Training course planning service method and system

Abstract

The invention belongs to the technical field of system design optimization driven by artificial intelligence, and discloses a training course planning service method and system. A correlation feature matrix of scene fluctuation parameters and resource requirements is extracted through a quantum principal component analysis module, a system health degree attenuation signal is analyzed in combination with quantum Fourier transform, and dynamic scene features are generated; constructing a parameter tuning instruction set based on a quantum annealing algorithm, and driving a genetic algorithm to optimize design topology; and the micro-optimization units are distributed to the digital twin platform by using quantum superposition state mapping, and the collaborative verification module solves a resource reuse strategy through a variable component sub-algorithm to form a data closed-loop feedback link. According to the method, the problem of mismatching of static data and dynamic requirements is solved, and the real-time responsiveness and the resource optimization efficiency in a complex scene are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of artificial intelligence-driven system design optimization, and specifically relates to a dynamic parameter tuning method and system based on quantum algorithms and genetic algorithms, which are used for design optimization and resource collaborative control of complex systems. By integrating genetic algorithms and quantum optimization models, a dynamic adjustment strategy for frequency modulation parameters is generated, and training course updates are synchronously triggered to verify the effectiveness of control instructions. Background Art

[0002] Existing system design optimization methods suffer from a mismatch between static data and dynamic demands. Existing optimization models use fixed parameter templates, making them difficult to adapt to the multi-objective collaborative optimization requirements of complex dynamic scenarios. For example, parameter tuning methods based on historical static data fail to integrate the correlation between dynamic scenario characteristics and resource constraints, resulting in optimization results that fail to respond to dynamic changes in real time, exacerbating the risk of system performance fluctuations. Existing curriculum planning methods based on historical static data fail to incorporate the dynamic correlation between renewable energy output volatility and frequency regulation capacity demand. This prevents dispatchers from mastering virtual inertia parameter tuning skills in emergency frequency regulation scenarios through practical training, exacerbating the risk of grid frequency fluctuations. Furthermore, existing training resource scheduling mechanisms are limited by single-timescale evaluation models, making it impossible to dynamically adjust the ratio of theoretical teaching and practical training based on the urgency of grid frequency regulation tasks. Furthermore, they struggle to adapt to the progressive development of specialized skills, such as energy storage system charging and discharging strategies. This results in redundant training resources and delayed personnel skill development. In addition, the lack of a coordination mechanism between equipment operation training and grid energy efficiency optimization knowledge modules makes it difficult for the existing system to achieve dynamic matching of theoretical teaching, simulation training and on-site operations when dealing with complex skill training such as source-grid-load-storage coordinated control. This ultimately restricts the training service's responsiveness to the complex operating conditions of the grid and the resource optimization efficiency under the background of the new dynamic system. Summary of the Invention

[0003] In response to the shortcomings of the existing technology, the present invention provides a training course planning service method and system to solve the problems in the existing technology. The dynamic system training course planning method based on static historical data lacks the ability to integrate multi-source heterogeneous data (including real-time working condition monitoring, new energy output fluctuation characteristics and multi-level scheduling coupling relationship modeling), resulting in a spatiotemporal mismatch between training content and dynamic control needs. At the same time, it is limited by the single time scale evaluation mechanism and insufficient collaborative optimization of resource scheduling, making it difficult to achieve dynamic matching of theoretical teaching, practical training and equipment operation modules, which ultimately restricts the real-time responsiveness, resource utilization and compound skill training efficiency of training services in complex scenarios of new dynamic systems.

[0004] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows: In a first aspect, the present invention provides a training course planning service method, comprising: Receive real-time operating data of the dynamic system, including scenario fluctuation parameters, resource gap rate, and performance deviation threshold. Use the quantum principal component analysis module to generate a correlation feature matrix that characterizes the relationship between the output fluctuation of renewable energy and the response delay of frequency modulation instructions. Simultaneously, input the energy storage system's state of charge time series data into the quantum Fourier transform module to analyze its health decay trend signal. Use the quantum principal component analysis module to perform dimensionality reduction processing on the real-time operating data to generate a correlation feature matrix between the output fluctuation of renewable energy and the response delay of frequency modulation instructions. Based on the correlation feature matrix, a system parameter tuning instruction set is generated through a quantum annealing algorithm. Based on the knowledge unit mastery and operation response speed in the student behavior data, a quantum state capability map is generated through quantum bit superposition state encoding, and input into a genetic algorithm to optimize the design topology. Applying the parameter tuning instruction set to the dynamic system controller to adjust the operating parameters of the system core components and resource nodes; Generate a course trigger priority sequence based on the correlation feature matrix and the real-time frequency modulation instruction, drive the digital twin platform to allocate micro-course units, generate a course trigger priority sequence based on the correlation feature matrix and the real-time frequency modulation instruction, and generate corresponding course modules based on the system parameter tuning instruction set; Generate a long-term steady-state course framework, including inputting the tie-line power constraint value and frequency regulation rate limit in the power grid dispatch regulations into the genetic algorithm to generate a long-term steady-state frequency regulation strategy training framework; Based on the health decay trend signal, a preventive maintenance training module is inserted into the long-term steady-state course framework; Resolve device occupancy conflicts through quantum superposition state mapping, generate verification course modules based on the execution results of the system parameter tuning instruction set, and allocate micro-course units to the digital twin platform through quantum superposition state mapping; The system parameter tuning instruction set is iteratively updated through the collaborative optimization model to form a data closed-loop feedback link.

[0005] Furthermore, the training course planning service method described in the present invention, the iterative updating of the system parameter tuning instruction set through the collaborative optimization model to form a data closed-loop feedback link includes: inputting the micro-course unit allocation results and the student learning trajectory data into the quantum Hamiltonian model, and solving the resource reuse binding strategy through the variational quantum algorithm; inputting the candidate course update plan into the genetic algorithm to generate an evolution plan, and verifying the matching degree of the evolution plan with the real-time operating condition data of the power grid through quantum parallel computing; and feeding back the verified course update plan to the fitness evaluation module of the genetic algorithm to iteratively update the optimized design topology.

[0006] Furthermore, the training course planning service method described in the present invention receives real-time operation data of a dynamic system, the real-time operation data including scenario fluctuation parameters, resource gap rate and performance deviation threshold, generates a correlation feature matrix characterizing the relationship between new energy output fluctuation and frequency modulation instruction response delay through a quantum principal component analysis module, and at the same time inputs the charge state time series data of the energy storage system into a quantum Fourier transform module to analyze its health attenuation trend signal, and performs dimensionality reduction processing on the real-time operation data through the quantum principal component analysis module, including: inputting the new energy output fluctuation parameters and frequency modulation demand parameters in the real-time operation data of the dynamic system into the quantum principal component analysis module, extracting the multidimensional feature vector associated with the new energy fluctuation and generating a correlation feature matrix after dimensionality reduction; inputting the charge state time series data of the energy storage system into the quantum Fourier transform module, analyzing its frequency domain periodic characteristics and generating a trend signal characterizing the energy storage health attenuation, and the trend signal is encrypted and transmitted to the scheduling automation system through the quantum communication protocol to trigger the course module update instruction of the training platform.

[0007] Furthermore, the training course planning service method of the present invention further includes: Based on the correlation feature matrix output by the quantum principal component analysis module, the knowledge unit mastery and operation response speed in the student behavior data are encoded in quantum bit superposition states to generate a quantum state capability map; the frequency modulation task completion rate parameter in the quantum state capability map is used as the fitness function input of the genetic algorithm to drive the cross-mutation process of the initial course topology, and generate an optimized design topology including the virtual synchronous machine inertia parameter adjustment and the energy storage charging and discharging coordinated control strategy.

[0008] Furthermore, the training course planning service method described in the present invention, the generation of the course trigger priority sequence includes: mapping the new energy fluctuation characteristics and real-time frequency modulation instructions in the associated feature matrix into a quantum bit energy model, solving the optimal course trigger sequence through a quantum annealing algorithm, and generating a trigger list including the priority of the frequency modulation virtual simulation module; binding the trigger list with the real-time frequency modulation instructions, driving the digital twin platform to allocate micro-course units and generate practical training tasks that match the current frequency modulation requirements.

[0009] Furthermore, in the training course planning service method of the present invention, generating a long-term steady-state course framework includes: The tie-line power constraint value and frequency regulation rate limit in the power grid dispatching regulations are input into the genetic algorithm to generate a long-term steady-state frequency regulation strategy training framework; The preventive maintenance training module inserted into the long-term steady-state course framework includes: The health decay trend signal output by the quantum Fourier transform module is input into the quantum Markov chain model to predict the maintenance demand time window of the energy storage equipment, and a preventive maintenance training module is inserted into the long-term steady-state course framework.

[0010] Furthermore, in the training course planning service method of the present invention, allocating micro-course units to the digital twin platform through quantum superposition state mapping includes: Mapping the course modules generated by the system parameter tuning instruction set and the training equipment status data into quantum superposition state mapping, and resolving equipment occupancy conflicts in concurrent requests from multiple students through the characteristics of quantum entanglement; Based on the device occupancy rate parameter in the quantum superposition state mapping and in combination with the course priority list generated by the quantum annealing algorithm, a conflict-free reservation sequence is generated in which the device occupancy rate matches the course priority; The conflict-free reservation sequence triggers the training equipment resource scheduling instruction and updates the equipment status data.

[0011] Furthermore, the training course planning service method of the present invention further includes: The resource utilization data in the conflict-free reservation sequence and the skill improvement rate parameters in the trainee learning trajectory data are encoded into a quantum Hamiltonian model, and a variational quantum algorithm is used to solve the reuse binding strategy of knowledge units and training equipment; Input the candidate course update plan into the genetic algorithm to generate an evolution plan, and verify the consistency of the evolution plan with the real-time operating data of the power grid through quantum parallel computing; The verified evolution scheme is fed back to the fitness evaluation module of the genetic algorithm to update the frequency modulation task weight coefficient and the device compatibility constraint threshold of the system parameter tuning instruction set.

[0012] Furthermore, the training course planning service method of the present invention further includes: a collaborative verification module; The verification result of the collaborative verification module is fed back to the fitness evaluation module of the genetic algorithm to drive the iterative update of the system parameter tuning instruction set; The device status update data generated by the conflict-free reservation sequence is encrypted and transmitted to the training platform via the quantum communication interface, forming a closed-loop process of data collection, optimization modeling, resource scheduling and verification feedback.

[0013] In a second aspect, the present invention provides a training course planning service system, which is applied to the training course planning service method, and includes: The quantum data processing module is used to perform quantum principal component analysis and dimensionality reduction on the scenario fluctuation parameters, resource gap rate, and performance deviation threshold in the real-time operation data of the dynamic system to generate an encrypted correlation feature matrix. At the same time, the quantum Fourier transform is performed on the charge state time series data of the energy storage system to generate a trend signal representing the decline of energy storage health. A hybrid optimization module is used to generate a quantum state capability map through quantum bit superposition state encoding based on the correlation feature matrix output by the quantum data processing module and the student behavior data, and input the quantum state capability map into a genetic algorithm to drive the generation of an optimized design topology; Inputting the quantum state capability map into a genetic algorithm to drive the generation of an optimized design topology, and combining the trend signal with the real-time frequency modulation instruction to generate a course trigger priority sequence through a quantum annealing algorithm; A resource scheduling module is used to input the optimized design topology and course trigger priority sequence generated by the hybrid optimization module into the multi-timescale course framework, allocate micro-course units to training equipment nodes in the digital twin platform through quantum superposition state mapping, and analyze equipment occupancy conflicts through quantum entanglement characteristics to generate a conflict-free reservation sequence; A long-term steady-state course framework construction module is used to input the tie-line power constraint value and frequency regulation rate limit in the power grid dispatching regulations into the genetic algorithm to generate a long-term steady-state frequency regulation strategy training framework, and insert a preventive maintenance training module based on the health decay trend signal; A collaborative verification module is configured to receive the equipment utilization data and trainee learning trajectory data fed back by the resource scheduling module, solve the reuse binding strategy of knowledge units and training equipment through a variational quantum algorithm, input the candidate course update plan into a genetic algorithm to generate an evolution plan, verify the consistency of the evolution plan with the real-time power grid operating data through quantum parallel computing, and feed back the verification results to the hybrid optimization module; The quantum data processing module, hybrid optimization module, resource scheduling module, long-term steady-state course framework construction module and collaborative verification module realize data interaction through the encrypted data transmission channel in the quantum communication protocol. The feedback results of the collaborative verification module drive the hybrid optimization module to dynamically adjust the frequency modulation task weight coefficient and device compatibility constraint threshold of the course topology, forming a closed-loop link of data collection, optimization modeling, resource scheduling and verification feedback.

[0014] Beneficial effects of the present invention: The beneficial effects of the present invention are as follows: a dynamic correlation feature matrix is generated by integrating the new energy output fluctuation parameters and frequency modulation demand data through the quantum principal component analysis module, and the health attenuation trend signal is analyzed in combination with the quantum Fourier transform, so as to realize the real-time feature extraction and encrypted transmission of multi-source heterogeneous data, and solve the spatiotemporal mismatch problem between static historical data and dynamic frequency modulation scenarios; a system parameter tuning instruction set is constructed by using the quantum annealing algorithm and coordinated with the course topology optimized by the genetic algorithm to generate a practical training module adapted to different student capabilities and grid conditions, and the multi-concurrent resource conflicts are analyzed through the quantum superposition state mapping and entanglement characteristics, so as to improve the dynamic resource allocation efficiency of the digital twin platform; a long-term steady-state course framework is constructed based on the genetic algorithm and a preventive maintenance module is dynamically inserted, and the closed-loop feedback mechanism of the collaborative verification module is combined to iteratively update the frequency modulation task weight and the equipment compatibility threshold, so as to realize the dynamic matching of theoretical teaching, practical training and equipment operation across time scales, and significantly improve the real-time responsiveness, resource utilization and compound skill training efficiency of training services in complex frequency modulation scenarios of new dynamic systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on the drawings without paying any creative labor.

[0016] Figure 1 This is a flowchart of a training course planning service method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. The technical solutions provided by each embodiment of the present invention are described in detail below in conjunction with the drawings. In order to better understand the purpose of the present invention, the present invention is further described in detail below.

[0018] First, see Figure 1 The present invention provides a training course planning service method, including: Step S101: Receive real-time operating data of the dynamic system, including scenario fluctuation parameters, resource gap rate, and performance deviation threshold. Generate a correlation feature matrix representing the relationship between renewable energy output fluctuations and frequency modulation command response delays using a quantum principal component analysis module. Simultaneously, input the energy storage system's state of charge time series data into a quantum Fourier transform module to analyze its health decay trend signal. Step S102: Based on the correlation feature matrix, a system parameter tuning instruction set is generated using a quantum annealing algorithm. Based on the knowledge unit mastery and operation response speed in the student behavior data, a quantum state capability map is generated using quantum bit superposition state encoding, and the map is input into a genetic algorithm to optimize the design topology. Step S103: Apply the parameter tuning instruction set to the dynamic system controller to adjust the operating parameters of the system core components and resource nodes. Step S104: Generate a course trigger priority sequence based on the correlation feature matrix and the real-time frequency modulation instruction, and drive the digital twin platform to allocate micro-course units; Step S105, generating a long-term steady-state course framework, including inputting the tie line power constraint value and the frequency regulation rate limit in the power grid dispatching regulations into the genetic algorithm to generate a long-term steady-state frequency regulation strategy training framework; Step S106: inserting a preventive maintenance training module into the long-term steady-state course framework based on the health decay trend signal; Step S107: Generate a verification course module based on the execution results of the system parameter tuning instruction set, distribute the micro-course units to the digital twin platform through quantum superposition state mapping, and iteratively update the system parameter tuning instruction set through the collaborative optimization model to form a data closed-loop feedback link.

[0019] The training course planning service method provided by the present invention includes the following steps: receiving real-time operating data of a dynamic system, the data including scenario fluctuation parameters, resource gap rate, and performance deviation threshold, performing dimensionality reduction processing on the data through quantum principal component analysis, extracting the nonlinear correlation characteristics between the fluctuation of renewable energy output and frequency regulation demand, and generating a low-dimensional correlation feature matrix; at the same time, inputting the time series data of the state of charge of the energy storage system into a quantum Fourier transform module, analyzing the frequency domain periodic characteristics of the charge and discharge cycle, and generating a signal representing the health decay trend of the energy storage device. The signal is transmitted to the scheduling system through a quantum encryption channel to trigger the course module update instruction. Based on the renewable energy fluctuation amplitude and frequency regulation response delay parameters in the correlation feature matrix, a system parameter tuning instruction set is constructed through a quantum annealing algorithm. The instruction set includes the virtual synchronous machine inertia parameter setting range and the energy storage charge and discharge rate threshold, which is used to adjust the dynamic response characteristics of the virtual synchronous machine and the power output mode of the energy storage device in the dynamic system frequency regulation controller in real time.

[0020] After generating a system parameter tuning instruction set, a genetic algorithm is used to perform cross-mutation on the initial course topology based on the associated feature matrix and the knowledge unit mastery and operational response speed parameters derived from the student behavior data. This algorithm retains the combination of knowledge modules related to frequency regulation strategy and energy storage control, generating an optimized design topology adapted to different student ability levels. After the system parameter tuning instruction set is input into the dynamic system frequency regulation controller, the primary frequency regulation response characteristics of the grid frequency are adjusted based on the virtual synchronous machine inertia parameters. The power regulation rate of the energy storage device is controlled based on the energy storage charge and discharge rate threshold, thereby improving grid frequency stability.

[0021] Based on the classification results of new energy fluctuation scenarios in the correlation feature matrix and the frequency deviation thresholds in real-time frequency regulation instructions, a quantum annealing algorithm was used to construct a course trigger priority sequence. This sequence prioritizes the emergency frequency regulation training module corresponding to high-volatility scenarios. Based on this priority sequence, the digital twin platform dynamically allocates frequency regulation virtual simulation micro-course units to generate real-time training tasks that match the current grid frequency regulation requirements. Micro-course parameters are updated synchronously with the response time constraints and regulation rate ranges in the frequency regulation instructions. A long-term steady-state course framework was constructed using a genetic algorithm. The tie-line power constraints and frequency regulation rate limits in the grid dispatching regulations were encoded into quantum chromosomes, generating a steady-state knowledge framework covering annual frequency regulation strategy optimization and the principles of source-grid-load-storage coordinated control. Health decay trend signals were input into a quantum Markov chain model to predict the time windows when battery capacity suddenly drops or internal resistance increases. Battery second-life strategy optimization and preventive maintenance procedure training modules were inserted into the corresponding nodes of the long-term course framework, synchronizing the training content with the actual health evolution of the equipment.

[0022] The execution results of the system parameter tuning instruction set generate a frequency modulation strategy verification course module, which is linked to the training equipment status in the digital twin platform through quantum superposition state mapping. The course module's equipment requirement parameters and equipment availability status are encoded as multi-qubit superposition states. Quantum entanglement is used to resolve equipment occupancy conflicts during concurrent operations by multiple students, generating a conflict-free reservation sequence that matches equipment occupancy rates with course priorities. The collaborative optimization model receives resource utilization data and student skill improvement rate parameters, and uses a variational quantum algorithm to solve the reuse binding strategy for knowledge units and training equipment. After candidate course update plans are evolved using a genetic algorithm, their consistency with real-time grid operating data is verified through quantum parallel computing. The verification results are fed back to the genetic algorithm's fitness evaluation module, driving the iterative update of the frequency modulation task weight coefficients and equipment compatibility constraint thresholds in the system parameter tuning instruction set, forming a closed-loop process of data acquisition, feature extraction, course generation, resource allocation, and verification feedback.

[0023] Specifically, the training course planning service method described in the present invention, the iterative updating of the system parameter tuning instruction set through the collaborative optimization model to form a data closed-loop feedback link includes: inputting the micro-course unit allocation results and the student learning trajectory data into the quantum Hamiltonian model, and solving the resource reuse binding strategy through the variational quantum algorithm; inputting the candidate course update plan into the genetic algorithm to generate an evolution plan, and verifying the matching degree of the evolution plan with the real-time operating condition data of the power grid through quantum parallel computing; and feeding back the verified course update plan to the fitness evaluation module of the genetic algorithm to iteratively update the optimized design topology.

[0024] The collaborative optimization model described in the present invention iteratively updates the system parameter tuning instruction set to form a data closed-loop feedback link. The specific implementation process is as follows: the training equipment load rate and course module reuse frequency parameters in the micro-course unit allocation results are encoded with the frequency modulation task response speed and operation accuracy parameters in the student learning trajectory data through quantum state tensor product to form a multi-body quantum Hamiltonian model, in which the equipment load rate is mapped to the amplitude parameter of the quantum bit, and the skill improvement rate parameter is converted into the superposition state phase angle through quantum phase encoding. A quantum energy model is constructed to characterize the coupling relationship between resource reuse efficiency and skill improvement effect. The variational quantum algorithm adjusts the ground state energy of the Hamiltonian through parameterized quantum circuits to solve the reuse binding strategy for theoretical course modules and training equipment nodes. Specifically, the high-frequency reuse frequency modulation principle knowledge unit is preferentially bound to low-load rate virtual device nodes, and the low-frequency maintenance operation module is adapted to high-availability physical device nodes, forming an optimized matching relationship between resource utilization and knowledge transfer efficiency.

[0025] Candidate course update plans are generated based on binding strategies and parameters such as renewable energy penetration and frequency regulation capacity gap ratios from real-time grid operating data. These plans are then fed into a genetic algorithm for genetic recombination. The genetic algorithm uses frequency regulation task completion rates and equipment compatibility parameters as fitness functions and performs crossover mutation on the course module topology. The crossover operation reorganizes the knowledge units of the virtual synchronous machine inertia setting module and the energy storage collaborative control strategy module. The mutation operation dynamically adjusts course duration based on the frequency deviation threshold in real-time frequency regulation instructions, generating an evolutionary plan that includes the newly added frequency regulation control logic and energy storage strategy. Quantum parallel computing maps the evolutionary plan and real-time grid data into quantum registers. Comparing quantum state amplitudes verifies the consistency of the course content with the frequency regulation requirements in terms of response time, regulation accuracy, and equipment compatibility, ultimately selecting candidate plans that meet preset matching thresholds.

[0026] Verified evolutionary schemes are fed back to the genetic algorithm's fitness evaluation module via the entangled photon pair transmission channel in the quantum communication protocol, updating the frequency modulation task weight coefficients and device compatibility constraint thresholds for the course topology. The fitness evaluation module performs quantum state interference analysis on the execution performance data of the frequency modulation strategy verification course module and the trainee skill improvement rate parameters, driving the next round of genetic evolution to generate an optimized design topology synchronized with the dynamic frequency modulation requirements of the power grid. Device status update data is encrypted and transmitted back to the resource scheduling module via the quantum key distribution protocol, updating the availability status table and load balancing strategy parameters of the training device resource pool. This forms a closed-loop optimization chain of data collection, feature extraction, course generation, resource allocation, and verification feedback, supporting the training system's dynamic adaptability to complex frequency modulation scenarios in dynamic systems.

[0027] Specifically, the training course planning service method described in the present invention receives real-time operation data of a dynamic system, where the real-time operation data includes scenario fluctuation parameters, resource gap rate, and performance deviation threshold. A correlation feature matrix characterizing the relationship between the new energy output fluctuation and the frequency modulation instruction response delay is generated through a quantum principal component analysis module. At the same time, the charge state time series data of the energy storage system is input into a quantum Fourier transform module to analyze its health attenuation trend signal. The real-time operation data is subjected to dimensionality reduction processing through the quantum principal component analysis module, including: inputting the new energy output fluctuation parameters and frequency modulation demand parameters in the real-time operation data of the dynamic system into the quantum principal component analysis module, extracting the multidimensional feature vector associated with the new energy fluctuation and generating a correlation feature matrix after dimensionality reduction; inputting the charge state time series data of the energy storage system into a quantum Fourier transform module, analyzing its frequency domain periodic characteristics and generating a trend signal characterizing the energy storage health attenuation. The trend signal is encrypted and transmitted to the scheduling automation system through a quantum communication protocol to trigger a course module update instruction of the training platform.

[0028] The technical implementation process of receiving the real-time operation data of the dynamic system and generating the correlation feature matrix and the health attenuation trend signal described in the present invention is as follows: the new energy output fluctuation parameters in the real-time operation data of the dynamic system, including the photovoltaic power generation power fluctuation rate and the wind power output ramp rate, are combined with the frequency deviation threshold and the regulation rate constraint value in the frequency regulation demand parameters, and input into the quantum principal component analysis module for feature extraction. The quantum principal component analysis module maps the multi-dimensional new energy output fluctuation parameters to a low-dimensional feature space through quantum state data projection and covariance matrix decomposition, eliminates noise interference and retains the key correlation between new energy fluctuations and frequency regulation instruction response delays, and generates a correlation feature matrix that characterizes the impact of new energy output on frequency stability under different frequency regulation scenarios. The row vectors of the correlation feature matrix correspond to the frequency regulation demand changes within the time window, and the column vectors characterize the coupling relationship between the new energy fluctuation amplitude, change rate and frequency regulation capacity demand, providing dynamic input for subsequent course topology optimization.

[0029] The state of charge time series data of the energy storage system is analyzed in the frequency domain through the quantum Fourier transform module. The charge and discharge depth sequence is converted into the frequency domain basis function space through quantum state phase encoding. The quantum amplitude modulation captures the energy distribution characteristics of the periodic charge and discharge behavior. The fundamental component in the frequency domain energy spectrum represents a typical charge and discharge cycle. The amplitude and phase deviation of the harmonic component reflect the performance degradation trend caused by battery aging, and a health decay trend signal containing the battery capacity decay rate and internal resistance growth rate parameters is generated. The trend signal is encrypted through the quantum key distribution protocol and transmitted to the scheduling automation system. When it is detected that the health decay rate exceeds the preset threshold, the scheduling system sends a course update instruction to the training platform, triggering the priority adjustment of the practical training modules related to the energy storage equipment maintenance strategy.

[0030] The processing results of the new energy output fluctuation parameters and the frequency modulation demand parameters form a correlation feature matrix, which is synchronously transmitted to the hybrid optimization module through the encrypted data transmission channel in the quantum communication protocol. The health decay trend signal and the real-time frequency modulation instruction work together in the course generation model to dynamically adjust the matching relationship between the training content and the health status of the equipment. The parallel processing mechanism of quantum principal component analysis and quantum Fourier transform reduces the computational complexity of data dimensionality reduction and feature extraction, and improves the efficiency of converting real-time working condition data into course planning decisions. The new energy fluctuation scenario classification results in the correlation feature matrix and the time window prediction parameters of the health decay trend signal jointly drive the construction of a multi-time scale course framework and the generation of a dynamic resource scheduling strategy.

[0031] Specifically, the training course planning service method of the present invention further includes: Based on the correlation feature matrix output by the quantum principal component analysis module, the knowledge unit mastery and operation response speed in the student behavior data are encoded in quantum bit superposition states to generate a quantum state capability map; the frequency modulation task completion rate parameter in the quantum state capability map is used as the fitness function input of the genetic algorithm to drive the cross-mutation process of the initial course topology, and generate an optimized design topology including the virtual synchronous machine inertia parameter adjustment and the energy storage charging and discharging coordinated control strategy.

[0032] The technical implementation process for generating a quantum state capability map and optimizing the design topology described in this invention is as follows: knowledge unit mastery parameters from student behavioral data, including frequency modulation principle understanding and energy storage strategy accuracy indicators, are converted into probability amplitude distributions through quantum bit amplitude encoding. Operational response speed parameters, such as instruction execution delay and adjustment accuracy deviation, are mapped into superposition state phase angles through quantum phase encoding, forming a multi-qubit superposition state model. This superposition state model constructs a knowledge unit association map in Hilbert space, with nodes representing frequency modulation control and energy storage charge and discharge strategy modules. Edge weights are determined by the quantum state interference results of knowledge mastery and response speed, generating a quantum state capability map that reflects the distribution characteristics of student skill weaknesses.

[0033] The frequency modulation task completion rate parameters in the quantum state capability map, including the frequency modulation response speed and regulation accuracy achieved in historical training, serve as inputs to the genetic algorithm's fitness function. The genetic algorithm uses these completion rate parameters and the edge weights of the knowledge unit association map as selection pressures, performing crossover and mutation operations on the initial course topology. The crossover operation reorganizes the knowledge units of the virtual synchronous machine inertia tuning theory module and the energy storage charge and discharge coordinated control practice module. The mutation operation dynamically adjusts course duration based on quantum phase deviation, retaining the knowledge combination of frequency modulation instruction parsing logic and energy storage strategy coordination, and generating an optimized design topology that includes dynamic virtual synchronous machine parameter adjustment rules and energy storage charge and discharge rate matching strategies.

[0034] The module connection sequence for the optimized design topology aligns with the actual workflow of grid frequency regulation control. The theoretical course covers the impact of virtual synchronous machine inertia parameters on primary grid frequency regulation. The practical training module simulates energy storage charging and discharging strategies under different renewable energy penetration scenarios. The iterative optimization process of the genetic algorithm, combined with real-time student skill assessment data from the quantum state capability map, dynamically adjusts the complexity of the course modules and the intensity of the practical training, aligning the training content with the evolution of individual student capabilities and enhancing the frequency regulation skills of dynamic system dispatchers in scenarios with high renewable energy fluctuations.

[0035] Specifically, the training course planning service method described in the present invention, the generation of the course trigger priority sequence includes: mapping the new energy fluctuation characteristics and real-time frequency modulation instructions in the associated feature matrix into a quantum bit energy model, solving the optimal course trigger sequence through the quantum annealing algorithm, and generating a trigger list including the priority of the frequency modulation virtual simulation module; binding the trigger list with the real-time frequency modulation instruction, driving the digital twin platform to allocate micro-course units and generate practical training tasks that match the current frequency modulation requirements.

[0036] The technical implementation process of generating a course trigger priority sequence and driving the allocation of practical training tasks described in the present invention is as follows: the new energy fluctuation characteristic parameters in the associated characteristic matrix, including the photovoltaic output fluctuation amplitude and the wind power change rate, are mapped together with the frequency deviation threshold and the regulation rate limit parameters in the real-time frequency modulation instruction to the quantum bit energy model. The new energy fluctuation amplitude corresponds to the amplitude parameter of the quantum bit, and the frequency deviation threshold is converted into a superposition state phase angle through quantum phase encoding, thereby constructing a quantum energy model that characterizes the urgency of frequency modulation needs and the complexity of new energy fluctuation scenarios. The quantum annealing algorithm traverses the local optimal solution of the energy model through the quantum tunneling effect, solves the trigger priority ranking of the frequency modulation virtual simulation module and the energy storage strategy optimization module, and generates a trigger list graded by the intensity of new energy fluctuations and the risk of frequency modulation response delay.

[0037] The priority parameters in the trigger list are dynamically bound to the frequency regulation target value and energy storage charge and discharge rate constraint value in the real-time frequency regulation instruction, driving the digital twin platform to allocate micro-course units. The virtual synchronous machine inertia parameter tuning module corresponding to the high-priority frequency regulation scenario is preferentially mapped to the low-latency simulation node. The energy storage collaborative control strategy module is allocated to the high-precision physical actuator node based on the charge and discharge rate threshold, generating a real-time training task that includes the dynamic response test of the virtual synchronous machine and the bidirectional regulation operation of the energy storage power. The parameter settings in the training task are synchronized with the current grid frequency regulation controller execution strategy, and the frequency fluctuation curve in the virtual simulation environment maintains time domain consistency with the actual dispatch system monitoring data.

[0038] During the execution of real-time frequency modulation instructions, changes in the output fluctuation characteristics of renewable energy sources trigger the dynamic reconstruction of the quantum energy model. The quantum annealing algorithm then recalculates the course module priorities based on the updated energy distribution. The digital twin platform adjusts the node mapping strategy for micro-course units based on the updated trigger list, adds a virtual inertia compensation training module for high-volatility scenarios, and optimizes the practical training content for energy storage device overload protection operations. The training task execution results are transmitted back to the collaborative optimization module via the quantum communication interface, updating the frequency modulation response delay coefficient and energy storage strategy matching parameters in the correlation feature matrix, thus forming a dynamic adaptation mechanism that aligns course priorities with grid frequency modulation requirements.

[0039] Specifically, the training course planning service method of the present invention, wherein generating a long-term steady-state course framework includes: The tie-line power constraint value and frequency regulation rate limit in the power grid dispatching regulations are input into the genetic algorithm to generate a long-term steady-state frequency regulation strategy training framework; The preventive maintenance training module inserted into the long-term steady-state course framework includes: The health decay trend signal output by the quantum Fourier transform module is input into the quantum Markov chain model to predict the maintenance demand time window of the energy storage equipment, and a preventive maintenance training module is inserted into the long-term steady-state course framework.

[0040] The technical implementation process of generating a long-term steady-state course framework and inserting a preventive maintenance training module described in the present invention is as follows: the interconnection line power constraint value in the power grid dispatching regulations includes the upper limit of the cross-region interconnection line transmission power and the inter-regional power exchange rate limit, and the frequency regulation rate limit covers the primary frequency regulation response time threshold and the secondary frequency regulation accuracy parameter, which are converted into the chromosome structure of the genetic algorithm through quantum bit amplitude encoding. The genetic algorithm maps the parameters into a quantum superposition state mapping population, realizes chromosome crossover mutation through a quantum rotating gate, and generates a long-term steady-state training framework that includes annual frequency regulation strategy optimization and source-grid-load-storage coordinated control principles. The course module layout of the framework corresponds to the multi-level frequency regulation time scale in the power grid dispatching regulations. The theoretical teaching module covers the inertia response and frequency regulation spare capacity allocation mechanism, and the practical training module integrates cross-region power support simulation and frequency coordinated control operations.

[0041] The health decay trend signal is analyzed by the quantum Fourier transform module. The frequency domain fundamental component of the charge and discharge cycle sequence represents a typical charge and discharge cycle, and the amplitude deviation of the harmonic component reflects the battery capacity decay rate. The quantum Markov chain model constructs a state transfer matrix by combining the frequency domain feature input with historical maintenance data. It predicts the time window of a sudden increase in battery internal resistance or a sudden decrease in capacity through quantum state evolution and outputs a maintenance demand prediction signal. The prediction signal triggers the training platform to insert a preventive maintenance training module into the corresponding teaching node of the long-term steady-state course framework, including battery health status assessment procedures, cascade utilization strategy optimization and fault warning and handling procedures. The course parameters are dynamically matched with the predicted maintenance time window and the actual degree of equipment degradation.

[0042] The construction of a long-term steady-state training framework and the insertion of a preventive maintenance module form a synergistic mechanism in the time dimension. The annual frequency modulation strategy course generated by the genetic algorithm and the maintenance cycle predicted by the quantum Markov chain are automatically aligned in the training progress. When the health decay trend signal indicates that the equipment performance has entered the critical decay stage, the training platform will simultaneously add an equipment maintenance strategy case analysis to the corresponding theoretical course module. The practical training module will load a battery parameter simulation environment that matches the actual equipment status, so that the maintenance skills mastered by the trainees are synchronized with the evolution of the actual operating status of the equipment. The framework interacts with the dispatching automation system data through the quantum communication protocol, receives real-time equipment operating status update data, and dynamically adjusts the urgency label of the course module and the priority of practical training resource allocation.

[0043] Specifically, the training course planning service method of the present invention, wherein the micro-course units are allocated to the digital twin platform through quantum superposition state mapping, comprises: Mapping the course modules generated by the system parameter tuning instruction set and the training equipment status data into quantum superposition state mapping, and resolving equipment occupancy conflicts in concurrent requests from multiple students through the characteristics of quantum entanglement; Based on the device occupancy rate parameter in the quantum superposition state mapping and in combination with the course priority list generated by the quantum annealing algorithm, a conflict-free reservation sequence is generated in which the device occupancy rate matches the course priority; The conflict-free reservation sequence triggers the training equipment resource scheduling instruction and updates the equipment status data.

[0044] The technical implementation process of the present invention for realizing micro-course unit allocation and resource conflict resolution through quantum superposition state mapping is as follows: the course module parameters generated by the system parameter tuning instruction set, including the virtual synchronous machine inertia adjustment range and the energy storage charge and discharge rate threshold, and the training equipment node status data in the digital twin platform, including the equipment load rate and response delay parameters, are converted into a multi-qubit superposition state through quantum state amplitude coding. The urgency label of the course module is mapped to the amplitude value of the quantum bit, and the availability status of the device node is characterized by quantum phase coding to construct a multi-dimensional superposition state model of course requirements and device status. The quantum entanglement characteristics resolve the device occupancy conflicts in the concurrent requests of multiple students through Bell state correlation measurement, identify the potential matching relationship between high-priority frequency modulation course modules and low-load device nodes, and screen out candidate allocation schemes with a probability of device occupancy conflict lower than a preset threshold.

[0045] The device occupancy parameters in the quantum superposition state mapping are extracted through quantum state projection measurement, including the real-time load rate of the physical device node and the concurrent processing capacity margin of the virtual simulation node. Combined with the frequency modulation scenario urgency label in the course priority list generated by the quantum annealing algorithm, a joint optimization model for device resource occupancy and course demand priority is constructed. The model traverses candidate allocation schemes through quantum parallel computing, evaluates the weighted scores of device utilization and course execution efficiency, and generates a conflict-free reservation sequence that matches the occupancy rate of the training device node with the course priority. The time window allocation parameters in the reservation sequence are dynamically associated with the device node load balancing strategy. High-priority frequency modulation virtual simulation tasks are preferentially allocated to low-latency simulation nodes, and the energy storage collaborative control operation module is adapted to high-precision actuator nodes.

[0046] The conflict-free reservation sequence triggers resource scheduling instructions for training equipment via the state transmission channel in the quantum communication protocol. The digital twin platform dynamically loads the frequency modulation strategy verification course module based on the node allocation parameters in the sequence. When the virtual synchronous machine inertia parameter tuning training task is mapped to a designated simulation node, the node load rate and task queue length parameters in the equipment status data are simultaneously updated. The execution results of the resource scheduling instructions are transmitted back to the collaborative optimization module via quantum key encryption, updating the real-time availability information in the equipment status database and providing dynamic input for the next round of quantum superposition state mapping. The equipment status data update process is synchronized in time with the course module execution progress, forming a closed-loop adaptation mechanism that aligns resource allocation strategies with frequency modulation training needs.

[0047] Specifically, the training course planning service method of the present invention further includes: The resource utilization data in the conflict-free reservation sequence and the skill improvement rate parameters in the trainee learning trajectory data are encoded into a quantum Hamiltonian model, and a variational quantum algorithm is used to solve the reuse binding strategy of knowledge units and training equipment; Input the candidate course update plan into the genetic algorithm to generate an evolution plan, and verify the consistency of the evolution plan with the real-time operating data of the power grid through quantum parallel computing; The verified evolution scheme is fed back to the fitness evaluation module of the genetic algorithm to update the frequency modulation task weight coefficient and the device compatibility constraint threshold of the system parameter tuning instruction set.

[0048] The resource allocation optimization and curriculum plan iterative update technology described in the present invention is implemented as follows: the resource utilization data in the conflict-free reservation sequence, including the load balancing rate of virtual simulation nodes and the frequency of use of physical actuator devices, and the frequency modulation task response speed improvement rate and operation accuracy growth parameters in the student learning trajectory data are encoded into a multi-body quantum Hamiltonian model through quantum state tensor product. The resource utilization parameters are mapped to the diagonal term coefficients of the Hamiltonian, and the skill improvement rate parameters are converted into non-diagonal coupling terms through quantum phase modulation to construct a quantum energy model that characterizes the reuse efficiency of knowledge units and the adaptability of equipment resources. The variational quantum algorithm adjusts the ground state energy distribution of the Hamiltonian through parameterized quantum circuits, solves the binding strategy of the high-frequency frequency modulation principle course module and the low-load virtual node, and the matching relationship between the low-frequency equipment maintenance course module and the high-availability physical node, forming a collaborative optimization solution for knowledge transfer efficiency and equipment utilization.

[0049] Candidate course update plans are generated based on the binding strategy and the renewable energy fluctuation characteristics in the real-time grid operating data, and then fed into a genetic algorithm for evolutionary operations. The genetic algorithm uses the execution performance data of the frequency regulation strategy verification course as a fitness function, performing genetic crossover and mutation on the course module combination. The crossover operation swaps the training node mapping relationship between the virtual synchronous machine parameter tuning module and the energy storage strategy optimization module. The mutation operation dynamically adjusts the course duration weight based on the frequency deviation change rate in the real-time frequency regulation instructions, generating an evolutionary plan that includes a new virtual inertia compensation strategy and energy storage overload protection logic. Quantum parallel computing maps the course parameters in the evolutionary plan and real-time grid data to multi-qubit registers. Quantum state amplitude comparison verifies the consistency between the course content and the frequency regulation requirements in terms of response time constraints, regulation accuracy thresholds, and equipment compatibility parameters, and selects candidate plans that meet the preset matching conditions.

[0050] The verified evolutionary scheme is fed back to the genetic algorithm's fitness evaluation module via a quantum teleportation protocol, updating the task weight coefficients and device compatibility constraint thresholds within the system parameter tuning instruction set. The task weight coefficients are dynamically adjusted based on the matching degree between the course modules and the real-time frequency modulation scenario, increasing the priority of the virtual synchronous machine inertia adjustment course in high-volatility scenarios. The device compatibility constraint thresholds are reset based on the correlation between node load rate and course execution efficiency, optimizing the binding strength between the energy storage strategy module and high-precision actuator nodes. The updated system parameter tuning instruction set is synchronized to the digital twin platform via a quantum communication interface, driving a new round of micro-course unit allocation and practical training task generation, forming a closed-loop iterative mechanism for resource optimization, dynamic course updates, and frequency modulation strategy verification.

[0051] Specifically, the training course planning service method of the present invention further includes: a collaborative verification module; The verification result of the collaborative verification module is fed back to the fitness evaluation module of the genetic algorithm to drive the iterative update of the system parameter tuning instruction set; The device status update data generated by the conflict-free reservation sequence is encrypted and transmitted to the training platform via the quantum communication interface, forming a closed-loop process of data collection, optimization modeling, resource scheduling and verification feedback.

[0052] The closed-loop verification feedback and data flow integration technology described in this invention is implemented as follows: The verification result data output by the collaborative verification module, including the matching index between the course update plan and the real-time grid operating conditions and the frequency modulation strategy execution effectiveness evaluation parameters, is transmitted to the genetic algorithm's fitness evaluation module via a quantum entanglement channel. This module performs quantum state coherent superposition analysis on the matching index and the historical student skill improvement rate to generate dynamic weight coefficients for the fitness function. This module drives the genetic algorithm to prioritize course module combinations that are suitable for high-fluctuation frequency modulation scenarios in the next round of evolution, and iteratively updates the virtual synchronous machine inertia adjustment weight and energy storage charge and discharge rate constraint thresholds in the system parameter tuning instruction set.

[0053] Device status update data generated by the conflict-free reservation sequence, including the real-time load rate of physical actuator nodes and the task queue depth parameters of virtual simulation nodes, is encrypted via the quantum key distribution protocol and transmitted to the training platform's data processing center. This data, after undergoing a quantum Fourier transform to extract periodic features, is then fed into the dynamic resource pool model of the hybrid optimization module, updating the device node's availability status labels and resource allocation priority parameters, providing real-time input for the next round of quantum superposition state mapping. The data processing center simultaneously receives grid frequency fluctuation curves and frequency regulation capacity gap rate data pushed by the dispatch automation system, reconstructing the renewable energy output fluctuation feature vectors within the correlation feature matrix.

[0054] Based on updated device status data and optimized design topology, the training platform generates a training task allocation strategy tailored to the current resource state. The virtual synchronous machine inertia parameter tuning module adjusts the computational resource allocation ratio of simulation nodes according to the latest weight coefficients of the system parameter tuning instruction set. The energy storage strategy training module dynamically binds high-availability physical nodes based on device compatibility constraint thresholds. Trainee operation data and device performance data collected during training tasks are transmitted back to the collaborative verification module via quantum teleportation channels, forming a closed-loop iterative chain of data collection, feature extraction, course optimization, resource scheduling, and effect verification, achieving multi-scale synchronous adaptation of training content to the dynamic frequency regulation requirements of the power grid.

[0055] In a second aspect, the present invention provides a training course planning service system, which is applied to the training course planning service method, and includes: The quantum data processing module is used to perform quantum principal component analysis and dimensionality reduction on the scenario fluctuation parameters, resource gap rate, and performance deviation threshold in the real-time operation data of the dynamic system to generate an encrypted correlation feature matrix. At the same time, the quantum Fourier transform is performed on the charge state time series data of the energy storage system to generate a trend signal representing the decline of energy storage health. A hybrid optimization module is configured to generate a quantum state capability map through quantum bit superposition state encoding based on the correlation feature matrix output by the quantum data processing module and the student behavior data, and input the quantum state capability map into a genetic algorithm to drive the generation of an optimized design topology; input the quantum state capability map into a genetic algorithm to drive the generation of an optimized design topology, and generate a course trigger priority sequence through a quantum annealing algorithm in combination with the trend signal and real-time frequency modulation instructions; A resource scheduling module is used to input the optimized design topology and course trigger priority sequence generated by the hybrid optimization module into the multi-timescale course framework, allocate micro-course units to training equipment nodes in the digital twin platform through quantum superposition state mapping, and analyze equipment occupancy conflicts through quantum entanglement characteristics to generate a conflict-free reservation sequence; A long-term steady-state course framework construction module is used to input the tie-line power constraint value and frequency regulation rate limit in the power grid dispatching regulations into the genetic algorithm to generate a long-term steady-state frequency regulation strategy training framework, and insert a preventive maintenance training module based on the health decay trend signal; A collaborative verification module is configured to receive the equipment utilization data and trainee learning trajectory data fed back by the resource scheduling module, solve the reuse binding strategy of knowledge units and training equipment through a variational quantum algorithm, input the candidate course update plan into a genetic algorithm to generate an evolution plan, verify the consistency of the evolution plan with the real-time power grid operating data through quantum parallel computing, and feed back the verification results to the hybrid optimization module; The quantum data processing module, hybrid optimization module, resource scheduling module, long-term steady-state course framework construction module and collaborative verification module realize data interaction through the encrypted data transmission channel in the quantum communication protocol. The feedback results of the collaborative verification module drive the hybrid optimization module to dynamically adjust the frequency modulation task weight coefficient and device compatibility constraint threshold of the course topology, forming a closed-loop link of data collection, optimization modeling, resource scheduling and verification feedback.

[0056] The technical implementation process of the training course planning service system described in the present invention is as follows: the quantum data processing module receives the new energy output fluctuation parameters and frequency modulation capacity gap rate in the real-time operation data of the dynamic system, performs covariance matrix decomposition on the multi-dimensional fluctuation characteristics through quantum principal component analysis, extracts the nonlinear correlation characteristics of the photovoltaic output fluctuation amplitude and the wind power ramp rate, and generates a low-dimensional encrypted correlation feature matrix; the charge state time series data of the energy storage system is analyzed for frequency domain periodic characteristics through the quantum Fourier transform module, and the harmonic component amplitude deviation of the charge and discharge cycle depth sequence is mapped to the battery capacity decay rate parameter, generating an energy storage health trend signal that is encrypted and transmitted to the scheduling system, triggering the training platform course module update instruction.

[0057] The hybrid optimization module quantum-encodes the classification results of new energy fluctuation scenarios in the associated feature matrix and the mastery parameters of knowledge units in the student behavior data. Quantum phase modulation is used to generate a superposition state capability map for operational response speed. A genetic algorithm performs crossover and mutation operations on the initial course topology based on the frequency modulation task completion rate parameters in the map. This crossover process reorganizes the knowledge units of the virtual synchronous machine inertia setting and energy storage strategy modules. The mutation operation adjusts the course duration weights based on real-time frequency modulation instructions to generate an optimized design topology covering inertia response and energy storage coordinated control. A quantum annealing algorithm combines frequency modulation demand urgency labels with energy storage health trend signals to construct a course trigger priority energy model, outputting a sequence of frequency modulation virtual simulation modules that prioritizes high-fluctuation scenarios.

[0058] The resource scheduling module inputs the optimized design topology and priority sequence into the multi-timescale course framework and allocates micro-course units to digital twin platform nodes through quantum superposition mapping. The course module's urgency tag and device node availability status are encoded in a multi-qubit superposition state. Quantum entanglement properties resolve virtual simulation node occupancy conflicts among multiple concurrent student requests and generate a conflict-free allocation scheme that matches the physical actuator node load rate with the course priority. This scheme dynamically adjusts the simulation node mapping strategy of the high-priority frequency modulation module and updates the node load parameters in the device status data in real time.

[0059] The long-term steady-state course framework module uses genetic algorithms to address tie-line power constraints and frequency regulation rate limits in grid dispatch regulations. Quantum chromosome encoding generates a steady-state training framework encompassing annual frequency regulation strategy optimization and regional coordinated control principles. Health decay trend signals are input into a quantum Markov chain model to predict maintenance demand windows. Practical training modules on battery health assessment and cascade utilization strategy optimization are incorporated into the corresponding teaching phases of the steady-state framework. Course parameters are dynamically synchronized with the actual equipment degradation process.

[0060] The collaborative verification module receives equipment utilization data fed back by the resource scheduling module, constructs a reused binding Hamiltonian model for knowledge units and training equipment through a variational quantum algorithm, and solves the optimal matching strategy between high-frequency theoretical modules and low-load virtual nodes. After the candidate course update plan is evolved by a genetic algorithm, quantum parallel computing verifies its consistency with the real-time operating conditions of the power grid. The verified plan is fed back to the hybrid optimization module through quantum teleportation, dynamically adjusting the inertia adjustment weight and equipment compatibility threshold in the course topology. The encrypted channel in the quantum communication protocol transmits equipment status update data and trainee skill assessment results, forming a closed-loop iterative link of data collection, course optimization, resource allocation, and effect verification, supporting the dynamic real-time adaptation capability of dynamic system frequency regulation training.

[0061] The specific implementation of the present invention is as follows: receiving scenario fluctuation parameters, resource gap rate and performance deviation threshold in the real-time operation data of the dynamic system, performing covariance matrix decomposition on the multi-dimensional fluctuation data through the quantum principal component analysis module, extracting the nonlinear correlation characteristics of the photovoltaic output fluctuation amplitude and the wind power ramp rate, and generating a low-dimensional encrypted correlation feature matrix; the state of charge time series data of the energy storage system is analyzed by the quantum Fourier transform module to obtain the frequency domain periodic characteristics, and the harmonic component amplitude deviation of the charge and discharge cycle depth sequence is mapped to the battery capacity decay rate parameter, generating an encrypted energy storage health trend signal transmitted to the dispatching system, and triggering the training platform course module update instruction. The row vectors of the correlation feature matrix represent the frequency modulation demand changes within the time window, and the column vectors associate the coupling relationship between the new energy fluctuation amplitude, the change rate and the frequency modulation capacity demand, providing dynamic input for the subsequent course topology optimization.

[0062] Based on the classification results of new energy fluctuation scenarios and frequency modulation response delay parameters in the associated feature matrix, a system parameter tuning instruction set is constructed using a quantum annealing algorithm. This generates the virtual synchronous machine inertia parameter setting range and energy storage charge and discharge rate thresholds, allowing real-time adjustment of the dynamic response characteristics of the virtual synchronous machine and the power output mode of the energy storage device in the dynamic system frequency modulation controller. The knowledge unit mastery and operation response speed parameters in the student behavior data are encoded using quantum bit amplitude and phase to generate a quantum state capability map. This is then input into a genetic algorithm to drive crossover and mutation operations on the initial course topology, retaining the combination of knowledge modules related to frequency modulation strategy and energy storage control to generate an optimized design topology adapted to different student ability levels.

[0063] Based on the frequency deviation thresholds and new energy fluctuation characteristics in real-time frequency modulation instructions, a quantum annealing algorithm was used to construct a course trigger priority energy model. This model determined the triggering order of the frequency modulation virtual simulation module and the energy storage strategy optimization module, generating a training task list prioritized for high-volatility scenarios. Based on this priority list, the digital twin platform dynamically allocated micro-course units to low-latency simulation nodes and high-precision physical actuator nodes. The frequency fluctuation curves in the virtual simulation environment were synchronized in the time domain with the actual dispatching system monitoring data, ensuring that the training task parameters were aligned in real time with the response time constraints and adjustment rate range of the frequency modulation instructions.

[0064] The long-term steady-state frequency regulation strategy training framework uses genetic algorithms to address the tie-line power constraints and frequency regulation rate limits in grid dispatch regulations. Quantum chromosome encoding generates course modules covering annual frequency regulation strategy optimization and the principles of coordinated control of power generation, grid load, and storage. Health decay trend signals are input into a quantum Markov chain model to predict maintenance demand windows. Battery health assessment and cascade utilization strategy optimization training modules are inserted into the corresponding teaching phases of the long-term course framework. Course parameters are dynamically synchronized with the actual degree of equipment degradation. When the health decay rate exceeds a preset threshold, the training platform automatically increases the proportion of preventive maintenance case analysis, and the simulation environment loads a battery parameter model that matches the actual equipment status.

[0065] During the micro-course unit allocation process, the course requirement parameters generated by the system parameter tuning instruction set and the device node availability status are encoded through quantum superposition mapping to construct a multi-dimensional optimization model. The characteristics of quantum entanglement resolve device occupancy conflicts in concurrent requests from multiple students, generating a conflict-free reservation sequence that matches device occupancy rates with course priorities. The collaborative verification module receives device utilization data and student skill improvement rate parameters, and uses a variational quantum algorithm to solve the reuse binding strategy for knowledge units and practical training equipment. After candidate course update plans are evolved through a genetic algorithm, quantum parallel computing verifies their consistency with the real-time operating conditions of the power grid. The updated frequency regulation task weight coefficient and device compatibility threshold are fed back to the digital twin platform via the quantum communication protocol, forming a closed-loop iterative link of data collection, optimization modeling, resource allocation, and verification feedback, supporting the training system's dynamic adaptability to complex frequency regulation scenarios.

[0066] The technical solution of the present invention solves the existing technical problems in the following ways: First, multi-source heterogeneous data fusion and dynamic feature extraction: The quantum principal component analysis module processes the scenario fluctuation parameters, resource gap rate, and performance deviation threshold in real-time operation data to extract the correlation feature matrix between new energy fluctuations and frequency modulation response delays. Simultaneously, the quantum Fourier transform module analyzes the energy storage system's state of charge time series data to generate a frequency domain trend signal representing battery health degradation. This correlation feature matrix and trend signal are encrypted and transmitted to the dispatching system via a quantum communication protocol, reflecting the dynamic frequency modulation needs of the power grid and changes in equipment health in real time. This addresses the problem of static historical data being unable to integrate new energy fluctuation characteristics with equipment degradation trends, and eliminates the spatiotemporal mismatch between training content and real-time operating conditions.

[0067] Second, dynamic course generation and resource collaborative optimization: A system parameter tuning instruction set is constructed based on a quantum annealing algorithm, combined with a genetic algorithm to optimize the design topology, generating theoretical teaching and practical training modules tailored to the capabilities of different learners. Micro-course units are assigned to the digital twin platform through quantum superposition state mapping, and the properties of quantum entanglement are used to resolve equipment occupancy conflicts and generate conflict-free appointment sequences. A genetic algorithm encodes interconnection line power constraints and frequency regulation rate limits into a long-term steady-state training framework. Preventive maintenance training modules are dynamically inserted based on energy storage health prediction signals, achieving a coordinated match between the multi-timescale course framework and equipment maintenance requirements, breaking through the limitations of a single-timescale evaluation mechanism.

[0068] Third, a closed-loop feedback and iterative update mechanism: The collaborative verification module receives data on equipment utilization and student learning trajectories, and uses a variational quantum algorithm to solve the reuse binding strategy for knowledge units and training equipment. Candidate course update plans are verified through quantum parallel computing and fed back to the genetic algorithm to dynamically adjust the frequency modulation task weight coefficients and device compatibility constraint thresholds. The results of resource scheduling instructions are encrypted and transmitted back via a quantum communication interface, forming a closed-loop link of data collection, course optimization, resource allocation, and verification feedback. This improves the training system's real-time responsiveness to complex frequency modulation scenarios and resource utilization efficiency, enhancing the effectiveness of complex skill training.

Claims

1. Training course planning service method, characterized by: include: Receive real-time operating data of the dynamic system, including scenario fluctuation parameters, resource gap rate, and performance deviation threshold. Use the quantum principal component analysis module to generate a correlation feature matrix that characterizes the relationship between the output fluctuation of renewable energy and the response delay of frequency modulation instructions. Simultaneously, input the energy storage system's state of charge time series data into the quantum Fourier transform module to analyze its health decay trend signal. Use the quantum principal component analysis module to perform dimensionality reduction processing on the real-time operating data to generate a correlation feature matrix between the output fluctuation of renewable energy and the response delay of frequency modulation instructions. Based on the correlation feature matrix, a system parameter tuning instruction set is generated through a quantum annealing algorithm. Based on the knowledge unit mastery and operation response speed in the student behavior data, a quantum state capability map is generated through quantum bit superposition state encoding, and input into a genetic algorithm to optimize the design topology. Applying the parameter tuning instruction set to the dynamic system controller to adjust the operating parameters of the system core components and resource nodes; Generate a course trigger priority sequence based on the correlation feature matrix and the real-time frequency modulation instruction, drive the digital twin platform to allocate micro-course units, generate a course trigger priority sequence based on the correlation feature matrix and the real-time frequency modulation instruction, and generate corresponding course modules based on the system parameter tuning instruction set; Generate a long-term steady-state course framework, including inputting the tie-line power constraint value and frequency regulation rate limit in the power grid dispatch regulations into the genetic algorithm to generate a long-term steady-state frequency regulation strategy training framework; Based on the health decay trend signal, a preventive maintenance training module is inserted into the long-term steady-state course framework; Device occupancy conflicts are resolved through quantum superposition state mapping, and a verification course module is generated based on the execution results of the system parameter tuning instruction set. Micro-course units are allocated to the digital twin platform through quantum superposition state mapping, and the system parameter tuning instruction set is iteratively updated through a collaborative optimization model.

2. The training course planning service method according to claim 1, characterized in that: The iterative updating of the system parameter tuning instruction set through the collaborative optimization model to form a data closed-loop feedback link includes: inputting the micro-course unit allocation results and the student learning trajectory data into the quantum Hamiltonian model, solving the resource reuse binding strategy through the variational quantum algorithm; inputting the candidate course update plan into the genetic algorithm to generate an evolution plan, and verifying the matching degree of the evolution plan with the real-time operating data of the power grid through quantum parallel computing; and feeding back the verified course update plan to the fitness evaluation module of the genetic algorithm to iteratively update the optimized design topology.

3. The training course planning service method according to claim 1, characterized in that: The method receives real-time operation data of a dynamic system, the real-time operation data including scenario fluctuation parameters, resource gap rate and performance deviation threshold, generates a correlation feature matrix representing the relationship between the fluctuation of new energy output and the response delay of frequency modulation instructions through a quantum principal component analysis module, and simultaneously inputs the state of charge time series data of the energy storage system into a quantum Fourier transform module to analyze its health attenuation trend signal. The method performs dimensionality reduction processing on the real-time operation data through the quantum principal component analysis module, including: inputting the new energy output fluctuation parameters and frequency modulation demand parameters in the real-time operation data of the dynamic system into the quantum principal component analysis module, extracting the multidimensional feature vector associated with the new energy fluctuation and generating a correlation feature matrix after dimensionality reduction; inputting the state of charge time series data of the energy storage system into the quantum Fourier transform module, analyzing its frequency domain periodic characteristics and generating a trend signal representing the health attenuation of the energy storage, and transmitting the trend signal to the dispatching automation system through an encrypted quantum communication protocol to trigger a course module update instruction of the training platform.

4. The training course planning service method according to claim 3, characterized in that: Also includes: Based on the correlation feature matrix output by the quantum principal component analysis module, the knowledge unit mastery and operation response speed in the student behavior data are encoded in quantum bit superposition states to generate a quantum state capability map; the frequency modulation task completion rate parameter in the quantum state capability map is used as the fitness function input of the genetic algorithm to drive the cross-mutation process of the initial course topology, and generate an optimized design topology including the virtual synchronous machine inertia parameter adjustment and the energy storage charging and discharging coordinated control strategy.

5. The training course planning service method according to claim 1, characterized in that: Generating a course trigger priority sequence includes: mapping the new energy fluctuation characteristics in the associated feature matrix and the real-time frequency modulation instructions into a quantum bit energy model, solving the optimal course trigger sequence through a quantum annealing algorithm, and generating a trigger list including the priority of the frequency modulation virtual simulation module; binding the trigger list with the real-time frequency modulation instructions, driving the digital twin platform to allocate micro-course units and generate practical training tasks that match the current frequency modulation requirements.

6. The training course planning service method according to claim 1, characterized in that: The framework for generating a long-term steady-state curriculum includes: The tie-line power constraint value and frequency regulation rate limit in the power grid dispatching regulations are input into the genetic algorithm to generate a long-term steady-state frequency regulation strategy training framework; The preventive maintenance training module inserted into the long-term steady-state course framework includes: The health decay trend signal output by the quantum Fourier transform module is input into the quantum Markov chain model to predict the maintenance demand time window of the energy storage equipment, and a preventive maintenance training module is inserted into the long-term steady-state course framework.

7. The training course planning service method according to claim 1, characterized in that: The allocation of micro-course units to the digital twin platform through quantum superposition state mapping includes: Mapping the course modules generated by the system parameter tuning instruction set and the training equipment status data into quantum superposition state mapping, and resolving equipment occupancy conflicts in concurrent requests from multiple students through the characteristics of quantum entanglement; Based on the device occupancy rate parameter in the quantum superposition state mapping and in combination with the course priority list generated by the quantum annealing algorithm, a conflict-free reservation sequence is generated in which the device occupancy rate matches the course priority; The conflict-free reservation sequence triggers the training equipment resource scheduling instruction and updates the equipment status data.

8. The training course planning service method according to claim 7, characterized in that: Also includes: The resource utilization data in the conflict-free reservation sequence and the skill improvement rate parameters in the trainee learning trajectory data are encoded into a quantum Hamiltonian model, and a variational quantum algorithm is used to solve the reuse binding strategy of knowledge units and training equipment; Input the candidate course update plan into the genetic algorithm to generate an evolution plan, and verify the consistency of the evolution plan with the real-time operating data of the power grid through quantum parallel computing; The verified evolution scheme is fed back to the fitness evaluation module of the genetic algorithm to update the frequency modulation task weight coefficient and the device compatibility constraint threshold of the system parameter tuning instruction set.

9. The training course planning service method according to claim 7, characterized in that: Also includes: Collaborative verification module; The verification result of the collaborative verification module is fed back to the fitness evaluation module of the genetic algorithm to drive the iterative update of the system parameter tuning instruction set; The device status update data generated by the conflict-free reservation sequence is encrypted and transmitted to the training platform via the quantum communication interface, forming a closed-loop process of data collection, optimization modeling, resource scheduling and verification feedback.

10. A training course planning service system, applied to the training course planning service method according to any one of claims 1 to 9, characterized in that: include: The quantum data processing module is used to perform quantum principal component analysis and dimensionality reduction on the scenario fluctuation parameters, resource gap rate, and performance deviation threshold in the real-time operation data of the dynamic system to generate an encrypted correlation feature matrix. At the same time, the quantum Fourier transform is performed on the charge state time series data of the energy storage system to generate a trend signal representing the decline of energy storage health. A hybrid optimization module is used to generate a quantum state capability map through quantum bit superposition state encoding based on the correlation feature matrix output by the quantum data processing module and the student behavior data, and input the quantum state capability map into a genetic algorithm to drive the generation of an optimized design topology; Inputting the quantum state capability map into a genetic algorithm to drive the generation of an optimized design topology, and combining the trend signal with the real-time frequency modulation instruction to generate a course trigger priority sequence through a quantum annealing algorithm; A resource scheduling module is used to input the optimized design topology and course trigger priority sequence generated by the hybrid optimization module into the multi-timescale course framework, allocate micro-course units to training equipment nodes in the digital twin platform through quantum superposition state mapping, and analyze equipment occupancy conflicts through quantum entanglement characteristics to generate a conflict-free reservation sequence; A long-term steady-state course framework construction module is used to input the tie-line power constraint value and frequency regulation rate limit in the power grid dispatching regulations into the genetic algorithm to generate a long-term steady-state frequency regulation strategy training framework, and insert a preventive maintenance training module based on the health decay trend signal; A collaborative verification module is configured to receive the equipment utilization data and trainee learning trajectory data fed back by the resource scheduling module, solve the reuse binding strategy of knowledge units and training equipment through a variational quantum algorithm, input the candidate course update plan into a genetic algorithm to generate an evolution plan, verify the consistency of the evolution plan with the real-time power grid operating data through quantum parallel computing, and feed back the verification results to the hybrid optimization module; The quantum data processing module, hybrid optimization module, resource scheduling module, long-term steady-state course framework construction module and collaborative verification module realize data interaction through the encrypted data transmission channel in the quantum communication protocol. The feedback results of the collaborative verification module drive the hybrid optimization module to dynamically adjust the frequency modulation task weight coefficient and device compatibility constraint threshold of the course topology, forming a closed-loop link of data collection, optimization modeling, resource scheduling and verification feedback.

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