Visualization method and system for electrical schematic diagram

Abstract

The present invention relates to the field of virtual simulation technology, and in particular to a visualization method and system for electrical schematics, which generates multi-dimensional model parameters by collecting data from multiple sensors, simulation engines and user inputs; inputs these parameters into a multi-level graph neural network and a dynamic cyclic association matrix to generate a dynamic mapping relationship between electrical components and a three-dimensional simulation model; through adaptive modeling and multi-level interactive processing, the system can dynamically generate feedback data and adjust the display status of the electrical schematic and the three-dimensional simulation model in real time; based on the generated task data, the system generates a dynamic visualization model of the electrical schematic to achieve real-time monitoring and optimization of the electrical system. The present invention improves the real-time performance, response speed and resource utilization efficiency of the system, and significantly improves the operability and safety of the electrical system.

Description

Technical Field

[0001] The present invention relates to the field of virtual simulation technology, and in particular to a visualization method and system for electrical schematic diagrams. Background Art

[0002] Electrical schematics are key tools for describing the working principle of an electrical system and the relationship between its components. They are of great significance in the design, maintenance, and teaching of electrical systems, helping engineers and technicians analyze electrical circuits, troubleshoot, and perform program design. However, traditional electrical schematics are displayed in two dimensions and lack intuitiveness, making it difficult for beginners or non-professionals to quickly connect electrical schematics with the electrical components of actual equipment.

[0003] The prior art attempts to realize the interactive display of three-dimensional and two-dimensional graphics by mapping and associating the three-dimensional simulation model with the electrical schematic diagram. However, this method has significant shortcomings in practical applications.

[0004] The existing technology relies on pre-set mapping relationships, which makes it difficult to dynamically reflect the real-time status changes of the system, resulting in poor synchronization and real-time performance between the three-dimensional model and the electrical schematic in complex electrical systems. In addition, the feedback mechanism of the existing technology is relatively simple and cannot effectively capture and process the complex correlations between components in the system, resulting in poor application effects in complex systems. Summary of the invention

[0005] In view of the many problems existing in the above-mentioned prior art, the present invention provides a visualization method and system for electrical schematics. The present invention generates and processes multi-dimensional model parameters and inputs them into a multi-level graph neural network and a dynamic circulatory association matrix to achieve a dynamic mapping relationship between electrical components and three-dimensional simulation models. The present invention also utilizes a hierarchical feedback mechanism of adaptive modeling and multi-level interactive processing to accurately display and optimize the real-time status of the electrical system, thereby greatly improving the real-time and operability of the system.

[0006] A method for visualizing an electrical schematic diagram comprises the following steps:

[0007] By collecting data from multiple sensors, simulation engines and user inputs, and fusing and processing the collected data, a multi-dimensional model parameter is generated, and the multi-dimensional model parameter is used for mapping the electrical schematic diagram with the three-dimensional simulation model;

[0008] Inputting the multi-dimensional model parameters into a multi-level graph neural network and a dynamic cyclic association matrix to generate mapping data reflecting a dynamic mapping relationship between electrical components and a three-dimensional simulation model, wherein the mapping data is used to establish complex associations of the electrical system;

[0009] Use mapping data for adaptive modeling and multi-level interactive processing, generate feedback data for system optimization through a hierarchical feedback mechanism, and dynamically adjust the interactive relationship between the electrical schematic and the 3D simulation model;

[0010] Based on feedback data, through multi-dimensional optimization algorithms and self-learning task scheduling models, task data for system execution and control is generated, and the allocation of system resources and the execution order of tasks are optimized in real time;

[0011] According to the task data, a dynamic visualization model of the electrical schematic diagram is generated, and the display status of the electrical schematic diagram and the three-dimensional simulation model is updated in real time through a synchronous adjustment mechanism.

[0012] Preferably, the step of collecting data from multiple sensors, simulation engines and user input includes: standardizing the collected data, wherein the standardization includes data normalization and denoising to generate standardized data with unified dimensions and reduced interference for subsequent multi-dimensional model parameter generation.

[0013] Preferably, the step of inputting the multidimensional model parameters into the multi-level graph neural network includes: performing multi-layer processing on the multidimensional model parameters through a nonlinear dynamic weight adjustment mechanism, wherein the nonlinear dynamic weight adjustment mechanism dynamically calculates weight values ​​based on real-time electrical component status data to ensure that the mapping relationship between the electrical components and the three-dimensional simulation model can adaptively reflect the complex correlation of the electrical system.

[0014] Preferably, the calculation expression of the nonlinear dynamic weight adjustment mechanism is:

[0015] in, Indicates The electrical components and The weights between nodes of a three-dimensional simulation model; represents the initial weight; is the dynamic adjustment coefficient; is an adjustment function based on the dynamic relationship between electrical components; Real-time data association between electrical components.

[0016] Preferably, the step of inputting the multidimensional model parameters into the dynamic cyclic association matrix includes: realizing complex correlation modeling between electrical components by constructing a time series model and a multidimensional feature matrix, wherein the time series model is used to capture the time correlation between electrical components, and the multidimensional feature matrix is ​​used to reflect the multidimensional attribute association between electrical components.

[0017] Preferably, the step of using mapping data for adaptive modeling and multi-level interactive processing includes: generating instant feedback data, short-term feedback data and long-term feedback data through a hierarchical feedback mechanism, the instant feedback data is used to fine-tune the real-time display status of the electrical schematic and the three-dimensional simulation model, the short-term feedback data is used to dynamically optimize the electrical system parameters during the execution of the current task, and the long-term feedback data is used to adjust and optimize global parameters based on the long-term accumulated data of the system's global performance.

[0018] Preferably, the generation of the instant feedback data, short-term feedback data and long-term feedback data is achieved through a multi-layer adaptive modeling algorithm, which dynamically generates feedback data at each level based on real-time input electrical component operation data, system feedback data and user interaction data. The generated feedback data is directly applied to the real-time display of electrical schematics and three-dimensional simulation models and the adjustment of system parameters.

[0019] Preferably, the step of using a multidimensional optimization algorithm and a self-learning task scheduling model based on feedback data includes: analyzing the dependency relationship of tasks through a multidimensional optimization algorithm, the multidimensional optimization algorithm generates task data by constructing a task priority matrix and calculating task priorities, and the calculation expression of the task priority matrix is:

[0020] in, Indicates The priority of each task; Indicates the task About Resources Dependence of Representation Resources The weight of Representation Resources Current availability of is the total number of resources;

[0021] The generated task data is used to dynamically adjust task priorities and resource allocation strategies during system execution to ensure efficient system operation and optimal resource utilization.

[0022] Preferably, the step of generating a dynamic visualization model of the electrical schematic diagram based on the task data includes: updating the display status of the electrical schematic diagram and the three-dimensional simulation model in real time through a synchronous adjustment mechanism, and the synchronous adjustment mechanism includes a comprehensive calculation of the electrical component status, user interaction input and system feedback data.

[0023] A system for implementing the electrical schematic visualization method, comprising:

[0024] A multi-source data acquisition module, for collecting data from multiple sensors, simulation engines and user inputs, and performing standardization on the data to generate multi-dimensional model parameters for mapping electrical schematics to three-dimensional simulation models;

[0025] A data fusion and processing module, used to receive and fuse the multi-dimensional model parameters, generate mapping data for complex correlation modeling of the electrical system, and input the mapping data into a multi-level graph neural network and a dynamic cyclic correlation matrix;

[0026] An adaptive modeling and interaction module, for performing adaptive modeling and multi-level interaction processing using the mapping data, generating instant feedback data, short-term feedback data and long-term feedback data through a hierarchical feedback mechanism, and dynamically adjusting the interaction relationship between the electrical schematic diagram and the three-dimensional simulation model;

[0027] A task optimization and execution control module, which is used to generate task data for system execution and control based on the feedback data through a multi-dimensional optimization algorithm and a self-learning task scheduling model, and optimize the allocation of system resources and the execution order of tasks in real time;

[0028] The dynamic visualization and adjustment module is used to generate a dynamic visualization model of the electrical schematic diagram according to the task data, and to update the display status of the electrical schematic diagram and the three-dimensional simulation model in real time through a synchronous adjustment mechanism.

[0029] Compared with the prior art, the advantages and beneficial effects of the present invention are:

[0030] By introducing a dynamic cyclic correlation matrix of multi-dimensional model parameters and a three-dimensional simulation model, as well as a hierarchical feedback mechanism of adaptive modeling and multi-level interactive processing, the present invention realizes real-time dynamic mapping between electrical schematics and three-dimensional simulation models;

[0031] Through the multi-level adaptive modeling algorithm, the present invention ensures that the system can be dynamically adjusted according to real-time feedback data to maintain the synchronization and real-time performance of the electrical schematic diagram and the three-dimensional simulation model;

[0032] Through multi-dimensional optimization algorithms and self-learning task scheduling models, the present invention effectively improves the resource utilization efficiency of the system and significantly improves the accuracy and response speed of task execution;

[0033] In summary, the present invention not only overcomes the deficiencies of the prior art in terms of dynamics and real-time performance, but also enhances the complex correlation modeling and optimization capabilities of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic diagram of the process of the present invention;

[0035] Figure 2A flow chart of generation and processing of multi-dimensional model parameters in the present invention;

[0036] Figure 3 It is a diagram of adaptive modeling and multi-level interactive processing in the present invention;

[0037] Figure 4 Generate task data and dynamically visualize model updates for the present invention;

[0038] Figure 5 It is a structural block diagram of the system of the present invention. DETAILED DESCRIPTION

[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 exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is obvious that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0040] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0041] All terms (including technical and scientific terms) used herein 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] like Figure 1 As shown, a method for visualizing an electrical schematic diagram includes the following steps:

[0043] By collecting data from multiple sensors, simulation engines and user inputs, and fusing and processing the collected data, a multi-dimensional model parameter is generated, and the multi-dimensional model parameter is used for mapping the electrical schematic diagram with the three-dimensional simulation model;

[0044] like Figure 2 As shown, preferably, the step of collecting data from multiple sensors, simulation engines and user input includes: standardizing the collected data, wherein the standardization includes data normalization and denoising to generate standardized data with unified dimension and reduced interference for subsequent multi-dimensional model parameter generation.

[0045] Through multi-source data acquisition technology, the system can obtain core data of electrical system operation from a variety of sensors, such as voltage, current, temperature and other physical parameters. These data reflect the real-time status and interaction of electrical components, and these data are simulated by the simulation engine to further supplement and improve the dynamic changes in actual operation. These simulation data are usually based on the mathematical model of the electrical system, which can simulate complex behaviors that are difficult to measure directly in the actual environment. In addition, user input data provides the system with operating instructions and environmental parameter adjustments, so that the visualization process of the electrical schematic can be highly consistent with user needs and achieve personalized display effects.

[0046] For data collected from multiple sensors, simulation engines, and user inputs, in order to ensure that these data can be effectively used in the subsequent processing, the system first standardizes the data. Standardization includes data normalization and denoising. The purpose of normalization is to convert data of different dimensions into a unified dimension so that various types of data can be compared and analyzed on the same platform. Denoising is to remove noise from the data and retain real and meaningful information, thereby improving the accuracy and reliability of the data. This standardization process can significantly reduce the calculation errors caused by inconsistent data or noise interference, ensuring that the multi-dimensional model parameters generated subsequently are highly accurate and consistent.

[0047] The generated multi-dimensional model parameters are the basis for mapping between electrical schematics and 3D simulation models. These model parameters usually include multi-dimensional information such as the position, state, and interaction relationship of electrical components. Through these parameters, the system can accurately present the actual operating status of electrical components in 3D space and dynamically reflect the relationship between them. Specifically, when the electrical system changes, these parameters will be updated in real time and mapped to the 3D simulation model to ensure that the electrical schematics seen by users in the interactive interface are always consistent with the actual situation.

[0048] Through standardization and multi-dimensional model parameter generation, the system can not only improve the visualization accuracy of electrical schematics, but also enhance the dynamic response capabilities of complex electrical systems. For example, when processing signals from current sensors, after normalization and denoising, the generated current state parameters can be accurately mapped to the current path in the three-dimensional simulation model. Users can observe the dynamic process of current changes in real time in the visualization interface, and with the support of the simulation engine, they can also predict the trend of current changes, so as to take preventive measures in advance in actual operations. This multi-dimensional model parameter generation and mapping process enables the visualization of electrical schematics to not only have display functions, but also have real-time monitoring and early warning effects, greatly improving the management and maintenance efficiency of electrical systems.

[0049] Inputting the multi-dimensional model parameters into a multi-level graph neural network and a dynamic cyclic association matrix to generate mapping data reflecting a dynamic mapping relationship between electrical components and a three-dimensional simulation model, wherein the mapping data is used to establish complex associations of the electrical system;

[0050] Multidimensional model parameters are data extracted from multiple sensors, simulation engines, and user inputs. These parameters include the state, position, and interaction relationship of each component in the electrical system. The multidimensional characteristics of these data make it impossible for traditional simple mapping methods to accurately reflect the complexity and dynamics of the system. To solve this problem, the present invention introduces multi-level graph neural networks and dynamic cyclic association matrices, which can effectively process complex graph structure data and time series data, thereby realizing accurate dynamic mapping of electrical systems.

[0051] The multi-level graph neural network uses a multi-layer neural network structure to abstract and process the relationship between each component in the electrical system layer by layer. Each layer of the neural network reprocesses the output of the previous layer, thereby gradually extracting the deep correlation information between electrical components. In this way, the system can capture the nonlinear relationship and complex interaction effects between electrical components. For example, in a power grid system, the mutual influence of voltage and current between different nodes can be modeled by a multi-level graph neural network, so as to accurately reflect the electrical state of each node in the three-dimensional simulation model.

[0052] The dynamic cyclic association matrix is ​​used to capture the time series correlation between electrical components. Many changes in electrical systems have strong time dependencies, such as current fluctuations and voltage transients. These phenomena are usually not isolated, but driven by state changes of other components. Through the dynamic cyclic association matrix, the system can model these time dependencies and generate association data that reflects the dynamic relationship between components. This association data further enriches the system's understanding of the relationship between components on the basis of multi-level graph neural networks, so that the final generated mapping data can not only reflect the system status at the current moment, but also predict future trends.

[0053] In practical applications, when the multi-dimensional model parameters are input into the multi-level graph neural network and the dynamic cyclic association matrix, the system will generate a set of mapping data, which represents the dynamic mapping relationship between the electrical components and the three-dimensional simulation model. These mapping data are not only the basis for the construction of the three-dimensional simulation model, but also used to establish the complex association of the electrical system. In the three-dimensional simulation model, these mapping data can dynamically adjust the representation of the electrical components, so that users can observe the changes in the electrical system in real time. For example, when the state of an electrical component changes, this change will be transmitted to the three-dimensional simulation model in real time through the mapping data, so that the state update of the component or the response change of the associated component can be shown on the interface.

[0054] By establishing this mapping relationship, the visualization of the electrical schematic is no longer just a static component arrangement diagram, but a dynamic model that can reflect the operating status of the electrical system and the complex relationships between components in real time. Such visualization greatly improves the system's ability to manage complex electrical systems, allowing operators to more intuitively understand the system's operating mechanism and quickly locate the source of the problem and take targeted measures when abnormal situations occur. In addition, the dynamic and real-time nature of this mapping relationship also provides a data basis for predictive maintenance. By analyzing the time series characteristics in the mapping data, the system can predict possible faults in advance, reduce the downtime of the electrical system, and improve overall operating efficiency.

[0055] Preferably, the step of inputting the multidimensional model parameters into the multi-level graph neural network includes: performing multi-layer processing on the multidimensional model parameters through a nonlinear dynamic weight adjustment mechanism, wherein the nonlinear dynamic weight adjustment mechanism dynamically calculates weight values ​​based on real-time electrical component status data to ensure that the mapping relationship between the electrical components and the three-dimensional simulation model can adaptively reflect the complex correlation of the electrical system.

[0056] Multi-level graph neural network is a deep learning model that can process complex graph structure data. It is particularly suitable for modeling the relationship between components in electrical systems. The structure of an electrical system can usually be represented as a graph containing nodes and edges. Nodes represent electrical components, and edges represent electrical connections and interactions between components. Traditional graph neural network processing methods may not accurately reflect the real-time correlation between components when facing dynamically changing electrical systems. Therefore, it is necessary to introduce a dynamic adjustment mechanism to update these correlations in real time.

[0057] In the present invention, the nonlinear dynamic weight adjustment mechanism adjusts the weight values ​​between components layer by layer by performing multi-layer processing on the input multi-dimensional model parameters. Specifically, the state data of each electrical component (such as voltage, current, temperature, etc.) will be gradually processed in each layer of the neural network to generate intermediate feature representations. After these feature representations pass through the nonlinear dynamic weight adjustment mechanism, the weight values ​​will be dynamically adjusted according to the current system state and the mutual influence between components. This weight value adjustment process is not a linear superposition, but reflects the complex dependencies and interactions between components through nonlinear functions.

[0058] For example, in a large power grid system, voltage changes in a transformer may cause current fluctuations in multiple nodes connected to it. Through the nonlinear dynamic weight adjustment mechanism, the system can capture such fluctuations in real time and reflect them in the three-dimensional simulation model connected to it. This not only enables the simulation model to dynamically display the state changes of each component, but also accurately reflects the global response characteristics of the entire electrical system.

[0059] The effect of this mechanism is that by dynamically adjusting the weights, the mapping relationship between electrical components and 3D simulation models can adaptively reflect the real-time status of the electrical system. This adaptability means that the system can automatically adjust the representation of each component in the 3D model as the state of the electrical component changes, making the entire simulation process more accurate and real-time. For example, when faced with power grid fault analysis, the system can quickly locate the source of the fault by dynamically adjusting the weights between components, and display the propagation path and impact range of the fault through 3D simulation, thereby providing users with intuitive and accurate decision support.

[0060] The nonlinear dynamic weight adjustment mechanism processes multi-dimensional model parameters in multiple layers, so that the visualization of electrical schematics not only has the function of static display, but also can realize the real-time reflection of the dynamic changes of the electrical system. This processing method ensures that the mapping relationship between electrical components and 3D simulation models can be automatically adjusted with the operating status of the electrical system, thus providing users with a more realistic and dynamic visualization experience. Through this mechanism, the complex correlation of the electrical system is accurately modeled and displayed, which significantly improves the practicality of electrical schematics in monitoring, analysis and fault diagnosis.

[0061] Preferably, the calculation expression of the nonlinear dynamic weight adjustment mechanism is:

[0062] in, Indicates The electrical components and The weights between nodes of a three-dimensional simulation model; represents the initial weight; is the dynamic adjustment coefficient; is an adjustment function based on the dynamic relationship between electrical components; Real-time data association between electrical components.

[0063] In the expression Represents the mapping strength between a specific electrical component and a node in the 3D simulation model. Initial weight The base values ​​that define this mapping relationship are usually determined by the system during the initialization phase based on the basic structure and component characteristics of the electrical system. This initial weight sets a baseline relationship that reflects the default association between the component and the simulation model under static conditions.

[0064] The electrical system is highly dynamic in actual operation, and the state of each component will change over time. Therefore, the initial weight is not enough to accurately reflect the complex dynamic relationship between components. In order to solve this problem, the present invention introduces a dynamic adjustment coefficient and adjustment function . Dynamic adjustment coefficient Determines the magnitude of the weight adjustment, and its value is usually set based on the real-time needs of the system and the sensitivity of the electrical components. For example, when the system detects that the state change of an electrical component has a significant impact on other components, The value of can be appropriately increased to more strongly reflect this effect.

[0065] Adjustment function It is the core of the entire weight adjustment mechanism, which is based on the real-time data correlation between electrical components. To calculate the adjustment value. This function can take many forms, depending on the specific application scenario. For example, in a power system, It may be a nonlinear function based on physical parameters such as current and voltage, which is used to reflect the interaction strength between components. If the current at a certain node fluctuates violently, this change will be reflected by The function is reflected in the weight adjustment, which affects the representation of the 3D simulation model nodes associated with it. This dynamic adjustment ensures that the simulation model can respond to the state changes of the electrical system in real time and provide more accurate visualization effects.

[0066] Through this nonlinear dynamic weight adjustment mechanism, the system can adaptively reflect the complex correlation of the electrical system in the three-dimensional simulation model. For example, in a power grid monitoring system, when a transformer fails, the system can quickly adjust the weight according to real-time data, and the associated nodes in the simulation model will immediately reflect this change. Users can intuitively see the impact range and propagation path of the fault, so as to quickly take corresponding measures. This adaptive mapping relationship not only improves the accuracy of the simulation model, but also enhances the system's monitoring and early warning capabilities for complex electrical systems.

[0067] Preferably, the step of inputting the multidimensional model parameters into the dynamic cyclic association matrix includes: realizing complex correlation modeling between electrical components by constructing a time series model and a multidimensional feature matrix, wherein the time series model is used to capture the time correlation between electrical components, and the multidimensional feature matrix is ​​used to reflect the multidimensional attribute association between electrical components.

[0068] The interaction between components in an electrical system depends not only on their current state, but also on their historical state. This kind of temporal dependency is very common in electrical systems. For example, changes in voltage or current are often gradually accumulated and will affect other parts of the system within a certain period of time. In order to capture this temporal correlation, the present invention introduces a time series model. The time series model can identify the laws and trends of component states changing over time by analyzing the historical data of electrical components. For example, in a power grid, the current load of a transformer may fluctuate periodically over time. The time series model can capture these fluctuation patterns and incorporate them into the associative modeling of electrical components. This modeling of time correlation enables the system to more accurately predict the future state of components and to show the dynamic process of electrical component states changing over time in a three-dimensional simulation model.

[0069] Secondly, the association between electrical components is not only reflected in the time dimension, but also involves multi-dimensional physical and electrical properties. To this end, the present invention constructs a multi-dimensional feature matrix to comprehensively reflect the multi-dimensional attribute association between electrical components. The multi-dimensional feature matrix contains multiple attributes of electrical components, such as voltage, current, impedance, temperature, etc., and the multi-dimensional attributes of each component can be described and compared through this matrix. Through this multi-dimensional feature matrix, the system can capture the complex relationship between components, such as the current change of some components may cause the voltage fluctuation of other components, and the correlation between these attributes can be clearly expressed through the feature matrix. In practical applications, when a certain attribute of an electrical component changes, this change will be propagated to other components associated with it through the multi-dimensional feature matrix, and reflected in the three-dimensional simulation model, so that users can observe the linkage effect in the electrical system in real time.

[0070] For example, suppose that in a large industrial electrical system, the temperature of a motor begins to rise. This change will first be captured in the time series model, and the system will identify the rising trend of temperature and predict the risk of overtemperature in the future. At the same time, this temperature change will be associated with other properties of the motor (such as current and voltage) through a multi-dimensional feature matrix to analyze the potential impact of temperature rise on the entire electrical system. For example, temperature rise may cause an increase in current, which in turn affects other parts of the system. Through the dynamic loop association matrix, these complex correlations are integrated into the three-dimensional simulation model, and users can intuitively see the impact of the motor temperature rise on the entire system in the visual interface, and then take corresponding measures to regulate it.

[0071] By combining the time series model and the multi-dimensional feature matrix in a dynamic cyclic correlation matrix, the present invention realizes the comprehensive modeling and dynamic display of the complex correlation between electrical components. This modeling method not only enhances the display capability of the electrical schematic, but also greatly improves the system's ability to analyze and predict the complex interactions between electrical components. Ultimately, users can use the three-dimensional simulation model to fully and in real time understand the operating status of the electrical system and respond quickly when an abnormality occurs in the system, thereby improving the safety and reliability of the electrical system.

[0072] Use mapping data for adaptive modeling and multi-level interactive processing, generate feedback data for system optimization through a hierarchical feedback mechanism, and dynamically adjust the interactive relationship between the electrical schematic and the 3D simulation model;

[0073] Mapping data is extracted from the complex relationship between electrical components and 3D simulation models, and contains information about the status and interaction of each component in the electrical system. Through adaptive modeling, the system can adjust modeling parameters in real time based on these mapping data to adapt to the dynamic changes of the electrical system. The core of adaptive modeling is that the model can automatically adjust its own structure and parameters based on real-time input data, thereby ensuring that the model always accurately reflects the current state of the electrical system. For example, when the system detects a sudden increase in the current of an electrical component, the adaptive model will automatically adjust the relevant parameters to reflect this change, so that the current path in the 3D simulation model and the representation in the electrical schematic are updated synchronously, showing a more accurate electrical state.

[0074] Multi-level interactive processing further enhances the system's responsiveness. In electrical systems, the interactions between components are often multi-level and multi-dimensional. For example, a change in the state of a component may directly affect the components connected to it, but it may also indirectly affect other distant components through the global state of the system. Multi-level interactive processing can capture these direct and indirect effects and reflect them in the three-dimensional simulation model through multi-level model adjustments. This processing method enables the system to not only display the immediate state of the component, but also the far-reaching impact of its changes on the global system, providing richer visualization information.

[0075] The hierarchical feedback mechanism plays a key role in this process. The mechanism generates immediate feedback data, short-term feedback data, and long-term feedback data by providing feedback on the interaction results at different levels, which are used to further optimize the system model. For example, when an abnormality occurs in a certain circuit in the electrical schematic, the hierarchical feedback mechanism will first generate immediate feedback data to quickly adjust the three-dimensional simulation model display of the circuit, so that users can immediately see the visual representation of the abnormality. Subsequently, the short-term feedback data generated by the system will optimize the execution parameters of the current task to ensure that the abnormality will not further affect other parts of the system. Long-term feedback data is based on the global analysis of the system and provides suggestions for overall optimization of the system, which may include adjusting the load distribution of the entire electrical network or performing hardware upgrades.

[0076] For example, in a large power transmission system, if the load on a certain transformer suddenly increases, the system first uses the mapping data to adjust the three-dimensional simulation model of the transformer and its associated nodes through adaptive modeling to reflect the current changes caused by the load increase. Multi-level interactive processing captures the potential impact of this change on other parts of the system, and adjusts the display status of related loops in real time through a hierarchical feedback mechanism to ensure that the visualization model of the entire power transmission system can accurately reflect the current operating status. Instant feedback data will quickly update the display content near the transformer, short-term feedback data may suggest reducing the load on certain lines, and long-term feedback data may suggest re-planning the load distribution of the power grid.

[0077] like Figure 3 As shown, preferably, the step of using mapping data for adaptive modeling and multi-level interactive processing includes: generating instant feedback data, short-term feedback data and long-term feedback data through a hierarchical feedback mechanism, the instant feedback data is used to fine-tune the real-time display status of the electrical schematic and the three-dimensional simulation model, the short-term feedback data is used to dynamically optimize the electrical system parameters during the execution of the current task, and the long-term feedback data is used to adjust and optimize the global parameters based on the long-term accumulated data of the system's global performance.

[0078] Mapping data is extracted from the complex association between electrical components and 3D simulation models, which reflects the state changes of each component in the electrical system and their interactions. Based on adaptive modeling, the system further optimizes the display and control strategies at each level through a hierarchical feedback mechanism. The hierarchical feedback mechanism is a mechanism that can dynamically adjust the system operating parameters according to feedback information at different time scales, thereby providing the best system performance at different times.

[0079] Instant feedback data is the most critical part of the hierarchical feedback mechanism, which is mainly used to fine-tune the real-time display status of electrical schematics and three-dimensional simulation models. Instant feedback data quickly analyzes the current status and associated changes of electrical components to generate fine-tuning parameters for adjusting the display content. For example, in an electric power system, if the voltage of a transformer fluctuates abnormally, the instant feedback data will immediately trigger adjustments to the simulation model of the relevant nodes, reflecting the voltage fluctuations in real time, ensuring that users can see abnormal dynamic changes at the first time. This fine-tuning not only makes the display of the three-dimensional simulation model more accurate and flexible, but also helps users quickly identify and respond to emergencies, reducing the possibility of accidents.

[0080] Short-term feedback data focuses on dynamically optimizing electrical system parameters during the execution of the current task. Compared with immediate feedback, short-term feedback pays more attention to the stability and efficiency of system operation. When the system detects that the parameters of certain components need to be optimized to improve the overall operation effect, short-term feedback data will recommend adjusting these parameters. For example, in the process of power load management, if the load of a transmission line is close to its safety upper limit, short-term feedback data may trigger adjustments to the load distribution of other lines, reduce risks and optimize the overall performance of the system. This dynamic optimization ensures that the electrical system can operate efficiently and stably during execution, avoiding system performance degradation or safety hazards caused by unreasonable local parameters.

[0081] Long-term feedback data is based on the long-term accumulated data of the system's global performance and is mainly used for global parameter adjustment and optimization. By analyzing the historical data of system operation, long-term feedback data can identify potential problems that may exist in the system during long-term operation and make global optimization suggestions. For example, some equipment in the power system may gradually age over time, causing its performance to gradually decline. Long-term feedback data can predict future performance trends by analyzing the historical operation data of the equipment, and make recommendations for replacement or maintenance to avoid the impact of equipment failure on the entire system. In addition, long-term feedback data can also be used to optimize the overall configuration of the system, such as by adjusting the power distribution strategy to further improve the efficiency and reliability of the system.

[0082] Through the synergistic effect of immediate feedback, short-term feedback, and long-term feedback, the present invention can accurately control and optimize the electrical system on different time scales. Immediate feedback ensures the system's agile response to rapid changes, short-term feedback ensures stability and efficiency during task execution, and long-term feedback provides a solid foundation for the global optimization of the system. This multi-level feedback mechanism enables the visualization of electrical schematics to not only accurately reflect the current system status, but also ensure the long-term stable operation of the system through continuous optimization, providing users with comprehensive and dynamic system monitoring and management tools. In this way, users can not only intuitively see the current performance of the system, but also make necessary adjustments and optimizations in a timely manner through the guidance of the feedback mechanism, thereby maximizing the safety and efficiency of the system.

[0083] Preferably, the generation of the instant feedback data, short-term feedback data and long-term feedback data is achieved through a multi-layer adaptive modeling algorithm, which dynamically generates feedback data at each level based on real-time input electrical component operation data, system feedback data and user interaction data. The generated feedback data is directly applied to the real-time display of electrical schematics and three-dimensional simulation models and the adjustment of system parameters.

[0084] The multi-layer adaptive modeling algorithm is one of the cores of the present invention. It can adaptively adjust the model parameters according to different types of data input to generate feedback data that adapts to the current system state. Instant feedback data, short-term feedback data and long-term feedback data correspond to system adjustment requirements under different time scales, respectively. Instant feedback data is usually used to deal with emergencies or rapidly changing states in the system, such as sudden failures of electrical components or sharp changes in loads. Short-term feedback data is mainly used to optimize the system performance during the execution of the current task, ensuring high efficiency and stability during the execution process. Long-term feedback data is based on the long-term operation data of the system, providing optimization suggestions for global parameters to help the system maintain the best state over a longer time span.

[0085] Specifically, the system first collects real-time electrical component operation data, which includes parameters such as voltage, current, and temperature, reflecting the immediate status of the components. These operation data are input into the adaptive modeling algorithm, which analyzes these operation data based on the current system status and historical data to generate immediate feedback data. For example, when the temperature of an electrical component suddenly rises, it may indicate an overheating risk. The adaptive modeling algorithm will quickly capture this change, generate immediate feedback data, and immediately trigger adjustments to the 3D simulation model, so that users can see this state change in real time on the visual interface and take corresponding measures.

[0086] At the same time, the system will also generate short-term feedback data by combining current operation data and historical system feedback data. These data are used to adjust the execution parameters of the current task to ensure that the system remains stable and efficient in the next operation. For example, during the power grid load management process, if it is detected that the current load of a certain line is close to its safety upper limit, the short-term feedback data will recommend adjusting the power distribution to balance the load of other lines, thereby avoiding potential overload risks. This dynamic optimization not only improves the safety of the system, but also ensures the rational use of resources.

[0087] The generation of long-term feedback data is achieved through comprehensive analysis of the system's long-term operation data and user interaction data. These data provide the basis for the global optimization of the system. Long-term feedback data usually identifies potential trends and risks in the system over a long period of time, such as the gradual aging of certain equipment or the decline in efficiency of certain operating modes. With this data, the system can recommend users to make necessary equipment replacements, maintenance, or operating strategy adjustments to ensure the reliability and efficiency of the electrical system in long-term operation.

[0088] Feedback data at each level generated by the multi-layer adaptive modeling algorithm is directly applied to the real-time display of electrical schematics and three-dimensional simulation models and the adjustment of system parameters. This means that when the system detects any signal that needs to be adjusted, the feedback data will immediately act on the three-dimensional simulation model to display the latest system status. At the same time, these feedback data will automatically adjust the operating parameters of the system to ensure that the system can always operate in the best condition. For example, when faced with a sudden power grid failure, the immediate feedback data will immediately adjust the display content, the short-term feedback data will optimize the fault response strategy, and the long-term feedback data may suggest optimizing the power grid structure to reduce the probability of similar failures in the future.

[0089] Based on feedback data, through multi-dimensional optimization algorithms and self-learning task scheduling models, task data for system execution and control is generated, and the allocation of system resources and the execution order of tasks are optimized in real time;

[0090] Preferably, the step of using a multidimensional optimization algorithm and a self-learning task scheduling model based on feedback data includes: analyzing the dependency relationship of tasks through a multidimensional optimization algorithm, the multidimensional optimization algorithm generates task data by constructing a task priority matrix and calculating task priorities, and the calculation expression of the task priority matrix is:

[0091] in, Indicates The priority of each task; Indicates the task About Resources Dependence of Representation Resources The weight of Representation Resources Current availability of is the total number of resources;

[0092] The generated task data is used to dynamically adjust task priorities and resource allocation strategies during system execution to ensure efficient system operation and optimal resource utilization.

[0093] The multi-dimensional optimization algorithm plays a core role in this process. The algorithm analyzes feedback data and deeply explores the dependencies between tasks to construct a task priority matrix. Task dependencies include factors such as the mutual influence between tasks and resources, the order of tasks, and possible resource competition during task execution. By analyzing these dependencies, the multi-dimensional optimization algorithm can identify which tasks have a higher priority in the current environment and which tasks may need to wait for other resources to be released before execution. This analysis ensures that during the task scheduling process, each task can be executed at the most appropriate time, thereby avoiding waste of resources or conflicts.

[0094] In order to specifically quantify the priority of tasks, the multi-dimensional optimization algorithm generates task data by constructing a task priority matrix and calculating the task priority. The calculation expression of the task priority matrix is ​​shown above. This priority calculation formula comprehensively considers the task's dependency on resources, the importance of resources, and the current availability of resources to generate a priority score for each task. This score directly affects the scheduling order of tasks, ensuring that high-priority tasks can be executed at the earliest time point, while low-priority tasks will wait for more suitable resource conditions.

[0095] Through the self-learning task scheduling model, the system can continuously optimize the scheduling strategy based on real-time feedback data and historical task execution. The introduction of the self-learning model enables the system to have the ability to dynamically adjust and continuously improve the efficiency of task scheduling as the environment changes. For example, when the availability of certain resources changes significantly, the system will automatically adjust the task priority to optimize the efficiency of resource use. In a power grid load management system, if the load on a transmission line suddenly increases, the self-learning task scheduling model will quickly adjust the priority of related tasks and reallocate resources to ensure the stable operation of the entire power grid.

[0096] Through this optimization, the task data generated by the system is not only used to guide the execution order of the current task, but also used to dynamically adjust the resource allocation strategy. In this way, the system can optimize the allocation of resources in real time according to changes in the actual situation during operation, so that resources can be used most effectively where they are most needed, avoiding resource waste and task delays. For example, when a certain area in the power system suddenly needs a large amount of power transmission, the system can quickly reallocate power resources by adjusting task priorities to ensure that critical tasks are given priority.

[0097] According to the task data, a dynamic visualization model of the electrical schematic diagram is generated, and the display status of the electrical schematic diagram and the three-dimensional simulation model is updated in real time through a synchronous adjustment mechanism.

[0098] like Figure 4 As shown, preferably, the step of generating a dynamic visualization model of the electrical schematic diagram based on the task data includes: updating the display status of the electrical schematic diagram and the three-dimensional simulation model in real time through a synchronous adjustment mechanism, and the synchronous adjustment mechanism includes a comprehensive calculation of the electrical component status, user interaction input and system feedback data.

[0099] The generation of dynamic visualization models depends on task data. Task data includes key parameters, task priorities, and resource allocation in the current operation of the electrical system. These data directly reflect the current operating status of the system and are the basis for building dynamic visualization models. Through task data, the system can accurately determine the status of electrical components in the 3D simulation model, including the physical properties (such as voltage, current, temperature) and logical properties (such as switch status, fault conditions) of the components. After comprehensive calculation of these status data, a dynamic visualization model is generated, which can truly reproduce the actual situation of the electrical system in 3D space.

[0100] The synchronous adjustment mechanism plays a key role in this process. The synchronous adjustment mechanism means that after the system generates a dynamic visualization model, it adjusts the display status of the electrical schematic and the 3D simulation model in real time to ensure that they are synchronized with the actual electrical system status. This mechanism includes the comprehensive calculation of electrical component status, user interaction input and system feedback data. The electrical component status data is the electrical parameters monitored by the system in real time, such as voltage, current, frequency, etc., and the changes in these parameters will be directly reflected in the 3D simulation model. For example, when the temperature of an electrical component rises, the system immediately updates the color or other visual representation of the component in the 3D model through the synchronous adjustment mechanism, prompting the user to pay attention to potential overheating problems.

[0101] User interactive input is another important part of the synchronous adjustment mechanism. In the visual interface, users can adjust or set the electrical system through the operation interface, such as changing the circuit switch state or adjusting the load distribution. The system will combine these user inputs with the current task data and electrical component status, and update the display status of the 3D simulation model in real time to ensure that users can see the immediate effect after the operation. For example, when the user closes an electrical circuit, the 3D simulation model will immediately reflect this operation and display the status after the circuit is interrupted, thereby helping users intuitively understand the impact of the operation on the system.

[0102] The system feedback data comes from the operation status of the electrical system itself and the accumulation of historical data. These feedback data are used to optimize the synchronization adjustment mechanism to ensure that the system can respond optimally under different circumstances. By analyzing the feedback data, the system can predict possible future state changes and make adjustments in advance. For example, when the load increases, the system can adjust the power distribution strategy based on the feedback data and display the adjusted effect in real time in the 3D simulation model, helping users to foresee and solve possible problems in advance.

[0103] For example, in a large industrial power management system, if the load of a key device suddenly increases, the system first generates a dynamic visualization model through task data to show the current load situation. Subsequently, the system updates the display status of the electrical schematic and the three-dimensional simulation model in real time through a synchronous adjustment mechanism, allowing users to intuitively see the impact of the load increase. At the same time, users can adjust the system through the interface, such as reducing the load of other equipment or switching the power path. These operations will be immediately reflected in the visualization model, ensuring that users have a sense of control over the entire system.

[0104] like Figure 5 As shown, a system for implementing the electrical schematic visualization method comprises:

[0105] The multi-source data acquisition module is used to collect data from multiple sensors, simulation engines and user inputs, and standardize the data to generate multi-dimensional model parameters for mapping electrical schematics to three-dimensional simulation models; this module collects data from multiple sensors, simulation engines and user inputs, and standardizes the data to generate unified multi-dimensional model parameters. These parameters serve as the basis for further processing by the system, ensuring that data from different sources can be analyzed and applied under the same standard.

[0106] The data fusion and processing module is used to receive and fuse the multi-dimensional model parameters, generate mapping data for complex correlation modeling of the electrical system, and input the mapping data into the multi-level graph neural network and the dynamic cyclic correlation matrix; after receiving the multi-dimensional model parameters, this module fuses these parameters to generate mapping data reflecting the complex correlation of the electrical system. By inputting these mapping data into the multi-level graph neural network and the dynamic cyclic correlation matrix, the system can build a complex relationship model between electrical components, thereby accurately reflecting the correlation between the electrical schematic and the three-dimensional simulation model.

[0107] The adaptive modeling and interaction module is used to use the mapping data to perform adaptive modeling and multi-level interactive processing, generate instant feedback data, short-term feedback data and long-term feedback data through a hierarchical feedback mechanism, and dynamically adjust the interactive relationship between the electrical schematic diagram and the three-dimensional simulation model; use the mapping data to perform adaptive modeling and multi-level interactive processing, and generate instant, short-term and long-term feedback data through a hierarchical feedback mechanism. The core of this module is to be able to dynamically adjust the interactive relationship between the electrical schematic diagram and the three-dimensional simulation model, so that the system can respond to changes in the state of electrical components in real time, ensuring the accuracy and real-time performance of the visualization model.

[0108] The task optimization and execution control module is used to generate task data for system execution and control based on the feedback data through a multi-dimensional optimization algorithm and a self-learning task scheduling model, and optimize the allocation of system resources and the execution order of tasks in real time; based on the feedback data, the module generates task data for system execution and control through a multi-dimensional optimization algorithm and a self-learning task scheduling model. These data help the system optimize resource allocation and task execution order, ensuring the operating efficiency and stability of the electrical system.

[0109] The dynamic visualization and adjustment module is used to generate a dynamic visualization model of the electrical schematic diagram according to the task data, and update the display status of the electrical schematic diagram and the three-dimensional simulation model in real time through a synchronous adjustment mechanism. This module generates a dynamic visualization model of the electrical schematic diagram according to the task data, and updates the display status of the electrical schematic diagram and the three-dimensional simulation model in real time through a synchronous adjustment mechanism. This dynamic visualization ensures that the user can view the current status of the system at any time and make adjustments when necessary to deal with emergencies or long-term trends in the system.

[0110] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware.

[0111] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for visualizing an electrical schematic diagram, characterized in that: The following steps are involved: By collecting data from multiple sensors, simulation engines and user inputs, and fusing and processing the collected data, a multi-dimensional model parameter is generated, and the multi-dimensional model parameter is used for mapping the electrical schematic diagram with the three-dimensional simulation model; Inputting the multi-dimensional model parameters into a multi-level graph neural network and a dynamic cyclic association matrix to generate mapping data reflecting a dynamic mapping relationship between electrical components and a three-dimensional simulation model, wherein the mapping data is used to establish complex associations of the electrical system; The step of inputting the multi-dimensional model parameters into the multi-level graph neural network includes: performing multi-layer processing on the multi-dimensional model parameters through a nonlinear dynamic weight adjustment mechanism, wherein the nonlinear dynamic weight adjustment mechanism dynamically calculates weight values ​​according to real-time electrical component state data to ensure that the mapping relationship between the electrical components and the three-dimensional simulation model can adaptively reflect the complex correlation of the electrical system; The step of inputting the multi-dimensional model parameters into the dynamic cyclic correlation matrix includes: implementing complex correlation modeling between electrical components by constructing a time series model and a multi-dimensional feature matrix, wherein the time series model is used to capture the time correlation between electrical components, and the multi-dimensional feature matrix is ​​used to reflect the multi-dimensional attribute correlation between electrical components; Use mapping data for adaptive modeling and multi-level interactive processing, generate feedback data for system optimization through a hierarchical feedback mechanism, and dynamically adjust the interactive relationship between the electrical schematic and the 3D simulation model; The step of using the mapping data to perform adaptive modeling and multi-level interactive processing includes: generating instant feedback data, short-term feedback data and long-term feedback data through a hierarchical feedback mechanism, wherein the instant feedback data is used to fine-tune the real-time display status of the electrical schematic diagram and the three-dimensional simulation model, the short-term feedback data is used to dynamically optimize the electrical system parameters during the execution of the current task, and the long-term feedback data is used to adjust and optimize the global parameters based on the long-term accumulated data of the system's global performance; The generation of the instant feedback data, short-term feedback data and long-term feedback data is achieved through a multi-layer adaptive modeling algorithm, which dynamically generates feedback data at each level according to real-time input electrical component operation data, system feedback data and user interaction data, and the generated feedback data is directly applied to the real-time display of the electrical schematic diagram and the three-dimensional simulation model and the adjustment of system parameters; Based on feedback data, through multi-dimensional optimization algorithms and self-learning task scheduling models, task data for system execution and control is generated, and the allocation of system resources and the execution order of tasks are optimized in real time; According to the task data, a dynamic visualization model of the electrical schematic diagram is generated, and the display status of the electrical schematic diagram and the three-dimensional simulation model is updated in real time through a synchronous adjustment mechanism.

2. The method for visualizing an electrical schematic diagram according to claim 1, characterized in that: The step of collecting data from multiple sensors, simulation engines and user inputs includes: standardizing the collected data, wherein the standardization includes data normalization and denoising to generate standardized data with unified dimensions and reduced interference for subsequent generation of multi-dimensional model parameters.

3. The method for visualizing an electrical schematic diagram according to claim 1, characterized in that: The calculation expression of the nonlinear dynamic weight adjustment mechanism is: ,in, Indicates The electrical components and The weights between nodes of a three-dimensional simulation model; represents the initial weight; is the dynamic adjustment coefficient; is an adjustment function based on the dynamic relationship between electrical components; Real-time data association between electrical components.

4. The method for visualizing an electrical schematic diagram according to claim 1, characterized in that: The step of generating task data for system execution and control based on feedback data through a multidimensional optimization algorithm and a self-learning task scheduling model includes: analyzing the dependency relationship of tasks through a multidimensional optimization algorithm, the multidimensional optimization algorithm generates task data by constructing a task priority matrix and calculating task priorities, and the calculation expression of the task priority is: ,in, Indicates The priority of each task; Representation Task About Resources Dependence of Representation Resources The weight of Representation Resources Current availability of is the total number of resources; The generated task data is used to dynamically adjust task priorities and resource allocation strategies during system execution to ensure efficient system operation and optimal resource utilization.

5. The method for visualizing an electrical schematic diagram according to claim 1, characterized in that: The step of generating a dynamic visualization model of an electrical schematic diagram according to task data includes: updating the display status of the electrical schematic diagram and the three-dimensional simulation model in real time through a synchronous adjustment mechanism, wherein the synchronous adjustment mechanism includes a comprehensive calculation of electrical component status, user interaction input and system feedback data.

6. A system for implementing the method for visualizing an electrical schematic diagram according to any one of claims 1 to 5, characterized in that: include: A multi-source data acquisition module, for collecting data from multiple sensors, simulation engines and user inputs, and performing standardization on the data to generate multi-dimensional model parameters for mapping electrical schematics to three-dimensional simulation models; A data fusion and processing module, used for receiving and fusing the multi-dimensional model parameters, and inputting the multi-dimensional model parameters into a multi-level graph neural network and a dynamic cyclic association matrix to generate mapping data for complex association modeling of an electrical system; An adaptive modeling and interaction module, for performing adaptive modeling and multi-level interaction processing using the mapping data, generating instant feedback data, short-term feedback data and long-term feedback data through a hierarchical feedback mechanism, and dynamically adjusting the interaction relationship between the electrical schematic diagram and the three-dimensional simulation model; A task optimization and execution control module, which is used to generate task data for system execution and control based on the feedback data through a multi-dimensional optimization algorithm and a self-learning task scheduling model, and optimize the allocation of system resources and the execution order of tasks in real time; The dynamic visualization and adjustment module is used to generate a dynamic visualization model of the electrical schematic diagram according to the task data, and to update the display status of the electrical schematic diagram and the three-dimensional simulation model in real time through a synchronous adjustment mechanism.

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