Training course planning service method and system

Abstract

The application belongs to the technical field of artificial intelligence driven system design optimization, and discloses a training course planning service method and system. A quantum principal component analysis module is used to extract a correlation feature matrix of scene fluctuation parameters and resource demand, a quantum Fourier transform is used to analyze system health degree attenuation signals, and dynamic scene features are generated. A parameter tuning instruction set is constructed based on a quantum annealing algorithm to drive a genetic algorithm to optimize the topology. A quantum superposition state mapping is used to distribute micro-optimization units to a digital twin platform, and a variation quantum algorithm is used by a collaborative verification module to solve resource multiplexing strategies to form a data closed-loop feedback link. The application solves the problem of static data and dynamic demand mismatch, and improves real-time responsiveness and resource optimization efficiency in complex scenarios.

Description

Technical Field

[0001] This invention belongs to the field of artificial intelligence-driven system design optimization technology, specifically involving a dynamic parameter tuning method and system based on quantum algorithm and genetic algorithm, which is used for the design optimization and resource collaborative control of complex systems. It generates a dynamic adjustment strategy for frequency modulation parameters by integrating genetic algorithm and quantum optimization model, and simultaneously triggers training course updates to verify the effectiveness of control commands. Background Technology

[0002] Existing system design optimization methods suffer from a mismatch between static data and dynamic demands. Current optimization models, employing fixed parameter templates, struggle 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 cannot respond to dynamic changes in real time, exacerbating the risk of system performance fluctuations. Similarly, existing curriculum planning methods based on historical static data fail to integrate the dynamic correlation between renewable energy output volatility and frequency regulation capacity demand, preventing dispatchers from mastering virtual inertia parameter tuning skills for emergency frequency regulation scenarios through practical training, further increasing the risk of grid frequency fluctuations. Furthermore, existing training resource scheduling mechanisms are limited by single-timescale evaluation models, failing to dynamically adjust the ratio of theoretical teaching to practical training based on the urgency of grid frequency regulation tasks, and also failing to adapt to the progressive training needs of specialized skills such as energy storage system charging and discharging strategies, leading to redundant consumption of training resources and lagging personnel skill iteration. Furthermore, the lack of a collaborative mechanism between equipment operation training and grid energy efficiency optimization knowledge modules makes it difficult for the existing system to dynamically match theoretical instruction, simulation training, and on-site operation when dealing with complex skills training such as source-grid-load-storage coordinated control. Ultimately, this restricts the training service's responsiveness to complex grid operating conditions and resource optimization efficiency in the context of new dynamic systems. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a training course planning service method and system. It solves the problem that existing methods for planning training courses for dynamic systems based on static historical data lack the ability to integrate multi-source heterogeneous data (including real-time operating condition monitoring, new energy output fluctuation characteristics, and multi-level scheduling coupling relationship modeling), leading to a spatiotemporal mismatch between training content and dynamic control needs. Furthermore, limitations in single-timescale evaluation mechanisms and insufficient resource scheduling collaborative optimization make it difficult to achieve dynamic matching of theoretical teaching, practical training, and equipment operation modules. Ultimately, this restricts the real-time responsiveness, resource utilization, and effectiveness of training services in complex scenarios of new dynamic systems.

[0004] To solve the above-mentioned technical problems, the specific technical solution of the present invention is as follows: In a first aspect, the training course planning service method provided by the present invention includes: The system receives real-time operational data from the dynamic system, including scenario fluctuation parameters, resource gap rate, and performance deviation threshold. It generates a correlation feature matrix characterizing the relationship between new energy output fluctuation and frequency regulation command response delay through the quantum principal component analysis module. Simultaneously, it inputs the state-of-charge time-series data of the energy storage system into the quantum Fourier transform module to analyze its health decay trend signal. The quantum principal component analysis module performs dimensionality reduction processing on the real-time operational data to generate a correlation feature matrix characterizing the relationship between new energy output fluctuation and frequency regulation command response delay. Based on the aforementioned correlation feature matrix, a system parameter tuning instruction set is generated using the 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 the genetic algorithm to optimize the topology design. The parameter tuning instruction set is applied to the dynamic system controller to adjust the operating parameters of the system's core components and resource nodes; The course trigger priority sequence is generated based on the associated feature matrix and real-time frequency tuning instructions, driving the digital twin platform to allocate micro-course units, and the corresponding course modules are generated based on the system parameter tuning instruction set. Generate a long-term steady-state curriculum framework, including inputting tie-line power constraints and frequency regulation rate limits from power grid dispatching procedures into a genetic algorithm to generate a long-term steady-state frequency regulation strategy training framework; Based on the aforementioned health decay trend signal, a preventive maintenance training module is inserted into the long-term steady-state curriculum framework. The device occupancy conflict is resolved by quantum superposition state mapping, and the execution result of the instruction set is optimized according to system parameters to generate a verification course module. The micro-course unit is then allocated to the digital twin platform through quantum superposition state mapping. The system parameter tuning instruction set is updated iteratively through a collaborative optimization model, forming a closed-loop data feedback chain.

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

[0006] Furthermore, in the training course planning service method of the present invention, the receiving of real-time operating data of the dynamic system, including scenario fluctuation parameters, resource gap rate, and performance deviation threshold, generates a correlation feature matrix characterizing the relationship between new energy output fluctuations and frequency regulation command response delay through a quantum principal component analysis module. Simultaneously, the state-of-charge (SOC) time-series data of the energy storage system is input into a quantum Fourier transform module to analyze its health degradation trend signal. The dimensionality reduction processing of the real-time operating data through the quantum principal component analysis module includes: inputting the new energy output fluctuation parameters and frequency regulation demand parameters from the real-time operating data of the dynamic system into the quantum principal component analysis module to extract multi-dimensional feature vectors associated with new energy fluctuations and generate a dimensionality-reduced correlation feature matrix; inputting the SOC time-series data of the energy storage system into the quantum Fourier transform module to analyze its frequency domain periodicity characteristics and generate a trend signal characterizing the energy storage health degradation. This trend signal is encrypted and transmitted to the scheduling automation system via a quantum communication protocol, triggering a course module update command from 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 a quantum bit superposition state 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 crossover and mutation process of the initial course topology, generating an optimized design topology including virtual synchronizer inertia parameter tuning and energy storage charging and discharging collaborative control strategy.

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

[0009] Furthermore, in the training course planning service method of the present invention, the generation of a long-term steady-state course framework includes: Input the tie-line power constraint value and frequency regulation rate limit value in the power grid dispatching procedure into the genetic algorithm to generate a long-term steady-state frequency regulation strategy training framework. The insertion of a preventative maintenance training module into the long-term steady-state curriculum 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 energy storage equipment, and a preventive maintenance training module is inserted into the long-term steady-state curriculum framework.

[0010] Furthermore, the training course planning service method of the present invention, wherein the allocation of micro-course units to the digital twin platform through quantum superposition state mapping includes: The course modules generated by the system parameter tuning instruction set are mapped to the status data of the training equipment as quantum superposition state mappings, and the device occupancy conflicts in the concurrent requests of multiple students are analyzed through the quantum entanglement characteristics. Based on the device occupancy rate parameter in the quantum superposition state mapping, and combined with the course priority list generated by the quantum annealing algorithm, a conflict-free reservation sequence matching the device occupancy rate and course priority is generated. 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 rate data in the conflict-free reservation sequence and the skill improvement rate parameter in the student learning trajectory data are encoded into a quantum Hamiltonian model, and the reuse binding strategy of knowledge units and training equipment is solved by the variable quantum algorithm. The candidate course update scheme is input into the genetic algorithm to generate an evolution scheme, and the consistency between the evolution scheme and the real-time operating data of the power grid is verified by quantum parallel computing. The validated evolution scheme is fed back to the fitness evaluation module of the genetic algorithm to update the frequency modulation task weight coefficient and device compatibility constraint threshold of the system parameter tuning instruction set.

[0012] Furthermore, the training course planning service method of the present invention also includes: a collaborative verification module; The verification results of the collaborative verification module are fed back to the fitness evaluation module of the genetic algorithm, driving 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 through a quantum communication interface, forming a closed-loop process of data acquisition, optimization modeling, resource scheduling, and verification feedback.

[0013] Secondly, the training course planning service system provided by the present invention, applied to the aforementioned training course planning service method, includes: The quantum data processing module is used to perform quantum principal component analysis to reduce the dimensionality of scene fluctuation parameters, resource gap rate and performance deviation threshold in the real-time operation data of dynamic systems, and generate an encrypted correlation feature matrix; at the same time, it performs quantum Fourier transform on the charge state time series data of the energy storage system to generate a trend signal characterizing the decline of energy storage health. The hybrid optimization module is used to generate a quantum state capability map based on the correlation feature matrix output by the quantum data processing module and the student behavior data through quantum bit superposition state encoding, and input the quantum state capability map into the genetic algorithm to drive the generation of an optimized design topology; The quantum state capability map is input into a genetic algorithm to drive the generation of an optimized design topology, and the trend signal and real-time frequency modulation command are combined to generate a course trigger priority sequence through a quantum annealing algorithm; The 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 the training equipment nodes in the digital twin platform through quantum superposition state mapping, and generate a conflict-free reservation sequence by resolving equipment occupancy conflicts through quantum entanglement characteristics. The long-term steady-state curriculum framework construction module is used to input the tie-line power constraint value and frequency regulation rate limit value in the power grid dispatching procedure into the genetic algorithm to generate a long-term steady-state frequency regulation strategy training framework, and insert it into the preventive maintenance training module based on the health decay trend signal. The collaborative verification module is used to receive equipment utilization data and student learning trajectory data fed back by the resource scheduling module, solve the reuse binding strategy of knowledge units and training equipment through the variable quantum algorithm, input the candidate course update scheme into the genetic algorithm to generate the evolution scheme, verify the consistency of the evolution scheme with the real-time operating data of the power grid through quantum parallel computing, and feed the verification result back to the hybrid optimization module. The quantum data processing module, hybrid optimization module, resource scheduling module, long-term steady-state curriculum framework construction module, and collaborative verification module achieve 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 curriculum topology, forming a closed-loop link of data acquisition, optimization modeling, resource scheduling, and verification feedback.

[0014] Beneficial effects of this invention; The beneficial effects of this invention are as follows: By integrating the power output fluctuation parameters of new energy sources with frequency regulation demand data through the quantum principal component analysis module to generate a dynamic correlation feature matrix, and combining it with quantum Fourier transform to analyze the health decay trend signal, real-time feature extraction and encrypted transmission of multi-source heterogeneous data are achieved, solving the spatiotemporal mismatch problem between static historical data and dynamic frequency regulation scenarios; The quantum annealing algorithm is used to construct a system parameter tuning instruction set and coordinate it with the course topology optimized by the genetic algorithm to generate training modules adapted to different student abilities and power grid conditions; The quantum superposition state mapping and entanglement characteristics are used to analyze multi-concurrent resource conflicts, improving 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; Combined with the closed-loop feedback mechanism of the collaborative verification module, the frequency regulation task weight and equipment compatibility threshold are iteratively updated, realizing the dynamic matching of theoretical teaching, practical training and equipment operation across time scales, significantly improving the real-time responsiveness, resource utilization and composite skill training efficiency of training services in complex frequency regulation scenarios of new dynamic systems. Attached Figure Description

[0015] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on the drawings without creative effort.

[0016] Figure 1 A flowchart of a training course planning service method provided in an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. The technical solutions provided by various embodiments of this invention will be described in detail below with reference to the accompanying drawings. To better understand the objectives of this invention, it will be described in further detail below.

[0018] Firstly, please refer to Figure 1 This invention provides a training course planning service method, including: Step S101: Receive real-time operating data of the dynamic system. The real-time operating data includes scenario fluctuation parameters, resource gap rate, and performance deviation threshold. Generate a correlation feature matrix characterizing the relationship between new energy output fluctuation and frequency regulation command response delay through the quantum principal component analysis module. At the same time, input the state of charge time series data of the energy storage system into the 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 by quantum annealing algorithm, and a quantum state capability map is generated by quantum bit superposition state encoding based on the knowledge unit mastery and operation response speed in the student behavior data, and input into the genetic algorithm to optimize the topology design. Step S103: Apply the parameter tuning instruction set to the dynamic system controller to adjust the operating parameters of the system's core components and resource nodes. Step S104: Generate a course trigger priority sequence based on the associated feature matrix and the real-time frequency modulation command, and drive the digital twin platform to allocate micro-course units; Step S105: Generate a long-term steady-state curriculum framework, including inputting tie-line power constraint values ​​and frequency regulation rate limits from the power grid dispatching procedure into a genetic algorithm to generate a long-term steady-state frequency regulation strategy training framework. Step S106: Based on the health decay trend signal, insert a preventive maintenance training module into the long-term steady-state curriculum framework; Step S107: Generate a verification course module based on the execution results of the system parameter tuning instruction set, allocate micro-course units to the digital twin platform through quantum superposition state mapping, and iteratively update the system parameter tuning instruction set through a collaborative optimization model to form a data closed-loop feedback link.

[0019] The training course planning service method provided by this 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 to extract the nonlinear correlation features between new energy output fluctuations and frequency regulation demand, generating a low-dimensional correlation feature matrix; simultaneously, inputting the state-of-charge time-series data of the energy storage system into a quantum Fourier transform module to analyze the frequency domain periodicity characteristics of the charge-discharge cycle, generating a signal characterizing the health degradation trend of the energy storage device; the signal is transmitted to the scheduling system through a quantum encrypted channel to trigger a course module update instruction. Based on the new energy fluctuation amplitude and frequency regulation response delay parameter in the correlation feature matrix, a system parameter tuning instruction set is constructed using a quantum annealing algorithm. The instruction set includes the virtual synchronous machine inertia parameter tuning range and the energy storage charge-discharge rate threshold, used to adjust the dynamic response characteristics of the virtual synchronous machine in the dynamic system frequency regulation controller and the power output mode of the energy storage device in real time.

[0020] After generating the system parameter tuning instruction set, based on the knowledge unit mastery and operation response speed parameters in the correlation feature matrix and student behavior data, a genetic algorithm is used to perform crossover and mutation operations on the initial course topology. This preserves the knowledge module combinations related to frequency regulation strategy and energy storage control, generating an optimized design topology structure 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 power grid are adjusted according to the virtual synchronous machine inertia parameters. Simultaneously, the power regulation rate of the energy storage device is controlled based on the energy storage charge / discharge rate threshold, thereby improving the stability of the power grid frequency.

[0021] Based on the classification results of new energy fluctuation scenarios in the correlation feature matrix and the frequency deviation threshold in the real-time frequency regulation command, a course trigger priority sequence is constructed using the quantum annealing algorithm. This sequence prioritizes the emergency frequency regulation training module corresponding to high-fluctuation scenarios. The digital twin platform dynamically allocates frequency regulation virtual simulation micro-course units based on the priority sequence, generating real-time training tasks that match the current power grid frequency regulation needs. The micro-course parameters are updated synchronously with the response time constraints and regulation rate range in the frequency regulation command. The construction of the long-term steady-state course framework is achieved through a genetic algorithm, encoding tie-line power constraints and frequency regulation rate limits in the power grid dispatching procedures into quantum chromosomes to generate a steady-state knowledge framework covering annual frequency regulation strategy optimization and the principle of source-grid-load-storage coordinated control. A health decay trend signal is input into the quantum Markov chain model to predict the time window of a sudden drop in battery capacity or a sharp increase in internal resistance. Battery cascade utilization strategy optimization and preventative maintenance procedure training modules are inserted into the corresponding nodes of the long-term course framework, ensuring that the training content is synchronized with the actual health status evolution of the equipment.

[0022] The execution results of the system parameter tuning instruction set generate a frequency modulation strategy verification course module. This module is associated with the status of training equipment in the digital twin platform through quantum superposition state mapping. The equipment requirement parameters and equipment availability status of the course module are encoded into a multi-qubit superposition state. The quantum entanglement property is used to resolve equipment occupancy conflicts when multiple students operate concurrently, generating a conflict-free reservation sequence that matches equipment occupancy rate with course priority. The collaborative optimization model receives resource utilization rate data and student skill improvement rate parameters, and solves the reuse binding strategy between knowledge units and training equipment through a variable quantum algorithm. After the candidate course update scheme is evolved by a genetic algorithm, its consistency with the real-time operating data of the power grid is verified by quantum parallel computing. The verification result is fed back to the fitness evaluation module of the genetic algorithm, driving the iterative update of the frequency modulation task weight coefficient and equipment compatibility constraint threshold of 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 of the present invention, wherein the step of iteratively updating the system parameter tuning instruction set through a collaborative optimization model to form a data closed-loop feedback link includes: inputting the micro-course unit allocation results and student learning trajectory data into a quantum Hamiltonian model, and solving the resource reuse binding strategy through a variable quantum algorithm; inputting candidate course update schemes into a genetic algorithm to generate evolution schemes, and verifying the matching degree between the evolution schemes and real-time power grid operating data through quantum parallel computing; and feeding back the verified course update schemes to the fitness evaluation module of the genetic algorithm to iteratively update the optimized design topology.

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

[0025] Candidate course update schemes are generated based on parameters such as renewable energy penetration rate and frequency regulation capacity gap rate from the binding strategy and real-time grid operating data. These are then input into a genetic algorithm for gene recombination. The genetic algorithm uses the frequency regulation task completion rate and equipment compatibility parameters as fitness functions to perform crossover and mutation operations on the course module topology. The crossover operation recombines knowledge units from the virtual synchronous machine inertia tuning module and the energy storage collaborative control strategy module. The mutation operation dynamically adjusts the course duration allocation based on the frequency deviation threshold in the real-time frequency regulation command, generating an evolutionary scheme that includes new frequency regulation control logic and energy storage strategies. Quantum parallel computing maps the evolutionary scheme and real-time grid data to a quantum register. The consistency of the course content with frequency regulation requirements in response time, regulation accuracy, and equipment compatibility parameters is verified through quantum state amplitude comparison, and candidate schemes that meet preset matching thresholds are selected.

[0026] The validated evolutionary scheme is fed back to the fitness evaluation module of the genetic algorithm via the entangled photon pair transmission channel in the quantum communication protocol, updating the frequency regulation task weight coefficients and equipment compatibility constraint thresholds of the course topology. The fitness evaluation module performs quantum state interference analysis on the execution effect data of the frequency regulation strategy validation course module and the student skill improvement rate parameter, driving the next round of genetic evolution to generate an optimized design topology synchronized with the dynamic frequency regulation requirements of the power grid. Equipment 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 equipment resource pool, forming a closed-loop optimization link of data acquisition, feature extraction, course generation, resource allocation, and validation feedback, supporting the training system's dynamic adaptation capability to complex frequency regulation scenarios in dynamic systems.

[0027] Specifically, the training course planning service method of the present invention involves receiving real-time operating data of a dynamic system, including scenario fluctuation parameters, resource gap rate, and performance deviation thresholds. A quantum principal component analysis (QPC) module is used to generate a correlation feature matrix representing the relationship between new energy output fluctuations and frequency regulation command response delays. Simultaneously, the state-of-charge (SOC) time-series data of the energy storage system is input into a quantum Fourier transform (QFT) module to analyze its health degradation trend signal. The dimensionality reduction processing of the real-time operating data using the QFT module includes: inputting the new energy output fluctuation parameters and frequency regulation demand parameters from the real-time operating data of the dynamic system into the QFT module to extract multi-dimensional feature vectors associated with new energy fluctuations and generate a dimensionality-reduced correlation feature matrix; inputting the SOC time-series data of the energy storage system into the QFT module to analyze its frequency domain periodicity characteristics and generate a trend signal representing the degradation of energy storage health. This trend signal is encrypted and transmitted to the scheduling automation system via a quantum communication protocol, triggering a course module update command from the training platform.

[0028] The technical implementation process of receiving real-time operating data of a dynamic system and generating a correlation feature matrix and a health decay trend signal, as described in this invention, is as follows: The new energy output fluctuation parameters in the real-time operating data of the dynamic system, including the photovoltaic power generation fluctuation rate and wind power output ramp rate, are combined with the frequency deviation threshold and regulation rate constraint value in the frequency regulation demand parameters and input into a quantum principal component analysis module for feature extraction. The quantum principal component analysis module maps the multidimensional new energy output fluctuation parameters to a low-dimensional feature space through quantum state data projection and covariance matrix decomposition, eliminating noise interference and retaining the key correlation between new energy fluctuations and frequency regulation command response delay, generating a correlation feature matrix characterizing 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 (POC) time-series data of the energy storage system is analyzed in the frequency domain using a quantum Fourier transform module. The charge / discharge depth sequence is converted into a frequency domain basis function space through quantum state phase encoding, and quantum amplitude modulation captures the energy distribution characteristics of periodic charge / discharge behavior. The fundamental component in the frequency domain energy spectrum characterizes a typical charge / discharge cycle, while the amplitude and phase deviation of the harmonic components reflect the performance degradation trend caused by battery aging, generating a health degradation trend signal containing parameters such as battery capacity decay rate and internal resistance growth rate. This trend signal is encrypted using a quantum key distribution protocol and transmitted to the scheduling automation system. When the health degradation rate is detected to exceed a preset threshold, the scheduling system sends a course update command to the training platform, triggering a priority adjustment of the relevant training modules for energy storage equipment maintenance strategies.

[0030] The processing results of new energy output fluctuation parameters and frequency regulation demand parameters form a correlation feature matrix, which is synchronously transmitted to the hybrid optimization module through an encrypted data transmission channel in the quantum communication protocol. The health degradation trend signal and real-time frequency regulation commands work synergistically in the course generation model, dynamically adjusting the matching relationship between training content and equipment health status. The parallel processing mechanism of quantum principal component analysis and quantum Fourier transform reduces the computational complexity of data dimensionality reduction and feature extraction, improving the efficiency of transforming real-time operating data into course planning decisions. The new energy fluctuation scenario classification results and the time window prediction parameters of the health degradation trend signal in the correlation feature matrix jointly drive the construction of a multi-timescale course framework and the generation of dynamic resource scheduling strategies.

[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 a quantum bit superposition state 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 crossover and mutation process of the initial course topology, generating an optimized design topology including virtual synchronizer inertia parameter tuning and energy storage charging and discharging collaborative control strategy.

[0032] The technical implementation process of generating a quantum state capability map and optimizing the topology design as described in this invention is as follows: Knowledge unit mastery parameters in student behavior data, including understanding of frequency modulation principles and accuracy indicators of energy storage strategies, are converted into probability amplitude distributions through quantum bit amplitude encoding; operation response speed parameters, such as instruction execution delay and adjustment accuracy deviation, are mapped to superposition phase angles through quantum phase encoding, forming a multi-qubit superposition model. This superposition model constructs a knowledge unit association map in Hilbert space, where nodes represent frequency modulation control and energy storage charging / discharging strategy modules, and edge weights are determined by the quantum state interference results of knowledge mastery and response speed, generating a quantum state capability map reflecting the distribution characteristics of students' skill weaknesses.

[0033] The frequency modulation task completion rate parameter in the quantum state capability graph, including the frequency adjustment response speed and adjustment accuracy achievement rate in historical training, is used as the input to the fitness function of the genetic algorithm. The genetic algorithm uses the completion rate parameter and the edge weights of the knowledge unit association graph as selection pressure to perform 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 charging and discharging collaborative control practice module; the mutation operation dynamically adjusts the course duration allocation according to the quantum phase deviation, retains the knowledge combination of frequency modulation command parsing logic and energy storage strategy collaboration, and generates an optimized design topology structure that includes the dynamic adjustment rules of virtual synchronous machine parameters and the energy storage charging and discharging rate matching strategy.

[0034] The module connection sequence of the optimized topology design is synchronized with the actual workflow of power grid frequency regulation control. The theoretical course content covers the influence mechanism of virtual synchronous machine inertia parameters on the primary frequency regulation of the power grid. The practical training module simulates the operation of 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 in the quantum state capability graph, dynamically adjusts the complexity and intensity of the course modules, making the training content adapt to the individual ability evolution path of the students and improving the frequency regulation operation skills of dynamic system dispatchers in renewable energy high-fluctuation scenarios.

[0035] Specifically, the training course planning service method of the present invention includes generating a course trigger priority sequence by: mapping the new energy fluctuation characteristics in the associated feature matrix to real-time frequency modulation commands as a quantum bit energy model; solving the optimal course trigger order through a quantum annealing algorithm; generating a trigger list including the priority of the frequency modulation virtual simulation module; binding the trigger list to the real-time frequency modulation commands; driving the digital twin platform to allocate micro-course units and generate 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 as described in this invention is as follows: New energy fluctuation characteristic parameters in the associated feature matrix, including the amplitude of photovoltaic power output fluctuation and the rate of change of wind power, are mapped together with the frequency deviation threshold and regulation rate limit parameters in the real-time frequency regulation command to the quantum bit energy model. The amplitude of new energy fluctuation 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, constructing a quantum energy model characterizing the urgency of frequency regulation demand and the complexity of new energy fluctuation scenarios. The quantum annealing algorithm traverses the local optimal solutions of the energy model through the quantum tunneling effect, solves the trigger priority ranking of the frequency regulation virtual simulation module and the energy storage strategy optimization module, and generates a trigger list classified according to the intensity of new energy fluctuations and the risk of frequency regulation response delay.

[0037] Priority parameters in the trigger list are dynamically bound to the frequency regulation target value and energy storage charge / discharge rate constraint value in the real-time frequency regulation command, driving the digital twin platform to allocate micro-course units. The virtual synchronous machine inertia parameter tuning module corresponding to high-priority frequency regulation scenarios is preferentially mapped to low-latency simulation nodes, while the energy storage collaborative control strategy module allocates it to high-precision physical actuator nodes based on the charge / discharge rate threshold, generating real-time training tasks that include virtual synchronous machine dynamic response testing and bidirectional energy storage power regulation operations. The parameter settings in the training tasks are updated synchronously 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 regulation commands, changes in the fluctuation characteristics of renewable energy output trigger the dynamic reconstruction of the quantum energy model. The quantum annealing algorithm recalculates the priority of course modules based on the updated energy distribution. The digital twin platform adjusts the node mapping strategy of micro-course units according to the updated trigger list, adds a virtual inertia compensation training module for high-fluctuation scenarios, and optimizes the training content for overload protection operation of energy storage equipment. The training task execution results are transmitted back to the collaborative optimization module through the quantum communication interface, updating the frequency regulation response delay coefficient and energy storage strategy matching parameters in the correlation feature matrix, forming a dynamic adaptation mechanism between course priorities and grid frequency regulation requirements.

[0039] Specifically, the training course planning service method of the present invention includes generating a long-term steady-state course framework, which comprises: Input the tie-line power constraint value and frequency regulation rate limit value in the power grid dispatching procedure into the genetic algorithm to generate a long-term steady-state frequency regulation strategy training framework. The insertion of a preventative maintenance training module into the long-term steady-state curriculum 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 energy storage equipment, and a preventive maintenance training module is inserted into the long-term steady-state curriculum framework.

[0040] The technical implementation process of generating a long-term steady-state curriculum framework and inserting a preventive maintenance training module as described in this invention is as follows: The tie-line power constraints in the power grid dispatching regulations include the upper limit of cross-regional tie-line transmission power and the limit of inter-regional power exchange rate. 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 a genetic algorithm through quantum bit amplitude encoding. The genetic algorithm maps the parameters to a quantum superposition state mapping population, and realizes chromosome crossover mutation through quantum rotation gates to generate a long-term steady-state training framework that includes annual frequency regulation strategy optimization and the principle of source-grid-load-storage coordinated control. The curriculum 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 inertial response and frequency regulation reserve capacity allocation mechanism, and the practical training module integrates cross-regional power support simulation and frequency coordinated control operation.

[0041] The battery health degradation trend signal is analyzed using a quantum Fourier transform module. The fundamental frequency component of its charge-discharge cycle sequence characterizes a typical charge-discharge cycle, while the amplitude deviation of the harmonic components reflects the battery capacity degradation rate. A quantum Markov chain model constructs a state transition matrix using the frequency domain features and historical maintenance data. Through quantum state evolution, it predicts the time window for a sudden increase in battery internal resistance or a sharp drop in capacity, outputting a maintenance demand prediction signal. This prediction signal triggers the training platform to insert a preventative maintenance training module into the corresponding teaching node of the long-term steady-state curriculum framework. This module includes battery health status assessment procedures, optimization of tiered utilization strategies, and fault early warning and handling procedures. Course parameters are dynamically matched with the predicted maintenance time window and the actual degree of equipment degradation.

[0042] The construction of the long-term steady-state training framework and the insertion of the 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 synchronously adds equipment maintenance strategy case analysis to the corresponding theoretical course module, and loads a battery parameter simulation environment that matches the actual equipment state into the practical training module, so that the maintenance skills mastered by the trainees are synchronized with the evolution of the actual operating state of the equipment. The framework interacts with the scheduling automation system through quantum communication protocol, receives real-time updates of equipment operating status data, and dynamically adjusts the urgency level labels of course modules and the priority of practical training resource allocation.

[0043] Specifically, the training course planning service method of the present invention, wherein the allocation of micro-course units to the digital twin platform through quantum superposition state mapping includes: The course modules generated by the system parameter tuning instruction set are mapped to the status data of the training equipment as quantum superposition state mappings, and the device occupancy conflicts in the concurrent requests of multiple students are analyzed through the quantum entanglement characteristics. Based on the device occupancy rate parameter in the quantum superposition state mapping, and combined with the course priority list generated by the quantum annealing algorithm, a conflict-free reservation sequence matching the device occupancy rate and course priority is generated. The conflict-free reservation sequence triggers the training equipment resource scheduling instruction and updates the equipment status data.

[0044] The technical implementation process of micro-course unit allocation and resource conflict resolution through quantum superposition state mapping described in this invention is as follows: Course module parameters generated by the system parameter tuning instruction set, including the virtual synchronizer inertia adjustment range and energy storage charging and discharging rate threshold, are converted into multi-qubit superposition states through quantum state amplitude encoding, along with the training equipment node status data in the digital twin platform, including equipment load rate and response delay parameters. The urgency level label of the course module is mapped to the amplitude value of the qubit, and the availability state of the equipment node is characterized by quantum phase encoding, constructing a multi-dimensional superposition state model of course requirements and equipment status. Quantum entanglement characteristics are used to resolve equipment occupancy conflicts in concurrent requests from multiple students through Bell state correlation measurement, identifying potential matching relationships between high-priority frequency-tuned course modules and low-load equipment nodes, and screening candidate allocation schemes with equipment occupancy conflict probabilities lower than a preset threshold.

[0045] The device occupancy rate parameter in the quantum superposition state mapping is extracted through quantum state projection measurement, including the real-time load rate of physical device nodes and the concurrent processing capacity margin of virtual simulation nodes. Combined with the frequency modulation scenario urgency label in the course priority list generated by the quantum annealing algorithm, a joint optimization model of device resource occupancy and course demand priority is constructed. This model traverses candidate allocation schemes through quantum parallel computing, evaluates the weighted score of device utilization and course execution efficiency, and generates a conflict-free reservation sequence that matches the occupancy rate of training device nodes with course priorities. The time window allocation parameter in the reservation sequence is dynamically correlated 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 the 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 synchronizer inertia parameter tuning training task is mapped to the designated simulation node, the node load rate and task queue length parameters in the equipment status data are updated synchronously. The execution result of the resource scheduling instruction is encrypted with a quantum key and transmitted back to the collaborative optimization module to update the real-time availability information in the equipment status database, providing dynamic input for the next round of quantum superposition state mapping. The equipment status data update process is synchronized in the time domain with the course module execution progress, forming a closed-loop adaptation mechanism between the resource allocation strategy and the frequency modulation training requirements.

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

[0048] The resource allocation optimization and curriculum scheme iterative update technology described in this invention is implemented as follows: Resource utilization data in the conflict-free reservation sequence, including the load balancing rate of virtual simulation nodes and the usage frequency of physical actuators, 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 coefficients of the Hamiltonian, and the skill improvement rate parameters are transformed into off-diagonal coupling terms through quantum phase modulation, constructing a quantum energy model characterizing the knowledge unit reuse efficiency and equipment resource adaptability. The variable quantum algorithm adjusts the ground state energy distribution of the Hamiltonian through parameterized quantum circuits, solves the binding strategy between the high-frequency modulation principle course module and the low-load virtual nodes, and the matching relationship between the low-frequency equipment maintenance course module and the high-availability physical nodes, forming a collaborative optimization scheme for knowledge transfer efficiency and equipment utilization.

[0049] The candidate course update scheme is generated based on the binding strategy and the new energy fluctuation characteristics in the real-time grid operating data, and input into a genetic algorithm for evolutionary operation. The genetic algorithm uses the execution effect data of the frequency regulation strategy verification course as the fitness function, and performs gene crossover and mutation on the course module combination: the crossover operation swaps the mapping relationship between the training nodes of the virtual synchronous machine parameter tuning module and the energy storage strategy optimization module; the mutation operation dynamically adjusts the course duration weight according to the frequency deviation change rate in the real-time frequency regulation command, generating an evolutionary scheme that includes a new virtual inertia compensation strategy and energy storage overload protection logic. Quantum parallel computing maps the course parameters in the evolutionary scheme and the real-time grid data to a multi-qubit register, and verifies the consistency between the course content and frequency regulation requirements in response time constraints, regulation accuracy thresholds, and equipment compatibility parameters by comparing quantum state amplitudes, and selects candidate schemes that meet preset matching conditions.

[0050] The validated evolutionary scheme is fed back to the fitness evaluation module of the genetic algorithm via the quantum teleportation protocol, updating the task weight coefficients and device compatibility constraint thresholds in the system parameter tuning instruction set. The task weight coefficients are dynamically adjusted based on the matching degree between the course module and the real-time frequency modulation scenario, increasing the priority of the virtual synchronous machine inertia adjustment course in high-fluctuation 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 the high-precision actuator node. The updated system parameter tuning instruction set is synchronized to the digital twin platform via the quantum communication interface, driving a new round of micro-course unit allocation and training task generation, forming a closed-loop iterative mechanism of 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 results of the collaborative verification module are fed back to the fitness evaluation module of the genetic algorithm, driving 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 through a quantum communication interface, forming a closed-loop process of data acquisition, 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 degree index between the course update scheme and the real-time operating conditions of the power grid, and the evaluation parameters of the frequency regulation strategy execution effect, is transmitted to the fitness evaluation module of the genetic algorithm through a quantum entanglement channel. The module performs quantum state coherent superposition analysis on the matching degree index and the historical student skill improvement rate to generate dynamic weight coefficients of the fitness function. This drives the genetic algorithm to prioritize the retention of course module combinations adapted to the high-fluctuation frequency regulation scenario in the next round of evolution, and iteratively updates the virtual synchronous machine inertia adjustment weight and energy storage charging and discharging rate constraint threshold in the system parameter tuning instruction set.

[0053] The equipment 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 using a quantum key distribution protocol and transmitted to the data processing center of the training platform. After extracting periodic features from the data using quantum Fourier transform, it is input into the dynamic resource pool model of the hybrid optimization module to update the availability status labels and resource allocation priority parameters of the equipment nodes, providing real-time input for a new round of quantum superposition state mapping. Simultaneously, the data processing center receives grid frequency fluctuation curves and frequency regulation capacity gap rate data pushed by the scheduling automation system, reconstructing the new energy output fluctuation feature vector in the associated feature matrix.

[0054] The training platform generates a training task allocation strategy adapted to the current resource status based on updated equipment status data and optimized design topology. 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, while the energy storage strategy training module dynamically binds high-availability physical nodes based on equipment compatibility constraint thresholds. During the execution of training tasks, student operation data and equipment performance data collected are transmitted back to the collaborative verification module via a quantum teleportation channel, forming a closed-loop iterative link of data acquisition, feature extraction, course optimization, resource scheduling, and effect verification. This achieves multi-scale synchronous adaptation of training content to the dynamic frequency regulation requirements of the power grid.

[0055] Secondly, the training course planning service system provided by the present invention, applied to the aforementioned training course planning service method, includes: The quantum data processing module is used to perform quantum principal component analysis to reduce the dimensionality of scene fluctuation parameters, resource gap rate and performance deviation threshold in the real-time operation data of dynamic systems, and generate an encrypted correlation feature matrix; at the same time, it performs quantum Fourier transform on the charge state time series data of the energy storage system to generate a trend signal characterizing the decline of energy storage health. The hybrid optimization module is used to generate a quantum state capability map based on the correlation feature matrix output by the quantum data processing module and the student behavior data through quantum bit superposition state encoding, and input the quantum state capability map into a genetic algorithm to drive the generation of an optimized design topology; the quantum state capability map is input into a genetic algorithm to drive the generation of an optimized design topology, and combined with the trend signal and real-time frequency modulation command, a course trigger priority sequence is generated through a quantum annealing algorithm; The 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 the training equipment nodes in the digital twin platform through quantum superposition state mapping, and generate a conflict-free reservation sequence by resolving equipment occupancy conflicts through quantum entanglement characteristics. The long-term steady-state curriculum framework construction module is used to input the tie-line power constraint value and frequency regulation rate limit value in the power grid dispatching procedure into the genetic algorithm to generate a long-term steady-state frequency regulation strategy training framework, and insert it into the preventive maintenance training module based on the health decay trend signal. The collaborative verification module is used to receive equipment utilization data and student learning trajectory data fed back by the resource scheduling module, solve the reuse binding strategy of knowledge units and training equipment through the variable quantum algorithm, input the candidate course update scheme into the genetic algorithm to generate the evolution scheme, verify the consistency of the evolution scheme with the real-time operating data of the power grid through quantum parallel computing, and feed the verification result back to the hybrid optimization module. The quantum data processing module, hybrid optimization module, resource scheduling module, long-term steady-state curriculum framework construction module, and collaborative verification module achieve 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 curriculum topology, forming a closed-loop link of data acquisition, optimization modeling, resource scheduling, and verification feedback.

[0056] The technical implementation process of the training course planning service system described in this invention is as follows: The quantum data processing module receives the new energy output fluctuation parameters and frequency regulation capacity gap rate from the real-time operation data of the dynamic system. It performs covariance matrix decomposition on the multidimensional fluctuation characteristics through quantum principal component analysis, extracts the nonlinear correlation characteristics between 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 by the quantum Fourier transform module to determine the frequency domain periodic characteristics. The harmonic component amplitude deviation of the charge-discharge cycle depth sequence is mapped to the battery capacity decay rate parameter, generating an encrypted energy storage health trend signal transmitted to the scheduling system, triggering the course module update instruction of the training platform.

[0057] The hybrid optimization module encodes the classification results of new energy fluctuation scenarios in the associated feature matrix and the knowledge unit mastery parameters in the student behavior data using quantum states. The operation response speed is generated into a superposition state capability map through quantum phase modulation. The genetic algorithm performs crossover and mutation operations on the initial course topology based on the frequency modulation task completion rate parameter in the map. The crossover process reorganizes the knowledge units of the virtual synchronizer inertia tuning and energy storage strategy modules, and the mutation operation adjusts the course duration weights according to real-time frequency modulation commands to generate an optimized design topology covering inertia response and energy storage coordinated control. The quantum annealing algorithm combines the urgency label of frequency modulation demand with the energy storage health trend signal to construct a course trigger priority energy model and output a sequence of frequency modulation virtual simulation modules that prioritize high-fluctuation scenarios.

[0058] The resource scheduling module optimizes the design topology and priority sequence input to a multi-timescale course framework, allocating micro-course units to digital twin platform nodes through quantum superposition mapping. The urgency labels of course modules and device node availability status are encoded as multi-qubit superposition states. Quantum entanglement characteristics are used to resolve virtual simulation node occupancy conflicts in concurrent requests from multiple students, generating a conflict-free allocation scheme that matches the physical actuator node load rate with course priority. This scheme dynamically adjusts the simulation node mapping strategy of high-priority frequency modulation modules and updates the node load parameters in the device status data in real time.

[0059] The long-term steady-state curriculum framework construction module uses a genetic algorithm to handle tie-line power constraints and frequency regulation rate limits in power grid dispatching procedures. Quantum chromosome encoding generates a steady-state training framework covering annual frequency regulation strategy optimization and regional coordinated control principles. A health degradation trend signal is input into a quantum Markov chain model to predict maintenance demand windows. Battery health assessment and tiered utilization strategy optimization training modules are inserted into the corresponding teaching stages of the steady-state framework, dynamically synchronizing course parameters with the actual equipment degradation process.

[0060] The collaborative verification module receives equipment utilization data from the resource scheduling module and constructs a reusable binding Hamiltonian model between knowledge units and training equipment using a variable quantum algorithm. This model solves for the optimal matching strategy between the high-frequency theory module and low-load virtual nodes. After candidate course update schemes evolve using a genetic algorithm, quantum parallel computing verifies their consistency with real-time grid conditions. Verified schemes are fed back to the hybrid optimization module via quantum teleportation, dynamically adjusting the inertia adjustment weights and equipment compatibility thresholds in the course topology. An encrypted channel in the quantum communication protocol transmits equipment status update data and student skill evaluation results, forming a closed-loop iterative link of data acquisition, course optimization, resource allocation, and effect verification. This supports the dynamic real-time adaptation capability of dynamic system frequency modulation training.

[0061] The specific implementation of this invention is as follows: Scene fluctuation parameters, resource gap rate, and performance deviation thresholds from real-time operating data of a dynamic system are received. A quantum principal component analysis module decomposes the multidimensional fluctuation data into a covariance matrix, extracting the nonlinear correlation features between photovoltaic power output fluctuation amplitude and wind power ramp-up rate, generating a low-dimensional encrypted correlation feature matrix. The state-of-charge time-series data of the energy storage system is analyzed for frequency domain periodicity using a quantum Fourier transform module. The harmonic component amplitude deviation of the charge-discharge cycle depth sequence is mapped to the battery capacity decay rate parameter, generating an encrypted energy storage health trend signal transmitted to the scheduling system, triggering an update command for the training platform's course module. The row vectors of the correlation feature matrix represent the frequency regulation demand changes within a time window, while the column vectors correlate the coupling relationship between the new energy fluctuation amplitude, change rate, and frequency regulation capacity demand, providing dynamic input for subsequent course topology optimization.

[0062] Based on the classification results of new energy fluctuation scenarios and frequency regulation response delay parameters in the correlation feature matrix, a system parameter optimization instruction set is constructed using the quantum annealing algorithm. This generates the virtual synchronizer inertia parameter tuning range and energy storage charge / discharge rate threshold, and adjusts the dynamic response characteristics of the virtual synchronizer and the power output mode of the energy storage device in real time within the dynamic system frequency regulation controller. The knowledge unit mastery and operational response speed parameters from student behavior data are encoded using qubit amplitude and phase to generate a quantum state capability map. This map is then input into a genetic algorithm to drive the crossover and mutation operations of the initial course topology, preserving the combination of knowledge modules related to frequency regulation strategy and energy storage control, and generating an optimized design topology structure adapted to different student ability levels.

[0063] Based on the frequency deviation threshold in the real-time frequency modulation command and the fluctuation characteristics of new energy sources, a course trigger priority energy model is constructed using the quantum annealing algorithm. This model solves for the triggering order of the frequency modulation virtual simulation module and the energy storage strategy optimization module, generating a training task list prioritizing high-fluctuation scenarios. The digital twin platform dynamically allocates micro-course units to low-latency simulation nodes and high-precision physical actuator nodes based on the priority list. The frequency fluctuation curve in the virtual simulation environment is synchronized in the time domain with the monitoring data of the actual scheduling system. The training task parameters are matched in real-time with the response time constraints and adjustment rate range of the frequency modulation command.

[0064] The long-term steady-state frequency regulation strategy training framework uses a genetic algorithm to handle tie-line power constraints and frequency regulation rate limits in power grid dispatching procedures. Quantum chromosome encoding generates course modules covering annual frequency regulation strategy optimization and the principles of source-grid-load-storage coordinated control. A health degradation trend signal is input into a quantum Markov chain model to predict maintenance demand windows. Battery health assessment and tiered utilization strategy optimization training modules are inserted into the corresponding teaching stages of the long-term course framework, with course parameters dynamically synchronized with the actual equipment degradation level. When the health degradation rate exceeds a preset threshold, the training platform automatically increases the proportion of preventative maintenance case analyses, and the simulation environment loads a battery parameter model consistent with the actual equipment state.

[0065] During the allocation of micro-course units, the course requirement parameters generated by the system parameter tuning instruction set and the availability status of equipment nodes are used to construct a multi-dimensional optimization model through quantum superposition state mapping encoding. Quantum entanglement characteristics are used to analyze equipment occupancy conflicts in concurrent requests from multiple students, generating a conflict-free reservation sequence that matches equipment occupancy rate with course priority. The collaborative verification module receives equipment utilization rate data and student skill improvement rate parameters, and solves the reuse binding strategy between knowledge units and training equipment through a variable quantum algorithm. After the candidate course update scheme is evolved by a genetic algorithm, quantum parallel computing is used to verify its consistency with the real-time operating conditions of the power grid. The updated frequency regulation task weight coefficient and equipment compatibility threshold are fed back to the digital twin platform through a quantum communication protocol, forming a closed-loop iterative link of data acquisition, optimization modeling, resource allocation, and verification feedback, supporting the training system's dynamic adaptation capability to complex frequency regulation scenarios.

[0066] The technical solution of this invention solves the problems of the prior art in the following ways: First, multi-source heterogeneous data fusion and dynamic feature extraction: A quantum principal component analysis module processes scene fluctuation parameters, resource gap rates, and performance deviation thresholds in real-time operational data to extract a correlation feature matrix between renewable energy fluctuations and frequency regulation response delays. Simultaneously, a quantum Fourier transform module analyzes the state-of-charge time-series data of the energy storage system to generate a frequency domain trend signal characterizing battery health degradation. The correlation feature matrix and trend signal are encrypted and transmitted to the scheduling system via a quantum communication protocol, reflecting real-time changes in grid dynamic frequency regulation demands and equipment health status. This addresses the problem that static historical data cannot integrate renewable energy fluctuation characteristics and equipment degradation trends, eliminating 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 the quantum annealing algorithm, and the topology is optimized using a genetic algorithm to generate theoretical teaching and practical training modules adapted to different learners' ability levels. Micro-course units are allocated to the digital twin platform through quantum superposition mapping, and equipment occupancy conflicts are resolved using quantum entanglement characteristics to generate conflict-free reservation sequences. The genetic algorithm encodes the tie-line power constraints and frequency regulation rate limits into a long-term steady-state training framework. Simultaneously, preventative maintenance training modules are dynamically inserted based on energy storage health prediction signals, achieving collaborative matching between the multi-timescale course framework and equipment maintenance needs, overcoming the limitations of single-timescale evaluation mechanisms.

[0068] Third, a closed-loop feedback and iterative update mechanism: The collaborative verification module receives equipment utilization and student learning trajectory data, and solves the reuse binding strategy between knowledge units and training equipment through a variable quantum algorithm; the candidate course update scheme is verified by quantum parallel computing and then fed back to the genetic algorithm to dynamically adjust the frequency modulation task weight coefficient and equipment compatibility constraint threshold. The execution results of resource scheduling instructions are encrypted and transmitted back through a quantum communication interface, forming a closed-loop link of data acquisition, course optimization, resource allocation and verification feedback, improving the training system's real-time response capability and resource utilization efficiency in complex frequency modulation scenarios, and enhancing the effectiveness of compound skills training.

Claims

1. A training course planning service method, characterized in that, include: The system receives real-time operating data from the dynamic system, including scenario fluctuation parameters, resource gap rate, and performance deviation threshold. The quantum principal component analysis module performs dimensionality reduction on the real-time operating data to generate a correlation feature matrix of the relationship between new energy output fluctuation and frequency regulation command response delay. At the same time, the system inputs the state of charge time series data of the energy storage system into the quantum Fourier transform module to analyze its health decay trend signal. Based on the aforementioned 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 through quantum bit superposition state encoding. 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 crossover and mutation process of the initial course topology, generating an optimized design topology including virtual synchronizer inertia parameter tuning and energy storage charging and discharging collaborative control strategy. The parameter tuning instruction set is applied to the dynamic system controller to adjust the operating parameters of the system's core components and resource nodes; Based on the associated feature matrix and real-time frequency modulation instructions, a course trigger priority sequence is generated to drive the digital twin platform to allocate micro-course units; Input the tie-line power constraint value and frequency regulation rate limit value in the power grid dispatching procedure into the genetic algorithm to generate a long-term steady-state frequency regulation strategy training framework. Based on the aforementioned health decay trend signal, a preventive maintenance training module is inserted into the long-term steady-state curriculum framework. The system parameter tuning instruction set 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. The system parameter tuning instruction set is iteratively updated through a collaborative optimization model. The step of iteratively updating the system parameter tuning instruction set through a collaborative optimization model to form a data closed-loop feedback link includes: inputting the micro-course unit allocation results and student learning trajectory data into a quantum Hamiltonian model, solving the resource reuse binding strategy through a variable quantum algorithm; inputting candidate course update schemes into a genetic algorithm to generate evolution schemes, and verifying the matching degree between the evolution schemes and real-time power grid operating data through quantum parallel computing; and feeding back the verified course update schemes to the fitness evaluation module of the genetic algorithm to iteratively update the optimized design topology. The generated course trigger priority sequence includes: mapping the new energy output fluctuations and real-time frequency modulation commands in the associated feature matrix to a quantum bit energy model; solving the optimal course trigger order through the quantum annealing algorithm; generating a trigger list including the priority of the frequency modulation virtual simulation module; binding the trigger list to the real-time frequency modulation command; driving the digital twin platform to allocate micro-course units and generate training tasks that match the current frequency modulation requirements.

2. The training course planning service method according to claim 1, characterized in that, The process involves using a quantum principal component analysis module to reduce the dimensionality of real-time operating data, generating a correlation feature matrix relating new energy output fluctuations to frequency regulation command response delays. Simultaneously, the state-of-charge (SOC) time-series data of the energy storage system is input into a quantum Fourier transform module to analyze its health degradation trend signal. This includes: inputting real-time operating data of the dynamic system into the quantum principal component analysis module to extract multi-dimensional feature vectors associated with new energy fluctuations and generating a dimensionality-reduced correlation feature matrix; inputting the SOC time-series data of the energy storage system into the quantum Fourier transform module to analyze its frequency domain periodicity characteristics and generate a trend signal characterizing the degradation of energy storage health. This trend signal is encrypted and transmitted to the scheduling automation system via a quantum communication protocol, triggering a course module update command on the training platform.

3. The training course planning service method according to claim 1, characterized in that, The insertion of a preventative maintenance training module into the long-term steady-state curriculum 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 energy storage equipment, and a preventive maintenance training module is inserted into the long-term steady-state curriculum framework.

4. The training course planning service method according to claim 1, characterized in that, The method of allocating micro-course units to training device nodes in the digital twin platform via quantum superposition state mapping includes: The course modules generated by the system parameter tuning instruction set are mapped to the status data of the training equipment as quantum superposition state mappings, and the device occupancy conflicts in the concurrent requests of multiple students are analyzed through the quantum entanglement characteristics. Based on the device occupancy rate parameter in the quantum superposition state mapping, and combined with the course priority list generated by the quantum annealing algorithm, a conflict-free reservation sequence matching the device occupancy rate and course priority is generated. The conflict-free reservation sequence triggers the training equipment resource scheduling instruction and updates the equipment status data.

5. The training course planning service method according to claim 4, characterized in that, Also includes: The resource utilization rate data in the conflict-free reservation sequence and the skill improvement rate parameter in the student learning trajectory data are encoded into a quantum Hamiltonian model, and the reuse binding strategy of knowledge units and training equipment is solved by the variable quantum algorithm. The candidate course update scheme is input into the genetic algorithm to generate an evolution scheme, and the consistency between the evolution scheme and the real-time operating data of the power grid is verified by quantum parallel computing. The validated evolution scheme is fed back to the fitness evaluation module of the genetic algorithm to update the frequency modulation task weight coefficient and device compatibility constraint threshold of the system parameter tuning instruction set.

6. The training course planning service method according to claim 4, characterized in that, Also includes: Collaborative verification module; The verification results of the collaborative verification module are fed back to the fitness evaluation module of the genetic algorithm, driving 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 through a quantum communication interface, forming a closed-loop process of data acquisition, optimization modeling, resource scheduling, and verification feedback.

7. A training course planning service system, applied to the training course planning service method as described in any one of claims 1 to 6, characterized in that, include: quantum The data processing module is used to perform quantum principal component analysis to reduce the dimensionality of scene fluctuation parameters, resource gap rate and performance deviation threshold in the real-time operation data of the dynamic system, and generate an encrypted correlation feature matrix; at the same time, it performs quantum Fourier transform on the charge state time series data of the energy storage system to generate a trend signal characterizing the decline of energy storage health. The hybrid optimization module is used to generate a quantum state capability map based on the correlation feature matrix output by the quantum data processing module and student behavior data through quantum bit superposition state encoding, and input the quantum state capability map into a genetic algorithm to drive the generation of an optimized design topology; and generate a course triggering priority sequence according to the correlation feature matrix and real-time frequency modulation instructions. The 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 the training equipment nodes in the digital twin platform through quantum superposition state mapping, and generate a conflict-free reservation sequence by resolving equipment occupancy conflicts through quantum entanglement characteristics. The long-term steady-state curriculum framework construction module is used to input the tie-line power constraint value and frequency regulation rate limit value in the power grid dispatching procedure into the genetic algorithm to generate a long-term steady-state frequency regulation strategy training framework, and insert it into the preventive maintenance training module based on the health decay trend signal. The collaborative verification module is used to receive equipment utilization data and student learning trajectory data fed back by the resource scheduling module, solve the reuse binding strategy of knowledge units and training equipment through the variable quantum algorithm, input the candidate course update scheme into the genetic algorithm to generate the evolution scheme, verify the consistency of the evolution scheme with the real-time operating data of the power grid through quantum parallel computing, and feed the verification result back to the hybrid optimization module. The quantum data processing module, hybrid optimization module, resource scheduling module, long-term steady-state curriculum framework construction module, and collaborative verification module achieve 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 curriculum topology, forming a closed-loop link of data acquisition, optimization modeling, resource scheduling, and verification feedback.

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