Electrical simulation interaction method and system based on quantum prediction

Abstract

The invention relates to the technical field of virtual simulation and electrical engineering, in particular to an electrical simulation interaction method and system based on quantum prediction, and the method comprises the steps: generating holographic mapping data through loading three-dimensional simulation model data and electrical schematic diagram data, and carrying out the initialization; after capturing user interaction operation data, analyzing and generating optimized path data through a quantum heuristic prediction model, and adjusting the states of a model unit and an electrical primitive in real time; the simulation result data is used for state verification and feedback analysis, adjusting model parameters and optimizing path selection; fault-tolerant adjustment data is generated through virtual sandbox verification, a prediction model and holographic mapping data are updated, and self-learning optimization is achieved; according to the invention, the real-time performance, the response efficiency and the system adaptability of interaction are improved, and the method is suitable for demonstration and simulation of the electrical principle in education and engineering application.

Description

Technical Field

[0001] The present invention relates to the technical fields of virtual simulation and electrical engineering, and particularly to an electrical simulation interaction method and system based on quantum prediction. Background Art

[0002] Through 3D simulation, users can more intuitively understand and operate electrical systems, making complex electrical principles vivid and easy to understand. This kind of interaction has advantages especially in teaching and engineering design, enabling learners to better combine theory with actual equipment. However, it is not easy to achieve real-time synchronization and interaction between 3D simulation models and electrical schematic diagrams.

[0003] The prior art usually uses static mapping relationships and basic event triggering mechanisms to achieve the interaction between 3D models and electrical schematic diagrams. In this method, simple event listening and model activation mechanisms are adopted to achieve the mutual mapping and status update between 3D models and electrical diagrams. However, due to relying on predefined static rules, this technology has several defects:

[0004] Due to the lack of a dynamic adjustment mechanism, it is difficult for the prior art to maintain real-time response under complex user operations, resulting in unstable interaction effects; using fixed model mapping relationships, it is unable to cope with complex real-time data changes, causing a synchronization lag between electrical schematic diagrams and 3D models; it is unable to self-optimize according to user operation data, resulting in the model being unable to improve interaction effects and response speeds. These defects make the prior art perform poorly in complex operation scenarios, restricting its application effects in education and engineering. Summary of the Invention

[0005] In view of the many problems existing in the above prior art, the present invention provides an electrical simulation interaction method and system based on quantum prediction. The present invention uses a quantum heuristic prediction model to analyze user interaction data, generate a real-time optimization path, and combines a holographic mapping engine to achieve state linkage between 3D simulation models and electrical schematic diagrams. At the same time, through a fault tolerance adjustment and self-learning mechanism, the response speed and accuracy of the system in complex operation scenarios are improved, thereby improving the user experience and operation efficiency.

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

[0007] Load 3D simulation model data and 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;

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

[0009] Generate real-time simulation data based on the optimized path data, update the status of the 3D simulation model data and the electrical schematic diagram data, generate simulation result data and use it for status verification;

[0010] Conduct feedback analysis based on the simulation result data, adjust the parameters of the quantum-inspired prediction model, and optimize the path selection to enhance the real-time response between the 3D simulation model data and the electrical schematic diagram data;

[0011] 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 the holographic mapping data, enabling the quantum-inspired prediction model and the holographic real-time mapping engine to achieve self-learning optimization.

[0012] Preferably, generating the holographic mapping data between the 3D simulation model data and the electrical schematic diagram data includes: calculating the relative positions, state weights, and logical association relationships of the 3D simulation model data and the electrical schematic diagram data in the three-dimensional space through a dynamic multi-dimensional mapping algorithm, and storing the holographic mapping data of the 3D simulation model data and the electrical schematic diagram data into the central data structure after each calculation.

[0013] Preferably, capturing user interaction operations and generating user interaction data are achieved by configuring multiple event listeners on the surfaces of the 3D simulation model data and the electrical schematic diagram data. The event listeners are used to identify the user's mouse click, drag, and zoom operations in real time, and generate timestamp and operation type information, thereby forming user interaction data.

[0014] Preferably, in generating the optimized path data for real-time simulation through the quantum-inspired prediction model, the quantum-inspired prediction model uses a dynamic multi-path adaptive prediction algorithm to parse the user interaction data into a multi-path operation tree, calculate the probability weights and priorities of each path, select the path with the highest weight as the real-time simulation path, and store the optimized path data into the cache module to support fast calling.

[0015] Preferably, generating real-time simulation data based on the optimized path data applies the optimized path data to the 3D simulation model data and the electrical schematic diagram data through the holographic real-time mapping engine to adjust the model unit positions, state displays, and connection relationships of the electrical graphic elements of the 3D simulation model data and the electrical schematic diagram data in real time, and generate a real-time updated status table.

[0016] Preferably, when the simulation result data is used for status verification, real-time difference detection is performed through the global status 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.

[0017] Preferably, 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, and the adjustment step includes the following formula:

[0018] W new =W old +α×(DP)

[0019] 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.

[0020] Preferably, during operation verification in a 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, mark potential error paths and generate complete fault-tolerant adjustment data.

[0021] Preferably, in updating the quantum heuristic prediction model and the holographic real-time mapping engine through fault-tolerant adjustment data, the quantum heuristic prediction model and the holographic real-time mapping engine dynamically adjust the internal parameters in combination with the historical operation data and feedback data, and optimize using the following formula:

[0022] θ new =θ old +β×E feedback

[0023] 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 A matrix of error values ​​representing feedback data, which reflects the difference between the model output and the expected result.

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

[0025] A data loading module is used to load the three-dimensional simulation model data and the electrical schematic diagram data, and generate holographic mapping data between the three-dimensional simulation model data and the electrical schematic diagram data, and verify the integrity of the holographic mapping data;

[0026] Event listener module, used to capture user interaction operations and generate user interaction data;

[0027] 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;

[0028] A holographic real-time mapping engine for generating real-time simulation data based on optimized path data, updating the states of 3D simulation model data and electrical schematic diagram data, and generating simulation result data for state verification;

[0029] A global feedback module for performing feedback analysis based on 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 3D simulation model data and the electrical schematic diagram data;

[0030] A virtual sandbox verification module for verifying the correctness of operations, 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 invention are as follows:

[0032] The present invention optimizes the real-time parsing and path prediction of user operation data through a quantum heuristic prediction model, achieving a more efficient and accurate model response;

[0033] The present invention makes the synchronous update of the 3D simulation model data and the electrical schematic diagram data smoother and more dynamic through a holographic real-time mapping engine;

[0034] The present invention introduces a self-learning feedback mechanism, using the fault-tolerant adjustment data to update the model parameters, enabling the system to automatically optimize according to the user's historical operations, and improving the interaction stability and intelligence in long-term use. Description of the Drawings

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

[0036] Figure 2 It is a schematic diagram of user interaction data capture and parsing in the present invention;

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

[0038] Figure 4 It is a structural block diagram of the system of the present invention. Detailed Embodiments

[0039] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying 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, for the sake of explanation, numerous specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, obviously, one or more embodiments can also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessarily confusing the concepts of the present disclosure.

[0040] The terms used herein are merely for describing specific embodiments and are not intended to limit the present disclosure. The terms "including", "comprising", etc. used herein indicate the presence of the described features, steps, operations, and / or components, but do not exclude 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 those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0042] As Figure 1 shown, an electrical simulation interaction method based on quantum prediction includes the following steps:

[0043] Load three-dimensional simulation model data and electrical schematic diagram data, generate holographic mapping data between the three-dimensional simulation model data and the electrical schematic diagram 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 diagram data includes: through a dynamic multi-dimensional mapping algorithm, calculating the relative positions, state weights, and logical association relationships of the three-dimensional simulation model data and the electrical schematic diagram data in three-dimensional space, and storing the holographic mapping data of the three-dimensional simulation model data and the electrical schematic diagram data into a central data structure after each calculation.

[0045] The core of generating holographic mapping data lies in using a dynamic multi-dimensional mapping algorithm. This algorithm matches and associates each unit in the model and electrical graphic elements by analyzing the geometric structures and logical relationships of the three-dimensional simulation model data and the electrical schematic diagram data in three-dimensional space. The inputs of the algorithm are three-dimensional simulation model data (including the spatial coordinates and physical attributes of the model) and electrical schematic diagram data (including the connection relationships and electrical characteristics of electrical components). By establishing a multi-dimensional vector matrix, the algorithm can accurately calculate the relative positions and state weights between these data.

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

[0047]

[0048] Among them, W ij represents the state weight of model unit i and electrical graphic element j; x i represents the feature vector of the 3D simulation model unit; y j represents the feature vector of the electrical graphic element; b is the bias term used to adjust the calculation result of the state weight.

[0049] This formula is based on the 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 graphic element. The state weight is stored in the holographic mapping data structure for use in user interaction and subsequent simulation processes.

[0050] After each calculation is completed, the system stores the generated holographic mapping data in the central data structure to ensure that it can be quickly called during real-time use. The central data structure uses an efficient multi-dimensional indexing technique, such as an R-tree-based spatial index, to store and retrieve holographic mapping data. Whenever the user performs an operation, such as moving or modifying a 3D model unit, the central data structure immediately updates the mapping data to reflect the latest state.

[0051] This implementation method ensures the synchronization and state consistency between the 3D 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 graphic elements during user operations, thus supporting efficient path planning and simulation.

[0052] In one embodiment, assume that the user selects and drags a 3D model unit in the simulation environment. This operation triggers the event listener to capture user interaction data and updates the mapping state between this unit and the relevant electrical graphic elements in real time through the holographic real-time mapping engine. The update of the mapping data recalculates the state weight based on the dynamic multi-dimensional mapping algorithm and synchronizes the new state in the central data structure. In this way, when the model state changes, the display of the electrical schematic can also be adjusted synchronously, allowing the user to intuitively see the real-time changes in the simulation model and the electrical system.

[0053] Capture user interaction operations, generate user interaction data, and transmit the user interaction data to the 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 operations involves setting event listeners on the interfaces of 3D simulation model data and electrical schematic diagram data. By continuously monitoring user operations on the interface (such as clicking, dragging, rotating, etc.), the event listener generates user interaction data, which includes detailed information such as operation type, operation target, operation location, and timestamp. This user interaction data is used to characterize the specific interaction behaviors of the user in the system and provides the basic input for subsequent analysis and calculations.

[0055] After the user interaction data is generated, it is passed to the quantum-inspired prediction model for path analysis and optimization. By simulating the characteristics of quantum state superposition and parallel computing, the quantum-inspired prediction model conducts multi-path analysis on the user interaction data to predict and select the optimal operation path. The model uses the states of qubits to represent multiple path possibilities and calculates the priority and probability of each path through probability amplitudes.

[0056] In the specific implementation, the calculation process of the quantum-inspired 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; and n is the total number of paths.

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

[0060] The generated optimized path data provides detailed information about the optimal path during the user interaction process, including the nodes of the path, operation weights, and priorities. This data is used for real-time simulation and model update to ensure that the system response after the user operation meets expectations and can provide a quick feedback. By introducing the quantum-inspired prediction model, the present invention realizes multi-path parallel computing and real-time analysis, overcomes the calculation bottleneck of traditional single-path prediction methods, and improves the efficiency and accuracy of path analysis.

[0061] In one embodiment, it is assumed that the user drags a 3D model unit in the interface to adjust its position. The event listener immediately captures this operation and generates user interaction data, including the operation type (drag), the target object (the ID of the 3D model unit), the operation position (the new coordinates), and the timestamp. After receiving this data, the quantum-inspired prediction model starts parallelly calculating the possible paths of the user, calculates the probability amplitude of the priority of each path, and selects the optimal path to generate optimized path data. The optimized path data is then used for real-time simulation updates, enabling the system to immediately respond to the user's operation, and synchronously updating the model position and the connection relationship of the corresponding components in the electrical schematic diagram, achieving the effect of what you see is what you get.

[0062] As Figure 2 shown, preferably, capturing the user interaction operation and generating the user interaction data are implemented by configuring multiple event listeners on the surfaces of the 3D simulation model data and the electrical schematic diagram data. The event listeners are used to real-time identify the user's mouse click, drag, and zoom operations, and generate the timestamp and operation type information, thereby forming the user interaction data.

[0063] An event listener is a mechanism used to capture user input in a software interface. To enable the user interaction to be immediately reflected in the 3D simulation model data and the electrical schematic diagram data, the system configures event listeners on the visualization surfaces of both. These listeners capture operations in real-time by detecting signals from input devices (such as a mouse or a touchpad). The operation types captured by the listeners include click, drag, and zoom, and the user interaction data generated by each operation includes the timestamp, the operation type, and the position coordinates.

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

[0065] (1) Listener configuration: Set up listeners on the surfaces of the 3D simulation model data and the 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, and the data format is as follows:

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

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

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

[0071] In one embodiment, when the user drags an electrical component in the three-dimensional simulation interface, the event listener immediately captures this drag operation and records it as user interaction data in the format of:

[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-inspired prediction model for analysis to determine the optimal prediction of the user interaction path, enabling the system to update the states of the three-dimensional simulation model and the electrical schematic diagram in real time.

[0074] To improve the response speed and data processing ability of the listener, the system adopts an efficient event queue mechanism to store the user operation inputs in the queue in chronological order, ensuring the orderliness of data processing. The implementation of the listener uses a non-blocking structure to ensure that the system still maintains a smooth response 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, in the optimized path data for real-time simulation generated by the quantum-inspired prediction model, the quantum-inspired prediction model uses a dynamic multi-path adaptive prediction algorithm to parse the user interaction data into a multi-path operation tree, calculate the probability weights and priorities 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 the cache module to support fast calling.

[0076] The quantum-inspired prediction model uses a dynamic multi-path adaptive prediction algorithm to parse user interaction data into a multi-path operation tree. User interaction data includes user operation types, locations, timestamps, and the identifiers of target objects. After this data is input into the quantum-inspired prediction model, it is parsed into a multi-path operation tree. This tree structure is used to represent all possible paths of the user under the current operation, where each path consists of multiple nodes, and each node represents an operation step or model state.

[0077] After parsing the user interaction data and generating the operation tree, the quantum-inspired prediction model begins to calculate the probability weights and priorities of each path. This process uses the superposition and parallelism of quantum computing and evaluates the 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 because they represent higher probabilities and priorities, meeting the expectations of the current user operation. 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 the 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 operations. This real-time path selection and storage mechanism ensures the interaction synchronization and efficiency between the 3D 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 3D 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] This user interaction data is input into a quantum-inspired prediction model. The model analyzes the data and generates a multi-path operation tree. Subsequently, the weights of each path are calculated 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 update. Finally, the system can update the states and connections of relevant components in the electrical schematic diagram in real time when the user adjusts the model unit.

[0086] Generate real-time simulation data based on the optimized path data, update the states of the 3D 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, this data 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 3D simulation model and the electrical schematic diagram data, keeping the two in sync during user interaction.

[0088] In specific implementation, the system gradually updates the states such as the position, rotation, and scaling of the 3D model units according to the optimized path data, and at the same time adjusts the connection relationships of the associated electrical primitives. This process realizes the real-time simulation data update through the following formula:

[0089]

[0090] where; M new represents the updated 3D simulation model data matrix; M old represents the current 3D 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 update of the model data, the state of the electrical schematic diagram data is also adjusted synchronously, including the activation state of electrical components, the connection path, and the change in the transmission of electrical signals. The system ensures that the simulation result data is generated in real time after each update.

[0092] The generation of the simulation result data not only includes the final states of the model and the electrical primitives, but also includes the intermediate states during the process, which are used for further analysis and state verification. The state verification process performs consistency verification 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 j-th element of the expected state data; Rj is the j-th 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 the three-dimensional simulation model data and electrical schematic diagram data, the present invention realizes an efficient mechanism for maintaining the synchronization of the model and primitive state during the user interaction process. The simulation result data is used to ensure that the operation of the model meets the expectations and perform consistency verification, providing accurate feedback and predictable operation responses for the user.

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

[0098] Preferably, generating real-time simulation data based on the optimized path data applies the optimized path data to the three-dimensional simulation model data and electrical schematic diagram data through the holographic real-time mapping engine to adjust the model unit positions, state displays, and connection relationships of electrical primitives in the three-dimensional simulation model data and electrical schematic diagram data in real time, and generates a real-time updated status 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 primitive changes. The optimized path data includes the steps, path nodes, weights, and related parameters of user interaction operations. The engine uses this data to update the status of the three-dimensional simulation model data and electrical schematic diagram data in real time.

[0100] The implementation process specifically includes:

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

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

[0103]

[0104] where M updatedDenote the updated 3D simulation model data matrix; M current is the current model data matrix; δ i is the state change vector of path node i; W i is the weight of path node i; k is the total number of path nodes.

[0105] During each adjustment, the engine modifies the positions and state displays of the model units in real time according to the weights of the paths and the state change vectors to ensure that the model conforms to the user's expected interaction.

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

[0107]

[0108] Among them, C new is the updated connection relationship matrix; C old is the current connection relationship matrix; T(δ i ,W i ) is the connection change function combining the state changes and weights of the path nodes.

[0109] (4) State table generation: The holographic real-time mapping engine generates a real-time state table after each adjustment, recording the latest states of the model units and electrical diagram elements, including detailed information such as positions, rotations, and connection states. 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 3D simulation model and the electrical schematic diagram in real time according to the user's operations, keeping the two highly synchronized. The generated real-time state table not only provides instant feedback of the model to the user but also serves as an internal record of the system for monitoring and verifying the accuracy of the interaction process.

[0111] In one embodiment, the user zooms in or out on a certain model unit in the 3D simulation interface. The holographic real-time mapping engine adjusts the size of the model unit according to the optimized path data and synchronizes the display states and connection relationships of the corresponding components in the electrical schematic diagram in real time. For example, when the user shrinks the model unit, the connection paths of the relevant electrical components are adjusted accordingly to ensure that the states of the model and the electrical diagram elements are consistent. The real-time state table generated during this process records the updated size, connection information, and node weights, 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 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.

[0113] Through the global state feedback module for real-time difference detection, the system compares the simulation result data with the initial path data, thereby identifying the deviation between the simulation path and the user path selection, and generating feedback data and a difference analysis report to guide subsequent adjustments. The global state feedback module receives the simulation result data R(t) and the initial path data P(t), where t represents the data point in the time series. The module uses the difference detection formula:

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

[0115] Calculate the absolute difference value between the simulation result and the initial path. 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. Through the calculated difference data D(t), the global state feedback module generates feedback data, indicating the deviation situation 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] The real-time difference detection is carried out through parallel computing technology to ensure that the comparison operation at each time point can be completed quickly, thereby accelerating the generation of feedback data. The feedback data is organized in a structured form, including time stamps, difference values D(t), deviation marks, and recommended adjustment schemes for subsequent use in system adjustment and path optimization. The global state feedback module uses a threshold ∈ to judge the severity of the deviation. If D(t) > ∈ at a certain time point, it is regarded as a significant deviation. The module will mark this difference and put forward adjustment suggestions in the report.

[0117] Through this difference detection and feedback mechanism, the system can quickly identify the deviation between the simulation path and the user path, provide instant feedback to ensure the accuracy of the interaction and the synchronization of the model and the electrical graphic element state. In implementation, for example, when the user drags and rotates the model unit in the simulation interface, the module compares the simulation result with the original operation path of the user. If the detected deviation exceeds the threshold ∈, the system will mark this deviation point in the feedback and provide adjustment suggestions, such as suggesting recalculating the path or adjusting the state of the model unit, to ensure that the final simulation effect is consistent with the user's expectations. Through this process, the system significantly improves the response speed, operation accuracy, and user experience, enabling the 3D simulation model and the electrical schematic diagram data to maintain consistency and efficiency in dynamic interaction.

[0118] Perform feedback analysis based on the simulation result data, adjust the parameters of the quantum-inspired prediction model, and optimize the path selection 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 parsing of the 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 sources and characteristics.

[0120] After identifying the deviation, the system improves the accuracy of the predicted path by adjusting the parameters of the quantum-inspired prediction model. The core of the parameter adjustment is to use the feedback data E(t) to update the weights and path selection mechanism of the model. 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, which controls the step size of parameter update; L(E(t)) is the loss function based on the 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 the gradient descent method, uses the feedback data to drive the model to self-correct, and enables it to better adapt to user interactions and system states in subsequent predictions.

[0124] After updating the parameters of the quantum-inspired prediction model, the system re-optimizes the path selection to improve the real-time performance and response speed of the simulation. The optimized model will recalculate the weights and priorities of the paths to ensure that when the user makes the next interaction, the system can select a more accurate path. 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 between the three-dimensional model and the electrical graphic elements.

[0128] This feedback analysis and model optimization method ensures that the quantum-inspired prediction model can be 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 the predicted path and the actual path, improving the simulation accuracy and interaction experience.

[0129] In one embodiment, when the user performs a complex rotation operation on the 3D simulation model, the initial simulation result may have a path deviation, resulting in the rotation state of the model not being exactly the same as the user's expectation. 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 simulations, reducing errors and improving the system's real-time response and accuracy.

[0130] Preferably, in adjusting the parameters of the quantum-inspired prediction model based on the feedback data, the weight parameters of the quantum-inspired prediction model are dynamically adjusted through a difference adjustment matrix to optimize path selection. The adjustment steps include the following formula:

[0131] W new =W old +α×(D - P)

[0132] where, 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 desired path data matrix; P represents the predicted path data matrix.

[0133] Adjusting the parameters of the quantum-inspired prediction model requires comparative analysis of the simulation result data and the user path data to generate feedback data. The feedback data is used to quantify the difference between the model prediction and the user path and guide the adjustment of the model parameters. The difference adjustment matrix plays a key role in this process. By applying it to dynamically update the weight matrix of the model, the model can be closer to the actual operation intention of the user in subsequent path predictions.

[0134] The adjustment process realizes element-by-element update through the above formula, that is, for each element W new,ij in the matrix, the current weight is adjusted according to the difference between the desired 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 and avoiding drastic changes in the model parameters due to too large a step size, which may affect the overall stability.

[0135] Through this adjustment process, the model can dynamically optimize its own parameters after each user operation, making future path predictions more in line with the user's operations. The adjusted weight matrix W newUsed to update the state of the quantum-inspired prediction model so that it can recalculate the weights and priorities of paths based on the optimized weight matrix during the next simulation path selection.

[0136] This dynamic adjustment mechanism ensures that the model has the ability of adaptive learning, enabling it to continuously improve the prediction accuracy. As the system is used for a longer time, the model continuously adjusts and optimizes its parameters, gradually improving the response speed and accuracy of the three-dimensional simulation model data and electrical schematic diagram data during user interaction.

[0137] In one embodiment, when the user adjusts the position of an electrical component 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 (i.e., a large value of D - P) is detected between the predicted path and the expected path, the system calculates a new weight matrix W new to adjust the parameters of the model. This adjustment ensures that in subsequent operations, the quantum-inspired 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, 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, enabling the quantum-inspired prediction model and the holographic real-time mapping engine to achieve self-learning optimization.

[0139] The virtual sandbox is a simulation environment used to test and verify the correctness of user interaction without affecting the actual simulation or operation. The system loads user interaction data and simulation result data through 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 will generate 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 impact of user operations on the three-dimensional simulation model and electrical schematic diagram data for observing the effects.

[0142] (2) Generation of fault-tolerant adjustment data: The virtual sandbox is built with a fault-tolerant analysis module to calculate the deviation between the user's actual operation and the ideal result and generate fault-tolerant adjustment data. The generation of the 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, which is used to control the influence range of the fault-tolerant data.

[0145] The fault-tolerant data is verified through multiple operations and compared with the expected path to identify deviation areas and adjustment needs in the operation.

[0146] (3) Model and engine update: Fault-tolerant adjustment data is used to update the quantum-inspired prediction model and the holographic real-time mapping engine. For the quantum-inspired 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] Among them, θ new represents the updated model parameters; θ old is the current model parameter and γ is the learning step size, which is used to control the update amplitude.

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

[0150] By applying fault-tolerant adjustment data to the quantum-inspired prediction model and holographic real-time mapping engine, the system achieves self-learning optimization. This optimization process ensures that the system can learn from the user's historical operations, improve the accuracy of the prediction model and the real-time response. The update of the holographic real-time mapping engine ensures that the 3D simulation model and electrical schematic data can be adjusted in real time in future user operations, reducing operational deviations and improving the operational 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 the user input and simulation response, and detects that some rotation angles in the operation deviate from the expected trajectory. The system generates fault tolerance adjustment data E corr (t) is used to quantify the deviation and the weight of the correction is controlled by adjusting the coefficient β. The fault-tolerant data is passed to the quantum heuristic prediction model to adjust its parameters and optimize future path predictions. The holographic real-time mapping engine is updated synchronously to ensure more accurate path selection for subsequent operations.

[0152] Preferably, during operation verification in a 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, mark potential error paths and generate complete fault-tolerant adjustment data.

[0153] The virtual sandbox, as an isolated test environment, simulates and calculates the optimized path data through a multi-path preview and backtracking algorithm to verify the logic and reliability of operations. The core of the multi-path preview and backtracking algorithm lies in dynamically calculating and detecting each possible path of user interaction to ensure that the paths are logically conflict-free and conform to the expected operation behavior.

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

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

[0156] (2) Path logical consistency detection: For the generated preview data, the system performs step-by-step logical checks on the nodes of each path. The logical consistency detection uses the following discriminant formula:

[0157]

[0158] where C logic (n) is the logical consistency mark of path node n; Path(n) represents node n in the path; 1 indicates that the path is logical, 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, that is, C logic (n) = 0, the system will mark this path as a potential error path and record the specific location and type of the conflict. Conflict detection and analysis include path node overlap, state change conflict, data consistency issues, etc.

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

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

[0164] Through the multi-path rehearsal backtracking algorithm, the virtual sandbox can conduct in-depth verification after user operations, generate rehearsal data, and mark logical conflicts and potential error paths. This process ensures the logical consistency between user operations and system responses, enhancing 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 the holographic mapping engine, enabling the system to have the ability of self-learning and gradual improvement.

[0165] In one embodiment, assume that 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. When a connection conflict is detected in a certain path, that path is marked as a potential error path. The virtual sandbox generates corresponding fault-tolerant adjustment data E corr (n), and uses this data to adjust the weight parameters of the quantum-inspired prediction model, optimize the path selection ability of the model, and reduce the bias of future similar operations.

[0166] Preferably, when updating the quantum-inspired prediction model and the holographic real-time mapping engine with the fault-tolerant adjustment data, the quantum-inspired prediction model and the holographic real-time mapping engine dynamically adjust their internal parameters by combining historical operation data and feedback data, and use the following formula for optimization:

[0167] θ new =θ old +β×E feedback

[0168] 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 represents the error value matrix of the feedback data, which is used to reflect the difference between the model output and the expected result.

[0169] The generation of the fault-tolerant adjustment data is based on the difference detection and analysis of user interaction operations. These data reflect the error between the simulation path and the expected path. The quantum-inspired 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-inspired prediction model and the holographic real-time mapping engine record user interaction data and simulation path data after each operation and store them as historical operation data. Combining this historical data, the system analyzes the patterns of user behavior and simulation errors as input for optimization.

[0172] (2) Error feedback data generation: The system generates error feedback data E by comparing the simulation results with the user's expected path. feedback . This matrix reflects the deviation values 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, the feedback data is used as the basis for adjusting the model, and the optimization formula θ new = θ old + β × E feedback is used to gradually update the model weights. The update process adopts a gradient descent strategy to ensure that the model parameters are adjusted along the direction of error reduction, ultimately improving the prediction accuracy.

[0174] When optimizing, the engine combines historical operation data and feedback data to adjust the weights and parameters in its mapping algorithm to improve the linkage effect between the 3D simulation model and the electrical schematic data. The engine updates the internal mapping relationship by comparing typical operations and simulation results in historical data to ensure more accurate and efficient path selection in future operations.

[0175] This dynamic adjustment mechanism enables the quantum-inspired 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 the user's operation mode, thereby reducing future deviations. The self-learning process may have a larger adjustment amplitude in the initial stage of optimization, while with the system accumulating more historical data, the adjustment under the control of the learning rate β will gradually stabilize, making the model stable and accurate in long-term applications.

[0176] Example: Suppose the user adjusts the position of an electrical component during a simulation operation, resulting in a slight synchronization deviation between the 3D 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 parameter θ new , enabling the model to more accurately predict the user's path in subsequent operations, reducing errors and improving the response efficiency. The holographic real-time mapping engine synchronously adjusts the mapping algorithm to optimize the linkage between the 3D model and the electrical graphic elements, ensuring the accuracy of real-time operation feedback.

[0177] Such as Figure 4As shown, a system for implementing the above-mentioned electrical simulation interaction method based on quantum prediction, the system includes:

[0178] A data loading module, configured to load three-dimensional simulation model data and electrical schematic diagram data, generate holographic mapping data between the three-dimensional simulation model data and the electrical schematic diagram data, and verify the integrity of the holographic mapping data; the data loading module generates holographic mapping data by reading and loading the three-dimensional simulation model data and the electrical schematic diagram 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 the accurate matching of the model and the electrical diagram.

[0179] An event listener module, configured to capture user interaction operations and generate user interaction data; the event listener module is used to capture user interaction operations, such as click, drag, and zoom operations, and generate user interaction data. It listens to user input in real time and passes the operation details to the subsequent processing module.

[0180] A quantum heuristic prediction module, configured to receive user interaction data and generate optimized path data for real-time simulation through a dynamic multi-path adaptive prediction algorithm; the quantum heuristic prediction module receives user interaction data, adopts 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 a complex path prediction algorithm.

[0181] A holographic real-time mapping engine, configured to generate real-time simulation data based on the optimized path data, update the states of the three-dimensional simulation model data and the electrical schematic diagram data, and generate simulation result data for state verification; the holographic real-time mapping engine updates the states of the three-dimensional simulation model data and the electrical schematic diagram data in real time according to the optimized path data. It generates real-time simulation data and performs state verification to ensure the real-time consistency of the simulation data and the electrical diagram components after user operations.

[0182] A global feedback module, configured to perform feedback analysis based on the simulation result data, adjust the parameters of the quantum heuristic prediction module, and optimize path selection to enhance the real-time response between the three-dimensional simulation model data and the electrical schematic diagram data; the global feedback module performs feedback analysis based on the simulation result data and adjusts the parameters of the quantum heuristic prediction module. This module optimizes path selection to improve the synchronization and response efficiency of the three-dimensional simulation model and the electrical schematic diagram during operations.

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

[0184] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects.

[0185] The above are only the embodiments of the present application and are not used to limit the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within 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 three-dimensional simulation model data and the electrical schematic diagram data, generating holographic mapping data between the three-dimensional 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 through the quantum-inspired prediction model; Generate real-time simulation data based on optimized path data, update the status of 3D simulation model data and electrical schematic data, generate simulation result data and use it for status verification; Conduct feedback analysis based on simulation result data, adjust parameters of quantum-inspired prediction models, 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 is 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 is characterized in that: 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 data. The event listeners are used to identify the user's 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 is 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 is 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 graphics 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 is 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 is 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 through 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 is 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, 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 is characterized in that: In the quantum heuristic prediction model and holographic real-time mapping engine, the quantum heuristic prediction model and holographic real-time mapping engine dynamically adjust the internal parameters by combining 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 A matrix of error values ​​representing feedback data, which 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 three-dimensional simulation model data and the electrical schematic diagram data, and generate holographic mapping data between the three-dimensional 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 three-dimensional simulation model data and the electrical schematic data, and generate simulation result data for status verification; A global feedback module for performing feedback analysis based on simulation result data, adjusting parameters of the quantum-inspired prediction module, and optimizing 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 the holographic real-time mapping engine to achieve self-learning optimization.

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