutions. The mutation formula is:

[0145]

[0146] Among them, γ i is the variation factor, For the current task T i The fitness change, k B is the Boltzmann constant, T is the system temperature;

[0147] S814. During the evolution process, the optimal quantum state of each task is generated through multi-task collaborative optimization And apply it to parameter adjustment in quantum computing optimization engine to optimize the overall performance of power system;

[0148] S82, based on the evolutionary learning mechanism of quantum multi-task evolutionary learning algorithm, regularly analyzes the system feedback data and calculates the feedback data matrix D feedback For the optimal quantum state Perform adaptive adjustment, including the following steps:

[0149] S821. Collect and analyze feedback data during system operation to generate feedback data matrix D feedback , where each column corresponds to task T i Feedback data;

[0150] S822, using the feedback data matrix D feedback For quantum states After adaptive adjustment, the adjusted quantum state is expressed as:

[0151]

[0152] in, For task T i The optimal fitness function value, σ i For task T i The associated standard deviation is used to adjust the update intensity of the quantum state;

[0153] S823, according to the adjusted quantum state Reoptimize the global path Pnew Cost control strategy

[0154] S83, the optimized global path P new Cost control strategy Deployed to each node of the power system, real-time monitoring and dynamic adjustment are carried out, including:

[0155] S831, through the quantum entanglement distributed computing architecture, real-time synchronization and adjustment of control parameters of each node;

[0156] S832, monitor the feedback information of each node device in real time, analyze the operating status and load of the device, and if abnormal fluctuations occur, trigger the quantum multi-task evolutionary learning algorithm to perform local optimization and adjustment;

[0157] S833. Based on the long-term operating data of the system, regularly review and adjust the parameters in the quantum computing optimization engine to cope with the long-term changes in the power system and new optimization needs.

[0158] The beneficial effects of the present invention are:

[0159] (1) The present invention proposes to process the multidimensional data of the power system through a quantum fractal mapping algorithm, map it to a multi-level fractal quantum state space, and extract preliminary optimization paths and key data patterns from it. This method effectively improves the accuracy and efficiency of power system data processing, especially when facing complex and large-scale data, and can significantly improve the effect of global optimization.

[0160] (2) The present invention introduces a topological quantum field algorithm to further analyze the preliminary path, and generates a global optimal control path with higher stability and adaptability through topological invariance feature identification and global optimization. Through this method, the system can maintain the optimal state under the condition of power load changes and user demand fluctuations, greatly enhancing the stability and responsiveness of the power system.

[0161] (3) The present invention uses quantum entanglement distributed computing architecture to adjust the operating parameters of multi-node devices in real time and implement optimized cost control strategies. This technology breaks through the limitations of traditional remote monitoring and control systems, achieves higher control accuracy and faster response speed, and is particularly suitable for widely distributed power system application scenarios.

[0162] (4) The present invention adopts quantum adaptive chaotic coding technology to encrypt the optimized cost control strategy and transmit it to the control module of the high-voltage dedicated transformer. This technology combines the adaptability of the chaotic system with the nonlinear characteristics of the quantum state, enhances the security of the data transmission process, can effectively resist external interference and data tampering, and ensure the safety and reliability of the power system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0163] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0164] Figure 1 This is a flow chart of a real-time optimization method for remote cost control of a high-voltage dedicated transformer based on quantum computing proposed by the present invention;

[0165] Figure 2 A flow chart of path generation and optimization of the quantum fractal mapping and topological quantum field algorithm proposed in the present invention;

[0166] Figure 3 This is the control and encryption flow chart of the quantum entanglement distributed computing and quantum adaptive chaos coding technology proposed in the present invention. DETAILED DESCRIPTION

[0167] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0168] refer to Figure 1-3 , a real-time optimization method for remote cost control of high-voltage special transformer based on quantum computing, comprising the following steps:

[0169] S1. Collecting real-time operating data of a high-voltage dedicated transformer, the data including voltage, current, load and temperature parameters;

[0170] S2. The collected multi-dimensional data is transmitted to the quantum computing optimization engine through the remote monitoring system, and the data of the power system is mapped to the multi-level fractal quantum state space using the quantum fractal mapping algorithm, from which the preliminary optimization path and key data patterns are extracted;

[0171] S3. Based on the preliminary optimization paths, the topological quantum field algorithm is applied to further analyze these paths, identify and utilize the topological invariance characteristics, perform global optimization on the preliminary paths, generate global optimal control paths with higher stability and adaptability, and determine the corresponding cost control strategy;

[0172] S4. Based on the generated global optimal path, the quantum entanglement distributed computing architecture is used to adjust the operating parameters of multi-node devices in real time and implement an optimized cost control strategy;

[0173] S5. Through quantum adaptive chaotic coding technology, the optimized fee control strategy is sent to the control module of the high-voltage dedicated transformer;

[0174] S6. Based on the preliminary optimization, the real-time data feedback mechanism is used to locally adjust and dynamically optimize the global optimal path through the quantum annealing algorithm to cope with sudden changes in the power system;

[0175] S7. Combined with the path optimized by the quantum annealing algorithm, the multi-node equipment in the power system is continuously monitored, and the comprehensive optimization of global and local paths is implemented through the quantum entanglement distributed computing architecture;

[0176] S8. Use the quantum multi-task evolutionary learning algorithm to adaptively learn and adjust the parameters in the quantum computing optimization engine to continuously improve the optimization capability and adaptability of the system.

[0177] In this implementation, S2 includes the following steps:

[0178] S21, standardize the collected multidimensional data, normalize different types of data, and generate a standardized matrix N, where each matrix N i Represents time series data of a data type;

[0179] S22. Map the standardized data matrix N to the high-dimensional quantum state space through quantum state encoding:

[0180] S221, define each row of data in the standardized matrix N as the input vector v i , where v i The feature vector representing the i-th data type;

[0181] S222. Use the quantum state mapping function to map the vector into the corresponding high-dimensional quantum state:

[0182]

[0183] Among them, α ij is the amplitude coefficient, θ ij is the phase angle, γ is the nonlinear modulation factor, c j is the reference vector in the quantum state space;

[0184] S23. Using the quantum fractal mapping algorithm, perform multi-level fractal mapping on the generated high-dimensional quantum state. The specific operations include:

[0185] S231. Quantum state fractal mapping is defined as:

[0186]

[0187] Among them, δ p is the fractal level weight, θ kp is the quantum state phase adjustment parameter, ψ p (N) is the fractal quantum state after mapping;

[0188] S232, by adjusting δ p and θ kp , achieving multi-level fractal decoupling to reveal the self-similarity and correlation structure between quantum states;

[0189] S24. Analysis of the coherence and fractal dimension characteristics of quantum states:

[0190] S241. Coherence calculation: For each fractal quantum state ψ p (N), calculate its coherence with other levels of quantum states, the formula is:

[0191]

[0192] in, is the coherence modulation phase, ψ r (N) is the reference quantum state;

[0193] S242, generate a coherence matrix Ξ, where each element ξ pq Reflects the degree of coherence between fractal levels p and q;

[0194] S243, Fractal dimension calculation: For each fractal quantum state ψ p (N), and its complexity is analyzed by the fractal dimension formula, which is:

[0195]

[0196] Among them, var(ψ p (N)) is the variance of the quantum state;

[0197] S244, summarize the fractal dimensions of all levels and generate a dimensional feature matrix D, where each element D p Indicates the fractal dimension of the corresponding level;

[0198] S25. Extract key data patterns and generate preliminary optimization paths in multi-level fractal quantum state space:

[0199] S251. Based on the local stability of the quantum state, its stability at adjacent time points is evaluated. The formula is:

[0200]

[0201] Among them, Δt is the time step, S i is the stability coefficient of the i-th path point;

[0202] S252, select the path point π with the highest local stability i , these waypoints will be used as preliminary waypoint candidates;

[0203] S253. Based on the global correlation of quantum states, the global optimal path of quantum states is constructed. The path construction is calculated based on the correlation between path points:

[0204]

[0205] Among them, ξ ij is the coherence between path points, S i and S j is the stability of the path point, P * is the global optimal path set;

[0206] S26: Storing the extracted key data patterns and preliminary optimization paths in a storage module of the quantum computing optimization engine.

[0207] In this implementation, S3 includes the following steps:

[0208] S31. Initialize the quantum field of the preliminary optimization path, map each path point in the path to the topological quantum field through the quantum field state, and generate the initial quantum field state Ψ(P), which is defined as:

[0209]

[0210] Among them, λ i is the weight coefficient of the path point, is the phase information of the path point, ψ(π i ) is the state function of the path point in the quantum field, is the nonlinear modulation enhancement function, defined as:

[0211]

[0212] Where α is the modulation intensity coefficient, which is used for the nonlinear interaction between the weight and phase of the path points to adapt to the dynamic environment in the complex power system;

[0213] S32. Based on the initial quantum field state, a topological quantum field algorithm is applied to perform invariance analysis, which specifically includes the following steps:

[0214] S321. Define the topological structure tensor F(Ψ), whose elements are composed of the quantum coherence and phase difference between the path points, combined with the energy distribution ε(π i ) for dynamic adjustment, specifically:

[0215]

[0216] Among them, ξ ij is the path point π i and π j The correlation between is the phase difference between the path points, ε(π i ) is the energy distribution of the path points, which is used to describe the global correlation between the path points;

[0217] S322. Generate topological invariants by integral calculation It is defined as:

[0218]

[0219] in, is the closed loop of the quantum field, Tr is the trace operation, dr is the differential element on the integral path, and K(Ψ) is the topological constraint condition;

[0220] S33, through topological invariance analysis, select the most stable path point for global optimization, and reconstruct the optimized path P * :

[0221]

[0222] in, is the global correlation function between path points, used to combine the global topological relationship between path points;

[0223] S34. Generate a cost control strategy function C(P * ), combining the energy distribution of the path points, the coherence of the quantum state and the phase information:

[0224]

[0225] Among them, ν k is the path point π k The priority coefficient of is the energy factor corresponding to the path point, is the dynamic modulation function:

[0226]

[0227] Where β is the dynamic modulation coefficient, is the path point π k The expected value of the quantum state is adjusted nonlinearly according to the priority of the path points and the characteristics of the quantum state through a dynamic modulation function;

[0228] S35. Store the optimized global path and the corresponding cost control strategy in the quantum computing optimization engine, ready to be transmitted to subsequent steps to execute the optimization control strategy.

[0229] In this implementation, S4 includes the following steps:

[0230] S41, the global optimal path P* ={π1,π2,...,π l Each path point π in i Initialize the quantum entangled state and generate the quantum entangled state Ψ E (P * ):

[0231]

[0232] Among them, ψ(π i ) represents the path point π i The quantum state of Represents entanglement operations of quantum states;

[0233] S42, the entangled state Ψ E (P * ) is transmitted to multiple node devices through the quantum state transmission channel to maintain the quantum entanglement between nodes. The coherence of the quantum state is maintained through the following phase adjustment formula:

[0234] ψ(π i )→ψ(π i )exp(iθ i );

[0235] Among them, θ i is the path point π during the transmission process i The phase adjustment parameter is used to correct the phase error of the quantum state during transmission;

[0236] S43, each node device receives the entangled state ψ(π i ) adjusts its operating parameters, and the adjustment process is calculated through the following correlation formula:

[0237]

[0238] Among them, ΔP i Indicates the adjustment amount of the device parameters, is the Hamiltonian operator, is the expectation value of the quantum state under the Hamiltonian, represents the derivative operation with respect to time;

[0239] S44, combined with the optimized cost control strategy C (P * ), implement specific control in each node device, and the fee control strategy is based on the quantum state component ψ(π i ) is constructed by:

[0240]

[0241] Among them, γ i is the strategy weight, is the operator related to the cost control operation, is the expected value of the fee control operation in the quantum state;

[0242] S45. Real-time monitoring of the feedback information of each node device, and dynamic correction of the quantum state. During the feedback process, the maintenance of the entangled state is achieved through the following feedback correction formula:

[0243]

[0244] Among them, δ j The feedback correction phase is used to correct the quantum state according to real-time feedback and maintain the coherence and synchronization of the entangled state.

[0245] In this implementation, S5 includes the following steps:

[0246] S51, optimize the cost control strategy C (P * ) to perform quantum state encoding processing and decompose it into a quantum state control instruction sequence {C1, C2, ..., C n}, where each control instruction C i and the quantum state ψ(C i ), the quantum state encoding is represented by:

[0247]

[0248] Among them, α k is the amplitude coefficient, θ k is the phase angle, f(C i ) is the control instruction C i Related nonlinear functions, |k is the quantum ground state;

[0249] S52, the quantum state ψ(C i ) generates the corresponding chaotic quantum state χ(C i ), which is achieved through the following formula:

[0250]

[0251] Among them, β j is the weight coefficient, λ j is the chaotic sequence parameter, ξ is the chaos control parameter, χ(C i ) is the generated chaotic quantum state;

[0252] S53, by real-time analysis of the noise characteristics of the quantum communication channel, dynamically modulating the chaotic quantum state χ(C i ), generate the encrypted quantum control instruction Φ(C i ), the formula is as follows:

[0253]

[0254] Among them, γ l is the modulation coefficient, Δ l is a phase correction parameter related to the noise characteristics of the communication channel;

[0255] S54, the encrypted quantum control instruction Φ(C i ) is transmitted to the control module of the high-voltage dedicated transformer through the quantum communication channel to ensure that it is not interfered with or eavesdropped by the outside world during the transmission process;

[0256] S55, in the control module of the high-voltage dedicated transformer, the received quantum control instruction Φ(C i ) is decoded, and the original quantum state ψ(C i ), and then restore to control instruction C i And perform fee control operations. The decoding process is implemented through the following formula:

[0257]

[0258] S56, decoded control instruction C i Used to guide the actual operation of high-voltage special transformers.

[0259] In this implementation, S6 includes the following steps:

[0260] S61, collect real-time data during system operation, including parameters such as voltage fluctuation V(t), current anomaly I(t), load change L(t), temperature fluctuation T(t), etc., and use data mining algorithms to analyze these data to identify potential local unstable areas U k , where k represents different local areas;

[0261] S62: for the identified local unstable region U k , the quantum annealing algorithm is applied to perform local path perturbation optimization, which specifically includes the following steps:

[0262] S621, initialize the global optimal path P * The path point π in i and its corresponding quantum state Where P * ={π1,π2,...,π n} represents a set of path points, each path point π i There is a quantum state associated with

[0263] S622. Define the objective function To measure the path point π iThe stability after local disturbance is based on the coherence of the path points, the local energy distribution and the fractal dimension characteristics. The specific expression is:

[0264]

[0265] Among them, ξ ij is the path point π i and adjacent path points π j The coherence between ij is the phase difference, is the quantum state of the adjacent path points, is the local energy distribution of the path point, is the fractal dimension feature of the path point;

[0266] S623, during quantum annealing, by adjusting the phase of the path point and amplitude Use the path perturbation formula to optimize the path points:

[0267]

[0268] in, is the quantum state of the next time step, k B is the Boltzmann constant, T is the annealing temperature, is the objective function value of the current quantum state;

[0269] S624, by gradually reducing the annealing temperature T, find the local optimal solution and generate an updated global path P # ;

[0270] S625: Calculate the updated path point coherence matrix for the local optimal solution after the path disturbance and path energy distribution matrix Save the optimization results to the quantum computing optimization engine;

[0271] S63, according to the real-time feedback data of the system, the path P # Perform dynamic adjustments. The specific steps include:

[0272] S631. Monitor the operating status of the power system in real time, and identify any dynamic changes that may cause global or local instability in combination with data feedback from distributed monitoring nodes;

[0273] S632. Use the quantum entanglement distributed computing architecture to continuously adjust the global path so that the system can maintain a stable control strategy when sudden changes occur. The specific adjustment method is:

[0274]

[0275] Among them, γ opt is the optimized path coherence parameter, P new is the new path after adjustment;

[0276] S64, finally generating the adjusted global optimal path P new And use it to guide the real-time cost control strategy of high-voltage dedicated transformers to achieve dynamic optimization and stable control of the power system.

[0277] In this implementation, S7 includes the following steps:

[0278] S71, combined with the global path P after quantum annealing algorithm optimization # , continuously monitor multi-node devices in the power system, the monitoring includes the following:

[0279] S711, collect the operating status data of each node device in real time through the distributed sensor network, including the node voltage V i (t), node current I i (t), node load L i (t) and node temperature T i (t), where i represents the i-th monitoring node;

[0280] S712, combined with the quantum entanglement distributed computing architecture, transmit the monitoring data to the central control system in real time and analyze the quantum entanglement state between each node The coherence changes of the system are used to evaluate the overall stability of the system;

[0281] S72. Based on real-time monitoring data, comprehensive optimization of global and local paths is implemented through a quantum entanglement distributed computing architecture, specifically including the following steps:

[0282] S721, coherence matrix between node devices Perform dynamic analysis to identify areas in the system where coherence decreases, using the following formula:

[0283]

[0284] in, and Represent the path point π i and π j The quantum state of is the coherence value before optimization, Δξ ij is the coherence change value;

[0285] S722, according to the coherence change value Δξ ij Adjust the optimization strategy of global and local paths. The specific optimization process includes:

[0286] S7221. Optimize and adjust the coherence reduction area using the path perturbation algorithm. The optimized path coherence matrix The following conditions are met:

[0287]

[0288] in, is the adjusted coherence matrix;

[0289] S7222. Update the operating parameters of each node device to make them consistent with the optimized path;

[0290] S73. Dynamically adjust and optimize cost control strategies based on real-time feedback from the system To make the system run optimally in a dynamic environment, the specific steps include:

[0291] S731, based on feedback data, real-time update of the cost control strategy function of the route point Optimize the energy distribution and load scheduling of the path points. The formula is:

[0292]

[0293] Among them, ν k is the path point priority coefficient, E k is the waypoint energy, is a dynamic modulation function used to adjust the control strategy of the path points;

[0294] S732. Send the adjusted fee control strategy to the control module of each node device to execute the optimized fee control operation in real time.

[0295] In this implementation, S8 includes the following steps:

[0296] S81. Adaptively learn and adjust the parameters in the quantum computing optimization engine using a quantum multi-task evolutionary learning algorithm, specifically including the following steps:

[0297] S811, initialize the multi-task set T in the power system = {T1, T2, ..., T n}, each task T i Corresponding to an optimization target in the power system, including voltage stability V stability , Load Balancing balance 、Energy efficiency E efficiency and temperature control T control wait;

[0298] S812. For each task T i Initialize quantum state And define the fitness function Used to evaluate the optimization effect of the task, the fitness function combines the coherence of the quantum state, the task energy distribution and the task priority, and the formula is:

[0299]

[0300] Among them, ξ ij is the coherence coefficient between tasks, θ ij is the phase adjustment factor, For task T j The energy distribution of i is the task priority, is the reduced Planck constant, ω is the frequency of the quantum state;

[0301] S813. Through the quantum genetic operation and quantum state mutation operation in the quantum multi-task evolutionary learning algorithm, the multi-task quantum state is collaboratively optimized, specifically including:

[0302] S8131. Perform quantum genetic operations, select quantum states with higher fitness for crossover and recombination, and generate next generation task quantum states

[0303] S8132. Perform quantum state mutation operation to explore the diversity of optimization solutions. The mutation formula is:

[0304]

[0305] Among them, γ i is the variation factor, For the current task T i The fitness change, k B is the Boltzmann constant, T is the system temperature;

[0306] S814. During the evolution process, the optimal quantum state of each task is generated through multi-task collaborative optimization And apply it to parameter adjustment in quantum computing optimization engine to optimize the overall performance of power system;

[0307] S82, based on the evolutionary learning mechanism of quantum multi-task evolutionary learning algorithm, regularly analyzes the system feedback data and calculates the feedback data matrix D feedback For the optimal quantum state Perform adaptive adjustment, including the following steps:

[0308] S821. Collect and analyze feedback data during system operation to generate feedback data matrix D feedback , where each column corresponds to task T i Feedback data;

[0309] S822, using the feedback data matrix Dfeedback For quantum states After adaptive adjustment, the adjusted quantum state is expressed as:

[0310]

[0311] in, For task T i The optimal fitness function value, σ i For task T i The associated standard deviation is used to adjust the update intensity of the quantum state;

[0312] S823, according to the adjusted quantum state Reoptimize the global path P new Cost control strategy

[0313] S83, the optimized global path P new Cost control strategy Deployed to each node of the power system, real-time monitoring and dynamic adjustment are carried out, including:

[0314] S831, through the quantum entanglement distributed computing architecture, real-time synchronization and adjustment of control parameters of each node;

[0315] S832, monitor the feedback information of each node device in real time, analyze the operating status and load of the device, and if abnormal fluctuations occur, trigger the quantum multi-task evolutionary learning algorithm to perform local optimization and adjustment;

[0316] S833. Based on the long-term operating data of the system, regularly review and adjust the parameters in the quantum computing optimization engine to cope with the long-term changes in the power system and new optimization needs.

[0317] Embodiment 1:

[0318] In a large industrial park, there are multiple high-voltage dedicated transformers that are responsible for providing a stable power supply to multiple high-energy-consuming enterprises. Due to the frequent changes in the power load in the park and the large differences in power demand among enterprises, the traditional power cost control system is difficult to meet the actual needs, and often encounters problems such as uneven distribution of power resources, delayed system response, and unstable power supply during peak hours. Especially during the high temperature period in summer, the demand for electricity surges, and the traditional cost control system is difficult to quickly and accurately adjust the power supply, resulting in frequent power shortages in some enterprises during peak power consumption periods, which not only affects the normal production of enterprises, but also increases the operating costs of the power system.

[0319] In order to solve the above problems, the park introduced the real-time optimization method of remote cost control of high-voltage special transformers based on quantum computing proposed in this invention. The system is deployed in the power monitoring center of the park, and the operating data of each high-voltage special transformer is collected in real time through the remote monitoring system, including voltage, current, load and temperature parameters. The collected data is first processed by the quantum fractal mapping algorithm to map the multi-dimensional data of the power system to the multi-level fractal quantum state space. This process helps to accurately extract key data patterns and generate preliminary optimization paths.

[0320] Next, the system applies the topological quantum field algorithm to analyze and optimize the initially generated path, and through the identification of topological invariance features, the preliminary path is globally optimized to finally generate the optimal power control path. This optimal path not only takes into account the current power demand and load changes, but also adjusts the power distribution strategy in advance by predicting future power demand, thereby improving the system's response speed and regulation accuracy.

[0321] In actual operation, the system uses a quantum entanglement distributed computing architecture to adjust the operating parameters of each node device in real time to ensure the stability and efficiency of power supply. For example, when a company's power demand suddenly increases, the system will automatically adjust the transformer output in its area to ensure that its production is not affected. At the same time, the system can also monitor and optimize the allocation of power resources in the entire park in real time to avoid waste of power resources and ensure that all companies can obtain sufficient power supply during peak power consumption periods.

[0322] Concrete data proves our beneficial effects

[0323] In the actual operation process, data collection starts from June 1, 2024, at the power monitoring center of the industrial park. The results of one month of operation after the system deployment are as follows:

[0324] Table 1 Data table

[0325] Data Item Traditional systems System of the present invention Difference (Improvement Effect) Average response time (peak hours, seconds) 12 seconds 3.8 seconds -8.2 seconds Average number of responses per day (times) 500 times 620 times +120 times Power distribution accuracy (%) 85％ 98.50％ 13.50％ Average load rate during high temperature period (%) 75％ 90％ 15％ Proportion of power resource waste (%) 12％ 2％ -10％ Number of power failures per month (times) 10 times 2 times -8 times Power restoration time after failure (minutes) 15 minutes 5 minutes -10 minutes Production stability during high temperature period (hours / day) 20 hours / day 23.5 hours / day +3.5 hours Number of data transmission errors per month (times) 2 times 0 times -2 times Data security incidents (times) 1 time 0 times -1 time System operating cost (yuan / month) 100,000 NTD 88,000 NTD -12,000 yuan System installation and debugging time (days) 10 days 7 days -3 days System maintenance frequency (monthly) 3 times 1 time -2 times

[0326] Through the above detailed data, the present invention shows obvious advantages in response speed, resource utilization efficiency, system stability, security and cost control.

[0327] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A real-time optimization method for remote cost control of high-voltage special transformers based on quantum computing, characterized in that: The following steps are involved: S1. Collecting real-time operating data of a high-voltage dedicated transformer, the data including voltage, current, load and temperature parameters; S2. The collected multi-dimensional data is transmitted to the quantum computing optimization engine through the remote monitoring system, and the data of the power system is mapped to the multi-level fractal quantum state space using the quantum fractal mapping algorithm, from which the preliminary optimization path and key data patterns are extracted; S3. Based on the preliminary optimization paths, the topological quantum field algorithm is applied to further analyze these paths, identify and utilize the topological invariance characteristics, perform global optimization on the preliminary paths, generate global optimal control paths with higher stability and adaptability, and determine the corresponding cost control strategy; S4. Based on the generated global optimal path, the quantum entanglement distributed computing architecture is used to adjust the operating parameters of multi-node devices in real time and implement an optimized cost control strategy; S5. Through quantum adaptive chaotic coding technology, the optimized fee control strategy is sent to the control module of the high-voltage dedicated transformer; S6. Based on the preliminary optimization, the real-time data feedback mechanism is used to locally adjust and dynamically optimize the global optimal path through the quantum annealing algorithm to cope with sudden changes in the power system; S7. Combined with the path optimized by the quantum annealing algorithm, the multi-node equipment in the power system is continuously monitored, and the comprehensive optimization of global and local paths is implemented through the quantum entanglement distributed computing architecture; S8. Use the quantum multi-task evolutionary learning algorithm to adaptively learn and adjust the parameters in the quantum computing optimization engine to continuously improve the optimization capability and adaptability of the system.

2. According to the method of real-time optimization of remote cost control of high-voltage special transformer based on quantum computing in claim 1, it is characterized in that: The S2 comprises the following steps: S21, standardize the collected multidimensional data, normalize different types of data, and generate a standardized matrix N, where each matrix N i Represents time series data of a data type; S22. Map the standardized data matrix N to the high-dimensional quantum state space through quantum state encoding: S221, define each row of data in the standardized matrix N as the input vector v i , where v i The feature vector representing the i-th data type; S222. Use the quantum state mapping function to map the vector into the corresponding high-dimensional quantum state: Among them, α ij is the amplitude coefficient, θ ij is the phase angle, γ is the nonlinear modulation factor, c j is the reference vector in the quantum state space; S23. Using the quantum fractal mapping algorithm, perform multi-level fractal mapping on the generated high-dimensional quantum state. The specific operations include: S231. Quantum state fractal mapping is defined as: Among them, δ p is the fractal level weight, θ kp is the quantum state phase adjustment parameter, ψ p (N) is the fractal quantum state after mapping; S232, by adjusting δ p and θ kp , achieving multi-level fractal decoupling to reveal the self-similarity and correlation structure between quantum states; S24. Analysis of the coherence and fractal dimension characteristics of quantum states: S241. Coherence calculation: For each fractal quantum state ψ p (N), calculate its coherence with other levels of quantum states, the formula is: in, is the coherence modulation phase, ψ r (N) is the reference quantum state; S242, generate a coherence matrix Ξ, where each element ξ pq Reflects the degree of coherence between fractal levels p and q; S243, Fractal dimension calculation: For each fractal quantum state ψ p (N), and its complexity is analyzed by the fractal dimension formula, which is: Among them, var(ψ p (N)) is the variance of the quantum state; S244, summarize the fractal dimensions of all levels and generate a dimensional feature matrix D, where each element D p Indicates the fractal dimension of the corresponding level; S25. Extract key data patterns and generate preliminary optimization paths in multi-level fractal quantum state space: S251. Based on the local stability of the quantum state, its stability at adjacent time points is evaluated. The formula is: Among them, Δt is the time step, S i is the stability coefficient of the i-th path point; S252, selecting the path points πi with the highest local stability, which will be used as preliminary path point candidates; S253. Based on the global correlation of quantum states, the global optimal path of quantum states is constructed. The path construction is calculated based on the correlation between path points: Among them, ξ ij is the coherence between path points, S i and S j is the stability of the path point, P * is the global optimal path set; S26: Storing the extracted key data patterns and preliminary optimization paths in a storage module of the quantum computing optimization engine.

3. According to the method of real-time optimization of remote cost control of high-voltage special transformer based on quantum computing in claim 1, it is characterized in that: The S3 comprises the following steps: S31. Initialize the quantum field of the preliminary optimization path, map each path point in the path to the topological quantum field through the quantum field state, and generate the initial quantum field state ψ(P), which is defined as: Among them, λ i is the weight coefficient of the path point, is the phase information of the path point, ψ(π i ) is the state function of the path point in the quantum field, is the nonlinear modulation function, defined as: Among them, α is the modulation intensity coefficient, which is used to optimize the nonlinear interaction between the weight and phase of the strong path points to adapt to the dynamic environment in the complex power system; S32. Based on the initial quantum field state, a topological quantum field algorithm is applied to perform invariance analysis, which specifically includes the following steps: S321. Define the topological structure tensor F(ψ), whose elements are composed of the quantum coherence and phase difference between the path points, combined with the energy distribution ε(π i ) for dynamic adjustment, specifically: Among them, ξ ij is the path point π i and π j The correlation between is the phase difference between the path points, ε(π i ) is the energy distribution of the path points, which is used to describe the global correlation between the path points; S322. Generate topological invariants by integral calculation It is defined as: in, is the closed loop of the quantum field, Tr is the trace operation, dr is the differential element on the integral path, and K(ψ) is the topological constraint condition; S33, through topological invariance analysis, select the most stable path point for global optimization, and reconstruct the optimized path P * : in, is the global correlation function between path points, used to combine the global topological relationship between path points; S34. Generate a cost control strategy function C(P * ), combining the energy distribution of the path points, the coherence of the quantum state and the phase information: Among them, v k is the path point π k The priority coefficient of is the energy factor corresponding to the path point, is the dynamic modulation function: Where β is the dynamic modulation coefficient, is the path point π k The expected value of the quantum state is adjusted nonlinearly according to the priority of the path points and the characteristics of the quantum state through a dynamic modulation function; S35. Store the optimized global path and the corresponding cost control strategy in the quantum computing optimization engine, ready to be transmitted to subsequent steps to execute the optimization control strategy.

4. According to the method of real-time optimization of remote cost control of high-voltage special transformer based on quantum computing in claim 1, it is characterized in that: The S4 comprises the following steps: S41, the global optimal path P * ={π1, π2, ..., π l Each path point π in i Initialize the quantum entangled state and generate the quantum entangled state Ψ E (P * ): Among them, ψ(π i ) represents the path point π i The quantum state of Represents entanglement operations of quantum states; S42, the entangled state Ψ E (P * ) is transmitted to multiple node devices through the quantum state transmission channel to maintain the quantum entanglement between nodes. The coherence of the quantum state is maintained through the following phase adjustment formula: ψ(π i )→ψ(π i )exp(iθ i ); Among them, θ i is the phase adjustment parameter for path point πi during the transmission process, which is used to correct the phase error of the quantum state during transmission; S43, each node device receives the entangled state ψ(π i ) adjusts its operating parameters, and the adjustment process is calculated through the following correlation formula: Among them, ΔP i Indicates the adjustment amount of the device parameters, is the Hamiltonian operator, is the expectation value of the quantum state under the Hamiltonian, represents the derivative operation with respect to time; S44, combined with the optimized cost control strategy C (P * ), implement specific control in each node device, and the fee control strategy is based on the quantum state component ψ(π i ) is constructed by: Among them, γ i is the strategy weight, is the operator related to the cost control operation, is the expected value of the fee control operation in the quantum state; S45. Real-time monitoring of the feedback information of each node device, and dynamic correction of the quantum state. During the feedback process, the maintenance of the entangled state is achieved through the following feedback correction formula: Among them, δ j The feedback correction phase is used to correct the quantum state according to real-time feedback and maintain the coherence and synchronization of the entangled state.

5. According to the quantum computing-based real-time optimization method for remote cost control of high-voltage special transformers according to claim 1, it is characterized in that: The S5 comprises the following steps: S51, optimize the cost control strategy C (P * ) to perform quantum state encoding processing and decompose it into a quantum state control instruction sequence {C1, C2, ..., C n }, where each control instruction C i and the quantum state ψ(C i ), the quantum state encoding is represented by: Among them, α k is the amplitude coefficient, θ k is the phase angle, f(C i ) is the control instruction C i Related nonlinear functions, |k is the quantum ground state; S52, the quantum state ψ(C i ) generates the corresponding chaotic quantum state χ(C i ), which is achieved through the following formula: Among them, β j is the weight coefficient, λ j is the chaotic sequence parameter, ξ is the chaos control parameter, χ(C i ) is the generated chaotic quantum state; S53, by real-time analysis of the noise characteristics of the quantum communication channel, dynamically modulating the chaotic quantum state χ(C i ), generate the encrypted quantum control instruction Φ(C i ), the formula is as follows: Among them, γ l is the modulation coefficient, Δ l is a phase correction parameter related to the noise characteristics of the communication channel; S54, the encrypted quantum control instruction Φ(C i ) is transmitted to the control module of the high-voltage dedicated transformer through the quantum communication channel to ensure that it is not interfered with or eavesdropped by the outside world during the transmission process; S55, in the control module of the high-voltage dedicated transformer, the received quantum control instruction Φ(C i ) is decoded, and the original quantum state ψ(C i ), and then restore to control instruction C i And perform fee control operations. The decoding process is implemented through the following formula: S56, decoded control instruction C i Used to guide the actual operation of high-voltage special transformers.

6. According to a quantum computing-based high-voltage dedicated transformer remote fee control real-time optimization method according to claim 1, it is characterized in that: The S6 comprises the following steps: S61, collect real-time data during system operation, including parameters such as voltage fluctuation V(t), current anomaly I(t), load change L(t), temperature fluctuation T(t), etc., and use data mining algorithms to analyze these data to identify potential local unstable areas U k , where k represents different local areas; S62: for the identified local unstable region U k , the quantum annealing algorithm is applied to perform local path perturbation optimization, which specifically includes the following steps: S621, initialize the global optimal path P * The path point π in i and its corresponding quantum state Where P * ={π1, π2, ..., π n } represents a set of path points, each path point π i There is a quantum state associated with S622. Define the objective function To measure the stability of the path point after local disturbance, combined with the coherence of the path point, local energy distribution and fractal dimension characteristics, the specific expression is: Among them, ξ ij is the path point π i and adjacent path points π j The coherence between ij is the phase difference, is the quantum state of the adjacent path points, is the local energy distribution of the path point, is the fractal dimension feature of the path point; S623, during quantum annealing, by adjusting the phase of the path point and amplitude Use the path perturbation formula to optimize the path points: in, is the quantum state of the next time step, kB is the Boltzmann constant, T is the annealing temperature, is the objective function value of the current quantum state; S624, by gradually reducing the annealing temperature T, find the local optimal solution and generate an updated global path P#; S625. Calculate the updated path point coherence matrix Ξ for the local optimal solution after the path disturbance P# And the path energy distribution matrix E P# , save the optimization results to the quantum computing optimization engine; S63, according to the real-time feedback data of the system, the path P # Perform dynamic adjustments. The specific steps include: S631. Monitor the operating status of the power system in real time, and identify any dynamic changes that may cause global or local instability in combination with data feedback from distributed monitoring nodes; S632. Use the quantum entanglement distributed computing architecture to continuously adjust the global path so that the system can maintain a stable control strategy when sudden changes occur. The specific adjustment method is: Among them, γ opt is the optimized path coherence parameter, P new is the new path after adjustment; S64, finally generating the adjusted global optimal path P new And use it to guide the real-time cost control strategy of high-voltage dedicated transformers to achieve dynamic optimization and stable control of the power system.

7. According to the quantum computing-based real-time optimization method for remote cost control of high-voltage special transformers according to claim 1, it is characterized in that: The S7 comprises the following steps: S71, combined with the global path P after quantum annealing algorithm optimization # , continuously monitor multi-node devices in the power system, the monitoring includes the following: S711, collect the operating status data of each node device in real time through the distributed sensor network, including the node voltage V i (t), node current I i (t), node load L i (t) and node temperature T i (t), where i represents the i-th monitoring node; S712, combined with the quantum entanglement distributed computing architecture, transmit the monitoring data to the central control system in real time, and analyze the quantum entanglement state ψ between each node EP# The coherence changes of the system are used to evaluate the overall stability of the system; S72. Based on real-time monitoring data, comprehensive optimization of global and local paths is implemented through a quantum entanglement distributed computing architecture, specifically including the following steps: S721, coherence matrix Ξ between node devices EP# Perform dynamic analysis to identify areas in the system where coherence decreases, using the following formula: in, and Represent the path point π i and π j The quantum state of is the coherence value before optimization, Δξ ij is the coherence change value; S722, according to the coherence change value Δξ ij Adjust the optimization strategy of global and local paths. The specific optimization process includes: S7221. Optimize and adjust the coherence reduction area using the path perturbation algorithm. The optimized path coherence matrix The following conditions are met: in, is the adjusted coherence matrix; S7222. Update the operating parameters of each node device to make them consistent with the optimized path; S73. Dynamically adjust and optimize the cost control strategy based on the real-time feedback from the system C P# , so that the system can operate optimally in a dynamic environment. The specific steps include: S731, based on feedback data, real-time update of the cost control strategy function of the route point Optimize the energy distribution and load scheduling of the path points. The formula is: Among them, v k is the path point priority coefficient, E k is the waypoint energy, is a dynamic modulation function used to adjust the control strategy of the path points; S732. Send the adjusted fee control strategy to the control module of each node device to execute the optimized fee control operation in real time.

8. According to the quantum computing-based real-time optimization method for remote cost control of high-voltage special transformers according to claim 1, it is characterized in that: The S8 comprises the following steps: S81. Adaptively learn and adjust the parameters in the quantum computing optimization engine using a quantum multi-task evolutionary learning algorithm, specifically including the following steps: S811, initialize the multi-task set T in the power system = {T1, T2, ..., T n }, each task T i Corresponding to an optimization target in the power system, including voltage stability V stability , Load Balancing balance 、Energy efficiency E efficiency and temperature control T control wait; S812. For each task T i Initialize quantum state And define the fitness function Used to evaluate the optimization effect of the task, the fitness function combines the coherence of the quantum state, the task energy distribution and the task priority, and the formula is: Among them, ξ ij is the coherence coefficient between tasks, θ ij is the phase adjustment factor, For task T j The energy distribution, v i is the task priority, is the reduced Planck constant, ω is the frequency of the quantum state; S813. Through the quantum genetic operation and quantum state mutation operation in the quantum multi-task evolutionary learning algorithm, the multi-task quantum state is collaboratively optimized, specifically including: S8131. Perform quantum genetic operations, select quantum states with higher fitness for crossover and recombination, and generate next generation task quantum states S8132. Perform quantum state mutation operation to explore the diversity of optimization solutions. The mutation formula is: Among them, γ i is the variation factor, For the current task T i The fitness change, k B is the Boltzmann constant, T is the system temperature; S814. During the evolution process, the optimal quantum state of each task is generated through multi-task collaborative optimization And apply it to parameter adjustment in quantum computing optimization engine to optimize the overall performance of power system; S82, based on the evolutionary learning mechanism of quantum multi-task evolutionary learning algorithm, regularly analyzes the system feedback data and calculates the feedback data matrix D feedback For the optimal quantum state Perform adaptive adjustment, including the following steps: S821. Collect and analyze feedback data during system operation to generate feedback data matrix D feedback , where each column corresponds to task T i Feedback data; S822, using the feedback data matrix D feedback For quantum states After adaptive adjustment, the adjusted quantum state is expressed as: in, For task T i The optimal fitness function value, σ i For task T i The associated standard deviation is used to adjust the update intensity of the quantum state; S823, according to the adjusted quantum state Reoptimize the global path P new Cost control strategy S83, the optimized global path P new Cost control strategy Deployed to each node of the power system, real-time monitoring and dynamic adjustment are carried out, including: S831, through the quantum entanglement distributed computing architecture, real-time synchronization and adjustment of control parameters of each node; S832, monitor the feedback information of each node device in real time, analyze the operating status and load of the device, and if abnormal fluctuations occur, trigger the quantum multi-task evolutionary learning algorithm to perform local optimization and adjustment; S833. Based on the long-term operating data of the system, regularly review and adjust the parameters in the quantum computing optimization engine to cope with the long-term changes in the power system and new optimization needs.

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