A configuration method and system based on BIM digital base

Abstract

This application relates to the field of data processing technology and provides a configuration method and system based on BIM digital base. Through the automated analysis and component feature extraction of BIM models, as well as the matching of configuration elements and derivation of control mechanisms, it greatly reduces manual intervention and reduces the uncertainty and error risks brought by human operation, ensuring that the entire configuration process is more efficient, accurate and stable; it derives the control mechanism based on topological relationships, and dynamically updates the visualization status based on real-time external data, so as to be able to closely follow the changes in equipment working conditions and respond, ensure the smoothness of collaborative work between equipment, and improve overall operational efficiency; it opens up the interactive channel between BIM model data, real-time equipment operation data and control instructions, realizes the organic integration of building information, equipment operation information and control logic, and lays a solid data and technical foundation for subsequent advanced applications such as intelligent operation and maintenance and digital twins.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a configuration method and system based on a BIM digital base. Background Art

[0002] With the continuous development of building information modeling (BIM) technology and industrial automation configuration technology, both have achieved remarkable results in their respective fields. BIM technology uses three-dimensional digital models as a carrier to integrate geometric information and semantic attributes of buildings or equipment throughout their life cycle, and can achieve accurate modeling and visual expression of physical objects. It has been widely used in architectural design, construction management, and facility operation and maintenance. Configuration technology uses graphical interface configuration to associate sensor data with virtual components, realizing real-time monitoring and control of industrial equipment, and plays an important role in industrial automation scenarios such as power, chemical and manufacturing.

[0003] Existing technologies still have shortcomings. On the one hand, traditional BIM applications focus on static model display and lack deep interaction with external data. It is difficult to dynamically reflect changes in equipment operating status and cannot meet the real-time and interactivity requirements of intelligent equipment operation and maintenance. On the other hand, industrial configuration technology mostly relies on manual configuration, requiring manual drawing of components and writing of control logic. When faced with complex equipment systems, it is not only inefficient, but also difficult to accurately match the spatial structure and topological relationship of the equipment, resulting in a disconnect between the control logic and actual working conditions.

[0004] Furthermore, existing BIM and configuration technologies are independent of each other, making it impossible to achieve the coordinated linkage of building information, equipment operation data, and control instructions, and thus failing to provide a comprehensive and unified data and control foundation for intelligent operations and maintenance. Most technologies fail to address how to integrate BIM models with configuration technologies to achieve model analysis, configuration component matching, and visualization. Summary of the Invention

[0005] In response to the deficiencies in the prior art, the present application provides a configuration method and system based on a BIM digital base.

[0006] In a first aspect, the present application provides a configuration method based on a BIM digital base, the method comprising: parsing a BIM model of a target device and extracting component attributes including geometric information, semantic attributes, and topological relationships of the components, identifying and marking configurable components, and generating component features including component identification, spatial coordinates, and component attributes;

[0007] Based on the domain knowledge base and component characteristics, the configuration elements associated with the configurable components are matched, the initial layout position of the configuration elements in the BIM model is determined according to the spatial coordinates, and the control mechanism of the configuration elements is derived based on the topological relationship. At the same time, a data channel with external data is established to determine the display properties of the configuration elements and the binding relationship between the component's visual state and external data, and the state mapping relationship;

[0008] Monitor the real-time external data of the target device, update the display properties of the configuration elements according to the binding relationship, and dynamically drive the visual state changes of the components based on the state mapping relationship. At the same time, respond to the user's operation on the configuration elements and send control instructions to the target device, and synchronously update the visual state of the configuration elements and components based on the execution results of the target device.

[0009] As an optional implementation, the component feature generation strategy includes:

[0010] According to the preset judgment rules and component properties, configurable components are identified and marked from the BIM model. At the same time, the spatial position relationship between components is analyzed through the spatial topology analysis algorithm to form spatial coordinates;

[0011] Encode the geometric information and semantic attributes of the component through the encrypted hash algorithm to generate the component identification;

[0012] Integrate component identification, spatial coordinates and component properties to generate component features.

[0013] As an optional implementation, the component attribute extraction sub-strategy includes:

[0014] Analyze the structured data of the BIM model of the target equipment, divide the geometric contours of the components in the BIM model through the point cloud segmentation algorithm, and extract the geometric information of the components;

[0015] Use natural language processing technology to perform semantic analysis on the attribute text of the BIM model, extract the semantic attributes of the components, and associate and match the semantic attributes of the components with the domain knowledge base;

[0016] The component topology structure is constructed based on the graph theory algorithm, with components as nodes and the connection relationships between components as edges. The component topology structure is analyzed to determine the topological relationships of the components and form component properties.

[0017] As an optional implementation, the derivation strategy of the control mechanism of the configuration element includes:

[0018] Based on the component topology, the path search algorithm is used to identify the connection paths and hierarchical relationships between components. The basic control mechanism is called based on the domain knowledge base, and the nodes of the component topology are marked as the trigger points of the basic control mechanism.

[0019] Extract spatial constraints based on the initial layout positions of configuration components in the BIM model, and transform them into additional rules for the basic control mechanism to form a constraint control mechanism.

[0020] The constraint control mechanism is used as the initial strategy of reinforcement learning, and the operating efficiency and energy consumption of the target equipment are used as the reward function of reinforcement learning. Multiple rounds of iterative training are carried out in combination with external data to derive the control mechanism of the configuration elements.

[0021] As an optional implementation, the matching sub-strategy of the configuration element includes:

[0022] Matching the semantic attributes of the configurable component with the semantic labels of the configurable elements in the domain knowledge base, and calculating the semantic similarity scores between the configurable component and the configurable element to screen out candidate configurable elements;

[0023] The state space of reinforcement learning is constructed by combining the topological relationship and geometric information of the components, and the adaptability of the candidate configuration components is used as the reward function of reinforcement learning;

[0024] Through multiple rounds of iterative training of reinforcement learning, candidate configuration elements are dynamically optimized and configuration elements associated with configurable components are matched.

[0025] As an optional implementation manner, the sub-strategy for determining the binding relationship includes:

[0026] Perform multi-dimensional analysis on external data and extract data features through principal component analysis. At the same time, analyze the display properties of configuration elements and parameter requirements of component visualization status to form attribute parameters.

[0027] With the data features of external data as the horizontal dimension and the attribute parameters as the vertical dimension, a feature mapping matrix is ​​constructed. The matching degree between the data features of external data and the attribute parameters is calculated using the cosine similarity algorithm to generate the initial binding relationship.

[0028] Taking data transmission efficiency and visualization accuracy as fitness functions, the initial binding relationship is iteratively optimized through genetic algorithm, and the binding relationship between external data and attribute parameters is determined through selection, crossover and mutation operations.

[0029] As an optional implementation, the update strategy of the visualization status of the configuration elements and components includes:

[0030] Monitor and respond to user operations on configuration components, convert the user operations on configuration components into control instructions based on the domain knowledge base, and transmit the control instructions to the target device;

[0031] Receive the execution result of the target device, verify the expected result of the control instruction and the execution validity of the execution result, and modify the display properties of the configuration component according to the execution validity and binding relationship;

[0032] The geometric shape and appearance status of the components are updated based on the execution validity and mapping actions, and the status update of the associated components is triggered according to the topological relationship of the components.

[0033] As an optional implementation, the sub-strategy for updating the display attributes of the configuration element includes:

[0034] Monitor the real-time external data of the target device and perform multi-scale analysis on it to extract spatiotemporal correlation features. Calculate the matching degree between the spatiotemporal correlation features and the feature templates in the binding relationship through cosine similarity to filter out update actions.

[0035] Dynamically adjust the display attributes of the configuration components according to the update action, and determine the type of the display attribute to determine the rendering parameters of the display attribute. The types of display attributes include numerical attributes, trend attributes, and abnormal attributes.

[0036] The state space of reinforcement learning is constructed according to the spatiotemporal correlation characteristics and the type of display attributes. The rendering parameter combination of display attributes is used as the action space of reinforcement learning. The reward function of reinforcement learning is comprehensively determined based on the efficiency of information transmission, visual comfort and convenience of interaction. Reinforcement learning is trained to optimize the visual mapping effect of display attributes.

[0037] As an optional implementation, the driving sub-strategy for the component visualization state change includes:

[0038] According to the spatiotemporal correlation characteristics, the mapping action associated with the component is found from the state mapping relationship, and the external data is converted into the visualization parameters of the component. The visualization parameters include geometric form, appearance state and rendering effect.

[0039] Determine the impact range of component visualization state changes based on the connection relationship between components, and modify the component's geometry and appearance according to the mapping action;

[0040] Dynamically adjust the rendering effect of the component according to the importance of the visualization state, and respond to the user's operation on the configuration element to feedback the visualization parameters of the component.

[0041] In a second aspect, the present application provides a configuration system based on a BIM digital base, which includes: a feature extraction module, a configuration configuration module and a mapping feedback module.

[0042] The feature extraction module is used to parse the BIM model of the target equipment and extract component properties including the geometric information, semantic attributes and topological relationships of the components, identify and mark configurable components, and generate component features including component identification, spatial coordinates and component attributes.

[0043] The configuration configuration module is used to match the configuration elements associated with the configurable components based on the domain knowledge base and component characteristics, determine the initial layout position of the configuration elements in the BIM model according to the spatial coordinates, and derive the control mechanism of the configuration elements based on the topological relationship. At the same time, it establishes a data channel with external data, configures the display properties of the configuration elements and the binding relationship between the component's visualization state and external data, and determines the state mapping relationship.

[0044] The mapping feedback module is used to monitor the real-time external data of the target device, update the display properties of the configuration elements according to the binding relationship, and dynamically drive the visual state changes of the components based on the state mapping relationship. At the same time, it responds to the user's operation on the configuration elements and sends control instructions to the target device, and synchronously updates the visual state of the configuration elements and components based on the execution results of the target device.

[0045] Compared with the existing technology, the beneficial effects of this application are: through the automated analysis and component feature extraction of BIM models, as well as the matching of configuration elements and the derivation of control mechanisms, manual intervention is greatly reduced and the uncertainty and error risks brought by human operations are reduced, ensuring that the entire configuration process is more efficient, accurate and stable; the control mechanism is derived based on the topological relationship, and the visualization status is dynamically updated according to real-time external data, so that it can closely follow the changes in equipment working conditions and respond, ensuring the smoothness of collaborative work between equipment and improving overall operational efficiency; real-time and intuitive visualization status feedback, and a convenient user operation response mechanism enable users to quickly and clearly grasp the equipment operating status, and efficiently control and manage equipment, significantly reducing the difficulty and complexity of operation and maintenance operations; it opens up the interaction channel between BIM model data, real-time equipment operation data and control instructions, and realizes the organic integration of building information, equipment operation information and control logic, laying a solid data and technical foundation for subsequent advanced applications such as intelligent operation and maintenance and digital twins.

[0046] Through point cloud segmentation algorithms, natural language processing technology and graph theory algorithms, the geometric information, semantic attributes and topological relationships of components are extracted respectively. Compared with a single data extraction method, it can obtain complete information of components in an all-round and in-depth manner, ensure the comprehensiveness and accuracy of the data, and provide reliable data support for subsequent work; identify and mark configurable components, and accurately screen out components that really need to be configured, avoiding invalid data processing and calculation of irrelevant components, greatly improving overall operating efficiency and reducing resource waste; generate component identification and integrate various types of information into component features, construct a standardized and structured data format, and provide a unified and standardized data interface for the subsequent process, ensuring the consistency and traceability of data in the interaction process of each link, and effectively reducing the possibility of errors in data interaction.

[0047] Configuration components are matched based on the domain knowledge base and component features, changing the limitations of traditional reliance on semantic matching. The compatibility of configuration components and components is evaluated from multiple dimensions, significantly improving the accuracy and reliability of matching, ensuring that the selected configuration components can perfectly match the component functions, and avoiding the problem of functions not being able to be properly implemented due to improper selection of configuration components; the control mechanism is derived based on spatial coordinates and topological relationships, breaking through the bottleneck of low efficiency and difficulty in adapting to complex working conditions of traditional manually written control rules. The automatically derived control mechanism can better fit the actual operation logic of the equipment, which not only improves the efficiency of control mechanism generation, but also enhances the applicability and effectiveness of the control mechanism in actual operation.

[0048] It monitors real-time external data and updates the display properties of configuration components in a timely manner according to the binding relationship, and dynamically drives the visual status changes of components based on the status mapping relationship, which changes the defects of traditional static monitoring information lag. It can reflect the changes in the operating status of the equipment in real time and dynamically, so that users can grasp the latest situation of the equipment at the first time, providing strong support for timely decision-making; by responding to user operations, verifying the execution results of control instructions and synchronously updating the visual status, a complete closed-loop control system is constructed. Compared with the traditional one-way control process, this closed-loop system can timely discover and correct deviations in the control process, ensure the accurate execution of control instructions, and effectively improve the stability and reliability of operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be derived from these drawings without inventive work. Among them:

[0050] Figure 1A flowchart of a configuration method based on a BIM digital base provided in an embodiment of the present application;

[0051] Figure 2 A matching sub-strategy diagram of configuration elements of a configuration method based on a BIM digital base provided in an embodiment of the present application;

[0052] Figure 3 A sub-strategy diagram for determining the binding relationship of a configuration method based on a BIM digital base provided in an embodiment of the present application. DETAILED DESCRIPTION

[0053] In order to make the objectives, technical solutions and advantages of the embodiments of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application are clearly and completely described below in conjunction with the drawings in the specification. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments.

[0054] Example 1

[0055] like Figure 1 As shown, a method flow chart of a configuration method based on a BIM digital base is provided for an embodiment of the present application, and the method includes:

[0056] S1. Analyze the BIM model of the target device and extract component attributes including geometric information, semantic attributes and topological relationships of the components, identify and mark configurable components, and generate component features including component identification, spatial coordinates and component attributes.

[0057] The extraction sub-strategies of component attributes include:

[0058] Analyze the structured data of the BIM model of the target equipment, divide the geometric contours of the components in the BIM model through the point cloud segmentation algorithm, and extract the geometric information of the components;

[0059] Use natural language processing technology to perform semantic analysis on the attribute text of the BIM model, extract the semantic attributes of the components, and associate and match the semantic attributes of the components with the domain knowledge base;

[0060] The component topology structure is constructed based on the graph theory algorithm, with components as nodes and the connection relationships between components as edges. The component topology structure is analyzed to determine the topological relationships of the components and form component properties.

[0061] The geometric information of components in the BIM model is the basis for realizing visual configuration. Accurate geometric contours and size parameters can ensure the authenticity and accuracy of the subsequent configuration component layout and visualization effect; extract the BIM model of the target device, convert the unstructured data in the BIM model into structured data, and import the BIM model of the target device into the parsing engine, and process the BIM model through the point cloud segmentation algorithm. The point cloud segmentation algorithm first converts the three-dimensional data in the BIM model into point cloud data, and by analyzing the density and distribution characteristics of the point cloud, the point cloud belonging to the same component is divided together through the region growing algorithm, thereby completing the division of the geometric contour, and then extracting the component's size parameters, shape characteristics and spatial positioning information from the divided geometric contour. The component's size parameters include length, width and height, the shape characteristics include cylinder and cuboid, and the spatial positioning information includes coordinates and orientation.

[0062] This enables the automated and precise extraction of geometric information of components in the BIM model. Compared with manual measurement or simple model analysis, it greatly improves the efficiency and accuracy of information extraction and reduces human errors. The extracted geometric information provides key data for the generation of component properties, among which spatial positioning information is directly used to generate spatial coordinates. Size and shape characteristics, as important components of component properties, also provide basic data support for the subsequent identification of configurable components and determination of layout positions based on component properties.

[0063] The semantic attributes of a component include important information such as its function and material. This information is key to understanding the characteristics and purpose of the component, and helps to accurately match functions and deduce control mechanisms during the configuration process. The attribute text of the BIM model is analyzed through natural language processing technology. Keywords in the attribute text are extracted through operations such as word segmentation, part-of-speech tagging, and named entity recognition, thereby obtaining the semantic attributes of the component. The extracted semantic attributes are then associated and matched with the domain knowledge base. The domain knowledge base pre-stores standard descriptions and associations of the semantic attributes of various components. The semantic description of the component is improved through keyword matching and semantic similarity calculation. Preferably, the valve component is supplemented with information such as its category and applicable working conditions. The semantic information of the component is deeply mined from the attribute text of the BIM model to make the description of the component more comprehensive and accurate, overcoming the problem that it is difficult to understand the functional characteristics of the component by relying solely on geometric information, and providing rich semantic support for subsequent intelligent configuration.

[0064] The topological relationship between components determines their functional connection and influence mode. Clarifying the topological relationship helps to deduce the control mechanism of the configuration elements and realize the coordinated operation and linkage control of the target equipment. Based on the graph theory algorithm, the components are regarded as nodes and the connection relationship between components as edges to construct the component topology structure, where the connection relationship between components includes pipeline connection and circuit connection. Then, by analyzing the component topology structure, the connection path and hierarchical relationship between components are identified through the path search algorithm, and then the topological relationship of the components is determined. Preferably, a depth-first search is used to determine the series topology of the pipeline connecting the pump and the valve, as well as the role and influence relationship of each component therein.

[0065] Constructing topological relationships based on graph theory can clearly and systematically describe the complex connection relationships between components, providing intuitive and structured data support for the derivation of control mechanisms. The determined topological relationships serve as the core content of component properties. After the component features are generated, they are used to derive the control mechanisms of configuration elements. At the same time, they also affect the linkage relationships between associated components when the visualization state changes.

[0066] Strategies for generating component features include:

[0067] According to the preset judgment rules and component properties, configurable components are identified and marked from the BIM model. At the same time, the spatial position relationship between components is analyzed through the spatial topology analysis algorithm to form spatial coordinates;

[0068] Encode the geometric information and semantic attributes of the component through the encrypted hash algorithm to generate the component identification;

[0069] Integrate component identification, spatial coordinates and component properties to generate component features.

[0070] Not all components require configuration operations. Identifying configurable components can reduce unnecessary calculations and processing and improve configuration efficiency. Spatial coordinates are the key to determining the layout position of configuration components, which directly affects the visualization effect and user operation experience. According to the preset judgment rules, that is, whether the component involves parameter adjustment and whether it is a key control component, etc., and then combined with the extracted component properties, components that can be configured are screened out from the BIM model and marked as configurable components. At the same time, the spatial position relationship between components is analyzed through the spatial topology analysis algorithm, and the spatial coordinates are formed by calculating the coordinate values ​​and orientation parameters of the components in three-dimensional space. This achieves accurate screening of configurable components, avoids resource waste, and improves operating efficiency. Accurate spatial coordinates lay the foundation for the reasonable layout of subsequent configuration components in the BIM model, making the visualization interface more consistent with the spatial structure of the actual equipment, reducing the complexity of layout planning, and facilitating user intuitive understanding and operation.

[0071] A unique identifier is generated for each component, which can accurately distinguish and track different components in the complex BIM model and data interaction process, ensuring the accuracy and consistency of data processing and operation; through the encrypted hash algorithm, the key features of the component's geometric information and semantic attributes are selected and combined, that is, the key size parameters and the unique name identifier are combined, and the combined data is input into the encrypted hash algorithm to generate a fixed-length and unique code as the component identifier. Due to the irreversibility and uniqueness of the encrypted hash algorithm, the uniqueness and security of each component identifier can be guaranteed; thereby providing a reliable unique identity identifier for the component. During data transmission, storage and processing, the component identifier can be used to quickly locate and identify the component, avoid confusion caused by repeated names or similar information, and improve the accuracy and efficiency of data management. As the core element of the component feature, the component identifier is used in subsequent steps to associate the component with configuration elements and external data and other information. Preferably, in the process of determining the binding relationship, the component identifier is used to accurately establish the correspondence between the component's visual state and the external data to ensure the accuracy of data binding.

[0072] Integrate component identification, spatial coordinates and component properties into component features to form a unified data structure, which facilitates the call and processing of component information in subsequent steps and improves data usage efficiency; establish a structured data format, and store and integrate the generated component identification, spatial coordinates and complete component properties according to the preset data format and hierarchical relationship to form a complete component feature; thereby constructing standardized and structured component features, making component information clearer and more orderly, facilitating users to quickly read and process, and reducing the difficulty of data interaction and analysis. The integrated component features provide a comprehensive and standardized data foundation for subsequent steps. Based on the component features, it is possible to efficiently match configuration elements, derive control mechanisms and establish data binding relationships, thereby promoting the smooth progress of the entire configuration process.

[0073] S2. Based on the domain knowledge base and component characteristics, the configuration elements associated with the configurable components are matched, the initial layout position of the configuration elements in the BIM model is determined according to the spatial coordinates, and the control mechanism of the configuration elements is derived based on the topological relationship. At the same time, a data channel with external data is established to determine the display properties of the configuration elements and the binding relationship between the visualization state of the component and the external data, and to determine the state mapping relationship.

[0074] like Figure 2 As shown, the matching sub-strategies of the configuration components include:

[0075] Matching the semantic attributes of the configurable component with the semantic labels of the configurable elements in the domain knowledge base, and calculating the semantic similarity scores between the configurable component and the configurable element to screen out candidate configurable elements;

[0076] The state space of reinforcement learning is constructed by combining the topological relationship and geometric information of the components, and the adaptability of the candidate configuration components is used as the reward function of reinforcement learning;

[0077] Through multiple rounds of iterative training of reinforcement learning, candidate configuration elements are dynamically optimized and configuration elements associated with configurable components are matched.

[0078] The functions of components and configuration elements in the BIM model need to correspond precisely. Semantic label matching can quickly narrow the screening scope, avoid blind matching, and improve the efficiency and accuracy of configuration element matching; the semantic attributes of the extracted configurable components are compared with the semantic labels of the configuration elements in the domain knowledge base, and the semantic similarity score of the semantic labels of the components and each configuration element is calculated through the text similarity algorithm. A similarity threshold is set, and configuration elements with a semantic similarity score greater than the similarity threshold are screened as candidate configuration elements; automatic screening is achieved based on semantic attributes, reducing manual intervention, and quickly locating suitable candidate configuration elements, providing a basis for subsequent optimized matching, reducing matching error rate, and the screened candidate configuration elements provide data objects for subsequent reinforcement learning optimization, limiting the optimization scope, and improving overall matching efficiency.

[0079] Single semantic matching is difficult to comprehensively evaluate the adaptability of configuration components. It is necessary to build a dynamic evaluation system based on the topological relationship and geometric information of the components to ensure that the matching results meet the actual operational requirements. The topological relationship and geometric information of the components are combined to construct the state space of reinforcement learning, in which the geometric information serves as a spatial constraint, that is, the limitation of the installation position size, and the action space is defined as the selection of different candidate configuration components. The reward function is the adaptability score of the candidate configuration components in the current state space, including whether it conforms to the topological connection relationship and whether it adapts to the geometric space. By integrating multi-dimensional information to build a dynamic evaluation system, the matching process is more in line with the actual working conditions, the adaptation accuracy of configuration elements and components is improved, and operational conflicts caused by static matching are avoided. The constructed state space, action space and reward function provide a framework for iterative training of reinforcement learning, so that the matching scheme of configuration elements can be dynamically optimized by simulating different operations.

[0080] Candidate configuration components may present multiple potential problems in actual operation. Through multiple rounds of iterative training using reinforcement learning, the matching strategy can be dynamically adjusted to obtain the optimal matching result. The reinforcement learning agent randomly selects one of the candidate configuration components as its initial action. Based on the calculated reward value in the current state space, the selected valve receives a positive score if the topological connection between the selected valve and the pipeline is correct, and a negative score if the size does not match. By continuously executing actions, observing environmental feedback, and updating the strategy, the selection strategy is gradually optimized over multiple rounds of iterations to ensure that the selected configuration component has the highest adaptability score under the reward function. The agent then determines whether the iteration termination conditions are met and determines the configuration component that is ultimately associated with the configurable component. This achieves adaptive optimization of configuration component matching, can cope with complex and changing operating conditions, improves the reliability and practicality of the matching solution, and ensures that the configuration components perform optimally in actual operation. The determined associated configuration components provide objects for the subsequent derivation of control mechanisms. Their matching results influence the relevance and effectiveness of the control mechanisms. They also provide basic data for determining binding relationships and state mapping relationships, clarifying the main body of data interaction and visualization.

[0081] The derivation strategy for the control mechanism of the configuration element includes:

[0082] Based on the component topology, the path search algorithm is used to identify the connection paths and hierarchical relationships between components. The basic control mechanism is called based on the domain knowledge base, and the nodes of the component topology are marked as the trigger points of the basic control mechanism.

[0083] Extract spatial constraints based on the initial layout positions of configuration components in the BIM model, and transform them into additional rules for the basic control mechanism to form a constraint control mechanism.

[0084] The constraint control mechanism is used as the initial strategy of reinforcement learning, and the operating efficiency and energy consumption of the target equipment are used as the reward function of reinforcement learning. Multiple rounds of iterative training are carried out in combination with external data to derive the control mechanism of the configuration elements.

[0085] The component topology relationship contains functional connection logic. Based on this, the basic control mechanism is called to quickly establish a preliminary control framework, providing a basis for the derivation of complex control mechanisms; based on the component topology structure, the connection path and hierarchical relationship between components are identified through the path search algorithm, and the corresponding basic control mechanism is called from the domain knowledge base with the topology structure as the index. Preferably, for the series topology structure of the pipeline connecting the pump and the valve, the basic control mechanism called is that the valve opens after the pump is started, and the key nodes in the topology structure are marked as the trigger points of the basic control mechanism, where the key nodes include the pump and the valve; the basic control mechanism is automatically called based on the topological relationship, which reduces the workload of manually designing the control logic, improves the derivation efficiency of the control mechanism, and ensures that the basic control mechanism conforms to the operating principle of the actual equipment. The obtained basic control mechanism and trigger point provide the original framework for the subsequent addition of spatial constraints and the formation of the constraint control mechanism, so that the control mechanism is gradually improved.

[0086] Components have spatial limitations in actual installation, and relying solely on the basic control mechanism cannot meet actual operational requirements. It is necessary to add spatial constraint rules in combination with the initial layout position of the configuration components to ensure the feasibility of the control mechanism; according to the initial layout position of the configuration components in the BIM model, the spatial constraint conditions are extracted, namely the spacing requirements of the components and the restrictions on the bending radius of the pipes, and these spatial constraints are converted into additional conditions of the basic control mechanism. Preferably, on the basic control mechanism of the valve opening after the pump is started, a constraint condition is added that when the distance between the valve and the pump is less than the safety threshold, the valve is delayed to open, thereby forming a constraint control mechanism; the control mechanism fully considers the actual spatial layout factors, avoids the inability to execute the control mechanism due to spatial conflicts, and improves the practicality and reliability of the control mechanism. The formed constraint control mechanism serves as the initial strategy of reinforcement learning, providing a more practical framework for subsequent dynamic optimization combined with external data.

[0087] During actual operation, the operating conditions of equipment are constantly changing, and fixed constraint control mechanisms are difficult to adapt. Through reinforcement learning combined with iterative optimization of external data, dynamic adaptive adjustment of the control mechanism can be achieved; the constraint control mechanism is used as the initial strategy of reinforcement learning, and the operating efficiency and energy consumption of the target equipment are used as the reward function of reinforcement learning. The operating efficiency of the target equipment includes the flow rate per unit time, and the energy consumption includes electricity consumption. Then, the monitored external data is obtained in real time. The reinforcement learning agent selects control actions according to the current operating conditions, that is, adjusts the valve opening and changes the pump speed. The action score is calculated based on the reward function, that is, positive points are awarded for efficiency improvement, while negative points are awarded for energy consumption increase. The strategy is continuously updated through the Q-learning algorithm. After multiple rounds of training, the control mechanism of the configuration components that adapt to different working conditions is derived.

[0088] This enables dynamic optimization of the control mechanism, enabling the target equipment to maintain efficient and energy-saving operation under different working conditions, improving the level of intelligence and operational stability. The determined control mechanism provides a logical basis for sending control instructions, ensuring that the control instructions meet the operating requirements of the target equipment. It also affects the determination of the state mapping relationship, so that the visual state changes of the components are synchronized with the control mechanism.

[0089] like Figure 3 As shown, the sub-strategies for determining the binding relationship include:

[0090] Perform multi-dimensional analysis on external data and extract data features through principal component analysis. At the same time, analyze the display properties of configuration elements and parameter requirements of component visualization status to form attribute parameters.

[0091] With the data features of external data as the horizontal dimension and the attribute parameters as the vertical dimension, a feature mapping matrix is ​​constructed. The matching degree between the data features of external data and the attribute parameters is calculated using the cosine similarity algorithm to generate the initial binding relationship.

[0092] Taking data transmission efficiency and visualization accuracy as fitness functions, the initial binding relationship is iteratively optimized through genetic algorithm, and the binding relationship between external data and attribute parameters is determined through selection, crossover and mutation operations.

[0093] External data must accurately correspond to the display properties of configuration elements and the visual status of components. Extracting the characteristics and parameters of both parties is a prerequisite for accurate binding. Multi-dimensional analysis is performed on the monitored external data. Principal component analysis is used to extract the data characteristics of the external data. The data characteristics include temperature change trends and pressure fluctuation peaks. At the same time, the display properties of configuration elements and the parameter requirements of the visual status of components are analyzed to form attribute parameters. The display properties of configuration elements include color, size and transparency, and the visual status of components includes geometric deformation and animation effects. The attribute parameters include the RGB value range of the color and the size scaling ratio. Preferably, the mean and variance of the temperature data are used as data features, and the RGB interval corresponding to the color change is used as the attribute parameter. Key information is extracted from a large amount of external data, and the required visualization parameters are clarified to provide a clear data basis for building a binding relationship and avoid data redundancy and invalid binding.

[0094] Establish a preliminary binding relationship between external data and attribute parameters to provide a starting point for subsequent optimization; use the extracted data features of external data as the horizontal dimension and the attribute parameters as the vertical dimension to construct a feature mapping matrix, calculate the matching degree between the data features of external data and the attribute parameters through the cosine similarity algorithm, and generate an initial binding relationship based on the matching degree. If the data features of temperature data match the color attribute parameters highly, the temperature value is bound to a specific color interval, and the initial relationship formed is that the color turns red when the temperature rises; thereby quickly establishing the association relationship between external data and attribute parameters, providing basic rules for visualization display, and being able to preliminarily display the visualization effect corresponding to data changes. The initial binding relationship is the object of genetic algorithm optimization, and is improved through iterative operations to achieve a better data visualization mapping effect.

[0095] The initial binding relationship is inaccurate or inefficient. The natural evolution process is simulated by genetic algorithm, and it is iteratively optimized to improve data transmission efficiency and visualization accuracy. The initial binding relationship is operated by genetic algorithm with data transmission efficiency and visualization accuracy as fitness functions, where data transmission efficiency includes reducing the amount of data transmission, and visualization accuracy includes accurately reflecting data changes. Through selection, crossover and mutation operations, after multiple rounds of iterations, the final binding relationship between external data and attribute parameters is determined, where selection is to retain the binding relationship with high fitness, crossover operation is to exchange some rules of different binding relationships, and mutation operation is to randomly modify the binding relationship. Preferably, the binding rules of temperature and color and the binding relationship of pressure and size are partially merged through crossover operation, and then the detailed parameters are adjusted through mutation.

[0096] The optimized binding relationship can more efficiently and accurately map external data to attribute parameters, improve data processing and display capabilities, and provide users with a clearer and more accurate visual interface. The determined binding relationship is used to update the display properties of the configuration components in subsequent steps to ensure that the display effect reflects the changes in external data in real time and accurately. It also provides a data association basis for determining the state mapping relationship.

[0097] The external data and component attributes are formed into a fused feature vector through feature splicing. Based on the fused fused feature vector, a dynamic state mapping relationship is constructed through the fuzzy logic algorithm, fuzzy language variables and membership functions are predefined, and the initial rule base is set according to historical data. At the same time, the equipment operation stability and user operation feedback are used as reward signals for reinforcement learning to continuously optimize the state mapping relationship. Through continuous optimization, the intelligent agent selects mapping actions according to the changes in current external data and the working conditions of the components, and obtains reward feedback based on the execution results, where the execution results include whether the state of the target device has improved and whether the user has confirmed it. Then, the value function of the state and action is updated through the Q-learning algorithm to dynamically adjust the state mapping relationship to make the state mapping relationship more in line with actual needs.

[0098] S3. Monitor the real-time external data of the target device, update the display properties of the configuration elements according to the binding relationship, and dynamically drive the visual state changes of the components based on the state mapping relationship. At the same time, respond to the user's operation on the configuration elements and send control instructions to the target device, and synchronously update the visual state of the configuration elements and components based on the execution results of the target device.

[0099] The update sub-strategies for the display properties of configuration components include:

[0100] Monitor the real-time external data of the target device and perform multi-scale analysis on it to extract spatiotemporal correlation features. Calculate the matching degree between the spatiotemporal correlation features and the feature templates in the binding relationship through cosine similarity to filter out update actions.

[0101] Dynamically adjust the display attributes of the configuration components according to the update action, and determine the type of the display attribute to determine the rendering parameters of the display attribute. The types of display attributes include numerical attributes, trend attributes, and abnormal attributes.

[0102] The state space of reinforcement learning is constructed according to the spatiotemporal correlation characteristics and the type of display attributes. The rendering parameter combination of display attributes is used as the action space of reinforcement learning. The reward function of reinforcement learning is comprehensively determined based on the efficiency of information transmission, visual comfort and convenience of interaction. Reinforcement learning is trained to optimize the visual mapping effect of display attributes.

[0103] Real-time external data has temporal and spatial correlation. By extracting spatiotemporal features and matching update actions, dynamic and accurate updates of the display properties of configuration components can be achieved. Through wavelet transform, multi-scale analysis of real-time external data is performed to extract features of different time windows and spatial dimensions, including pressure fluctuation frequency and temperature gradient changes. The extracted spatiotemporal correlation features are compared with the preset feature templates in the binding relationship through cosine similarity calculation to obtain the matching degree, and a matching threshold is configured. When the matching degree is greater than the matching threshold, the corresponding update action is selected. Through spatiotemporal feature matching, a rapid response to changes in external data is achieved, making the display property updates of configuration components more timely and targeted, avoiding invalid updates, and the selected update actions provide a basis for subsequent adjustment of display properties, directly determining the adjustment direction and amplitude of the display properties.

[0104] Different types of display attributes require different rendering methods. Clarifying the attribute type and determining the rendering parameters can optimize the visualization effect. According to the update action, the display attributes of the configuration components are dynamically adjusted, where the display attributes include color, size, and transparency. Then, the type of display attribute is determined. If it is a numerical attribute, the rendering parameters such as the digital font, color, and precision are determined. If it is a trend attribute, the rendering parameters such as the line chart style and time window are determined. If it is an abnormal attribute, the rendering parameters such as the warning icon and flashing frequency are determined. Numerical attributes include temperature values, trend attributes include pressure change trends, and abnormal attributes include equipment failures. Differentiated rendering strategies are adopted for different types of display attributes to improve the efficiency of information transmission and enable users to obtain key data and abnormal conditions more intuitively. The determined rendering parameters provide the initial configuration for reinforcement learning optimization, which affects the training direction of subsequent visual mapping effects.

[0105] The initial rendering parameters are not optimal. Through dynamic optimization using reinforcement learning, a balance can be achieved between information transmission efficiency, visual comfort, and interaction convenience. Reinforcement learning is constructed, with spatiotemporal correlation features and the type of display attributes as the state space, and the rendering parameter combination of display attributes as the action space. A reward function is designed based on information transmission efficiency, visual comfort, and interaction convenience. Information transmission efficiency includes the speed at which users recognize data, visual comfort includes color contrast and layout rationality, and interaction convenience includes operation response speed. Through iterative training using the Q-learning algorithm, the rendering parameter combination of display attributes is continuously adjusted to maximize the reward function value. This achieves adaptive optimization of the visual mapping of display attributes, enhances the human-computer interaction experience, reduces the user's cognitive burden, and improves monitoring efficiency. The optimized visual mapping effect provides a unified display standard for the visual state changes of components, ensuring the coordination of their visual performance.

[0106] The driving sub-strategies for component visualization state changes include:

[0107] According to the spatiotemporal correlation characteristics, the mapping action associated with the component is found from the state mapping relationship, and the external data is converted into the visualization parameters of the component. The visualization parameters include geometric form, appearance state and rendering effect.

[0108] Determine the impact range of component visualization state changes based on the connection relationship between components, and modify the component's geometry and appearance according to the mapping action;

[0109] Dynamically adjust the rendering effect of the component according to the importance of the visualization state, and respond to the user's operation on the configuration element to feedback the visualization parameters of the component.

[0110] To convert external data into visualization parameters, it is necessary to establish a correspondence between data and visual representation through a state mapping relationship to achieve precise driving of state changes; based on the spatiotemporal correlation characteristics, the mapping actions associated with the component are found from the state mapping relationship, and the external data is converted into the component's visualization parameters. The visualization parameters include geometric shape, appearance shape, and rendering effect; thereby achieving standardized conversion from data to visual, ensuring that component state changes are consistent with actual working conditions, and improving the authenticity and credibility of visualization.

[0111] There are topological associations between components. A change in the state of one component will affect other components. The scope of influence is determined and updated in a linked manner to fully present the operating status. Based on the component topological relationship, a breadth-first search algorithm is used, starting from the current component, to traverse all directly or indirectly connected components to determine the scope of influence of the state change. The geometric shape and appearance state of the components within the range are modified according to the mapping action. The geometric shape includes the pipe diameter, and the appearance state includes the color and transparency. Preferably, when the valve is closed, in addition to modifying its own state, the state of the downstream pipeline is also marked as fluid stagnation, and the color is adjusted to gray. This achieves a coordinated update of the status of associated components, reflects the overall operating status of the components, avoids information deviation caused by isolated display, and improves the integrity of visualization.

[0112] Components in different states have different importance. Dynamically adjusting the rendering effect can highlight key information, respond to user operation feedback, and enhance the interactive experience. According to the importance of the component visualization state, the rendering parameters are dynamically adjusted, where the importance of the fault state is greater than that of the normal state. The rendering parameters include brightness, transparency, and hierarchy. Highlight, magnify, or animate components in important states, and reduce transparency or simplify the display of components in secondary states. At the same time, the user's operations on the configuration elements, i.e., clicks and drags, are monitored, and real-time feedback on changes in the component's visualization parameters is provided, including displaying detailed data and adjusting the viewing angle. By sorting the importance of the visualization state, users are guided to focus on key information, and interactive feedback enhances user participation and improves system usability. The adjusted rendering effect directly affects the user's perception of the component state, and thus affects the user's operation decision-making and the generation of control instructions.

[0113] The update strategies for the visualization status of configuration elements and components include:

[0114] Monitor and respond to user operations on configuration components, convert the user operations on configuration components into control instructions based on the domain knowledge base, and transmit the control instructions to the target device;

[0115] Receive the execution result of the target device, verify the expected result of the control instruction and the execution validity of the execution result, and modify the display properties of the configuration component according to the execution validity and binding relationship;

[0116] The geometric shape and appearance status of the components are updated based on the execution validity and mapping actions, and the status update of the associated components is triggered according to the topological relationship of the components.

[0117] To convert user operations into executable control instructions, semantic parsing and logical conversion must be performed in combination with the domain knowledge base; user operation events on configuration components are monitored, and user operations on configuration components are converted into control instructions based on the domain knowledge base. According to the component topology relationship and control mechanism, instruction parameters are supplemented, including pump speed and start time, to generate complete control instructions; thereby achieving a seamless conversion from user intention to device control, lowering the operating threshold, improving control efficiency, and avoiding control errors caused by semantic ambiguity. The generated control instructions are directly sent to the target device to determine the operating status of the device, thereby affecting the verification and status update of subsequent execution results.

[0118] Verify the effectiveness of the control instruction execution, ensure that the execution feedback is consistent with the user's expectations, and update the display properties of the configuration components according to the results to keep the information real-time; receive the execution results returned by the target device, compare the expected results with the actual execution results, and calculate the execution deviation rate, that is, the execution deviation rate between the speed set in the control instruction and the actual speed. When the execution deviation rate is within the set threshold range, the execution is determined to be valid, and then the display properties of the configuration components are updated according to the binding relationship, that is, the button color is changed to green; when the execution deviation rate exceeds the set threshold range, the execution is determined to have failed, and a warning message is displayed and the abnormality is marked; the display properties of the configuration components are updated in real time, so that users can understand the operation effect in a timely manner and enhance their confidence in operation. The results of the execution effectiveness and the updated display properties provide a basis for updating the component visualization status and determine the direction and method of the status update.

[0119] Based on the execution results, the component status is updated, and a chain reaction of related components is triggered to fully present the component change process; according to the execution validity and mapping action, the geometric shape and appearance status of the component are updated, and according to the topological relationship of the component, the graph traversal algorithm is used to identify the affected related components, and their status updates are recursively triggered. Preferably, the fluid status and pressure display of the downstream pipeline are automatically updated after the pump is started; thereby achieving the synchronous update of the status of all components, reflecting the collaborative working relationship between equipment, providing a complete component operation view, and assisting users in fully understanding the working conditions. The updated component status serves as the new initial state, affecting the next round of data monitoring and state change driving process, forming a closed-loop feedback mechanism.

[0120] Example 2

[0121] An embodiment of the present application provides a configuration system based on a BIM digital base, which includes a feature extraction module, a configuration configuration module and a mapping feedback module.

[0122] The feature extraction module is used to parse the BIM model of the target equipment and extract component properties including the geometric information, semantic attributes and topological relationships of the components, identify and mark configurable components, and generate component features including component identification, spatial coordinates and component attributes.

[0123] The configuration configuration module is used to match the configuration elements associated with the configurable components based on the domain knowledge base and component characteristics, determine the initial layout position of the configuration elements in the BIM model according to the spatial coordinates, and derive the control mechanism of the configuration elements based on the topological relationship. At the same time, it establishes a data channel with external data, configures the display properties of the configuration elements and the binding relationship between the component's visualization state and external data, and determines the state mapping relationship.

[0124] The mapping feedback module is used to monitor the real-time external data of the target device, update the display properties of the configuration elements according to the binding relationship, and dynamically drive the visual state changes of the components based on the state mapping relationship. At the same time, it responds to the user's operation on the configuration elements and sends control instructions to the target device, and synchronously updates the visual state of the configuration elements and components based on the execution results of the target device.

[0125] Since the principle of the system for solving the problem in the embodiment of the present application is similar to the method described above in the embodiment of the present application, the implementation of the method refers to the implementation of the method, and the repeated parts will not be repeated.

Claims

1. A configuration method based on BIM digital base, characterized in that: include: Parse the BIM model of the target device and extract component attributes including geometric information, semantic attributes and topological relationships of the components, identify and mark configurable components, and generate component features including component identification, spatial coordinates and component attributes; The generation strategy of the component features includes: According to the preset judgment rules and component properties, configurable components are identified and marked from the BIM model. At the same time, the spatial position relationship between components is analyzed through the spatial topology analysis algorithm to form spatial coordinates; Encode the geometric information and semantic attributes of the component through the encrypted hash algorithm to generate the component identification; Integrate component identification, spatial coordinates and component attributes to generate component features; The component attribute extraction sub-strategy includes: Analyze the structured data of the BIM model of the target equipment, divide the geometric contours of the components in the BIM model through the point cloud segmentation algorithm, and extract the geometric information of the components; Use natural language processing technology to perform semantic analysis on the attribute text of the BIM model, extract the semantic attributes of the components, and associate and match the semantic attributes of the components with the domain knowledge base; Build component topology based on graph theory algorithm, take components as nodes and connections between components as edges, analyze component topology to determine the topological relationship of components and form component attributes; Based on the domain knowledge base and component characteristics, the configuration elements associated with the configurable components are matched, the initial layout position of the configuration elements in the BIM model is determined according to the spatial coordinates, and the control mechanism of the configuration elements is derived based on the topological relationship. At the same time, a data channel with external data is established to determine the display properties of the configuration elements and the binding relationship between the component's visual state and external data, and the state mapping relationship; Monitor the real-time external data of the target device, update the display properties of the configuration elements according to the binding relationship, and dynamically drive the visual state changes of the components based on the state mapping relationship. At the same time, respond to the user's operation on the configuration elements and send control instructions to the target device, and synchronously update the visual state of the configuration elements and components based on the execution results of the target device.

2. A configuration method based on a BIM digital base according to claim 1, characterized in that: The derivation strategy of the control mechanism of the configuration element includes: Based on the component topology, the path search algorithm is used to identify the connection paths and hierarchical relationships between components. The basic control mechanism is called based on the domain knowledge base, and the nodes of the component topology are marked as the trigger points of the basic control mechanism. Extract spatial constraints based on the initial layout positions of configuration components in the BIM model, and transform them into additional rules for the basic control mechanism to form a constraint control mechanism. The constraint control mechanism is used as the initial strategy of reinforcement learning, and the operating efficiency and energy consumption of the target equipment are used as the reward function of reinforcement learning. Multiple rounds of iterative training are carried out in combination with external data to derive the control mechanism of the configuration elements.

3. A configuration method based on a BIM digital base as claimed in claim 2, characterized in that: The matching sub-strategy of the configuration element includes: Matching the semantic attributes of the configurable component with the semantic labels of the configurable elements in the domain knowledge base, and calculating the semantic similarity scores between the configurable component and the configurable element to screen out candidate configurable elements; The state space of reinforcement learning is constructed by combining the topological relationship and geometric information of the components, and the adaptability of the candidate configuration components is used as the reward function of reinforcement learning; Through multiple rounds of iterative training of reinforcement learning, candidate configuration elements are dynamically optimized and configuration elements associated with configurable components are matched.

4. A configuration method based on a BIM digital base as claimed in claim 3, characterized in that: The sub-strategies for determining the binding relationship include: Perform multi-dimensional analysis on external data and extract data features through principal component analysis. At the same time, analyze the display properties of configuration elements and parameter requirements of component visualization status to form attribute parameters. With the data features of external data as the horizontal dimension and the attribute parameters as the vertical dimension, a feature mapping matrix is ​​constructed. The matching degree between the data features of external data and the attribute parameters is calculated using the cosine similarity algorithm to generate the initial binding relationship. Taking data transmission efficiency and visualization accuracy as fitness functions, the initial binding relationship is iteratively optimized through genetic algorithm, and the binding relationship between external data and attribute parameters is determined through selection, crossover and mutation operations.

5. A configuration method based on a BIM digital base as claimed in claim 4, characterized in that: The update strategy of the visualization status of the configuration elements and components includes: Monitor and respond to user operations on configuration components, convert the user operations on configuration components into control instructions based on the domain knowledge base, and transmit the control instructions to the target device; Receive the execution result of the target device, verify the expected result of the control instruction and the execution validity of the execution result, and modify the display properties of the configuration component according to the execution validity and binding relationship; The geometric shape and appearance status of the components are updated based on the execution validity and mapping actions, and the status update of the associated components is triggered according to the topological relationship of the components.

6. A configuration method based on a BIM digital base according to claim 5, characterized in that: The updating sub-strategy of the display attribute of the configuration element includes: Monitor the real-time external data of the target device and perform multi-scale analysis on it to extract spatiotemporal correlation features. Calculate the matching degree between the spatiotemporal correlation features and the feature templates in the binding relationship through cosine similarity to filter out update actions. Dynamically adjust the display attributes of the configuration components according to the update action, and determine the type of the display attribute to determine the rendering parameters of the display attribute. The types of display attributes include numerical attributes, trend attributes, and abnormal attributes. The state space of reinforcement learning is constructed according to the spatiotemporal correlation characteristics and the type of display attributes. The rendering parameter combination of display attributes is used as the action space of reinforcement learning. The reward function of reinforcement learning is comprehensively determined based on the efficiency of information transmission, visual comfort and convenience of interaction. Reinforcement learning is trained to optimize the visual mapping effect of display attributes.

7. A configuration method based on a BIM digital base according to claim 6, characterized in that: The driving sub-strategies for the visual state change of the component include: According to the spatiotemporal correlation characteristics, the mapping action associated with the component is found from the state mapping relationship, and the external data is converted into the visualization parameters of the component. The visualization parameters include geometric form, appearance state and rendering effect. Determine the impact range of component visualization state changes based on the connection relationship between components, and modify the component's geometry and appearance according to the mapping action; Dynamically adjust the rendering effect of the component according to the importance of the visualization state, and respond to the user's operation on the configuration element to feedback the visualization parameters of the component.

8. A configuration system based on a BIM digital base, used to implement a configuration method based on a BIM digital base according to any one of claims 1 to 7, characterized in that: include: Feature extraction module, configuration module and mapping feedback module; The feature extraction module is used to parse the BIM model of the target equipment and extract component attributes including geometric information, semantic attributes and topological relationships of the components, identify and mark configurable components, and generate component features including component identification, spatial coordinates and component attributes; The configuration module is used to match configuration elements associated with configurable components based on the domain knowledge base and component characteristics, determine the initial layout position of configuration elements in the BIM model according to spatial coordinates, and derive the control mechanism of configuration elements based on topological relationships. At the same time, it establishes a data channel with external data, configures the display properties of configuration elements and the binding relationship between the component's visual state and external data, and determines the state mapping relationship; The mapping feedback module is used to monitor the real-time external data of the target device, update the display properties of the configuration elements according to the binding relationship, and dynamically drive the visual state changes of the components based on the state mapping relationship. At the same time, it responds to the user's operation on the configuration elements and sends control instructions to the target device, and synchronously updates the visual state of the configuration elements and components based on the execution results of the target device.

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