Electrical simulation interaction method and system based on quantum prediction

Abstract

The present application relates to the technical field of virtual simulation and electrical engineering, and particularly relates to an electrical simulation interaction method and system based on quantum prediction, which comprises the following steps: generating holographic mapping data and initializing by loading three-dimensional simulation model data and electrical schematic diagram data; after capturing user interaction operation data, generating optimized path data by analyzing quantum heuristic prediction model, and adjusting model unit and electrical diagram element state in real time; simulation result data is used for state checking and feedback analysis, adjusting model parameters and optimizing path selection; fault tolerance adjustment data is generated by virtual sandbox verification, updating prediction model and holographic mapping data, and realizing self-learning optimization; the present application improves the real-time performance, response efficiency and system adaptability of interaction, and is suitable for electrical principle demonstration and simulation in education and engineering application.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of virtual simulation and electrical engineering, and particularly relates to an electrical simulation interaction method and system based on quantum prediction. BACKGROUND

[0002] Through three-dimensional simulation, users can more intuitively understand and operate electrical systems, making complex electrical principles more vivid and understandable. This interaction is particularly advantageous in teaching and engineering design, allowing learners to better integrate theory with actual equipment. However, it is not easy to achieve real-time synchronization and interaction between three-dimensional simulation models and electrical schematics.

[0003] Existing technologies typically use static mapping relationships and basic event triggering mechanisms to achieve interaction between three-dimensional models and electrical schematics. In this method, simple event listening and model activation mechanisms are used to achieve mutual mapping and state updating between three-dimensional models and electrical diagrams. However, this technology has several drawbacks due to its reliance on predefined static rules:

[0004] Due to the lack of dynamic adjustment mechanism, the existing technology is difficult to maintain real-time response under complex user operations, resulting in unstable interaction effect; using fixed model mapping relationship, it cannot cope with complex real-time data changes, causing synchronization lag between electrical schematics and three-dimensional models; unable to optimize itself according to user operation data, resulting in models unable to improve interaction effect and response speed. These defects make the existing technology perform poorly in complex operation scenarios, limiting its application effect in education and engineering. SUMMARY

[0005] To address the many problems existing in the above-mentioned prior art, the present application provides an electrical simulation interaction method and system based on quantum prediction. The present application uses a quantum heuristic prediction model to analyze user interaction data, generates real-time optimization paths, and combines a holographic mapping engine to achieve state linkage between three-dimensional simulation models and electrical schematics. At the same time, through fault-tolerant adjustment and self-learning mechanism, the response speed and accuracy of the system in complex operation scenarios are improved, thereby improving user experience and operation efficiency.

[0006] An electrical simulation interaction method based on quantum prediction, comprising the following steps:

[0007] Load three-dimensional simulation model data and electrical schematic data, generate holographic mapping data between three-dimensional simulation model data and electrical schematic data, and verify the integrity of the holographic mapping data;

[0008] Capture user interaction operations, generate user interaction data, and pass the user interaction data to a quantum heuristic prediction model to generate optimization path data for real-time simulation through the quantum heuristic prediction model;

[0009] Based on the optimized path data, real-time simulation data is generated, the states of the three-dimensional simulation model data and the electrical schematic diagram data are updated, simulation result data is generated and used for state verification;

[0010] Based on the simulation result data, feedback analysis is performed, the parameters of the quantum heuristic prediction model are adjusted, and the path selection is optimized to enhance the real-time response between the three-dimensional simulation model data and the electrical schematic diagram data;

[0011] The correctness of the operation is verified in the virtual sandbox, fault-tolerant adjustment data is generated, and the quantum heuristic prediction model and the holographic mapping data are updated using the fault-tolerant adjustment data, so that the quantum heuristic prediction model and the holographic real-time mapping engine realize self-learning optimization.

[0012] Preferably, the holographic mapping data between the three-dimensional simulation model data and the electrical schematic diagram data includes: through a dynamic multi-dimensional mapping algorithm, the relative position, state weight and logical correlation of the three-dimensional simulation model data and the electrical schematic diagram data in the three-dimensional space are calculated, and the holographic mapping data of the three-dimensional simulation model data and the electrical schematic diagram data is stored in the central data structure after each calculation.

[0013] Preferably, capturing user interaction operations and generating user interaction data is achieved by configuring multiple event listeners on the surface of the three-dimensional simulation model data and the electrical schematic diagram data, said event listeners are used to identify mouse clicks, drag and zoom operations of the user in real time, and generate timestamp and operation type information, thereby forming user interaction data.

[0014] Preferably, in the optimized path data generated by the quantum heuristic prediction model for real-time simulation, the quantum heuristic prediction model uses a dynamic multi-path adaptive prediction algorithm to parse the user interaction data into a multi-path operation tree, calculates the probability weight and priority of each path, and selects the path with the highest weight as the real-time simulation path, while storing the optimized path data in the cache module to support fast calling.

[0015] Preferably, based on the optimized path data, real-time simulation data is generated by applying the optimized path data to the three-dimensional simulation model data and the electrical schematic diagram data through the holographic real-time mapping engine, to real-time adjust the model unit position, state display and connection relationship of the electrical graph element of the three-dimensional simulation model data and the electrical schematic diagram data, and generate a real-time updated state table.

[0016] Preferably, when the simulation result data is used for state verification, real-time difference detection is performed through a global state feedback module, the simulation result data is compared with the initial path data, and feedback data and difference analysis report are generated, which are used to identify the deviation between the simulation path and the user path selection.

[0017] In the preferred embodiment, the parameters of the quantum heuristic prediction model are adjusted based on the feedback data. The weight parameters of the quantum heuristic prediction model are dynamically adjusted through a difference adjustment matrix to optimize path selection. The adjustment process includes the following formula:

[0018] W new =W old +α×(D-P)

[0019] Where W new represents the updated weight matrix; W old represents the current weight matrix; α represents the learning rate, which controls the step size of weight update; D represents the expected path data matrix; P represents the predicted path data matrix.

[0020] In the preferred embodiment, the operation verification is performed in a virtual sandbox. The virtual sandbox uses a multi-path prediction backtracking algorithm to simulate the optimization path data, generate a pre-performance data, and detect the logical consistency and potential conflicts in each path. The potential error paths are marked and complete fault tolerance adjustment data is generated.

[0021] In the preferred embodiment, the quantum heuristic prediction model and the holographic real-time mapping engine are updated by the fault tolerance adjustment data. The quantum heuristic prediction model and the holographic real-time mapping engine dynamically adjust the internal parameters using historical operation data and feedback data. The following formula is used for optimization:

[0022] θ new =θ old +β×E feedback

[0023] Where θ new represents the optimized model parameters; θ old represents the current model parameters; β represents the self-learning rate, which controls the step size of parameter update; E feedback represents the error value matrix of the feedback data, which reflects the difference between the model output and the expected result.

[0024] A system for implementing the quantum prediction-based electrical simulation interaction method, the system comprising:

[0025] A data loading module for loading three-dimensional simulation model data and electrical schematic data, and generating holographic mapping data between the three-dimensional simulation model data and the electrical schematic data, while verifying the integrity of the holographic mapping data;

[0026] An event listener module for capturing user interaction operations and generating user interaction data;

[0027] A quantum heuristic prediction module for receiving user interaction data and generating optimization path data for real-time simulation through a dynamic multi-path adaptive prediction algorithm;

[0028] a holographic real-time mapping engine for generating real-time simulation data based on optimized path data, updating the state of three-dimensional simulation model data and electrical schematic data, and generating simulation result data for state verification;

[0029] a global feedback module for feedback analysis according to the simulation result data, adjusting the parameters of the quantum heuristic prediction module, and optimizing path selection to enhance the real-time response between the three-dimensional simulation model data and the electrical schematic data;

[0030] a virtual sandbox verification module for verifying the correctness of the operation, generating fault-tolerant adjustment data, and using the fault-tolerant adjustment data to update the quantum heuristic prediction module and the holographic real-time mapping engine to achieve self-learning optimization.

[0031] Compared with the prior art, the advantages and beneficial effects of the present application are:

[0032] The present application optimizes real-time analysis and path prediction of user operation data through a quantum heuristic prediction model, achieving more efficient and accurate model response.

[0033] The present application makes the synchronous update of three-dimensional simulation model data and electrical schematic data smoother and more dynamic through a holographic real-time mapping engine.

[0034] The present application introduces a self-learning feedback mechanism, updates model parameters using fault-tolerant adjustment data, so that the system can automatically optimize according to user historical operations, improving the interaction stability and intelligence in long-term use. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 is a flowchart of the method of the present application;

[0036] Figure 2 is a schematic diagram of user interaction data capture and analysis in the present application;

[0037] Figure 3 is a schematic diagram of fault-tolerant adjustment data generation and model update in the present application;

[0038] Figure 4 is a structural block diagram of the system of the present application. DETAILED DESCRIPTION

[0039] Embodiments of the present disclosure will be described below with reference to the drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. However, it will be apparent to one skilled in the art that one or more embodiments can be practiced without these specific details. In addition, in the following description, descriptions of well-known structures and techniques have been omitted to avoid unnecessarily obscuring the concepts of the present disclosure.

[0040] The terminology used herein is for the purpose of describing specific embodiments only and is not intended to limit the scope of the present disclosure. The terms "comprising," "including," and the like as used herein specify the presence of stated features, steps, operations, and / or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.

[0041] All terms used herein, including technical and scientific terms, have the meanings commonly understood by one of ordinary skill in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted in a manner consistent with the context of the present specification, and should not be interpreted in an idealized or overly formal manner.

[0042] As shown in Figure 1 A quantum prediction-based electrical simulation interaction method includes the following steps:

[0043] Load three-dimensional simulation model data and electrical schematic data, generate holographic mapping data between the three-dimensional simulation model data and the electrical schematic data, and verify the integrity of the holographic mapping data;

[0044] Preferably, generating holographic mapping data between the three-dimensional simulation model data and the electrical schematic data includes: calculating the relative position, state weight, and logical correlation of the three-dimensional simulation model data and the electrical schematic data in the three-dimensional space by a dynamic multidimensional mapping algorithm, and storing the holographic mapping data of the three-dimensional simulation model data and the electrical schematic data into a central data structure after each calculation.

[0045] The core of generating holographic mapping data is to use a dynamic multidimensional mapping algorithm. This algorithm matches and correlates each unit in the model and electrical graph element by analyzing the geometric structure and logical relationship of the three-dimensional simulation model data and the electrical schematic data in the three-dimensional space. The input of the algorithm is the three-dimensional simulation model data (including the spatial coordinates and physical properties of the model) and the electrical schematic data (including the connection relationship and electrical characteristics of the electrical elements). By establishing a multidimensional vector matrix, the algorithm can accurately calculate the relative position and state weight between these data.

[0046] The calculation expression is as follows: The following formula is used in the algorithm to calculate the state weight between the three-dimensional simulation model data and the electrical schematic data:

[0047]

[0048] where W ij represents the state weight of model unit i and electrical diagram unit j; x i represents the feature vector of three-dimensional simulation model unit; y j represents the feature vector of electrical diagram unit; b is the bias term, used to adjust the calculation result of state weight.

[0049] This formula is based on a logistic regression model, ensuring that the state weight is between [0, 1], representing the degree of association between the two. A higher W ij value indicates a strong association between the model unit and the electrical diagram unit. The state weight is stored in the holographic mapping data structure for use during user interaction and subsequent simulation.

[0050] After each calculation is completed, the system stores the generated holographic mapping data into a central data structure to ensure that it can be quickly called in real-time use. The central data structure uses efficient multi-dimensional indexing techniques, such as R-tree-based spatial indexing, to store and retrieve holographic mapping data. Whenever a user performs an operation, such as moving or modifying a certain three-dimensional model unit, the central data structure updates the mapping data in real time to reflect the latest state.

[0051] This implementation ensures synchronization and state consistency between the three-dimensional simulation model and the electrical schematic data. Through the dynamic generation and storage of holographic mapping data, the system can provide immediate feedback and path adjustment during user interaction, improving the response speed and accuracy of the overall interaction. In addition, the dynamic multi-dimensional mapping algorithm can effectively identify and calculate the spatial and logical relationships between complex model units and electrical diagram units during user operations, thereby supporting efficient path planning and simulation.

[0052] In one embodiment, assuming that a user selects and drags a three-dimensional model unit in the simulation environment, this operation triggers the event listener to capture user interaction data and updates the mapping state of the unit and related electrical diagram units in real time through the holographic real-time mapping engine. The update of mapping data is based on the dynamic multi-dimensional mapping algorithm to recalculate the state weight, and the new state is synchronized in the central data structure. This way, when the model state changes, the display of the electrical schematic can also be adjusted synchronously, and the user can intuitively see the real-time changes of the simulation model and the electrical system.

[0053] Capture user interaction operations, generate user interaction data, and pass the user interaction data to a quantum heuristic prediction model to generate optimized path data for real-time simulation through the quantum heuristic prediction model;

[0054] The process of capturing user interaction involves setting up event listeners on the interface of three-dimensional simulation model data and electrical schematic data. Event listeners generate user interaction data by monitoring user operations on the interface in real time, such as clicks, drags, rotations, etc. This data includes detailed information such as operation type, operation target, operation location, and timestamp. This user interaction data is used to represent specific interaction behaviors in the system and provides basic input for subsequent analysis and calculation.

[0055] After the user interaction data is generated, it is passed to the quantum heuristic prediction model for path analysis and optimization. The quantum heuristic prediction model simulates the characteristics of quantum state superposition and parallel computing to analyze multiple paths of user interaction data to predict and select the optimal operation path. The model uses quantum bit states to represent multiple path possibilities and calculates the priority and probability of each path through probability amplitude.

[0056] In specific implementations, the calculation process of the quantum heuristic prediction model involves the following core expressions:

[0057]

[0058] where P opt represents the maximum probability of the selected path; ψ i represents the probability amplitude of the quantum state of path i; f(i) represents the characteristic function of path i, which is used to calculate the priority of the path; n is the total number of paths.

[0059] Through the above formula, the quantum heuristic prediction model can analyze and select the path with the highest probability to generate optimized path data for real-time simulation. This calculation process is based on the characteristics of quantum superposition and interference, allowing multi-path analysis to be completed in a short time, greatly improving the calculation efficiency.

[0060] The generated optimized path data provides detailed information about the optimal path in the user interaction process, including path nodes, operation weights, and priorities. This data is used for real-time simulation and model updating to ensure that the system response after user operation meets expectations and can quickly feedback. Through the introduction of the quantum heuristic prediction model, the invention realizes multi-path parallel computing and real-time analysis, overcoming the computational bottleneck of traditional single-path prediction methods and improving the efficiency and accuracy of path analysis.

[0061] In one embodiment, it is assumed that the user drags a three-dimensional model unit in the interface to adjust its position. The event listener captures this operation in real time and generates user interaction data, including the operation type (drag), the target object (the ID of the three-dimensional model unit), the operation position (new coordinates), and the timestamp. After receiving this data, the quantum heuristic prediction model starts parallel computing the user's possible paths, calculates the priority of each path in terms of probability amplitude, and selects the optimal path to generate optimization path data. The optimization path data is then used for real-time simulation update, so that the system responds to user operations in real time, and the model position and the connection relationship of the corresponding components in the electrical schematic diagram are updated synchronously, achieving the effect of what you see is what you get.

[0062] As Figure 2 shown, preferably, capturing user interaction operations and generating user interaction data is achieved by configuring multiple event listeners on the surfaces of three-dimensional simulation model data and electrical schematic diagram data. The event listeners are used to identify user mouse clicks, drags, and zoom operations in real time and generate timestamp and operation type information, thereby forming user interaction data.

[0063] An event listener is a mechanism in a software interface for capturing user input. In order to enable instant feedback of user interaction to three-dimensional simulation model data and electrical schematic diagram data, the system configures event listeners on the visual surfaces of both. These listeners capture operations in real time by detecting signals from input devices such as mice or touchpads. The operation types captured by the listeners include clicks, drags, and zooms, and the user interaction data generated by each operation includes a timestamp, operation type, and position coordinates.

[0064] The generation process of user interaction data is as follows:

[0065] (1) Listener configuration: Set up listeners on the surfaces of three-dimensional simulation model data and electrical schematic diagram data. The listeners are distributed in key interaction areas, such as the boundaries of model units and the connection nodes of electrical components.

[0066] (2) Real-time identification and capture: When the user performs an operation in the interface (such as clicking or dragging a model unit), the listener identifies the operation type and position and immediately records the timestamp of the operation.

[0067] (3) Data generation: The system organizes the captured information into user interaction data, with the following data format:

[0068] User interaction data = {operation type, timestamp, position coordinates, object ID}

[0069] Among them, operation type: represents the specific operation performed by the user (such as "click", "drag"); timestamp: records the specific time of the operation, used for operation sorting and path analysis; position coordinates: represent the three-dimensional spatial position where the operation occurs; object ID: identifies the target object of the user's operation (such as a specific model unit or electrical element).

[0070] By capturing user interaction data in real time, the system can quickly convert the user's input into a processable data structure and use it for further path prediction and real-time simulation. This process ensures that the user's operation on the three-dimensional simulation model data and electrical schematic data is synchronized and accurate. By recording the timestamp and operation type, the system can track the sequence and frequency of user operations, providing a basis for subsequent path optimization and feedback analysis.

[0071] In one embodiment, when the user drags a certain electrical element in the three-dimensional simulation interface, the event listener captures this drag operation and records it as user interaction data in real time, in the format:

[0072] {"drag", 2024-05-09 14:23:45, (15.2, 34.5, 10.7), object ID: 1023}

[0073] This data is then passed to the quantum heuristic prediction model for analysis to determine the optimal prediction of the user interaction path, allowing the system to update the state of the three-dimensional simulation model and electrical schematic in real time.

[0074] To improve the response speed and data processing capacity of the listener, the system uses an efficient event queue mechanism to store user operation inputs in chronological order, ensuring the orderliness of data processing. The listener is implemented in a non-blocking structure to ensure smooth response of the system under high-frequency operations. Each time the listener captures a new interaction operation, the system will call the data parsing module in real time to parse the input data and store it in the central data structure for subsequent processing.

[0075] Preferably, the optimized path data for real-time simulation is generated by a quantum heuristic prediction model that uses a dynamic multi-path adaptive prediction algorithm to parse user interaction data into a multi-path operation tree, calculates the probability weight and priority of each path, and selects the path with the highest weight as the real-time simulation path. At the same time, the optimized path data is stored in the cache module to support fast calling.

[0076] The quantum-inspired prediction model utilizes a dynamic multi-path adaptive prediction algorithm to parse user interaction data into a multi-path operation tree. User interaction data includes user operation type, position, timestamp, and target object identification, which are parsed into a multi-path operation tree after inputting the quantum-inspired prediction model. This tree structure is used to represent all possible paths for the user under the current operation, where each path consists of multiple nodes, each representing an operation step or model state.

[0077] After parsing user interaction data and generating the operation tree, the quantum-inspired prediction model begins to calculate the probability weight and priority of each path. This process uses the superposition and parallelism of quantum computing to evaluate path weights by calculating the following formula:

[0078]

[0079] where W k represents the total probability weight of path k; ψ i,k represents the quantum state probability amplitude of the i-th node in path k; R(i,k) represents the priority coefficient of the i-th node in path k; N represents the total number of nodes in the path.

[0080] The model evaluates the priority of each path by calculating its weight. Paths with higher weights are considered better paths, as they represent higher likelihood and priority, consistent with the user's current operation expectations. The path with the highest weight is selected as the optimal path for real-time simulation.

[0081] After selecting the optimal path, the system stores the path data in the cache module for subsequent simulation and operation calls. The cache module uses an efficient data indexing mechanism (such as a hash table) to ensure that path data can be quickly retrieved and applied when called.

[0082] In this way, the quantum-inspired prediction model not only improves the system's response speed to user input, but also enables the system to perform real-time simulation updates during user operation. This real-time path selection and storage mechanism ensures the synchronization and efficiency of interaction between the three-dimensional simulation model and the electrical schematic diagram.

[0083] In one embodiment, assume that the user continuously adjusts the positions of multiple model units in the three-dimensional simulation model. The event listener captures these operations and generates user interaction data, such as:

[0084] {"drag", 2024-05-09 15:45:30, (20.3, 42.1, 12.0), object ID: 1056}

[0085] The user interaction data input quantum heuristic prediction model analyzes the data and generates a multi-path operation tree, and then calculates the weight of each path through a dynamic multi-path adaptive prediction algorithm. After the calculation is completed, the model selects the path with the highest weight as the optimized path and stores it in the cache module for the simulation module to call for model updating. Finally, the system can update the state and connection of the relevant components in the electrical schematic diagram in real time when the user adjusts the model unit.

[0086] Based on the optimized path data, real-time simulation data is generated to update the state of the three-dimensional simulation model data and the electrical schematic diagram data, generate simulation result data and use it for state verification;

[0087] Once the optimized path data is generated, it is passed to the holographic real-time mapping engine for real-time simulation. The holographic real-time mapping engine uses the optimized path data to drive the dynamic update of the three-dimensional simulation model and the electrical schematic diagram data, keeping them synchronized in user interaction.

[0088] In specific implementation, the system updates the position, rotation, scaling, etc. of the three-dimensional model unit based on the optimized path data, and adjusts the connection relationship of the electrical diagram element associated with it. This process realizes real-time simulation data update through the following formula:

[0089]

[0090] Where: M new represents the updated three-dimensional simulation model data matrix; M old represents the current three-dimensional simulation model data matrix; δ i represents the state change increment of the path node; P i represents the weight coefficient of the path node; k is the total number of path nodes.

[0091] This algorithm traverses the optimized path data node by node, and updates the model state through the accumulation of state increments and weights. After each model data update, the state of the electrical schematic diagram data is also adjusted synchronously, including the activation state of the electrical component, the connection path, and the transmission change of the electrical signal. The system ensures that simulation result data is generated in real time after each update.

[0092] The generation of simulation result data not only contains the final state of the model and electrical diagram element, but also contains the intermediate state in the process, which is used for further analysis and state verification. The state verification process verifies the consistency by comparing the simulation result data with the expected state data. The verification formula is as follows:

[0093]

[0094] Where, C is the verification error; E j is the jth element of the expected state data; Rj is the jth element of the simulation result data; n is the total number of data elements.

[0095] When the error C is controlled within an acceptable range, the simulation is considered valid, otherwise the system will trigger a feedback mechanism for correction.

[0096] By generating simulation data in real time and updating three-dimensional simulation model data and electrical schematic data, the present application realizes an efficient mechanism for keeping the model and graph state synchronized during user interaction. Simulation result data is used to ensure that the model's operation meets expectations and performs consistency checks, providing accurate feedback and predictable operation responses for users.

[0097] In one embodiment, assuming that a user drags a certain electrical element in the simulation environment, the system determines the impact of this operation by optimizing path data and updates the position and angle of the three-dimensional model in the holographic real-time mapping engine. In real-time feedback of this process, the connection path related to the element in the electrical schematic is changed synchronously. The generated simulation result data is compared with the expected state data to ensure the accuracy of the simulation operation. If the error is high in the check, the system will adjust the feedback to ensure that the final result is consistent with the expected state.

[0098] Preferably, real-time simulation data is generated based on optimized path data, which is applied to three-dimensional simulation model data and electrical schematic data by the holographic real-time mapping engine to adjust the model element position, state display, and electrical graph element connection relationship of three-dimensional simulation model data and electrical schematic data in real time, and generate a real-time updated state table.

[0099] The core function of the holographic real-time mapping engine is to convert the optimized path data generated by user operations into actual model adjustments and electrical graph changes. The optimized path data contains the steps, path nodes, weights, and related parameters of user interaction operations. The engine uses this data to update the state of three-dimensional simulation model data and electrical schematic data in real time.

[0100] The implementation process specifically includes:

[0101] (1) Optimized path data analysis: The holographic real-time mapping engine receives optimized path data and analyzes the operation type, position, and weight of each path node. The analysis process scans the path data and extracts the necessary information through an algorithm.

[0102] (2) Model element adjustment: The engine adjusts the element position and state display of the three-dimensional simulation model data in real time according to the analyzed path nodes through the following formula:

[0103]

[0104] where M updatedrepresents the updated three-dimensional simulation model data matrix; M current represents the current model data matrix; δ i represents the state change vector of path node i; W i represents the weight of path node i; k is the total number of path nodes.

[0105] At each adjustment, the engine modifies the position and state display of model units in real-time based on the weight and state change vector of the path, ensuring that the model aligns with the user's intended interaction.

[0106] (3) Electrical diagram element connection update: For electrical schematic data, the engine synchronously updates the electrical component connections corresponding to the three-dimensional simulation model. This process is achieved by calculating the association and state change of each electrical diagram element. The adjustment of connection relationships follows the following rules:

[0107]

[0108] where C new represents the updated connection relationship matrix; C old represents the current connection relationship matrix; T(δ i , W i ) is the connection change function combined with the state change and weight of the path node.

[0109] (4) State table generation: The holographic real-time mapping engine generates a real-time state table after each adjustment, recording the latest state of model units and electrical diagram elements, including position, rotation, connection state, and other detailed information. The state table is formatted as structured data for subsequent verification and system feedback.

[0110] In this way, the system can adjust the data of the three-dimensional simulation model and the electrical schematic diagram in real-time according to the user's operation, keeping them highly synchronized. The generated real-time state table not only provides immediate feedback to the user, but also serves as an internal record for the system, monitoring and verifying the accuracy of the interaction process.

[0111] In one embodiment, the user scales a certain model unit in the three-dimensional simulation interface. The holographic real-time mapping engine adjusts the size of the model unit according to the optimized path data, and synchronously adjusts the display state and connection relationship of the corresponding element in the electrical schematic diagram in real-time. For example, when the user scales down the model unit, the connection path of the related electrical component is adjusted accordingly, ensuring that the state of the model and the electrical diagram element remains consistent. The real-time state table generated during this process records the updated size, connection information, and node weight, providing a basis for subsequent system analysis and verification.

[0112] Preferably, when the simulation result data is used for state verification, real-time difference detection is performed by the global state feedback module, which compares the simulation result data with the initial path data and generates feedback data and a difference analysis report to identify deviations between the simulation path and the user path selection.

[0113] Real-time difference detection is performed by the global state feedback module, which compares the simulation result data with the initial path data to identify deviations between the simulation path and the user path selection and generates feedback data and a difference analysis report to guide subsequent adjustments. The global state feedback module receives simulation result data R(t) and initial path data P(t), where t represents data points in the time series. The module uses the difference detection formula:

[0114] D(t) = |R(t) - P(t)|

[0115] The absolute difference value between the simulation result and the initial path is calculated. In this formula, D(t) represents the difference value at time point t, R(t) is the simulation result data matrix, and P(t) is the initial path data matrix. Based on the calculated difference data D(t), the global state feedback module generates feedback data indicating the deviation at each time point and further analyzes the trend and cumulative effect of the deviation to generate a difference analysis report to help identify the specific reasons for the deviation of the simulation path from the user path selection.

[0116] Real-time difference detection is performed through parallel computing technology to ensure that the comparison operation at each time point can be quickly completed, thereby accelerating the generation of feedback data. The feedback data is organized in a structured form, including timestamps, difference values D(t), deviation markers, and recommended adjustment schemes, to facilitate subsequent system adjustments and path optimization. The global state feedback module uses a threshold value ∈ to determine the severity of the deviation, and if D(t) > ∈ at a certain time point, it is considered a significant deviation, and the module will mark this difference and make adjustment recommendations in the report.

[0117] Through this difference detection and feedback mechanism, the system can quickly identify deviations between the simulation path and the user path, providing immediate feedback to ensure the accuracy of the interaction and the synchronization of the model and the electrical diagram element states. In implementation, for example, when the user drags and rotates a model element in the simulation interface, the module compares the simulation result with the user's original operation path, and if a deviation exceeding the threshold value ∈ is detected, the system will indicate the deviation point in the feedback and provide adjustment suggestions, such as recalculating the path or adjusting the state of the model element, to ensure that the final simulation effect is consistent with the user's expectations. Through this process, the system significantly improves response speed, operation accuracy, and user experience, ensuring consistency and efficiency of the three-dimensional simulation model and electrical schematic diagram data in dynamic interaction.

[0118] Based on the feedback analysis of simulation result data, the parameters of the quantum heuristic prediction model are adjusted, and the path selection is optimized to enhance the real-time response between the three-dimensional simulation model data and the electrical schematic diagram data;

[0119] The process of feedback analysis begins with the comparison and analysis of simulation result data. The global state feedback module compares the simulation result data R(t) with the initial path data P(t) and calculates the deviation value D(t) at each time point. If the deviation exceeds the preset threshold ∈, the system will further analyze these deviation points to determine the specific error source and characteristics.

[0120] After identifying the deviation, the system improves the accuracy of the predicted path by adjusting the parameters of the quantum heuristic prediction model. The core of parameter adjustment is to update the model's weights and path selection mechanism using feedback data E(t). The adjustment formula is as follows:

[0121]

[0122] Where θ new is the updated model parameter; θ old is the current model parameter; η is the learning rate, controlling the step size of parameter update; L(E(t)) is the loss function based on feedback data, measuring the difference between the predicted path and the actual path; is the gradient of the loss function with respect to the model parameter.

[0123] This process realizes parameter optimization through gradient descent method, and uses feedback data to drive the model to self-correct, so that it can better adapt to user interaction and system state in subsequent prediction.

[0124] After updating the parameters of the quantum heuristic prediction model, the system re-optimizes the path selection to improve the real-time performance and response speed of the simulation. The optimized model recalculates the weights and priorities of the paths to ensure that the system can select more accurate paths when the user performs the next interaction. At this time, the weight calculation formula of the path is:

[0125]

[0126] Where W opt is the optimized path weight; ψ i is the probability amplitude of path node i; θ i is the updated weight of node i; n is the total number of path nodes.

[0127] By updating the weights and path selection, the system can respond more quickly to user operations and adjust the simulation state in real time, maintaining the synchronization of the three-dimensional model and electrical diagram elements.

[0128] This feedback analysis and model optimization method ensures that the quantum heuristic prediction model is adjusted and improved after each user interaction, making the system more accurate and efficient in predicting and simulating path selection. By combining feedback data for parameter optimization, the system can gradually reduce the deviation between predicted paths and actual paths, improving simulation accuracy and interactive experience.

[0129] In one embodiment, when the user performs a complex rotation operation on the three-dimensional simulation model, the initial simulation result may deviate from the path, causing the rotation state of the model to be inconsistent with the user's expectations. The global state feedback module transmits this deviation data D(t) to the prediction model, triggering the parameter adjustment process. The adjusted model will better predict the user's path in subsequent rotation simulation, reducing errors and improving the system's real-time response and accuracy.

[0130] Preferably, in adjusting the parameters of the quantum heuristic prediction model based on feedback data, the weight parameters of the quantum heuristic prediction model are dynamically adjusted through a difference adjustment matrix to optimize path selection, and the adjusting step comprises the following formula:

[0131] W new = W old + α × (D - P)

[0132] wherein W new represents the updated weight matrix; W old represents the current weight matrix; α represents the learning rate, used to control the step size of weight update; D represents the expected path data matrix; P represents the predicted path data matrix.

[0133] Adjusting the parameters of the quantum heuristic prediction model requires comparing simulation result data and user path data to generate feedback data. Feedback data is used to quantify the difference between model prediction and user path, and guide the adjustment of model parameters. The difference adjustment matrix plays a key role in this process, by dynamically updating the weight matrix of the model, so that the model can be closer to the user's actual operation intention in subsequent path prediction.

[0134] The adjustment process realizes element-by-element update through the above formula, i.e. for each element W new,ij in the matrix, the current weight is adjusted according to the difference between the expected path data and the predicted path data. The learning rate α controls the step size of the update, ensuring the stability of the model adjustment, avoiding the model parameters changing dramatically due to too large step size, affecting the overall stability.

[0135] Through this adjustment process, the model can dynamically optimize its parameters after each user operation, making future path prediction more consistent with user operation. The adjusted weight matrix W newto update the state of the quantum heuristic prediction model, enabling it to recalculate the weights and priorities of the paths based on the optimized weight matrix during the next simulation path selection.

[0136] This dynamic adjustment mechanism ensures that the model has adaptive learning capabilities, allowing it to continuously improve prediction accuracy. As the system is used over time, the model continuously adjusts and optimizes parameters, gradually improving the response speed and accuracy of three-dimensional simulation model data and electrical schematic data in user interactions.

[0137] In one embodiment, when the user adjusts the position of electrical components in the three-dimensional simulation model, the system generates simulation result data and compares it with the user's expected path data. If a significant difference is detected between the predicted path and the expected path (i.e., the value of D-P is large), the system adjusts the parameters of the model by calculating a new weight matrix W new to ensure that in subsequent operations, the quantum heuristic prediction model can better identify the user's operation intention, select a path that better meets the user's expectations, and reduce future deviations.

[0138] As Figure 3 shown, the correctness of the operation is verified in the virtual sandbox, fault-tolerant adjustment data is generated, and the quantum heuristic prediction model and holographic mapping data are updated using the fault-tolerant adjustment data, allowing the quantum heuristic prediction model and the holographic real-time mapping engine to achieve self-learning optimization.

[0139] The virtual sandbox is a simulated environment used to test and verify the correctness of user interactions without affecting the actual simulation or operation. The system loads user interaction data and simulation result data into the virtual sandbox, simulates the actual effects of user operations, and compares the results with the expected state. If an operation deviation is detected, the system generates fault-tolerant adjustment data for self-learning adjustment.

[0140] The specific implementation process is as follows:

[0141] (1) Operation verification: The virtual sandbox receives user interaction data and simulation path data and simulates the entire operation process. This process reproduces the effects of user operations on the three-dimensional simulation model and electrical schematic data to observe their effects.

[0142] (2) Fault-tolerant adjustment data generation: The virtual sandbox has a fault-tolerant analysis module that calculates the deviation between the user's actual operation and the ideal result, generating fault-tolerant adjustment data. The generation of fault-tolerant data is achieved through the following formula:

[0143] E corr (t)=(R(t)-U(t))·β

[0144] where E corr(t) is the fault-tolerant adjustment data at time t; R(t) is the simulation result data matrix; U(t) is the user interaction data matrix; β is the adjustment coefficient, used to control the influence range of the fault-tolerant data.

[0145] The fault-tolerant data is compared with the expected path through multiple operation verifications to identify the deviation area and adjustment requirements in the operation.

[0146] (3) Model and engine update: The fault-tolerant adjustment data is used to update the quantum heuristic prediction model and the holographic real-time mapping engine. For the quantum heuristic prediction model, the adjustment data is applied in the update formula of the model parameters:

[0147] θ new = θ old + γ × E corr (t)

[0148] where θ new represents the updated model parameters; θ old is the current model parameter γ is the learning step, used to control the update amplitude.

[0149] The holographic real-time mapping engine adjusts the holographic mapping data according to the fault-tolerant data, so that the model and the electrical schematic data can more accurately map the user's intention and operation results in future operations.

[0150] By applying the fault-tolerant adjustment data to the quantum heuristic prediction model and the holographic real-time mapping engine, the system realizes self-learning optimization. This optimization process ensures that the system can learn from the user's historical operations to improve the accuracy of the prediction model and the real-time response of the response. The update of the holographic real-time mapping engine ensures that the three-dimensional simulation model and the electrical schematic data can be adjusted in real time in future user operations, reducing operation deviations and improving the operation consistency of the system.

[0151] In one embodiment, the user performs complex model rotation and scaling operations in the simulation environment, and the virtual sandbox simulates user input and simulation response, detects that certain rotation angles in the operation deviate from the expected trajectory. The system generates fault-tolerant adjustment data E corr (t) to quantify the deviation, and controls the weight of the correction through the adjustment coefficient β. The fault-tolerant data is passed to the quantum heuristic prediction model to adjust its parameters and optimize future path prediction. The holographic real-time mapping engine is updated synchronously to ensure that the path selection in subsequent operations is more accurate.

[0152] Preferably, in the operation verification in the virtual sandbox, the virtual sandbox uses a multi-path prediction backtracking algorithm to simulate the optimized path data, generates a pre-performance data, and detects the logical consistency and potential conflicts in each path, marks the potential error path and generates complete fault-tolerant adjustment data.

[0153] As an isolated testing environment, the virtual sandbox simulates the optimized path data through the multi-path pre-backtracking algorithm to verify the logic and reliability of operations. The core of the multi-path pre-backtracking algorithm lies in dynamic calculation and detection of each possible path of user interaction, ensuring that the path is logically conflict-free and consistent with the expected operation behavior.

[0154] The specific implementation of the multi-path pre-backtracking algorithm includes:

[0155] (1) Path simulation and pre-performance: The virtual sandbox loads the optimized path data and decomposes it into multiple branch paths, each representing a different result that user operations may lead to. Through recursive calculation and state backtracking, the algorithm performs complete calculation on each path to generate pre-performance data P sim .

[0156] (2) Path logic consistency detection: For the generated pre-performance data, the system performs step-by-step logic checks on each path node. The logic consistency detection uses the following discriminant formula:

[0157]

[0158] Where C logic (n) is the logic consistency marker of path node n; Path(n) represents node n in the path; 1 indicates that the path is logically consistent, and 0 indicates that the path has a logical conflict.

[0159] (3) Conflict detection and marking: If a logical conflict is detected in the path, i.e., C logic (n) = 0, the system will mark the path as a potential error path and record the specific location and type of the conflict. Conflict detection analysis includes path node overlap, state change conflict, data consistency issues, etc.

[0160] (4) Fault-tolerant adjustment data generation: Based on the marked potential error path, the virtual sandbox generates complete fault-tolerant adjustment data E corr . This data is used to quantify path deviation and adjustment requirements, and the formula is:

[0161]

[0162] Where E corr (n) is the fault-tolerant adjustment data of path node n; δ i is the deviation value of path node i; μ i is the weight adjustment factor of path node i; and m is the total number of path nodes.

[0163] This fault-tolerant adjustment data is then used to update the quantum heuristic prediction model and the holographic real-time mapping engine, enhancing the self-learning capabilities of the model and engine, thereby better adapting to user interactions and reducing potential biases in future operations.

[0164] Through the multi-path pre-rehearsal algorithm, the virtual sandbox can perform in-depth verification after user operations, generating rehearsal data and marking logical conflicts and potential error paths. This process ensures logical consistency between user operations and system responses, improving the stability and accuracy of the system in future interactions. The generated fault-tolerant adjustment data can be used to dynamically update the prediction model and holographic mapping engine, enabling the system to self-learn and gradually improve.

[0165] In one embodiment, assuming the user performs complex electrical component dragging and connection operations in the simulation interface, the system will decompose and rehearse the user operation path through the virtual sandbox. The algorithm calculates the logical consistency of each path and marks a path as a potential error path when a connection conflict is detected. The virtual sandbox generates corresponding fault-tolerant adjustment data E corr (n) and uses this data to adjust the weight parameters of the quantum heuristic prediction model, optimizing the model's path selection ability and reducing the bias of future similar operations.

[0166] Preferably, the quantum heuristic prediction model and the holographic real-time mapping engine are updated through fault-tolerant adjustment data, and the quantum heuristic prediction model and the holographic real-time mapping engine dynamically adjust internal parameters using historical operation data and feedback data, and are optimized using the following formula:

[0167] θ new = θ old + β × E feedback

[0168] where θ new represents the optimized model parameters; θ old represents the current model parameters; β represents the self-learning rate, used to control the step size of parameter updating; E feedback represents the error value matrix of feedback data, reflecting the difference between model output and expected results.

[0169] The generation of fault-tolerant adjustment data is based on the difference detection and analysis of user interaction operations, and these data reflect the error between simulation paths and expected paths. The quantum heuristic prediction model and the holographic real-time mapping engine use these data to adjust their internal parameters through the self-learning process.

[0170] The optimization process specifically includes:

[0171] (1) Historical Data Integration: The quantum heuristic prediction model and the holographic real-time mapping engine record user interaction data and simulation path data after each operation, storing them as historical operation data. By combining these historical data, the system analyzes the patterns of user behavior and simulation errors as inputs for optimization.

[0172] (2) Error Feedback Data Generation: The system generates error feedback data E feedback by comparing the simulation results with the user's intended path. This matrix reflects the deviation of each node in the path and is the core driving factor in the optimization process.

[0173] (3) Model Parameter Update: During the model update process, feedback data is used as the basis for adjusting the model, applying the optimization formula θ new = θ old + β × E feedback to gradually update the model weights. The update process uses a gradient descent strategy to ensure that model parameters adjust in the direction of error reduction, ultimately improving prediction accuracy.

[0174] When optimizing, the engine adjusts the weights and parameters in its mapping algorithm based on historical operation data and feedback data to improve the linkage effect between the three-dimensional simulation model and the electrical schematic data. By comparing typical operations in historical data with simulation results, the engine updates the internal mapping relationship to ensure more accurate and efficient path selection in future operations.

[0175] This dynamic adjustment mechanism enables the quantum heuristic prediction model and the holographic real-time mapping engine to self-optimize after each user operation, improving the system's response speed and accuracy in complex interactions. By combining historical data and real-time feedback, the model can gradually learn and adapt to user operation patterns, reducing future deviations. The self-learning process may have a larger adjustment at the beginning of optimization, but as the system accumulates more historical data, the adjustment under the control of learning rate β will gradually stabilize, making the model stable and accurate in long-term application.

[0176] Example: Suppose a user adjusts the position of an electrical component during simulation, causing a small synchronization deviation between the three-dimensional simulation model and the electrical schematic data. The system detects this deviation through the feedback module and generates an error feedback matrix E feedback . Using this matrix, the system updates the model parameters θ new so that the model can more accurately predict user paths in subsequent operations, reducing errors and improving response efficiency. The holographic real-time mapping engine adjusts the mapping algorithm to optimize the linkage between the three-dimensional model and the electrical diagram elements, ensuring the accuracy of real-time operation feedback.

[0177] As Figure 4As shown, a system for implementing the quantum prediction-based electrical simulation interaction method, the system comprising:

[0178] A data loading module for loading three-dimensional simulation model data and electrical schematic data, and generating holographic mapping data between the three-dimensional simulation model data and the electrical schematic data, while verifying the integrity of the holographic mapping data; the data loading module generates the holographic mapping data by reading and loading the three-dimensional simulation model data and the electrical schematic data. The principle of holographic mapping is to logically associate and spatially synchronize the two through a mapping algorithm, and verify the data integrity to ensure accurate matching of the model and the electrical diagram.

[0179] An event listener module for capturing user interaction operations and generating user interaction data; the event listener module is used to capture user interaction operations such as clicking, dragging and zooming, and generate user interaction data. It listens to user input in real time and passes operation details to subsequent processing modules.

[0180] A quantum heuristic prediction module for receiving user interaction data and generating optimized path data for real-time simulation through a dynamic multi-path adaptive prediction algorithm; the quantum heuristic prediction module receives user interaction data, uses a dynamic multi-path adaptive prediction algorithm, analyzes possible operation paths and generates optimized path data for real-time simulation. The core of this module is to improve the accuracy and response speed of path selection through complex path prediction algorithms.

[0181] A holographic real-time mapping engine for generating real-time simulation data based on optimized path data, updating the state of three-dimensional simulation model data and electrical schematic data, and generating simulation result data for state verification; the holographic real-time mapping engine updates the state of three-dimensional simulation model data and electrical schematic data according to the optimized path data. It generates real-time simulation data and performs state verification to ensure real-time consistency of simulation data and electrical diagram elements after user operation.

[0182] A global feedback module for feedback analysis based on simulation result data, adjusting parameters of the quantum heuristic prediction module, and optimizing path selection to enhance real-time response between three-dimensional simulation model data and electrical schematic data; the global feedback module adjusts the parameters of the quantum heuristic prediction module based on the feedback analysis of the simulation result data. This module optimizes path selection to improve the synchronization and response efficiency of three-dimensional simulation models and electrical schematics in operation.

[0183] A virtual sandbox verification module is used to verify the correctness of the operation, generate fault-tolerant adjustment data, and use the fault-tolerant adjustment data to update the quantum heuristic prediction module and the holographic real-time mapping engine to achieve self-learning optimization. The virtual sandbox verification module verifies the correctness of the user operation in a simulated environment, generates fault-tolerant adjustment data, and uses the data to update the quantum heuristic prediction module and the holographic real-time mapping engine. This mechanism realizes self-learning optimization, so that the system has higher adaptability and stability in future operations.

[0184] Those skilled in the art will appreciate that embodiments of the present application can be provided as methods, systems or computer program products. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects.

[0185] The above merely provides embodiments of the present application and is not intended to limit the present application. Various modifications and changes can be made by those skilled in the art based on the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the scope of the claims of the present application.

Claims

1. An electrical simulation interaction method based on quantum prediction, characterized in that: The following steps are involved: Loading the 3D simulation model data and the electrical schematic diagram data, generating holographic mapping data between the 3D simulation model data and the electrical schematic diagram data, and verifying the integrity of the holographic mapping data; Capture user interaction operations, generate user interaction data, and pass the user interaction data to the quantum-inspired prediction model to generate optimized path data for real-time simulation; Generate real-time simulation data based on optimized path data, update the status of 3D simulation model data and electrical schematic data, and generate simulation result data for status verification; Conduct feedback analysis based on simulation result data, adjust parameters of the quantum-inspired prediction model, and optimize path selection to enhance real-time response between 3D simulation model data and electrical schematic data; Verify the correctness of the operation in the virtual sandbox, generate fault-tolerant adjustment data, and use the fault-tolerant adjustment data to update the quantum-inspired prediction model and holographic mapping data, so that the quantum-inspired prediction model and holographic real-time mapping engine can achieve self-learning optimization.

2. The electrical simulation interaction method based on quantum prediction according to claim 1, characterized in that: Generating holographic mapping data between three-dimensional simulation model data and electrical schematic data includes: calculating the relative position, state weight and logical association relationship of the three-dimensional simulation model data and the electrical schematic data in the three-dimensional space through a dynamic multi-dimensional mapping algorithm, and storing the holographic mapping data of the three-dimensional simulation model data and the electrical schematic data in a central data structure after each calculation.

3. The electrical simulation interaction method based on quantum prediction according to claim 1, characterized in that: Capturing user interaction operations and generating user interaction data is achieved by configuring multiple event listeners on the surface of three-dimensional simulation model data and electrical schematic data. The event listeners are used to identify user mouse clicks, drags, and zoom operations in real time, and generate timestamps and operation type information, thereby forming user interaction data.

4. The electrical simulation interaction method based on quantum prediction according to claim 1, characterized in that: The optimized path data for real-time simulation is generated by a quantum-inspired prediction model. The quantum-inspired prediction model uses a dynamic multi-path adaptive prediction algorithm to parse user interaction data into a multi-path operation tree, calculate the probability weight and priority of each path, and select the path with the highest weight as the real-time simulation path. At the same time, the optimized path data is stored in a cache module to support fast calling.

5. The electrical simulation interaction method based on quantum prediction according to claim 1, characterized in that: Generate real-time simulation data based on optimized path data, apply the optimized path data to the three-dimensional simulation model data and electrical schematic data through the holographic real-time mapping engine, so as to adjust the model unit position, status display and connection relationship of electrical elements of the three-dimensional simulation model data and electrical schematic data in real time, and generate a real-time updated status table.

6. The electrical simulation interaction method based on quantum prediction according to claim 1, characterized in that: When the simulation result data is used for state verification, real-time difference detection is performed through the global state feedback module, the simulation result data is compared with the initial path data, and feedback data and a difference analysis report are generated to identify the deviation between the simulation path and the user path selection.

7. The electrical simulation interaction method based on quantum prediction according to claim 1, characterized in that: In adjusting the parameters of the quantum heuristic prediction model based on the feedback data, the weight parameters of the quantum heuristic prediction model are dynamically adjusted by the difference adjustment matrix to optimize the path selection. The adjustment step includes the following formula: W new =W old +α×(D-P) Among them, W new Represents the updated weight matrix; W old Represents the current weight matrix; α represents the learning rate, which is used to control the step size of weight update; D represents the expected path data matrix; P represents the predicted path data matrix.

8. The electrical simulation interaction method based on quantum prediction according to claim 1, characterized in that: During operation verification in the virtual sandbox, the virtual sandbox uses a multi-path preview backtracking algorithm to simulate and calculate the optimized path data, generate preview data, and detect logical consistency and potential conflicts in each path one by one, mark potential error paths and generate complete fault-tolerant adjustment data.

9. The electrical simulation interaction method based on quantum prediction according to claim 1, characterized in that: By adjusting data through fault tolerance, the quantum heuristic prediction model and the holographic real-time mapping engine are updated. The quantum heuristic prediction model and the holographic real-time mapping engine dynamically adjust internal parameters based on historical operation data and feedback data, and optimize using the following formula: i new =θ old +β×E feedback Among them, θ new represents the optimized model parameters; θ old Represents the current model parameters; β represents the self-learning rate, which is used to control the step size of parameter update; E feedback The error matrix representing the feedback data reflects the difference between the model output and the expected result.

10. A system for implementing the electrical simulation interaction method based on quantum prediction according to any one of claims 1 to 9, characterized in that: The system includes: A data loading module is used to load the 3D simulation model data and the electrical schematic diagram data, generate holographic mapping data between the 3D simulation model data and the electrical schematic diagram data, and verify the integrity of the holographic mapping data; Event listener module, used to capture user interaction operations and generate user interaction data; A quantum-inspired prediction module, which receives user interaction data and generates optimized path data for real-time simulation through a dynamic multi-path adaptive prediction algorithm; A holographic real-time mapping engine is used to generate real-time simulation data based on the optimized path data, update the status of the 3D simulation model data and electrical schematic data, and generate simulation result data for status verification; A global feedback module, which performs feedback analysis based on simulation result data, adjusts parameters of the quantum-inspired prediction module, and optimizes path selection to enhance real-time response between 3D simulation model data and electrical schematic data; The virtual sandbox verification module is used to verify the correctness of the operation, generate fault-tolerant adjustment data, and use the fault-tolerant adjustment data to update the quantum-inspired prediction module and holographic real-time mapping engine to achieve self-learning optimization.

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