Heating room modeling method and system based on BIMBase

Abstract

The present invention provides a heating machine room modeling method and system based on BIMBase. First, the heating machine room component data structure in the BIMBase platform is hierarchically parsed to obtain the component structure parsing result. Then, based on the result, the dynamic and static data sets of the heating machine room are collected and integrated. Then, the component structure parsing result and the dynamic and static data sets are extracted from the component association features to generate a component association feature set. A layout generation tool is called to perform spatial layout mapping on the component association feature set to generate a preliminary layout association model of the heating machine room. Spatial conflict detection and optimization processing are performed on the model to obtain the spatially optimized layout association model. The model is converted into modeling data that meets the BIMBase data interaction standard and output to the BIMBase platform to complete the construction of the heating machine room model, thereby improving the accuracy and efficiency of the heating machine room modeling.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a heating machine room modeling method and system based on BIMBase. Background Art

[0002] In the field of design and construction of heating rooms, traditional modeling methods face many challenges. Most existing modeling methods rely on manual experience to plan component layouts and lack a systematic analysis of the data structure of heating room components. The complex relationships between different components, such as connection relationships, functional dependencies, etc., are difficult to accurately capture and present. At the same time, a large amount of dynamic operation data is generated during the operation of the heating room, while traditional methods often only focus on the static attribute data of the equipment and are unable to effectively integrate dynamic and static data, resulting in the model not being able to truly reflect the actual operating conditions of the heating room. In addition, in terms of spatial layout, traditional modeling methods are prone to spatial conflict problems and lack effective detection and optimization mechanisms, making the modeling process inefficient and the model quality uneven, making it difficult to meet the needs of refined design and efficient operation management of modern heating rooms. Summary of the Invention

[0003] In view of the above-mentioned problems, in combination with the first aspect of the present invention, an embodiment of the present invention provides a heating room modeling method based on BIMBase, the method comprising:

[0004] Perform hierarchical parsing on the heating room component data structure in the BIMBase platform to obtain component structure parsing results that include component type attributes and hierarchical relationship characteristics;

[0005] Based on the component structure analysis results, the dynamic and static data sets of the heating room are collected and integrated, wherein the dynamic and static data sets of the heating room include a dynamic operation data set and a static attribute data set;

[0006] Performing component association feature extraction processing on the component structure analysis results and the dynamic and static data set of the heating machine room to generate a component association feature set including connection relationship features, function dependency features, and dynamic and static data association features;

[0007] Calling a layout generation tool to perform spatial layout mapping processing on the component association feature set to generate a preliminary layout association model of the heating machine room;

[0008] The preliminary layout association model of the heating room is subjected to spatial conflict detection and optimization processing to obtain a spatially optimized layout association model, and the spatially optimized layout association model is converted into modeling data that conforms to the BIMBase data interaction standard. The modeling data is output to the BIMBase platform to complete the construction of the heating room model.

[0009] On the other hand, an embodiment of the present invention also provides a heating room modeling system based on BIMBase, including a processor and a machine-readable storage medium, the machine-readable storage medium is connected to the processor, the machine-readable storage medium is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the machine-readable storage medium to implement the above method.

[0010] Based on the above aspects, the embodiment of the present invention can accurately obtain component type attributes and hierarchical relationship characteristics by performing hierarchical parsing on the heating room component data structure in the BIMBase platform, collect and integrate the dynamic and static data sets of the heating room based on the component structure parsing results, realize the comprehensive integration of dynamic operation data sets and static attribute data, so that the model can truly reflect the actual operation status of the heating room, perform inter-component association feature extraction on the component structure parsing results and dynamic and static data sets, and the generated component association feature set covers multiple features such as connection relationships, functional dependencies, and dynamic and static data associations, effectively improving the relevance and integrity of the model, calling the layout generation tool to perform spatial layout mapping processing, and the generated preliminary layout association model provides a scientific basis for the reasonable layout of the heating room, performs spatial conflict detection and optimization processing on the preliminary layout association model, and converts it into modeling data that meets the BIMBase data interaction standard, which not only solves the spatial conflict problem, but also ensures that the model can be smoothly constructed and applied on the BIMBase platform, significantly improving the accuracy, scientificity and efficiency of the heating room modeling as a whole. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a schematic diagram of the execution flow of the heating room modeling method based on BIMBase provided by an embodiment of the present invention.

[0012] Figure 2 Schematic diagram of exemplary hardware and software components of a BIMBase-based heating room modeling system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0013] The present invention will be described in detail below with reference to the accompanying drawings. Figure 1 This is a flow chart of a heating machine room modeling method based on BIMBase provided by an embodiment of the present invention. The heating machine room modeling method based on BIMBase is introduced in detail below.

[0014] Step S110: performing hierarchical parsing processing on the heating room component data structure in the BIMBase platform to obtain a component structure parsing result including component type attributes and hierarchical relationship characteristics.

[0015] In this embodiment, taking a heating room in a large industrial park as an example, the specific process of hierarchical parsing of the heating room component data structure in the BIMBase platform is described in detail.

[0016] The heating rooms in large industrial parks are usually large in scale and contain many components of different types and functions, such as boilers, water pumps, valves, heat exchangers, pipes, etc. These components are stored and managed in the form of a set data structure in the BIMBase platform. The purpose of hierarchical analysis is to deeply understand the hierarchical relationship between components and the specific type attributes of each component.

[0017] Step S111: reading the original data structure of the heating room components stored in the BIMBase platform, wherein the original data structure of the heating room components includes component identification information, geometric description information and attribute field set.

[0018] First, the raw data structure of the heating room components is read from the BIMBase platform. Component identification information is the key to distinguishing different components. It is a unique code that may consist of letters, numbers, or a combination of these. In the heating room of a large industrial park, each component has its own unique identification code. For example, a specific model of boiler may be assigned an identification such as "GL-001," while a specific specification of water pump may be identified as "SB-002." This identification information allows specific components to be quickly and accurately located within the massive amount of data on the BIMBase platform.

[0019] Geometric descriptions detail the component's external features and spatial form. For a boiler, this geometric description includes its overall shape, which could be cylindrical or square, as well as specific dimensional parameters such as length, diameter, and height. This information precisely defines the boiler's three-dimensional form. Similarly, for pipes, the geometric description includes parameters such as length, diameter, and bend angles, allowing the model to accurately simulate the pipe's layout and spatial orientation.

[0020] The attribute field collection contains various component attribute information, which describes the component's characteristics and performance from multiple perspectives. For example, for a boiler, the power attribute reflects the boiler's heating capacity, which determines the amount of heat it can generate per unit time. The material attribute describes the boiler's manufacturing material. For example, the boiler's outer shell may be made of stainless steel, which not only determines the boiler's durability and corrosion resistance but also affects its heat dissipation performance. In addition, the attribute field collection may also include information such as the manufacturer, production date, and maintenance records. This information is of great reference value for component management, maintenance, and updates. For a water pump, the attribute field collection may include performance parameters such as flow rate, head, and speed, as well as information such as the pump's installation method and motor power.

[0021] Step S112: performing structured decomposition processing on the component identification information, extracting component classification codes and hierarchical path codes, and establishing a component basic classification index.

[0022] After obtaining component identification information, it needs to be structured and split. The purpose of this step is to extract valuable classification and hierarchical information from the complex component identification information to better organize and manage component data. By setting splitting rules, the component classification code and hierarchical path code can be separated from the component identification information.

[0023] Component classification codes are used to categorize components into different groups. In a heating plant room in a large industrial park, all components might be divided into different categories based on their function and purpose. For example, all boilers might be grouped into one category, all pumps into another, and valves, heat exchangers, and pipes into separate categories. Component classification codes effectively reflect the component's category; for example, "GL" might represent a boiler, "SB" might represent a pump, and so on.

[0024] The hierarchical path code reflects the component's location within the entire heating room's hierarchical structure. Within the heating room's system architecture, different hierarchical relationships exist. For example, the entire heating room can be divided into a main system and multiple subsystems, each of which can be further divided into smaller branches. The hierarchical path code accurately identifies the component's location within that layer and branch. For example, if a water pump is located at a specific location within first-level subsystem A under the main system, its hierarchical path code may record this location information in detail.

[0025] Based on the extracted component classification codes and hierarchical path codes, a component basic classification index can be established. This component basic classification index arranges components in an orderly manner according to their classification and hierarchical relationships. Using the component basic classification index, specific components can be quickly located based on their classification and hierarchical information. For example, if you need to find the location of a specific type of valve within the entire heating room hierarchical structure, you only need to enter the relevant classification code and hierarchical path code in the component basic classification index to quickly find the corresponding valve component and its related information.

[0026] Step S113: Based on the component basic classification index, the geometric description information is format converted to convert the non-standardized geometric data into a three-dimensional coordinate data format compatible with the BIMBase platform.

[0027] In the actual data collection and storage process, component geometry data from different sources may use different formats. This non-standardized geometry data cannot be directly used in the BIMBase platform. Therefore, it is necessary to convert the geometry description information into a 3D coordinate data format compatible with the BIMBase platform based on the component basic classification index.

[0028] Some geometric data in the form of two-dimensional drawings lacks three-dimensional coordinate information and needs to be converted into three-dimensional coordinate data through a series of conversion algorithms. For example, a pipeline represented on a two-dimensional drawing may only record the length, diameter, and general direction of the pipeline, but not its specific position and direction in three-dimensional space. Based on the information on the two-dimensional drawing, combined with the overall spatial layout and coordinate system of the heating room, the pipeline's geometric data can be converted into three-dimensional coordinate data. This may involve determining the coordinates of the pipeline's starting and ending points, as well as calculating parameters such as the pipe's bending angle and inclination in three-dimensional space.

[0029] Some geometric data acquired from different software or devices may experience data format incompatibility. For example, some devices may store geometric data in a proprietary format, while the BIMBase platform only supports a standard format. In these cases, data format conversion is necessary. This may require the use of specialized data conversion tools or the development of custom conversion programs to convert the original geometric data into a 3D coordinate data format that the BIMBase platform can recognize and process. This converted 3D coordinate data accurately represents the component's 3D model within the BIMBase platform.

[0030] Step S114: parsing the type attribute field in the attribute field set, extracting component function type, material type and installation type parameters, and generating a component type attribute vector.

[0031] Parsing the type attribute fields within the attribute field set is an important step in further exploring component characteristics. By analyzing these fields, we can extract the component's function type, material type, and installation type parameters, which can describe the component's essential characteristics from different perspectives.

[0032] The functional type of a component clarifies its role and function within the heating room system. In the heating rooms of large industrial parks, different components have different functional types. A boiler's function is to convert the chemical energy of fuel into thermal energy, providing heat for the entire heating system; a water pump's function is to provide power, circulating hot water through the pipes; a valve's function is to control the flow, pressure, and direction of fluids; and a heat exchanger's function is to facilitate heat exchange between different media. Accurately identifying a component's functional type is crucial for understanding the operating principles and system architecture of a heating room.

[0033] The material type describes the material used to manufacture a component. Different materials have different physical and chemical properties, which directly affect the performance and service life of the component. For example, the outer shell of a boiler may be made of stainless steel, which has excellent corrosion resistance and high strength, ensuring that the boiler is protected from environmental erosion during long-term operation. Piping may be made of steel or plastic pipes. Steel pipes have high strength and pressure resistance, making them suitable for high-pressure and high-temperature environments, while plastic pipes are lightweight and easy to install, making them suitable for low-pressure and normal-temperature environments. Determining the material type is important for component selection, maintenance, and replacement.

[0034] The installation type parameter describes the installation method and requirements for a component. Different components may have different installation types. For example, a water pump may be installed horizontally or vertically. Horizontal installation is suitable for spacious environments, while vertical installation saves space. Valves may have different installation types, such as flanged or threaded. Different installation types require different installation tools and processes. Extracting the installation type parameter plays an important role in guiding component installation and commissioning.

[0035] The extracted component function type, material type, and installation type parameters are combined to generate a component type attribute vector. This component type attribute vector stores the key type attribute information of the component in a structured manner, which can comprehensively and accurately describe the characteristics of the component.

[0036] Step S115: constructing a parent-child hierarchical relationship tree between components according to the hierarchical path encoding, wherein each node in the parent-child hierarchical relationship tree includes a type attribute vector and three-dimensional coordinate data of the corresponding component.

[0037] Based on the hierarchical path coding, a parent-child hierarchical relationship tree between components can be constructed. The hierarchical path coding effectively reflects the position and hierarchical relationship of the components in the entire heating room hierarchy. By analyzing and processing these codes, the parent-child relationship between components can be determined.

[0038] In the heating room of a large industrial park, the entire system can be understood as a tree structure with different levels and branches. For example, the entire heating room is the root node of the tree, and the main system and various subsystems are the first-level child nodes under the root node. Each subsystem can be further divided into smaller child nodes until it reaches a specific component. Based on the hierarchical path encoding, it is possible to determine which components are parent components and which components are child components. For example, a large heating subsystem can be considered a parent component, while the water pumps, valves, and other components within it are child components under this subsystem.

[0039] In the constructed parent-child hierarchical relationship tree, each node represents a component, and each node contains the type attribute vector and three-dimensional coordinate data of the corresponding component. The type attribute vector provides characteristic information about the component's function, material, installation, etc., while the three-dimensional coordinate data determines the specific position of the component in three-dimensional space. Through the parent-child hierarchical relationship tree, the hierarchical structure and relationships between components can be intuitively monitored. For example, it is possible to effectively monitor which components are part of a subsystem, as well as the connection and collaboration relationships between these components. At the same time, through the type attribute vector and three-dimensional coordinate data contained in the node, each component can be analyzed and evaluated in detail.

[0040] Step S116: Integrate the component type attribute vector and the parent-child hierarchical relationship tree into structured data to obtain a component structure parsing result including component type attributes and hierarchical relationship features.

[0041] After generating the component type attribute vector and constructing the parent-child hierarchical relationship tree, the two need to be integrated into structured data. The purpose of this step is to organically combine the component type attribute information and hierarchical relationship information to form a complete and orderly data structure for better subsequent analysis and application.

[0042] The component type attribute vector can be integrated with the information in the parent-child hierarchical relationship tree. During the integration process, it is necessary to ensure that the type attribute vector of each component is accurately associated with the corresponding node in the parent-child hierarchical relationship tree. For example, for a component represented by a node in the parent-child hierarchical relationship tree, the function type, material type, installation type, and other information in the corresponding type attribute vector should be consistent with the actual situation of the component.

[0043] The integrated structured data includes both component type attributes and hierarchical relationship characteristics. From the perspective of type attributes, it effectively displays information such as each component's function, material, and installation method; from the perspective of hierarchical relationship characteristics, it accurately reflects the component's position and interrelationships within the overall heating room hierarchy. This structured data enables the modeling process to more accurately reflect the actual conditions of the heating room, improving the model's reliability and practicality.

[0044] Step S120: collecting and integrating the dynamic and static data sets of the heating room based on the component structure analysis results, wherein the dynamic and static data sets of the heating room include a dynamic operation data set and a static attribute data set.

[0045] After obtaining the component structure analysis results, the next step is to collect and integrate the dynamic and static data sets of the heating room. The heating room in a large industrial park generates a large amount of dynamic operating data during operation. At the same time, each device also has some relatively stable static attribute data. Effectively collecting and integrating these two types of data helps to gain a deeper understanding of the heating room's operating status and performance.

[0046] Step S121: extracting the unique identification codes of all components from the component structure parsing result and generating a component identification list.

[0047] First, the unique identification codes of all components are extracted from the component structure analysis results. Since the component identification information has already been processed and analyzed in the previous steps, it is now easy to obtain the unique identification code of each component from the component structure analysis results. These unique identification codes are aggregated to generate a component identification list. This component identification list contains the identification information of all components in the heating room of a large industrial park. This component identification list ensures that data collection is accurately targeted for each component during data collection, avoiding data omissions or incorrect associations.

[0048] Step S122: Based on the component identification list, collect the real-time status data of each component during operation, the real-time status data including operation status records, performance monitoring records and abnormal alarm records, and mark the real-time status data as a dynamic operation data set.

[0049] Based on the component identification list, we start collecting real-time status data of each component during operation. This real-time status data can reflect the component's current operating status and performance.

[0050] Operational status records record component operating status information, such as whether a boiler is running, its start time, and its stop time. Pump operation records may include information about the pump's on and off status and operating duration. By analyzing operation status records, we can understand the component's operating mode and behavior, and determine whether the component is operating properly.

[0051] Performance monitoring records contain various component performance parameters that provide a direct reflection of component performance. For example, for a boiler, performance monitoring records might include parameters such as temperature, pressure, and thermal efficiency; for a water pump, performance monitoring records might include parameters such as flow rate, head, and power. Real-time monitoring of these performance parameters can promptly identify component performance changes and potential problems.

[0052] Abnormal alarm records are generated when a component experiences an abnormality. When a component's operating status or performance parameters exceed normal ranges, the system triggers an abnormality alarm mechanism and records the relevant abnormality information. For example, if the boiler's temperature or pressure is too high, the system will issue an alarm signal and record the alarm time, abnormal parameter value, and other information. Abnormal alarm records are crucial for timely detection and resolution of equipment failures, preventing them from seriously impacting the normal operation of the heating room.

[0053] The collected real-time status data are marked as dynamic operation data sets. These data change continuously as the components run, reflecting the real-time operation status and performance of the components.

[0054] Step S123: Based on the component identification list, the inherent attribute data of each component is collected, the inherent attribute data including design parameters, specifications and models, and factory information, and the inherent attribute data is marked as a static attribute data set.

[0055] Similarly, based on the component identification list, the inherent attribute data of each component is collected. This inherent attribute data is relatively stable and changes little throughout the component's life cycle. It describes the basic characteristics and performance of the component from different aspects.

[0056] Design parameters are parameters determined during the component design phase. These parameters determine the component's basic performance and functionality. For example, for a boiler, these parameters might include rated power, rated pressure, and rated temperature. For a water pump, these might include flow rate, head, and speed. Design parameters are crucial for evaluating component performance and suitability, helping to understand the component's performance under ideal conditions.

[0057] Specifications and models define the specific specifications and models of a component, reflecting its size, structure, and performance characteristics. Different specifications and models correspond to different component performance and application ranges. For example, a boiler model may have a specific size and heating capacity, suitable for a specific scale of heating needs; a pump model may have a specific flow rate and head, suitable for a specific piping system.

[0058] Factory information includes the component's manufacturer, production date, and product number. The manufacturer's reputation and technical expertise can influence the quality and reliability of the component. The production date can help understand the component's age and aging. The product number uniquely identifies the component.

[0059] Therefore, the collected inherent attribute data are marked as a static attribute data set.

[0060] Step S124: the dynamic operation data set and the static attribute data set are associated and integrated according to the component identification code to generate a dynamic and static data set of the heating room including the dynamic operation data set and the static attribute data set.

[0061] After collecting the dynamic operation data set and the static attribute data set, the two sets need to be associated and integrated according to the component identification code. Since each component has a unique identification code, the dynamic operation data and static attribute data of the component can be associated through the identification code.

[0062] The specific association and integration process involves matching and combining each component's dynamic operating data and static attribute data, using the component identification code as an index. For example, for a specific boiler, its real-time operating status data (such as temperature, pressure, and thermal efficiency) is integrated with its inherent attribute data, including its design parameters, specifications, model, and factory information. This creates a complete data set containing information about the boiler, including both its real-time operating status and its basic attributes and performance parameters.

[0063] By performing the aforementioned association and integration operations on all components, a dynamic and static data set for the heating room is generated, which includes a dynamic operation data set and a static attribute data set. This dynamic and static data set covers the dynamic and static data of all components in the heating room of a large industrial park.

[0064] Step S130: performing component association feature extraction processing on the component structure analysis result and the dynamic and static data set of the heating room to generate a component association feature set including connection relationship features, function dependency features and dynamic and static data association features.

[0065] After obtaining the component structure analysis results and the dynamic and static data sets of the heating room, they need to be processed to extract the correlation features between the components. In the heating room of a large industrial park, the components do not exist in isolation, but rather have complex correlations. Extracting these correlation features helps to gain a deeper understanding of the heating room's system architecture and operating mechanisms.

[0066] Step S131: extracting connection port information of adjacent level components from the parent-child hierarchical relationship tree of the component structure parsing result, wherein the connection port information includes port type, interface size and connection direction parameters.

[0067] Extracting the connection port information of adjacent hierarchical components from the parent-child hierarchical relationship tree of the component structure analysis result is the basis for analyzing the connection relationship between components. In the parent-child hierarchical relationship tree, there are usually physical connections between components of adjacent hierarchies, and these connections are realized through connection ports.

[0068] Connection port information includes important information such as port type, interface dimensions, and connection direction parameters. The port type specifies the specific type of connection port, for example, flanged, threaded, or welded. Different port types are suitable for different connection methods and applications. Flanges offer secure connections and easy disassembly, making them suitable for components requiring frequent maintenance and replacement. Threaded ports offer the advantages of simple installation and excellent sealing, making them suitable for smaller pipe connections.

[0069] Port size is a crucial parameter for connecting two components, determining whether they can achieve an effective connection. Precise port size matching ensures a tight and reliable connection. For example, when connecting a pipe to a valve, the pipe and valve port sizes must match; otherwise, leaks or unstable connections may occur.

[0070] The connection direction parameter specifies the orientation and angle of the connection port. During installation, the correct connection direction is essential to ensure proper connection between components and smooth fluid flow. For example, the inlet and outlet connections of a water pump must align with the flow direction of the pipe to ensure effective water pumping and delivery.

[0071] Step S132: Based on the connection port information, determine the matching relationship between different component ports, mark the successfully matched port pairs as direct connection relationships, and generate a connection relationship feature vector.

[0072] Based on the extracted connection port information, it is necessary to determine the matching relationship between different component ports. This is a key step in determining the direct connection relationship between components. By comparing and analyzing the port type, interface size, and connection direction parameters, it can be determined whether two ports are compatible.

[0073] When determining the port type, if two ports are of the same type, they are compatible in terms of connection method. For example, two flanged ports can be connected using flange bolts, while a flanged port and a threaded port cannot be directly connected.

[0074] For interface dimensions, the two ports are compared to see if they are within the allowable tolerance. If the difference between the interface dimensions of the two ports is within the preset dimensional tolerance, they are considered dimensionally matched. For example, if the diameter difference between two pipes is within the set range, they can be connected using the appropriate connector.

[0075] Matching the connection direction parameters is also very important. The connection direction of the two ports must be consistent or within the allowable angular deviation range to ensure the connection is effective. For example, when the angle between the connection directions of the two pipes is within the specified angle range, the connection can be made using an elbow or other connector.

[0076] Port pairs that simultaneously match the port type, interface size, and connection direction parameters are marked as directly connected. For each directly connected port pair, the relevant information is collated and combined to generate a connection feature vector. This connection feature vector contains information such as the connected component's identifier, port type, interface size, and connection direction, effectively describing the direct connection between components.

[0077] Step S1321: extracting the interface size parameters and connection direction parameters of each port from the connection port information, and constructing a port attribute matrix.

[0078] In order to more conveniently determine the port matching relationship, it is necessary to extract the interface size parameters and connection direction parameters of each port from the connection port information and construct a port attribute matrix.

[0079] The port attribute matrix is ​​a two-dimensional matrix, where each row represents a port, and each column corresponds to the interface size parameters and connection direction parameters. By constructing a port attribute matrix, key information about all ports can be centrally managed and analyzed. For example, for the numerous pipe and equipment connection ports in the heating room of a large industrial park, their interface size and connection direction parameters can be organized into the port attribute matrix, allowing intuitive comparison of parameter differences between different ports.

[0080] Step S1322: Calculate the absolute value of the difference between the interface size parameters of any two ports. When the absolute value of the difference is smaller than a preset size tolerance range, mark them as a size-matched port pair.

[0081] After constructing the port attribute matrix, the interface size parameters of any two ports need to be compared. By calculating the absolute value of the difference between the interface size parameters of any two ports, the degree of size matching between them can be determined.

[0082] The preset dimensional tolerance range is determined based on actual project requirements and connection standards. When the absolute difference between the interface dimensional parameters of two ports is less than the dimensional tolerance range, the two ports are considered dimensionally matched and marked as a dimensionally matched port pair. For example, if the absolute difference between the diameters of two pipes is less than the preset diameter tolerance range, the two pipes are considered dimensionally compatible for connection.

[0083] Step S1323: For the size-matched port pair, calculate the angle difference of the connection direction parameters thereof. When the angle difference is less than a preset direction tolerance range, mark it as a direction-matched port pair.

[0084] For port pairs marked as size-matched, we need to further determine whether their connection directions match. By calculating the angle difference between the connection direction parameters of size-matched port pairs, we can evaluate their consistency in connection direction.

[0085] The preset directional tolerance range is determined based on actual installation requirements and fluid flow characteristics. When the angle difference between the connection direction parameters of two ports is less than the directional tolerance range, the two ports are considered to be matched in the connection direction and are marked as a direction-matched port pair. For example, if the angle difference between the connection directions of two pipes is less than the preset angular tolerance range, the two pipes can be considered to be correctly connected in the direction.

[0086] Step S1324: Determine the port pairs that satisfy both size matching and direction matching as directly connected port pairs.

[0087] Port pairs that meet both size and orientation matching are identified as directly connected. Only pairs that meet both conditions can achieve a truly effective physical connection, ensuring proper functioning of components and smooth fluid flow. In the heating equipment rooms of large industrial parks, accurately determining directly connected port pairs is crucial to system stability and reliability. For example, in the connection between a water pump and a pipeline, only when both port size and orientation match can the pump effectively deliver water to the pipeline.

[0088] Step S1325: Allocate a connection type label to each directly connected port pair, where the connection type label includes rigid connection, flexible connection, and detachable connection.

[0089] Assigning a connection type label to each directly connected port pair helps describe the connection method and characteristics between components in more detail. Connection type labels mainly include rigid connection, flexible connection, and detachable connection.

[0090] A rigid connection refers to a connection between two components achieved through welding, bolting, or other methods. These connections offer high strength and stability, but lack flexibility. In heating equipment rooms, some pipe connections may use rigid connections to ensure the piping system's tightness and pressure resistance.

[0091] Flexible connections are achieved through flexible elements such as rubber joints and bellows. These connections offer a degree of elasticity and flexibility, capable of absorbing vibration and displacement. For example, when connecting a water pump to a pipe, using a flexible connection can reduce the impact of vibrations generated by the pump's operation on the piping system.

[0092] Removable connections are connections achieved through flanges, threads, and other methods that facilitate disassembly and maintenance. In heating rooms, components that require frequent repair and replacement, such as valves and filters, often use removable connections.

[0093] Step S1326: extracting the identification codes of the components to which the directly connected port pairs belong, and constructing a component connection relationship matrix, wherein each element in the component connection relationship matrix includes a connection type label and a port matching parameter.

[0094] Extract the identification codes of the components to which the directly connected ports belong and construct a component connection matrix. This component connection matrix can effectively display the connection relationships and connection characteristics between components.

[0095] The component connection matrix is ​​a two-dimensional matrix in which each row and column represents a component. Each element in the matrix contains information such as the connection type label and port matching parameters. For example, if there is a direct connection between components A and B, the elements corresponding to A and B in the matrix will contain the connection type label (such as rigid connection, flexible connection, or detachable connection) and port matching parameters (such as interface size and connection direction). The component connection matrix provides an intuitive understanding of the connections between components.

[0096] Step S1327: Convert the component connection relationship matrix into a structured connection relationship feature vector, where the connection relationship feature vector includes a connection component identification pair, a connection type label, and a port matching parameter.

[0097] The component connection relationship matrix is ​​converted into a structured connection relationship feature vector. The purpose of this step is to convert the connection relationship information in matrix form into a vector form that is easier to process and analyze.

[0098] The connection relationship feature vector contains information such as the identification pairs of connected components, the connection type label, and port matching parameters. By organizing and combining the information in the component connection relationship matrix, a vector containing all direct connection relationship information can be obtained. For example, for the connection relationships between multiple components in the heating room of a large industrial park, the connection relationship feature vector can effectively list the identification of each pair of connected components, the connection type between them, and the specific port matching parameters.

[0099] Step S133: extracting the function description field of each component from the static attribute data set of the dynamic and static data set of the heating room, parsing the input and output relationship in the function description field, and determining the upstream and downstream function dependency paths between components.

[0100] Extracting the functional description fields of each component from the static attribute data set of the heating room's dynamic and static data sets provides the basis for analyzing the functional dependencies between components. The functional description fields detail the component's function and operating principle. By parsing these fields, we can determine the input and output relationships between components.

[0101] In the heating room of a large industrial park, different components have upstream and downstream functional dependencies. For example, the boiler outputs hot water or steam, which is fed into a heat exchanger. The heat exchanger transfers the heat from the hot water or steam to another medium, achieving heat exchange. The water pump provides power to the entire system, circulating the hot water through the pipes. Its output affects the input flow and pressure of other components. By analyzing the input-output relationships in the functional description fields, we can determine the upstream and downstream functional dependency paths between components, namely, which components are upstream and which are downstream, and the functional transfer relationships between them.

[0102] Step S134: Based on the upstream and downstream functional dependency paths, the functional dependency strength value between any two components is calculated to generate a functional dependency feature vector.

[0103] Based on the determined upstream and downstream functional dependency paths, the functional dependency strength value between any two components can be further calculated. The functional dependency strength value reflects the closeness of the functional dependency between the two components.

[0104] When calculating the functional dependency strength value, multiple factors need to be considered. For example, the frequency of functional transfer between two components, the amount of energy or material transferred, and the stability of the functional transfer. If the functional transfer between two components is frequent, the amount of energy or material transferred is large, and the transfer process is stable, then the functional dependency strength value between them is high. Conversely, if the functional transfer is infrequent, the amount of energy or material transferred is small, or the transfer process is unstable, then the functional dependency strength value is low.

[0105] By calculating the functional dependency strength between any two components, a functional dependency feature vector can be generated. This functional dependency feature vector contains the functional dependency strength information between all component pairs and can effectively represent the functional dependency network between components.

[0106] Step S1341: converting the upstream and downstream functional dependency paths into a directed graph structure, wherein nodes in the directed graph structure represent components and directed edges represent functional dependency directions.

[0107] To more intuitively represent the upstream and downstream functional dependency paths between components, we can convert them into a directed graph structure. In a directed graph structure, each node represents a component, and directed edges represent the functional dependency directions between components.

[0108] For example, in a heating room at a large industrial park, if the boiler is an upstream component and the heat exchanger is a downstream component, with the boiler's output providing input to the heat exchanger, then in a directed graph, there would be a directed edge from the node representing the boiler to the node representing the heat exchanger. This directed graph structure effectively monitors the functional transfer direction and dependencies between components, facilitating further analysis and calculations.

[0109] Step S1342: Based on the directed graph structure, calculate the out-degree value of each component as an upstream node and the in-degree value of each component as a downstream node, wherein the out-degree value represents the functional impact of the component on other components, and the in-degree value represents the functional impact of other components on the component.

[0110] In a directed graph, we can calculate the out-degree of each component as an upstream node and the in-degree of each component as a downstream node. The out-degree refers to the number of directed edges emanating from that node, indicating the impact that component has on the functionality of other components. For example, if a component has an out-degree of 3, it means that it provides functional input to three other components and has a direct impact on the functioning of these three components.

[0111] The in-degree value refers to the number of directed edges pointing to the node, which indicates the amount of influence other components have on the component's functionality. For example, if a component has an in-degree value of 2, it means that two other components provide functional input to the component, and the component's function depends on these two components.

[0112] By calculating the out-degree and in-degree values, we can understand the position and role of each component in the functional dependency network. Components with large out-degree values ​​are usually key supply components in the system; components with large in-degree values ​​are usually key demand components in the system, and their functional operation depends on multiple other components.

[0113] Step S1343: extracting the shortest dependency path length between any two components in the directed graph structure, where the shortest dependency path length represents the minimum number of links for two components to establish functional dependencies through intermediate components.

[0114] The shortest dependency path length between any two components is extracted from the directed graph structure. This length represents the minimum number of links through which the two components establish functional dependencies through intermediate components.

[0115] In the heating equipment room of a large industrial park, some components may need to pass through multiple intermediate components to achieve functional dependencies. For example, the output of component A may need to pass through component B, then component C, and finally reach component D. In this case, the shortest dependency path length between components A and D is 3. The shortest dependency path length reflects the convenience and efficiency of function transfer between two components. A shorter shortest dependency path length means that the function transfer between two components is more direct and efficient, while a longer shortest dependency path length may indicate more intermediate links and potential delays in the function transfer process.

[0116] Step S1344: Based on the out-degree value, in-degree value and the shortest dependency path length, a functional dependency strength calculation formula is constructed. The functional dependency strength value is positively correlated with the out-degree value and in-degree value, and negatively correlated with the shortest dependency path length.

[0117] Based on the calculated out-degree and in-degree values ​​and the shortest dependency path length, a formula can be constructed to calculate the functional dependency strength. The functional dependency strength is positively correlated with the out-degree and in-degree values. This means that the larger the out-degree and in-degree values ​​of a component, the stronger its functional dependency strength with other components. This is because a large out-degree value indicates a wide range of functional influence on other components, while a large in-degree value indicates a deep functional influence on the component from other components.

[0118] The functional dependency strength value is negatively correlated with the length of the shortest dependency path. That is, the longer the shortest dependency path, the lower the functional dependency strength between two components. This is because a longer shortest dependency path means that the function transfer needs to go through more intermediate links, which increases the uncertainty and delay of the function transfer.

[0119] Step S1345: Calculate the functional dependency strength value between any two components and generate a functional dependency strength matrix.

[0120] The functional dependency strength calculation formula is used to calculate the functional dependency strength between any two components. These calculated functional dependency strength values ​​are organized into a matrix to generate a functional dependency strength matrix.

[0121] The functional dependency strength matrix is ​​a two-dimensional matrix in which each row and column represents a component, and each element in the matrix represents the functional dependency strength between two corresponding components. The functional dependency strength matrix allows you to intuitively monitor the closeness of the functional dependency relationship between any two components. For example, larger element values ​​in the matrix indicate a high degree of functional dependency between the two components, while smaller element values ​​indicate a low degree of functional dependency.

[0122] Step S1346: Add a dependency direction label to each element in the functional dependency strength matrix, where the dependency direction label includes unidirectional dependency and bidirectional dependency.

[0123] Adding dependency direction labels to each element in the functional dependency strength matrix helps to more accurately describe the functional dependency relationship between components. Dependency direction labels mainly include unidirectional dependency and bidirectional dependency.

[0124] A unidirectional dependency means that the functionality of one component depends on another, but the functionality of the other component does not depend on it. For example, in a heating plant, the heat exchanger's functionality depends on the hot water or steam provided by the boiler, but the boiler's functionality does not depend on the heat exchanger. In the functional dependency strength matrix, for unidirectional dependencies, the dependency direction can be clearly marked.

[0125] A bidirectional dependency indicates a mutual dependence between two components: the output of one component is the input of another, and the output of the other component also affects the input of the first. For example, a bidirectional dependency might exist between a water pump and a piping system. The pump provides power to the piping system, allowing water to flow through the pipes, while the resistance and flow rate of the piping system also affect the operation of the water pump. Bidirectional dependencies are also noted in the functional dependency strength matrix.

[0126] Step S1347: Integrate the functional dependency strength matrix and the dependency direction label into a structured functional dependency feature vector, wherein the functional dependency feature vector includes a dependency component identification pair, a functional dependency strength value, and a dependency direction label.

[0127] The functional dependency strength matrix and dependency direction labels are integrated into a structured functional dependency feature vector. The purpose of this step is to convert the functional dependency information in matrix form into a vector form that is easier to process and analyze.

[0128] The functional dependency feature vector contains information such as the identification pairs of dependent components, the functional dependency strength values, and dependency direction labels. By organizing and combining the information in the functional dependency strength matrix and the dependency direction labels, a vector containing the functional dependency information between all component pairs can be obtained. For example, for the functional dependencies between multiple components in the heating room of a large industrial park, the functional dependency feature vector can effectively list the identification of each pair of dependent components, the functional dependency strength value between them, and the dependency direction.

[0129] Step S135: extracting the real-time performance parameters of each component from the dynamic operation data set of the dynamic and static data set of the heating room, calculating the correlation between the change trends of the real-time performance parameters of different components, and generating a dynamic correlation coefficient.

[0130] The real-time performance parameters of each component are extracted from the dynamic and static data sets of the heating room. These parameters reflect the actual performance of the component during operation. For example, the real-time performance parameters of a boiler may include temperature, pressure, and thermal efficiency; the real-time performance parameters of a water pump may include flow rate, head, and power.

[0131] Calculating the correlation between the changing trends of different components' real-time performance parameters helps us understand the dynamic relationships between them. By analyzing the changing trends of different components' real-time performance parameters, we can determine whether they are changing synchronously or influencing each other. For example, if the water pump flow rate increases as the boiler temperature rises, then we can conclude that there is a certain dynamic relationship between the boiler and the water pump.

[0132] The dynamic correlation coefficient reflects the similarity and strength of the correlation between the real-time performance parameters of different components. A higher dynamic correlation coefficient indicates that the real-time performance parameter trends of two components are similar, and the dynamic correlation between them is strong; a lower dynamic correlation coefficient indicates that the trends are significantly different, and the dynamic correlation is weak.

[0133] Step S136: extracting the design parameters of each component from the static attribute data set of the dynamic and static data set of the heating room, calculating the specification matching degree between the design parameters of different components, and generating a static correlation coefficient.

[0134] Extract the design parameters of each component from the static attribute data set of the dynamic and static data set of the heating room. These design parameters are the basic parameters determined during the component design phase and determine the component's performance and functionality. For example, the design parameters of a boiler may include rated power, rated pressure, and rated temperature; the design parameters of a water pump may include flow rate, head, and speed.

[0135] Computing the degree of specification matching between component design parameters helps assess the compatibility and interdependence between components at the design level. For example, if the design parameters of two components match each other, they are more likely to work together in actual operation. By comparing the design parameters of different components, the degree of specification matching between them can be determined.

[0136] The static correlation coefficient reflects the degree of specification matching between the design parameters of different components. A higher static correlation coefficient indicates that the design parameters of two components are closely matched, and their design-level correlation is strong; a lower static correlation coefficient indicates that the design parameters are significantly different and the correlation is weak.

[0137] Step S137: Integrate the dynamic correlation coefficient and the static correlation coefficient into a dynamic and static data correlation feature vector.

[0138] The generated dynamic correlation coefficients and static correlation coefficients are integrated into dynamic and static data correlation feature vectors. The purpose of this step is to integrate the dynamic correlation information and static correlation information between components to form a more comprehensive correlation feature representation.

[0139] The dynamic and static data association feature vectors contain both dynamic and static correlation coefficients between different components. This allows for the simultaneous consideration of both dynamic component associations during operation and static design-level associations. For example, for multiple components in a heating room at a large industrial park, the dynamic and static data association feature vectors can effectively demonstrate the comprehensive degree of association between any two components.

[0140] Step S138: Associating and integrating the connection relationship feature vector, the function dependency feature vector and the dynamic and static data association feature vector according to the component identification code to generate a component association feature set including the connection relationship feature, the function dependency feature and the dynamic and static data association feature.

[0141] The connection relationship feature vector, function dependency feature vector, and dynamic and static data association feature vector are associated and integrated according to the component identification code. Since each component has a unique identification code, the connection relationship feature, function dependency feature, and dynamic and static data association feature of the component can be associated through the identification code.

[0142] The specific association and integration process involves matching and combining each component's connection relationship feature vector, functional dependency feature vector, and dynamic and static data association feature vector, using the component identification code as an index. For example, for a specific component, its connection relationship features (such as connection type and port matching parameters), functional dependency features (such as functional dependency strength and dependency direction), and dynamic and static data association features (such as dynamic and static association coefficients) are integrated.

[0143] By performing this association and integration operation on all components, a component association feature set is generated, including connection relationship features, functional dependency features, and dynamic and static data association features. This component association feature set covers the association feature information between all components in the heating room of a large industrial park.

[0144] Step S140: calling a layout generation tool to perform spatial layout mapping processing on the component association feature set to generate a preliminary layout association model of the heating room.

[0145] After obtaining the component association feature set, the layout generation tool is invoked to perform spatial layout mapping on it to generate a preliminary layout association model for the heating room. The layout generation tool is a software tool specifically designed for spatial layout planning based on the association relationships between components. It can rationally arrange the position and connection methods of components in space based on the information in the component association feature set.

[0146] Step S141: extracting a connection relationship feature vector and a function dependency feature vector from the component association feature set, and constructing a layout constraint condition set, wherein the layout constraint condition set includes a spatial position constraint, a connection distance constraint, and a function area constraint.

[0147] From the component association feature set, we extract the connection relationship feature vector and the functional dependency feature vector. These two vectors contain important information about the connection relationship and functional dependency between components. Based on this information, we can construct a set of layout constraints.

[0148] Spatial location constraints define the permitted location ranges for components within a space. In the heating equipment room of a large industrial park, certain components can only be placed within specific areas due to site limitations and safety requirements. For example, boilers typically need to be placed in well-ventilated areas away from flammable materials, while water pumps need to be located in locations that are easily accessible for maintenance and operation.

[0149] Connection distance constraints require that the distance between components be within a specified range. Excessively long connections increase pipe length and resistance, reducing system efficiency; too close connections can lead to installation and maintenance difficulties. For example, the distance between a pump and a valve must be appropriately set based on pipe specifications and system requirements.

[0150] Functional area constraints divide components into different functional areas based on their function. For example, all heating components (such as boilers and heat exchangers) are grouped into the heating area, and all power components (such as pumps) are grouped into the power area. This division helps improve system management efficiency and operational stability.

[0151] Step S142: using the component three-dimensional coordinate data in the component structure analysis result as initial layout position parameters, and inputting them into the initial positioning module of the layout generation tool.

[0152] The component's 3D coordinate data from the component structure analysis results is used as the initial layout position parameters and input into the initial positioning module of the layout generation tool. In the previous step, the component's geometric description information was converted to its 3D coordinate data. This 3D coordinate data can be used as the component's initial position information.

[0153] The initial positioning module performs a preliminary positioning of components in virtual 3D space based on the input component 3D coordinate data. This positioning is only an initial position arrangement and needs to be adjusted based on the layout constraint set.

[0154] Step S143: Based on the layout constraint condition set, the layout generation tool performs spatial coordinate adjustment processing on the initial layout position parameters output by the initial positioning module, so that the relative positions between components meet the connection distance constraints.

[0155] Based on the constructed set of layout constraints, the layout generation tool performs spatial coordinate adjustments on the initial layout position parameters output by the initial positioning module. The purpose of this step is to ensure that the relative positions between components meet the connection distance constraints.

[0156] The layout generation tool analyzes the connection requirements between components based on the information in the connection relationship feature vector. For components with direct connections, the layout generation tool adjusts their spatial coordinates to keep the connection distance between them within the allowed range. For example, if the initial connection distance between a pump and a valve is too long, the layout generation tool will adjust the position of the pump or valve to shorten the connection distance. If the connection distance is too short, the layout generation tool will move them further apart.

[0157] When adjusting spatial coordinates, the layout generation tool also considers spatial position constraints and functional area constraints, ensuring that the adjusted component position remains within the permitted spatial range and complies with functional area division requirements. For example, when adjusting the position of a boiler, it can be ensured to remain in a well-ventilated heating area away from flammable materials.

[0158] For example, step S1431: extracting connection distance constraint parameters from the layout constraint condition set, where the connection distance constraint parameters include a minimum connection distance and a maximum connection distance.

[0159] Extract connection distance constraint parameters from the layout constraint set. These parameters are important for the layout generation tool to adjust spatial coordinates. Connection distance constraint parameters include minimum connection distance and maximum connection distance.

[0160] The minimum connection distance refers to the shortest allowable distance between components and is determined based on factors such as pipe installation requirements and fluid flow characteristics. If the connection distance between components is less than the minimum connection distance, it may cause problems such as difficult pipe installation and increased fluid resistance.

[0161] The maximum connection distance refers to the longest allowable distance between components, which is determined based on factors such as system efficiency and cost. If the connection distance between components is greater than the maximum connection distance, the length and resistance of the pipeline may increase, reducing the efficiency of the system and increasing construction costs.

[0162] Step S1432: Calculate the spatial straight-line distance between any two directly connected components in the initial layout position parameters.

[0163] Based on the initial layout position parameters, the linear distance between any two directly connected components is calculated. This calculation is performed using the components' 3D coordinate data. The layout generation tool uses spatial geometry algorithms to calculate the linear distance between the components based on their coordinate positions in 3D space.

[0164] For example, for a directly connected component like a pump and a valve, the layout generation tool calculates their linear distance in space based on their 3D coordinate data. This linear distance is the basis for the layout generation tool to determine whether the connection distance between the components meets the requirements.

[0165] Step S1433: When the spatial straight-line distance is less than the minimum connection distance, the layout generation tool adjusts the spatial coordinates of the downstream component in the reverse direction along the connection direction until the spatial straight-line distance reaches the minimum connection distance.

[0166] When the calculated spatial straight-line distance is less than the minimum connection distance, the layout generation tool will take appropriate adjustment measures. The layout generation tool will adjust the spatial coordinates of the downstream components in the opposite direction of the connection.

[0167] In heating room systems, there are upstream and downstream components. For example, a pump is an upstream component, and a valve is a downstream component. If the linear distance between the pump and valve is less than the minimum connection distance, the layout generation tool will move the valve in the opposite direction of the connection until the linear distance between them reaches the minimum connection distance. This adjustment ensures sufficient space between components for piping connections and installation and maintenance.

[0168] Step S1434: When the spatial straight-line distance is greater than the maximum connection distance, the layout generation tool adjusts the spatial coordinates of the downstream component in the positive direction along the connection direction until the spatial straight-line distance reaches the maximum connection distance.

[0169] When the calculated spatial straight-line distance is greater than the maximum connection distance, the layout generation tool will positively adjust the spatial coordinates of the downstream components along the connection direction.

[0170] Using the same example of a pump and valve, if the distance between them is greater than the maximum connection distance, the layout generator will reposition the valve in the direction of the connection until the distance between them reaches the maximum connection distance. This adjustment can prevent system inefficiencies and increased costs caused by excessively long pipes.

[0171] Step S1435: Perform chain effect detection on the adjusted component space coordinates to determine whether the position adjustment of the component causes other components with functional dependencies to violate layout constraints.

[0172] After adjusting the spatial coordinates of a component, you need to perform chain effect detection on the adjusted spatial coordinates of the component. This is because the position adjustment of a component may affect other components that have functional dependencies with it.

[0173] The layout generation tool analyzes the functional dependencies between the adjusted component and other components to determine whether the component's position adjustment will cause other components to violate layout constraints. For example, adjusting the position of a water pump may affect the connection distance and spatial position of components such as valves and heat exchangers that have functional dependencies on the water pump. The layout generation tool checks whether these affected components still meet spatial position constraints, connection distance constraints, and functional area constraints.

[0174] Step S1436: When there is a chain reaction, the spatial coordinate adjustment process is repeatedly performed on the affected components until the relative positions of all components satisfy the constraint parameters in the layout constraint condition set.

[0175] When a chain reaction is detected, the layout generation tool repeatedly adjusts the spatial coordinates of the affected components. This process continues iteratively until the relative positions of all components satisfy the constraints in the layout constraint set.

[0176] For example, if adjusting the position of a water pump causes the connection distance of a valve to not meet the requirements, the layout generation tool will adjust the spatial coordinates of the valve; if the adjustment of the valve affects other components, the layout generation tool will continue to adjust these affected components until all components in the entire system meet the layout constraints.

[0177] Step S1437: Record the final adjusted spatial coordinate parameters of all components and generate an adjusted spatial coordinate parameter set.

[0178] After completing all spatial coordinate adjustments and chain reaction processing, the layout generation tool will record the final adjusted spatial coordinate parameters of all components and organize these parameters into a set to generate the adjusted spatial coordinate parameter set.

[0179] The adjusted set of spatial coordinate parameters accurately reflects the final spatial position of the component while satisfying the layout constraints.

[0180] Step S144: Based on the dynamic and static data association feature vectors in the component association feature set, the layout generation tool allocates a data interface position to each component, and the data interface position is used for subsequent dynamic and static data attachment.

[0181] The layout generation tool assigns data interface locations to each component based on the dynamic and static data association feature vectors in the component association feature set. The dynamic and static data association feature vectors contain information about the dynamic and static associations between components. By analyzing this information, it can determine where each component needs to interact with other components for data.

[0182] Data interfaces are the points where components connect and exchange external data. They are used to connect to subsequent dynamic and static data. For example, for a boiler, these interfaces can be used to connect to devices like temperature sensors and pressure sensors to obtain real-time operating data. For a water pump, these interfaces can be used to connect to devices like flow sensors and power sensors to obtain pump performance parameters. The layout generation tool will rationally assign data interface locations based on component functions and data exchange requirements to ensure accurate data collection and transmission.

[0183] Step S145: Based on the adjusted spatial coordinate parameters and data interface positions, the layout generation tool constructs a spatial association relationship network between components, where the spatial association relationship network includes physical connection links and data interaction links.

[0184] Based on the adjusted spatial coordinate parameters and data interface locations, the layout generation tool constructs a spatial association network between components. This spatial association network includes physical connection links and data interaction links between components.

[0185] Physical connection links reflect the actual physical connection relationships between components, such as pipe connections, cable connections, etc. The layout generation tool determines the physical connection paths between components based on the spatial positions and connection relationship feature vectors of the components and constructs the corresponding physical connection links in space.

[0186] Data exchange links reflect the data transmission and interaction relationships between components. The layout generation tool determines the data transmission paths between components based on the data interface locations and the dynamic and static data association feature vectors, and constructs the corresponding data exchange links in space. For example, a data exchange link might exist between a boiler and a heat exchanger to transmit data such as hot water temperature and pressure.

[0187] Step S146: Fusing the spatial association relationship network with the component three-dimensional geometric model to generate a preliminary layout association model of the heating room including spatial location information, connection relationship information and data interface information.

[0188] The constructed spatial association network is integrated with the component's 3D geometric model. The component's 3D geometric model has been constructed in the previous step based on the component's geometric description information, which accurately presents the component's shape and size.

[0189] Through fusion processing, the physical connection links and data interaction links in the spatial association network are integrated with the component 3D geometric model to generate a preliminary layout association model of the heating room. This preliminary layout association model includes the spatial location information, connection relationship information, and data interface information of the components. It can intuitively display the spatial layout, interconnection relationships, and data interaction of each component in the heating room.

[0190] Step S150: Perform spatial conflict detection and optimization processing on the preliminary layout association model of the heating room to obtain a spatially optimized layout association model, and convert the spatially optimized layout association model into modeling data that complies with the BIMBase data interaction standard, and output the modeling data to the BIMBase platform to complete the construction of the heating room model.

[0191] After generating the preliminary layout model for the heating room, spatial conflict detection and optimization were performed to ensure the model's rationality and feasibility. The optimized model was then converted into modeling data that complies with BIMBase data exchange standards and exported to the BIMBase platform, completing the construction of the heating room model.

[0192] Step S151: extracting the three-dimensional geometric models and spatial coordinate parameters of all components from the preliminary layout association model of the heating room to construct a layout space model.

[0193] The 3D geometric models and spatial coordinate parameters of all components are extracted from the preliminary layout model of the heating room. The 3D geometric model accurately describes the shape and size of the components, while the spatial coordinate parameters determine the position of the components in 3D space.

[0194] These 3D geometric models and spatial coordinate parameters are integrated to construct a layout space model. This model is a virtual 3D space that contains the actual form and spatial location information of all components in the heating room. This model allows for intuitive visualization of the spatial relationships and layout of components.

[0195] Step S152: performing collision detection processing on the layout space model, calculating the spatial intersection volume between the three-dimensional geometric models of any two components, and marking them as a spatial conflict component pair when the spatial intersection volume is greater than a preset collision tolerance volume.

[0196] Perform collision detection on the layout space model and calculate the spatial intersection volume between the three-dimensional geometric models of any two components.

[0197] The preset collision tolerance volume is determined based on actual project requirements and installation specifications. When the calculated spatial intersection volume is larger than the collision tolerance volume, the two components are spatially conflicting and are marked as a spatially conflicting component pair. For example, if the 3D geometric models of two pipes have a large spatial intersection that exceeds the collision tolerance volume, the two pipes are marked as a spatially conflicting component pair.

[0198] Step S153: Analyze the connection relationship characteristics and functional dependency characteristics of the spatial conflicting component pairs to determine the conflict type, which includes physical collision conflict, functional path conflict, and data interface conflict.

[0199] Analyze the connection relationship characteristics and functional dependency characteristics of spatial conflicting component pairs to determine the type of conflict. Conflict types mainly include physical collision conflict, functional path conflict, and data interface conflict.

[0200] A physical collision occurs when the physical entities of two components overlap or interfere with each other in space. For example, the housings of two devices may squeeze each other in space, which can affect the installation and normal operation of the devices.

[0201] Functional path conflict occurs when the functional transfer path between components is blocked. For example, if the output pipe of a water pump is blocked by another component, hot water cannot flow properly, affecting the function of the system.

[0202] A data interface conflict occurs when the data interfaces of a component conflict in location, preventing data from being transmitted normally. For example, two devices may have overlapping data interfaces, preventing simultaneous connection to a data acquisition device.

[0203] Step S154: For a component pair of physical collision conflict type, based on the connection direction parameter in the connection relationship feature, adjust the spatial coordinates of one of the components along the conflict direction until the spatial intersection volume is smaller than the collision tolerance volume.

[0204] For component pairs with physical collision conflicts, the spatial coordinates of one component are adjusted along the conflict direction based on the connection direction parameters in the connection relationship feature. The purpose of this adjustment is to eliminate the physical overlap between components and make the spatial intersection volume smaller than the collision tolerance volume.

[0205] For example, if two pipes physically collide, the layout optimization tool determines the direction of the conflict based on the pipe connection parameters and adjusts the spatial coordinates of one pipe in the conflicting direction. The physical collision conflict is resolved by continuously adjusting and testing the spatial intersection volume until it is smaller than the collision tolerance volume.

[0206] Step S155: For component pairs with conflicting functional paths, based on the dependency strength values ​​in the functional dependency features, the spatial coordinates of the components with dependency strength values ​​less than the set strength values ​​are preferentially adjusted to restore the functional dependency paths to smooth flow.

[0207] For component pairs with functional path conflicts, adjustments are made based on the dependency strength value in the functional dependency feature. The dependency strength value reflects the closeness of the functional dependency between components. When a functional path conflict occurs, the spatial coordinates of components with a dependency strength value lower than the set strength value are adjusted first.

[0208] The set strength value is a critical value determined based on the overall performance and operating requirements of the heating room system. For components with a dependency strength value less than the set strength value, they are relatively less critical in the functional dependency relationship. For example, in the heating room, an auxiliary valve has a low functional dependency strength value with other components. When its position affects the functional paths of other components, its spatial coordinates are adjusted first. By adjusting the position of the component, the originally obstructed functional dependency path is restored to normal operation of the system. During the adjustment process, it is also necessary to constantly check whether the functional path is unobstructed to ensure that the adjusted layout can meet the functional requirements of the system.

[0209] Step S156: for component pairs with data interface conflict types, reallocate data interface positions so that the spatial distance between the data interfaces meets the data transmission requirement.

[0210] For component pairs with data interface conflicts, the data interface locations need to be reallocated. Data interface conflicts typically manifest as data interfaces being too close together or overlapping, which can affect normal data transmission.

[0211] When relocating data interfaces, consider data transmission requirements. For example, data transmission may require a certain amount of space to avoid signal interference, and different types of data transmission may have different spatial distance requirements. High-speed data transmission may require greater spacing to ensure signal stability. The layout optimization tool will replan and adjust the data interface positions of conflicting components based on the data interface type and specific data transmission requirements. By rationally arranging the data interface positions, the spatial distance between them meets data transmission requirements, ensuring accurate and stable data transmission.

[0212] Step S157: re-execute the collision detection process on the optimized layout space model. When there are still spatial conflicting component pairs, repeat the conflict optimization process until the spatial intersection volume of all component pairs is smaller than the collision tolerance volume.

[0213] After completing the optimization processing of different types of conflicts, it is necessary to re-execute the collision detection processing on the optimized layout space model to ensure that no new space conflicts are introduced during the optimization process.

[0214] If spatially conflicting component pairs still exist after re-detection, it means that the previous optimization process was not perfect enough and the conflict optimization process needs to be repeated. This process will be iterated continuously. For newly discovered conflicts, the conflict type (physical collision conflict, functional path conflict, or data interface conflict) will be analyzed again, and corresponding optimization measures will be taken. For example, if a physical collision conflict is detected between two pipes again, the spatial coordinates of the pipes can be adjusted again; if a functional path conflict is found, the positions of the relevant components can be adjusted again to restore the smooth flow of the functional path. By repeatedly detecting and optimizing until the spatial intersection volume of all component pairs is less than the collision tolerance volume, it is ensured that there are no obvious spatial conflicts in the layout space model.

[0215] Step S158: Integrate the optimized layout space model with the component association feature set to obtain a space-optimized layout association model.

[0216] The layout space model after multiple optimization processes is integrated with the component association feature set. The component association feature set contains important information such as the connection relationship features between components, functional dependency features, and dynamic and static data association features.

[0217] Through integration, the actual spatial positions of components in the layout space model are combined with the various association information in the component association feature set. For example, the specific spatial positions of the pump and valve are determined in the layout space model. This is combined with the connection relationship characteristics (such as connection type and port matching parameters), functional dependency characteristics (functional dependency strength value and dependency direction), and dynamic and static data association characteristics (dynamic association coefficient and static association coefficient) of the pump and valve in the component association feature set to form a more complete and accurate model. The resulting spatially optimized layout association model not only reflects the spatial layout of the components but also reflects the various associations between them.

[0218] Step S159: converting the space-optimized layout association model into modeling data that complies with the BIMBase data interaction standard, and outputting the modeling data to the BIMBase platform to complete the construction of the heating room model.

[0219] The last step is to convert the space-optimized layout association model into modeling data that complies with the BIMBase data interaction standard and output it to the BIMBase platform.

[0220] First, read the BIMBase platform's modeling data standard specifications. These include key aspects such as data format requirements, field definition rules, and relationship description methods. Data format requirements specify the storage format of modeling data within the BIMBase platform. For example, a specific file format (such as a 3D model file format) may be required to store component geometry and relationship information. Field definition rules clarify the meaning and value range of each data field, such as how component identification fields and attribute fields should be defined and filled in. Relationship description methods explain how to accurately describe the connection relationships and functional dependencies between components in the data.

[0221] Extract component 3D geometry data from the spatially optimized layout context model. This data describes the component's shape and dimensions and is converted into a BIMBase-compatible geometry description file according to the BIMBase platform's data format requirements. A geometry description file typically contains information such as vertex coordinates, face indices, and material properties. Vertex coordinates precisely define the component's 3D shape, face indices determine the connectivity between vertices, and thus form the component's surface. Material properties describe the component's appearance and physical properties.

[0222] Extract component association feature sets from the spatially optimized layout association model. According to the BIMBase platform's field definition rules, convert connection relationship features, functional dependency features, and dynamic and static data association features into a structured attribute data table. This structured attribute data table contains a component ID column, a feature type column, and a feature value column. The component ID column uniquely identifies each component, the feature type column describes the specific feature type (e.g., connection relationship, functional dependency, etc.), and the feature value column records the specific feature values ​​(e.g., connection type label, functional dependency strength value, etc.).

[0223] The geometry description file is associated and bound to the structured attribute data table using component identification codes. Since each component has a unique identification code, this identification code accurately matches the component's geometry and associated information. Through this association and binding, integrated modeling data containing both geometry and attribute information is generated.

[0224] Perform data verification on the integrated modeling data. Check the integrity of the geometry description file and the consistency of the structured attribute data table. The integrity check mainly checks whether the geometry description file contains all necessary information, such as whether the vertex coordinates are complete and the patch index is correct. The consistency check ensures that the data in the structured attribute data table complies with the field definition rules, such as whether the eigenvalues ​​are within a reasonable range and whether the component identifiers are unique and accurate. If missing or inconsistent data is found during the data verification process, it is necessary to return to the data conversion step and reprocess the data to correct and improve it.

[0225] The validated integrated modeling data is encapsulated into a model file format supported by the BIMBase platform. This file format typically includes model version information, a creation timestamp, and a data checksum. Model version information records model updates, facilitating model management and traceability; the creation timestamp clearly identifies the model's creation time; and the data checksum verifies the integrity and accuracy of the model data, preventing errors during data transmission or storage.

[0226] Upload the integrated modeling data in the encapsulated model file format to the BIMBase platform database through the BIMBase platform's data interface. The data interface bridges external data with the BIMBase platform, ensuring accurate and secure data transfer to the platform database. Once uploaded, the platform triggers the model loading process. The platform parses and processes the uploaded model data and loads it into the BIMBase platform's modeling environment.

[0227] After the platform model is loaded, perform a model visualization preview. This allows you to intuitively view the overall effect of the heating room model and check whether the spatial layout, component connection relationships, and data associations of the model meet your expectations. If problems are found in the model during the visualization preview process, such as an unreasonable spatial layout or incorrect connection relationships, the model can be further adjusted and optimized. Once the model is confirmed to be correct in terms of spatial layout and data associations, the construction of the heating room model is completed. At this point, an accurate and complete heating room model has been established on the BIMBase platform.

[0228] The entire data collection process involves privacy-sensitive data, such as equipment manufacturer information and maintenance records. To protect this privacy-sensitive data, a variety of privacy protection and anti-leakage technologies are employed. For data storage, encryption technology is used to encrypt privacy-sensitive data, converting the data into ciphertext for storage. Only authorized personnel can decrypt and access the data using a pre-set key. During data transmission, secure transmission protocols, such as SSL / TLS, are used to encrypt data and prevent it from being stolen or tampered with during transmission. Furthermore, strict access rights management is implemented, ensuring that only personnel with appropriate permissions can access and process privacy-sensitive data. Through user authentication and authorization mechanisms, data security and privacy are ensured.

[0229] Figure 2 A schematic diagram illustrates exemplary hardware and software components of a BIMBase-based heating room modeling system 100 that can implement the concepts of the present application, as provided in some embodiments of the present application. For example, a processor 120 can be used in the BIMBase-based heating room modeling system 100 to perform the functions of the present application.

[0230] The BIMBase-based heating room modeling system 100 can be a general-purpose server or a special-purpose server, both of which can be used to implement the BIMBase-based heating room modeling method of this application. Although only one server is shown in this application, for convenience, the functions described in this application can be implemented in a distributed manner on multiple similar platforms to balance the processing load.

[0231] For example, the heating room modeling system 100 based on BIMBase may include a network port 110 connected to a network, one or more processors 120 for executing program instructions, a communication bus 130, and storage media 140 in different forms, such as a disk, ROM, or RAM, or any combination thereof. Exemplarily, the heating room modeling system 100 based on BIMBase may also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The method of the present application can be implemented according to these program instructions. The heating room modeling system 100 based on BIMBase also includes an I / O interface 150 between the computer and other input and output devices.

[0232] For ease of explanation, only one processor is described in the BIMBase-based heating room modeling system 100. However, it should be noted that the BIMBase-based heating room modeling system 100 in the present application may also include multiple processors, so the steps performed by one processor described in the present application may also be performed jointly or individually by multiple processors. For example, if the processor of the BIMBase-based heating room modeling system 100 executes step A and step B, it should be understood that step A and step B may also be performed jointly by two different processors or individually in one processor. For example, the first processor executes step A, the second processor executes step B, or the first processor and the second processor execute steps A and B together.

[0233] In addition, an embodiment of the present invention further provides a readable storage medium, in which computer-executable instructions are preset. When a processor executes the computer-executable instructions, the above-mentioned heating room modeling method based on BIMBase is implemented.

[0234] It should be noted that in order to simplify the description of the present invention and thus help understand one or more embodiments of the invention, in the foregoing description of the embodiments of the present invention, multiple features are sometimes combined into one embodiment, figure or description thereof.

Claims

1. A heating room modeling method based on BIMBase, characterized in that: The method comprises: Perform hierarchical parsing on the heating room component data structure in the BIMBase platform to obtain a component structure parsing result containing component type attributes and hierarchical relationship characteristics, including: reading the original data structure of the heating room component stored in the BIMBase platform, the original data structure of the heating room component containing component identification information, geometric description information and attribute field set; performing structured splitting processing on the component identification information, extracting component classification code and hierarchical path code, and establishing a component basic classification index; based on the component basic classification index, performing format conversion processing on the geometric description information, and converting non-standardized geometric data into a three-dimensional coordinate data format compatible with the BIMBase platform; parsing the type attribute field in the attribute field set, extracting component function type, material type and installation type parameters, and generating a component type attribute vector; constructing a parent-child hierarchical relationship tree between components according to the hierarchical path code, each node in the parent-child hierarchical relationship tree contains the type attribute vector and three-dimensional coordinate data of the corresponding component; integrating the component type attribute vector and the parent-child hierarchical relationship tree into structured data to obtain a component structure parsing result containing component type attributes and hierarchical relationship characteristics; Based on the component structure analysis results, the dynamic and static data sets of the heating room are collected and integrated, wherein the dynamic and static data sets of the heating room include a dynamic operation data set and a static attribute data set; The component structure analysis result and the dynamic and static data set of the heating machine room are subjected to inter-component association feature extraction processing to generate a component association feature set including connection relationship features, function dependency features and dynamic and static data association features, including: extracting connection port information of adjacent hierarchical components from the parent-child hierarchical relationship tree of the component structure analysis result, the connection port information including port type, interface size and connection direction parameters; based on the connection port information, judging the matching relationship between different component ports, marking the successfully matched port pairs as direct connection relationships, and generating a connection relationship feature vector; extracting the function description field of each component from the static attribute data set of the dynamic and static data set of the heating machine room, parsing the input and output relationship in the function description field, and determining the upstream and downstream function dependency paths between components; based on the upstream and downstream function dependency paths path, calculate the functional dependency strength value between any two components, and generate a functional dependency feature vector; extract the real-time performance parameters of each component from the dynamic operation data set of the dynamic and static data set of the heating room, calculate the correlation of the change trend between the real-time performance parameters of different components, and generate a dynamic correlation coefficient; extract the design parameters of each component from the static attribute data set of the dynamic and static data set of the heating room, calculate the specification matching degree between the design parameters of different components, and generate a static correlation coefficient; integrate the dynamic correlation coefficient and the static correlation coefficient into a dynamic and static data association feature vector; associate and integrate the connection relationship feature vector, the functional dependency feature vector and the dynamic and static data association feature vector according to the component identification code to generate a component association feature set including connection relationship features, functional dependency features and dynamic and static data association features; Calling a layout generation tool to perform spatial layout mapping processing on the component association feature set to generate a preliminary layout association model of the heating machine room; The preliminary layout association model of the heating room is subjected to spatial conflict detection and optimization processing to obtain a spatially optimized layout association model, and the spatially optimized layout association model is converted into modeling data that conforms to the BIMBase data interaction standard. The modeling data is output to the BIMBase platform to complete the construction of the heating room model.

2. The heating room modeling method based on BIMBase according to claim 1 is characterized in that: The dynamic and static data sets of the heating room are collected and integrated based on the component structure analysis results. The dynamic and static data sets of the heating room include a dynamic operation data set and a static attribute data set, including: Extracting unique identification codes of all components from the component structure parsing result to generate a component identification list; Based on the component identification list, real-time status data of each component during operation is collected, the real-time status data including operation status records, performance monitoring records, and abnormal alarm records, and the real-time status data is marked as a dynamic operation data set; Based on the component identification list, collecting inherent attribute data of each component, the inherent attribute data including design parameters, specifications and model, and factory information, and marking the inherent attribute data as a static attribute data set; The dynamic operation data set and the static attribute data set are associated and integrated according to the component identification code to generate the dynamic and static data set of the heating room.

3. The heating room modeling method based on BIMBase according to claim 1 is characterized in that: The step of determining the matching relationship between different component ports based on the connection port information, marking the successfully matched port pairs as direct connection relationships, and generating a connection relationship feature vector includes: Extracting interface size parameters and connection direction parameters of each port from the connection port information, and constructing a port attribute matrix; Calculating the absolute value of the difference between the interface size parameters of any two ports, and marking them as a size-matched port pair when the absolute value of the difference is less than a preset size tolerance range; For the size-matched port pair, calculating the angle difference of the connection direction parameters thereof, and marking the port pair as a direction-matched port pair when the angle difference is less than a preset direction tolerance range; Determine the port pairs that satisfy both size matching and direction matching as directly connected port pairs; Allocating a connection type label to each directly connected port pair, wherein the connection type label includes rigid connection, flexible connection, and detachable connection; Extracting identification codes of components to which directly connected ports belong, and constructing a component connection relationship matrix, wherein each element in the component connection relationship matrix includes a connection type label and a port matching parameter; The component connection relationship matrix is ​​converted into a structured connection relationship feature vector, wherein the connection relationship feature vector includes a connection component identification pair, a connection type label, and a port matching parameter.

4. The heating room modeling method based on BIMBase according to claim 1 is characterized in that: The step of calculating the functional dependency strength value between any two components based on the upstream and downstream functional dependency paths and generating a functional dependency feature vector includes: Converting the upstream and downstream functional dependency paths into a directed graph structure, wherein nodes in the directed graph structure represent components and directed edges represent functional dependency directions; Based on the directed graph structure, calculating the out-degree value of each component as an upstream node and the in-degree value of each component as a downstream node, wherein the out-degree value represents the functional impact of the component on other components, and the in-degree value represents the functional impact of other components on the component; Extracting the shortest dependency path length between any two components in the directed graph structure, wherein the shortest dependency path length represents the minimum number of links between the two components to establish functional dependency through intermediate components; Based on the out-degree value, in-degree value and the shortest dependency path length, a functional dependency strength calculation formula is constructed, wherein the functional dependency strength value is positively correlated with the out-degree value and in-degree value, and negatively correlated with the shortest dependency path length; Calculate the functional dependency strength value between any two components and generate a functional dependency strength matrix; Adding a dependency direction label to each element in the functional dependency strength matrix, wherein the dependency direction label includes unidirectional dependency and bidirectional dependency; The functional dependency strength matrix and the dependency direction label are integrated into a structured functional dependency feature vector, wherein the functional dependency feature vector includes a dependency component identification pair, a functional dependency strength value, and a dependency direction label.

5. The heating room modeling method based on BIMBase according to claim 1 is characterized in that: The calling of the layout generation tool to perform spatial layout mapping processing on the component association feature set to generate a preliminary layout association model of the heating machine room includes: Extracting a connection relationship feature vector and a function dependency feature vector from the component association feature set to construct a layout constraint condition set, wherein the layout constraint condition set includes a spatial position constraint, a connection distance constraint, and a function area constraint; The component three-dimensional coordinate data in the component structure analysis result is used as the initial layout position parameter and input into the initial positioning module of the layout generation tool; Based on the layout constraint condition set, the layout generation tool performs spatial coordinate adjustment processing on the initial layout position parameters output by the initial positioning module so that the relative positions between components meet the connection distance constraints; According to the dynamic and static data association feature vectors in the component association feature set, the layout generation tool allocates a data interface position to each component, wherein the data interface position is used for subsequent dynamic and static data attachment; Based on the adjusted spatial coordinate parameters and data interface positions, the layout generation tool constructs a spatial association relationship network between components, wherein the spatial association relationship network includes physical connection links and data interaction links; The spatial association relationship network is fused with the component three-dimensional geometric model to generate a preliminary layout association model of the heating room containing spatial position information, connection relationship information and data interface information.

6. The heating room modeling method based on BIMBase according to claim 1 is characterized in that: The performing of spatial conflict detection and optimization processing on the preliminary layout association model of the heating room to obtain a spatially optimized layout association model includes: Extracting the three-dimensional geometric models and spatial coordinate parameters of all components from the preliminary layout association model of the heating room to construct a layout space model; Performing collision detection on the layout space model to calculate the spatial intersection volume between the three-dimensional geometric models of any two components. When the spatial intersection volume is greater than a preset collision tolerance volume, the components are marked as spatial conflicting pairs. Analyze the connection relationship characteristics and functional dependency characteristics of the spatial conflicting component pairs to determine the conflict type, which includes physical collision conflict, functional path conflict, and data interface conflict; For component pairs with physical collision conflict type, based on the connection direction parameter in the connection relationship feature, the spatial coordinates of one component are adjusted along the conflict direction until the spatial intersection volume is smaller than the collision tolerance volume; For component pairs with conflicting functional paths, based on the dependency strength value in the functional dependency feature, the spatial coordinates of components with dependency strength values ​​less than the set strength value are adjusted first to restore the functional dependency path to a smooth state. For component pairs with data interface conflict types, reallocate the data interface positions so that the spatial distance between the data interfaces meets the data transmission requirements; Re-execute collision detection processing on the optimized layout space model. If there are still spatially conflicting component pairs, repeat the conflict optimization processing until the spatial intersection volume of all component pairs is smaller than the collision tolerance volume. The optimized layout space model is integrated with the component association feature set to obtain a spatially optimized layout association model.

7. The heating room modeling method based on BIMBase according to claim 1 is characterized in that: The step of converting the optimized layout association model into modeling data that complies with the BIMBase data interaction standard, and outputting the modeling data to the BIMBase platform to complete the construction of the heating room model, includes: Read the modeling data standards of the BIMBase platform, extract data format requirements, field definition rules and relationship description methods; Extracting component three-dimensional geometric model data from the spatially optimized layout association model and converting the data into a BIMBase-compatible geometric description file according to data format requirements, wherein the geometric description file includes a vertex coordinate set, a face index, and material properties; Extracting a component association feature set from the spatially optimized layout association model, and converting the connection relationship features, function dependency features, and dynamic and static data association features into a structured attribute data table according to field definition rules, wherein the structured attribute data table includes a component identification column, a feature type column, and a feature value column; Associating and binding the geometric description file with the structured attribute data table through component identification coding to generate integrated modeling data containing geometric information and attribute information; Performing data verification on the integrated modeling data to check the integrity of the geometry description file and the consistency of the structured attribute data table. If there is data missing or inconsistency, returning to the data conversion step for reprocessing; Encapsulate the verified integrated modeling data into a model file format supported by the BIMBase platform, wherein the model file format includes model version information, creation timestamp, and data verification code; Uploading the integrated modeling data in the model file format to the platform database through the data interface of the BIMBase platform to trigger the platform model loading process; After the platform model is loaded, perform the model visualization preview operation, and complete the construction of the heating room model when the spatial layout and data association are correct.

8. A heating room modeling system based on BIMBase, characterized in that: It includes a processor and a memory, the memory is connected to the processor, the memory is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the memory to implement the BIMBase-based heating room modeling method described in any one of claims 1 to 7.

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