A data collaboration platform management system and method integrating all project participants

Abstract

This invention discloses a data collaboration platform management system and method integrating all project participants, relating to the field of data collaboration platform management technology. The method includes: simplifying and compressing triangular faces and textures of a lightweight 3D model to generate a lightweight 3D scene; matching the time-weighted index set with the spatial topology and sensor coordinates of the lightweight 3D scene; calculating a comprehensive risk probability value using a risk probability equation; subsequently classifying the comprehensive risk probability value by hazard level to generate a high-risk spatial coordinate set. This achieves precise digital integration of component information and physical attributes throughout the project's entire lifecycle, enabling all participants to obtain the latest component status and parameter data on a unified 3D data platform.

Description

Technical Field

[0001] This invention relates to the field of data collaboration platform management technology, and in particular to a data collaboration platform management system and method that integrates all project participants. Background Technology

[0002] In the field of Building Information Management (BIM), with the widespread application of BIM technology, project participants have an increasing demand for digital management and collaborative operation of building components. Traditional data collaboration methods typically obtain building component information through independent BIM software interfaces or local databases, managing and visualizing the geometry, physical properties, and spatial location of the components. Building upon this, some methods employ lightweight 3D models or triangular patch simplification techniques to improve the rendering efficiency of 3D models. Simultaneously, they combine sensor data for spatial location matching and physical parameter monitoring to support data analysis and decision-making in project management, construction monitoring, and operation and maintenance. Conventional technologies in this field have formed relatively mature practical solutions in ensuring information availability, component parameter visualization, and basic collaborative operation, providing fundamental support for the full lifecycle management of building projects.

[0003] However, existing methods still have certain limitations in multi-party collaboration and data integration. First, conventional methods often rely on a single data source or local interface, making it difficult to achieve real-time data synchronization across disciplines and enterprises, thus limiting the efficiency of data integration among different participants. Second, they lack sufficient support for risk analysis and optimization control functions for component physical parameters and 3D models. Conventional methods typically cannot tightly integrate weighted calculation of physical parameters, risk probability assessment, and visual operation interfaces, making it difficult to form a decision-making closed loop based on risk assessment and limiting the intelligent handling capabilities of complex projects. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a data collaboration platform management method that integrates all project participants to solve the problems of real-time data integration among project participants and risk-based intelligent optimization and handling.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a data collaboration platform management method integrating all project participants, comprising, BIM component data and physical parameters are collected in real time through the OpenBIM standard interface, and the BIM component data is parsed to generate a lightweight 3D model. Then, the physical parameters are weighted and reduced to generate a time-weighted index set. The lightweight 3D model is simplified by triangular facets and texture compression to generate a lightweight 3D scene. The time-weighted index set is matched with the spatial topology of the lightweight 3D scene and the sensor coordinates. The comprehensive risk probability value is calculated through the risk probability equation. Then, the comprehensive risk probability value is classified by hazard level to generate a set of high-risk spatial coordinates. Map the high-risk spatial coordinate set onto a lightweight 3D scene to perform shading pulses and generate a three-party collaborative operation interface. Based on the three-party collaborative operation interface, a multi-objective optimization algorithm is used to select an optimized treatment plan, and the parameters of the data acquisition equipment are adjusted according to the optimized treatment plan. Then, the risk decay rate in the data acquisition equipment parameters is monitored in real time and tracked against the preset stability criteria to generate a risk treatment audit report.

[0007] As a preferred embodiment of the data collaboration platform management method integrating all project participants described in this invention, the steps include: real-time acquisition of BIM component data and physical parameters through the OpenBIM standard interface, parsing the BIM component data to generate a lightweight 3D model, and then performing weighted calculations and weight reduction processing on the physical parameters to generate a time-sensitive weighted index set. BIM component data and physical parameters are collected in real time through the OpenBIM standard interface, and spatiotemporal coordinate identifiers and version tags are added to generate original BIM data packages with spatiotemporal tags and physical parameter sets. The original BIM data package with spatiotemporal tags is simplified and texture compressed to generate a lightweight 3D model. Based on the acquisition delay of the physical parameter set, half-life weighting is performed to generate a weighted physical parameter set. Extract the origin of spatial coordinates from the lightweight 3D model, and then merge the origin of spatial coordinates, the weighted set of physical parameters, and the version tag to generate a time-weighted index set.

[0008] As a preferred embodiment of the data collaboration platform management method integrating all project participants described in this invention, the version tag is identification information used to mark the order and iteration status of BIM component data and physical parameters during the collection and updating process.

[0009] As a preferred embodiment of the data collaboration platform management method integrating all project participants described in this invention, the following steps are taken: simplifying the lightweight 3D model by triangular facets and compressing textures to generate a lightweight 3D scene; matching the time-weighted index set with the spatial topology of the lightweight 3D scene and sensor coordinates; calculating the comprehensive risk probability value through a risk probability equation; and then classifying the hazard levels to generate a high-risk spatial coordinate set. Perform topology correction on the lightweight 3D model to generate a lightweight 3D scene file with complete spatial topology; Parse the component IDs in the lightweight 3D scene file to associate them with device types, and accurately bind the spatial location with the weighted physical parameter set to establish a spatial location mapping table; Based on the deviation between the physical parameter weights and safety benchmark values ​​in the lightweight 3D scene, the risk factor of the physical parameter weights is calculated, and the BIM confidence in the BIM component data is superimposed to generate a comprehensive risk probability value. Then, the risk level is divided according to the preset threshold to generate a component risk level list. Based on the preset risk and safety level threshold, spatial coordinate data and equipment type information are filtered, and the hazard characteristics of the equipment type information are classified into levels to generate a set of high-risk spatial coordinates.

[0010] As a preferred embodiment of the data collaboration platform management method integrating all project participants described in this invention, the preset risk and safety level threshold is set based on the following factors: industry safety norms and standards, actual engineering operation data and historical risk cases, as well as risk assessment methods and risk management strategies.

[0011] As a preferred embodiment of the data collaboration platform management method integrating all project participants described in this invention, the steps of mapping the high-risk spatial coordinate set onto a lightweight 3D scene for color pulse generation to create a three-party collaborative operation interface are as follows: The high-risk coordinates in the high-risk spatial coordinate set are spatially matched with the equipment components in the lightweight 3D scene to construct a device-coordinate binding table. Based on the device type and hazard level in the device-coordinate binding table, the shading rule library is called to shading the lightweight 3D scene, generating a lightweight 3D scene file after shading pulse rendering; Based on the lightweight 3D scene file rendered by shading pulses, we perform three-party differentiated data encapsulation and hierarchical permission control to generate a three-party collaborative operation interface.

[0012] As a preferred embodiment of the data collaboration platform management method integrating all project participants described in this invention, the coloring rule library refers to a set of visual coloring schemes corresponding to device types and their corresponding hazard levels.

[0013] As a preferred embodiment of the data collaboration platform management method integrating all project participants described in this invention, the steps include: selecting an optimized handling scheme based on a multi-objective optimization algorithm using a three-party collaborative operation interface, adjusting the data acquisition device parameters according to the optimized handling scheme, then monitoring the risk decay rate in the data acquisition device parameters in real time, tracking and processing it against preset stability criteria, and generating a risk handling audit report. Based on a three-party collaborative operation interface, a multi-objective optimization algorithm is adopted to calculate a multi-objective weighted decision score. The highest decision score scheme is automatically selected based on the multi-objective weighted decision score to generate smart contract execution instructions. Finally, the optimized disposal scheme execution instruction set is integrated and obtained. The edge controller executes the instruction set of the optimized treatment plan to adjust parameters and obtains the equipment parameter adjustment records. Based on the equipment parameter adjustment records, obtain the risk value before the equipment parameters were adjusted, calculate the current risk value based on the collected equipment parameters, and finally integrate them to obtain a risk decay rate report; Integrate risk decay rate reports and equipment parameter adjustment records, and generate a risk disposal audit report using the six-element method.

[0014] As a preferred embodiment of the data collaboration platform management method integrating all project participants described in this invention, the six-element method refers to classifying and expressing the comprehensive data set from six dimensions—time element, location element, object element, event element, behavior element, and result element—when generating a risk disposal audit report.

[0015] Secondly, this invention provides a data collaboration platform management system that integrates all project participants, including, The data acquisition module collects BIM component data and physical parameters in real time through the OpenBIM standard interface, parses the BIM component data to generate a lightweight 3D model, and then performs weighted calculation and weight reduction processing on the physical parameters to generate a time-weighted index set. The model component module simplifies the triangular facets and compresses the textures of the lightweight 3D model to generate a lightweight 3D scene. It matches the time-weighted index set with the spatial topology of the lightweight 3D scene and the sensor coordinates, calculates the comprehensive risk probability value through the risk probability equation, and then classifies the comprehensive risk probability value into hazard levels to generate a set of high-risk spatial coordinates. The risk analysis module maps a set of high-risk spatial coordinates onto a lightweight 3D scene to generate color pulses and a three-party collaborative operation interface. The optimization and control module, based on a three-party collaborative operation interface, uses a multi-objective optimization algorithm to select an optimized treatment plan, adjusts the parameters of the acquisition equipment according to the optimized treatment plan, monitors the risk decay rate in the acquisition equipment parameters in real time, tracks and processes it against preset stability criteria, and generates a risk treatment audit report.

[0016] The beneficial effects of this invention are as follows: By using the OpenBIM standard interface to collect BIM component data and physical parameters in real time and generate lightweight 3D models, the invention achieves precise digital integration of component information and physical attributes throughout the entire project lifecycle. This enables all participating parties to obtain the latest component status and parameter data on a unified 3D data platform, thereby providing comprehensive data support and traceable information sources. This lays a precise data foundation for risk analysis and optimization, and effectively improves information transparency and data availability. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating the data collaboration platform management method for integrating all project participants.

[0019] Figure 2 A flowchart for generating data collection and timeliness-weighted indicator sets.

[0020] Figure 3 A flowchart for lightweight 3D scene construction and risk analysis.

[0021] Figure 4 This is a flowchart for tripartite collaborative operation and optimized control. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0025] Reference Figures 1-4As one embodiment of the present invention, this embodiment provides a data collaboration platform management method integrating all project participants, comprising the following steps: S1: Real-time acquisition of BIM component data and physical parameters through the OpenBIM standard interface, parsing of BIM component data to generate a lightweight 3D model, and then weighted calculation and weight reduction processing of physical parameters to generate a time-weighted index set.

[0026] Specifically, the steps are as follows: S1.1: Real-time acquisition of BIM component data and physical parameters through the OpenBIM standard interface, and addition of spatiotemporal coordinate identifiers and version tags to the BIM component data and physical parameters to generate original BIM data packages with spatiotemporal tags and physical parameter sets.

[0027] Specifically, the OpenBIM standard interface is called to establish a data acquisition channel, receive BIM component data and physical parameters, and format and structure the acquired BIM component data and physical parameters to extract the component type, component ID, and set information of the BIM components, as well as the material properties and related physical parameters of the physical parameters, generating BIM component data and physical parameters that conform to the standard format. Subsequently, spatiotemporal coordinate identifiers are added to the parsed BIM component data and physical parameters to mark the acquisition location and acquisition time of the parsed physical parameters, as well as the spatial location and time stamp corresponding to the parsed BIM component data. At the same time, version tags are added to the parsed BIM component data and physical parameters to mark the acquisition and update order, and finally, the original BIM data package and physical parameter set with spatiotemporal tags are generated.

[0028] It should be explained that BIM component data refers to a collection of multi-dimensional information related to building components, which is collected and parsed through the OpenBIM standard interface. BIM component data includes the component's geometric information (such as the component's shape, size, volume, and spatial coordinates), attribute information (such as the component's material type, fire resistance rating, service life, and manufacturer information), functional information (such as the component's structural role, pipeline function, or partition function in the building), and additional information related to operation and maintenance (such as the component's maintenance cycle and installation time).

[0029] It should be explained that physical parameters refer to a dynamic set of data collected through the OpenBIM standard interface and used to describe the operating status and performance characteristics of BIM components in the actual environment. Physical parameters typically include environmental condition parameters (such as temperature, humidity, pressure, and wind speed), structural state parameters (such as stress, strain, vibration frequency, and displacement), energy consumption performance parameters (such as power consumption, fluid flow rate, and heat load), and safety risk parameters (such as smoke concentration, gas leak concentration, and fire alarm trigger value).

[0030] It should be explained that spatiotemporal coordinate identification refers to the marking information used to describe the state of BIM component data and physical parameters at a specific location and time. Spatiotemporal coordinate identification mainly includes two parts: spatial coordinate information and time stamp. The spatial coordinate information is usually in the form of three-dimensional coordinates (e.g., X, Y, and Z dimension values) to determine the specific location of BIM components in the lightweight three-dimensional scene or building physical space. The time stamp records the specific time point of data acquisition or update in a standardized time format (e.g., UTC timestamp or local timestamp).

[0031] It should be explained that version tags are identification information used to mark the order and iteration status of BIM component data and physical parameters during the acquisition and update process. Version tags typically contain three core elements: version number, update sequence number, and timestamp. The version number is used to distinguish data batches generated at different stages; the update sequence number is used to record the number of consecutive modifications within the same version; and the timestamp records the specific time point of each acquisition or update to ensure the chronological order of versions.

[0032] S1.2: Simplify and compress the original BIM data package with spatiotemporal tags to generate a lightweight 3D model, and perform half-life weighting based on the acquisition delay of the physical parameter set to generate a weighted physical parameter set.

[0033] Specifically, the process involves parsing each BIM component data in the original BIM data package with spatiotemporal tags, extracting geometric dimensions, component IDs, and texture information from each BIM component data, and then reconstructing a triangular network based on the geometric dimensions to convert the component's geometric contour and dimensions into a set of vertex coordinates. Initial triangular patches are then generated based on the geometric dimensions, and the vertex coordinate sets are combined with the initial triangular patches to form the mesh structure of the BIM component, serving as a preliminary mesh model. This preliminary mesh model of all BIM components is then integrated with the corresponding texture information, and a lightweight 3D model is formed based on the component ID and spatial location. Finally, based on the acquisition delay of the physical parameter set, each parameter in the physical parameter set undergoes half-life weighting, and an attenuation factor is calculated. The weight values ​​of the physical parameter set are then adjusted according to the attenuation factor to generate a weighted physical parameter set.

[0034] The formula for weighting by half-life is as follows: ; in, Indicates the attenuation factor. This represents the natural exponential function. Indicates the data acquisition delay. Indicates half-life.

[0035] It should be explained that the geometric dimension information in BIM component data refers to the physical form and structural characteristics of each BIM component in three-dimensional space, including the component's length, width, height, thickness, curve radius, hole size, and geometric contour information of the component surface; the component ID information in BIM component data refers to the unique identifier of each BIM component in the entire BIM data package, used to distinguish different components, track component attribute updates and operation history, and perform component positioning, mesh integration, and texture binding in the lightweight 3D model; the texture information in BIM component data refers to the visual feature information attached to the surface of each BIM component, including surface color, material type, texture image, and surface detail effects.

[0036] It needs to be explained that a triangular facet is a basic geometric facet generated during the reconstruction of a triangular mesh, consisting of the coordinates of three vertices. Triangular facests are the smallest components of a triangular mesh model. Combining multiple triangular facests according to vertex indices and boundary relationships can accurately express the curved surface or complex geometric contour of a component, and play a fundamental role in the geometric appearance representation and texture mapping in lightweight 3D models.

[0037] S1.3: Extract the spatial coordinate origin from the lightweight 3D model, then fuse the spatial coordinate origin, weighted physical parameter set, and version tag to generate a time-weighted index set. The visual feature information attached to the surface of each BIM component includes surface color, material type, texture image, and surface detail effect.

[0038] Specifically, the spatial coordinate origin information of each BIM component data is extracted from the lightweight 3D model, and the spatial coordinate origin information is fused with the weighted physical parameter set. Each physical parameter in the weighted physical parameter set is mapped to the spatial coordinate origin information according to the component ID information. At the same time, version tags are added to complete the fusion, generating the time-weighted index data corresponding to each BIM component data. Finally, the data is fused to obtain a complete time-weighted index set.

[0039] For example, the spatial coordinate origin of a beam member is (x, y, z), and the weighted physical parameters corresponding to the coordinate origin include temperature and stress. The version label is V2. After merging the spatial coordinate origin, weighted physical parameters, and version label, the time-weighted index data of the beam member is generated.

[0040] S2: Simplify the lightweight 3D model by performing triangular facet simplification and texture compression to generate a lightweight 3D scene. Then, match the time-weighted index set with the spatial topology of the lightweight 3D scene and the sensor coordinates. Calculate the comprehensive risk probability value through the risk probability equation, and then classify the comprehensive risk probability value into hazard levels to generate a set of high-risk spatial coordinates.

[0041] Specifically, the steps are as follows: S2.1: Perform topology correction on the lightweight 3D model to generate a lightweight 3D scene file with complete spatial topology.

[0042] Specifically, the boundary connectivity of the triangular mesh model of each BIM component data in the lightweight 3D model is checked to identify mesh topology defects. Based on the adjacency conditions, the vertex index and boundary relationship of the lightweight 3D model are adjusted to complete the splicing correction of the triangular mesh model. Then, the spatial positional relationship between adjacent BIM component data in the lightweight 3D model is processed by topological constraint. The spatial topology relationship type in each BIM component data is uniformly encoded to form spatial topology relationship. Finally, the texture data and spatial topology relationship in the lightweight 3D model are integrated to generate a lightweight 3D scene file with complete spatial topology.

[0043] It needs to be explained that the triangular mesh model extracts the boundary contours and spatial shape features of BIM component data from the geometric dimensions of the lightweight 3D model. The geometric shape of the BIM component data is converted into a set of vertex coordinates. Then, triangular patches are generated based on the spatial distribution of the vertex coordinates. These triangular patches are combined according to an index to form a mesh structure composed of vertices, edges, and triangular patches, which serves as the triangular mesh model. During the training process, by adjusting the vertex distribution and the triangular patch splicing method, the triangular mesh model can reduce the amount of redundant data while maintaining geometric accuracy, thus ensuring the lightweight processing effect.

[0044] It needs to be explained that boundary connection refers to the topological connection method of triangular mesh models at the boundary in a lightweight 3D model. It is used to describe the relationship between adjacent geometric elements. Specifically, it includes the boundary relationship of the lightweight 3D model. The start and end positions of the edges are determined by the vertex index, and the closed region of the mesh is formed by the connection relationship of the edges. When different triangular mesh models in the lightweight 3D model are missing, misaligned or discontinuous at the boundary, it will lead to defects in the boundary connection relationship. Therefore, it is necessary to correct the vertex index and boundary relationship based on the adjacency condition to ensure the continuity and integrity of the lightweight 3D model in the overall space.

[0045] It should be explained that the adjacency condition refers to the condition in a triangular mesh model where two triangular faces share the same vertex index or the same boundary line segment, and are therefore considered adjacent. Additionally, when triangular mesh models of different BIM component data in a lightweight 3D model have overlapping vertices or common boundaries in space, the adjacency condition is used to identify them as adjacent.

[0046] It should be explained that the boundary relationships of a lightweight 3D model describe the geometric connections between triangular facets established through shared vertices or edges, as well as the open boundary conditions of the mesh at the outer contour, and are used to identify the overall continuity and integrity of the lightweight 3D model.

[0047] S2.2: Parse the component IDs associated with device types in the lightweight 3D scene file, and accurately bind the spatial location with the weighted physical parameter set to establish a spatial location mapping table.

[0048] Specifically, the lightweight 3D scene file is parsed to extract component ID information. Based on the component ID information, equipment type information is retrieved, generating a set of correspondences between component ID information and equipment type information. Subsequently, using the set of correspondences between component ID information and equipment type information as an index, the coordinate information of each BIM component data in the lightweight 3D scene file is extracted. The coordinate information of each BIM component data is then bound to a weighted physical parameter set to generate a component information mapping set. Finally, the component information mapping sets are uniformly organized to generate a spatial location mapping table.

[0049] It should be explained that the equipment type information refers to the specific equipment category and functional attributes corresponding to the component ID information. It is used to distinguish and identify the role of various equipment in the engineering scene in the lightweight 3D scene file. Specifically, it includes the equipment category name (such as HVAC equipment, electrical equipment, piping equipment and monitoring sensors, etc.), functional purpose (such as ventilation, power supply, water supply and environmental monitoring, etc.), and the equipment's operating characteristics (such as power, flow rate and load capacity, etc.).

[0050] S2.3: Based on the deviation between the physical parameter weights and safety benchmark values ​​in the lightweight 3D scene, calculate the risk factor of the physical parameter weights, and superimpose the BIM confidence level in the BIM component data to generate a comprehensive risk probability value. Then, classify the risk levels according to the preset threshold and generate a list of component risk levels.

[0051] Specifically, the weight values ​​for each physical parameter are extracted from the weights of the physical parameters in the lightweight 3D scene. These weight values ​​are then compared item by item with the safety benchmark values ​​to calculate the deviation between the physical parameter weight values ​​and the safety benchmark values. Based on the deviation, a risk assessment method is used to calculate the risk factor corresponding to each physical parameter. Subsequently, the confidence value corresponding to each BIM component data is extracted from the BIM confidence value of the BIM component data. The risk factors and confidence values ​​are then superimposed to generate a comprehensive risk probability value. Finally, the comprehensive risk probability value is compared with a preset risk level judgment threshold range. Risk levels are classified according to the preset threshold range and organized according to the component ID information to generate a component risk level list.

[0052] The formula for the risk assessment method is as follows: ; in, Indicates the physical parameter index. This represents a stabilizing factor to prevent the denominator from being zero. Indicates the first Risk factors corresponding to each physical parameter Indicates the first The weight values ​​of each physical parameter, Indicates the first The safety baseline value corresponding to each physical parameter This represents the risk assessment function.

[0053] It should be explained that the safety benchmark value refers to the benchmark value of the safety reference standard or allowable range corresponding to each physical parameter in a lightweight 3D scene. The safety benchmark value is usually derived from industry specifications, engineering technical standards, operation and management requirements or historical experience data, and is used to measure the normal level that a certain physical parameter should maintain under safe conditions.

[0054] It should be explained that the preset risk level determination threshold range refers to the numerical range set in the risk assessment process to divide different risk levels. The preset threshold range is usually determined by a combination of industry safety standards, engineering technical specifications, historical monitoring data and risk management strategies, and is used to define the overall risk probability value at different levels of safety, controllability or danger.

[0055] S2.4: Based on the preset risk and safety level threshold, filter the spatial coordinate data and equipment type information in the component risk level list that exceed the risk and safety level threshold, and classify the hazard characteristics of the equipment type information to generate a set of high-risk spatial coordinates.

[0056] Specifically, the component risk level list is analyzed, and each risk level in the list is compared with the preset risk safety level threshold. Spatial coordinate data and equipment type information with risk levels exceeding the preset risk safety level threshold are selected. Then, based on the equipment type information exceeding the preset risk safety level threshold, the hazard characteristics corresponding to the equipment type information are extracted. The spatial coordinate data and equipment type information exceeding the preset risk safety level threshold are classified into risk levels according to the hazard characteristics. Finally, the spatial coordinate data and equipment type information after risk level classification are integrated to generate a high-risk spatial coordinate set.

[0057] It should be explained that the preset risk safety level threshold refers to the boundary of the acceptable risk level defined in the process of risk control and safety management. It is used to determine whether BIM component data is within the safe range. Furthermore, the setting of the preset risk safety level threshold is based on the following factors: industry safety norms and standards, such as building safety standards and mechanical and electrical equipment operation specifications, to ensure that the risk safety level threshold meets mandatory requirements; actual project operation data and historical risk cases: determining the safety boundary through statistical analysis; project management requirements and risk management strategies: defining the risk tolerance in combination with project characteristics and safety management objectives.

[0058] It should be explained that the hazard characteristics corresponding to equipment type information refer to the specific risk manifestations and harmful effects that may be caused by different types of equipment during operation, failure, or malfunction. For example, electrical equipment may pose risks of electric shock, short circuit, or fire; pressure equipment may pose risks of explosion or leakage; ventilation and duct equipment may pose risks of harmful gas diffusion or air quality degradation. The hazard characteristics corresponding to equipment type information are directly related to the equipment type and are used to determine the potential risk types and severity of equipment that exceeds the preset risk safety level threshold in a spatial scenario.

[0059] S3: Map the high-risk spatial coordinate set onto a lightweight 3D scene to perform shading pulses and generate a three-party collaborative operation interface.

[0060] Specifically, the steps are as follows: S3.1: Match the high-risk coordinates in the high-risk spatial coordinate set with the equipment components in the lightweight 3D scene to construct a device-coordinate binding table.

[0061] Specifically, the high-risk spatial coordinate set is parsed, high-risk coordinates are read from the high-risk spatial coordinate set, and the high-risk coordinates are spatially matched with the BIM component data in the lightweight 3D scene. Using the spatial coordinate information of the BIM component data in the lightweight 3D scene as a reference, spatial coordinate information that matches the high-risk coordinates is retrieved, and the high-risk coordinates that match the spatial coordinates are bound to the corresponding BIM component data to form a device-coordinate correspondence. Finally, the device-coordinate correspondence is uniformly organized using the component ID information as an index to generate a device-coordinate binding table.

[0062] It needs to be explained that the device-coordinate correspondence relationship refers to the relationship formed by binding the high-risk coordinates with consistent spatial coordinates in the high-risk spatial coordinate set with the BIM component data corresponding to the high-risk coordinates during the process of matching the spatial positions of BIM component data in the lightweight 3D scene.

[0063] It should be explained that equipment components refer to objects in a lightweight 3D scene that use BIM component data as a carrier and can be matched with high-risk coordinates in spatial location. Equipment components include BIM component data identified by component ID information in the lightweight 3D scene file, and the BIM component data also carries spatial coordinate information.

[0064] S3.2: Based on the device type and hazard level in the device-coordinate binding table, call the shading rule library to shading the lightweight 3D scene and generate a lightweight 3D scene file after shading pulse rendering.

[0065] Specifically, the equipment type information and hazard level are extracted from the equipment-coordinate binding table, and the corresponding shading rules are retrieved from the shading rule library based on the equipment type information and hazard level. Then, the shading rules are applied to the BIM component data in the lightweight 3D scene that correspond to the equipment type information and hazard level. According to the spatial coordinate information bound in the equipment-coordinate binding table, the BIM component data is shading pulse rendering. Finally, the shading pulse rendering BIM component data is uniformly integrated to generate the shading pulse rendering lightweight 3D scene file.

[0066] It should be explained that the coloring rule library is a collection used to store the visual coloring schemes corresponding to the equipment type and the corresponding hazard level. It is used to perform pulse rendering on BIM component data in a lightweight 3D scene. Each coloring rule in the coloring rule library refers to the visual presentation scheme based on the equipment type and the corresponding hazard level, including rendering control parameters such as color selection, brightness adjustment, pulse frequency setting, and texture mapping method.

[0067] S3.3: Based on the lightweight 3D scene file rendered by shading pulses, perform third-party differentiated data encapsulation and hierarchical permission control to generate a third-party collaborative operation interface.

[0068] Specifically, the lightweight 3D scene file rendered by shading pulses is parsed. According to the three-party differentiated data encapsulation requirements, the device type information, spatial coordinate information, and risk level information in the lightweight 3D scene file are encapsulated respectively, obtaining the device type encapsulation set, spatial coordinate encapsulation set, and risk level encapsulation set. Corresponding permission hierarchical control rules are set for the device type encapsulation set, spatial coordinate encapsulation set, and risk level encapsulation set, obtaining the permission hierarchical control rule set. Then, according to the correspondence between the three-party differentiated data encapsulation requirements and the permission hierarchical control rule set, the binding of the three-party differentiated data and the permission hierarchical control rule set is completed, generating a three-party collaborative operation interface.

[0069] It should be explained that the permission hierarchical control rule refers to the specification for hierarchical management of access and operation permissions for device type information, spatial coordinate information and risk level information in lightweight 3D scene files based on different users or user groups in the three-party collaborative operation interface. Each permission hierarchical control rule is used to specify the access permissions of users or user groups to specific data types, including the ability to view, modify, interact or hide, while limiting the operable scope and constraints of the corresponding encapsulated set in the three-party collaborative operation interface.

[0070] It needs to be explained that the correspondence between the three-party differentiated data encapsulation requirements and the permission hierarchical control rule set refers to matching different types of encapsulated data (e.g., equipment type encapsulation set, spatial coordinate encapsulation set, and risk level encapsulation set) in the three-party collaborative operation interface with the permission hierarchical control rules set for the encapsulated data, so that each type of encapsulated data follows the visualization, operable permissions, and constraints in the three-party collaborative operation interface.

[0071] S4: Based on the three-party collaborative operation interface, a multi-objective optimization algorithm is used to select an optimized treatment plan, and the parameters of the data acquisition equipment are adjusted according to the optimized treatment plan. Then, the risk decay rate in the data acquisition equipment parameters is monitored in real time and tracked with the preset stability criteria to generate a risk treatment audit report.

[0072] Specifically, the steps are as follows: S4.1: Based on the three-party collaborative operation interface, a multi-objective optimization algorithm is adopted to calculate the multi-objective weighted decision score, and the highest decision score scheme is automatically selected according to the multi-objective weighted decision score to generate smart contract execution instructions. Finally, all smart contract execution instructions are integrated to obtain the optimized disposal scheme execution instruction set.

[0073] Specifically, a multi-objective optimization algorithm is used to optimize the differentiated data and permission hierarchical control rule set of the three parties in the three-party collaborative operation interface, generating a set of candidate decision schemes. Then, a weighted sum method is used to calculate the multi-objective weighted decision score of each candidate decision scheme in the set. Subsequently, based on the multi-objective weighted decision score, the decision scheme with the highest score is selected from the set of candidate decision schemes. The operation steps, target quantitative index parameters and resource allocation information in the decision scheme with the highest score are encoded and converted according to the predefined smart contract execution rules to generate smart contract execution instructions. Finally, all smart contract execution instructions are integrated to obtain the optimized disposal scheme execution instruction set.

[0074] The formula for calculating the multi-objective weighted decision score is as follows: ; in, ; in, Index representing candidate decision options. This indicates the index in which the input data is extracted from the three-way collaborative operation interface. This represents the total number of decision indicators for each candidate decision-making option. Indicates the first Multi-objective weighted decision scoring of candidate solutions Indicates the first Weighting coefficients for each input data point Indicates the first The candidate solutions in the first The values ​​in the input data, This represents the total number of candidate decision options.

[0075] It should be explained that predefined smart contract execution rules refer to the pre-set coding specifications and operating procedures in the process of generating the execution instruction set of the optimized disposal plan. These rules are used to convert the operation steps, target quantitative index parameters, and resource allocation information in the candidate decision plan into a set of instructions that can be automatically executed in the smart contract. The predefined smart contract execution rules clearly stipulate the coding method of the operation steps in the candidate decision plan, the unit and quantification standard of the target quantitative index parameters, and the allocation priority and constraints of resource allocation information in the contract.

[0076] It needs to be explained that the operation steps refer to the specific sequence of actions required to achieve the processing requirements in the highest-scoring decision scheme. The operation steps are arranged in a clear order and describe the operation process in actual execution, such as equipment start-up and shutdown, parameter adjustment, or system linkage triggering. Ultimately, the operation steps are transformed into executable instructions in the smart contract. The target quantification index parameters refer to the specific values ​​or parameters used to clarify and measure the degree to which the index parameters are achieved in the highest-scoring decision scheme, such as risk level thresholds, runtime, energy consumption values, alarm triggering conditions, etc. The resource allocation information refers to the usage and allocation of various resources required to execute the operation steps in the highest-scoring decision scheme, such as the power allocation ratio of the acquisition equipment, the network bandwidth usage, and the sensor monitoring frequency allocation, etc.

[0077] S4.2: The edge controller performs parameter adjustment on the instruction set of the optimization treatment plan, synchronously triggers parameter updates in the lightweight 3D model, records the updated parameter content, and obtains the equipment parameter adjustment record.

[0078] Specifically, the edge controller parses the instruction set of the optimization solution execution one by one, extracting the operation steps, target quantitative index parameters, and resource allocation information contained in each optimization solution execution instruction. Based on the operation steps, target quantitative index parameters, and resource allocation information, the BIM component data, the corresponding physical parameter weights, and texture information in the lightweight 3D model are adjusted item by item, so that each BIM component data is updated. Finally, the original parameter values ​​of the BIM component data, the updated parameter values ​​of the BIM component data, the time point of the BIM component data parameter adjustment operation, and the component ID information of the BIM component data are recorded to obtain the equipment parameter adjustment record.

[0079] It needs to be explained that the process of adjusting each item involves the edge controller executing the operation steps in the instruction set of the optimization and disposal plan to determine the range of BIM components that need to be adjusted. Then, based on the target quantitative index parameters, the physical parameters of the components (such as operating pressure, temperature setpoint, and energy consumption coefficient) are numerically adjusted. In addition, the weights and texture information of the physical parameters corresponding to the BIM component data are matched and corrected in conjunction with resource allocation information to ensure that resource allocation is consistent with the risk disposal objectives.

[0080] S4.3: Based on the equipment parameter adjustment records, obtain the risk value before the equipment parameters were adjusted, calculate the current risk value based on the collected equipment parameters, and finally integrate them to obtain a risk decay rate report.

[0081] Specifically, the original parameter values ​​of BIM component data are extracted from the equipment parameter adjustment records as the risk values ​​before the equipment parameters are adjusted. Then, the weighted fuzzy comprehensive evaluation method is used to calculate the current risk value of the equipment parameters based on the updated parameter values ​​in the equipment parameter adjustment records. Finally, the current risk value of the equipment parameters is integrated with the risk value before the equipment parameters are adjusted to generate a risk decay rate report.

[0082] The formula for the weighted fuzzy comprehensive evaluation method is as follows: ; in, This indicates the current risk value of the equipment parameters. Indicates the total number of equipment parameter items. This represents the total number of risk levels. Indicates the index of the device parameter item. Indicates the risk level index. This indicates the avoidance of extremely small positive numbers with a denominator of zero. Indicates the first The weighting coefficient of each device parameter item. Indicates the first Updated parameter values ​​for each device parameter item. Indicates the first The original parameter values ​​for each device parameter item. Indicates the first A quantitative score for each risk level. Indicates the first The quantitative score value corresponding to each risk level; It is obtained by assigning values ​​from low to high based on the risk level determination range, combined with industry safety standards and historical risk data, and normalizing them.

[0083] S4.4: Integrate the risk decay rate report and equipment parameter adjustment records, and generate a risk disposal audit report according to the six-element method.

[0084] Specifically, the risk decay rate report and equipment parameter adjustment records are integrated to generate a comprehensive data set containing information on risk value changes and equipment parameter adjustments. Then, according to the six-element method rule, the comprehensive data set is broken down into time elements, location elements, object elements, event elements, behavior elements, and result elements. The time elements, location elements, object elements, event elements, behavior elements, and result elements are then formatted and arranged to generate a risk disposal audit report.

[0085] It should be explained that the six-element method rule refers to classifying and expressing the comprehensive data set from six dimensions—time element, location element, object element, event element, behavior element, and result element—when generating a risk disposal audit report. The six-element method rule requires that the risk decay rate report and equipment parameter adjustment record in the comprehensive data set be broken down and summarized according to the time element, location element, object element, event element, behavior element, and result element to ensure that the final risk disposal audit report is comprehensive, well-organized, and traceable.

[0086] It needs to be explained that the steps of formatted expression and arrangement are to perform structured processing on time elements, location elements, and object elements. For example, time elements are converted into standard timestamps or time intervals, location elements are converted into coordinates or regional identifiers, and object elements are identified with a unified ID or unified name to obtain an identifier data set. Then, event elements, behavior elements, and result elements are semantically classified and textually standardized to obtain a standardized semantic description set. Finally, the identifier data set and the standardized semantic description set are arranged sequentially to form a structured, traceable, and comparable risk disposal audit report.

[0087] This embodiment also provides a data collaboration platform management system that integrates all project participants, including: The data acquisition module collects BIM component data and physical parameters in real time through the OpenBIM standard interface, parses the BIM component data to generate a lightweight 3D model, and then performs weighted calculation and weight reduction processing on the physical parameters to generate a time-weighted index set. The model component module simplifies the triangular facets and compresses the textures of the lightweight 3D model to generate a lightweight 3D scene. It matches the time-weighted index set with the spatial topology of the lightweight 3D scene and the sensor coordinates, calculates the comprehensive risk probability value through the risk probability equation, and then classifies the comprehensive risk probability value into hazard levels to generate a set of high-risk spatial coordinates. The risk analysis module maps a set of high-risk spatial coordinates onto a lightweight 3D scene to generate color pulses and a three-party collaborative operation interface. The optimization and control module, based on a three-party collaborative operation interface, uses a multi-objective optimization algorithm to select an optimized treatment plan, adjusts the parameters of the acquisition equipment according to the optimized treatment plan, monitors the risk decay rate in the acquisition equipment parameters in real time, tracks and processes it against preset stability criteria, and generates a risk treatment audit report.

[0088] This embodiment also provides a computer device applicable to the data collaboration platform management method integrating all project participants, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the data collaboration platform management method integrating all project participants as proposed in the above embodiment.

[0089] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0090] This embodiment also provides a storage medium storing a computer program. When executed by a processor, the program implements the data collaboration platform management method for integrating all project participants as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0091] In summary, this invention achieves precise digital integration of component information and physical attributes throughout the entire project lifecycle by using the OpenBIM standard interface to collect BIM component data and physical parameters in real time and generate lightweight 3D models. This enables all participating parties to obtain the latest component status and parameter data on a unified 3D data platform, thereby providing comprehensive data support and traceable information sources. This lays a precise data foundation for risk analysis and optimization, effectively improving information transparency and data availability.

[0092] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A data collaboration platform management method integrating all project participants, characterized in that: include, BIM component data and physical parameters are collected in real time through the OpenBIM standard interface, and the BIM component data is parsed to generate a lightweight 3D model. Then, the physical parameters are weighted and reduced to generate a time-weighted index set. The lightweight 3D model is simplified by triangular facets and texture compression to generate a lightweight 3D scene. The time-weighted index set is matched with the spatial topology of the lightweight 3D scene and the sensor coordinates. The comprehensive risk probability value is calculated through the risk probability equation. Then, the comprehensive risk probability value is classified by hazard level to generate a set of high-risk spatial coordinates. Map the high-risk spatial coordinate set onto a lightweight 3D scene to perform shading pulses and generate a three-party collaborative operation interface. Based on the three-party collaborative operation interface, a multi-objective optimization algorithm is used to select an optimized treatment plan, and the parameters of the data acquisition equipment are adjusted according to the optimized treatment plan. Then, the risk decay rate in the data acquisition equipment parameters is monitored in real time and tracked against the preset stability criteria to generate a risk treatment audit report.

2. The data collaboration platform management method integrating all project participants as described in claim 1, characterized in that: The process involves real-time acquisition of BIM component data and physical parameters through the OpenBIM standard interface, parsing the BIM component data to generate a lightweight 3D model, and then performing weighted calculations and weight reduction processing on the physical parameters to generate a time-sensitive weighted index set. The steps are as follows. BIM component data and physical parameters are collected in real time through the OpenBIM standard interface, and spatiotemporal coordinate identifiers and version tags are added to generate original BIM data packages with spatiotemporal tags and physical parameter sets. The original BIM data package with spatiotemporal tags is simplified and texture compressed to generate a lightweight 3D model. Based on the acquisition delay of the physical parameter set, half-life weighting is performed to generate a weighted physical parameter set. Extract the origin of spatial coordinates from the lightweight 3D model, and then merge the origin of spatial coordinates, the weighted set of physical parameters, and the version tag to generate a time-weighted index set.

3. The data collaboration platform management method integrating all project participants as described in claim 2, characterized in that: The version tag is an identifier used to mark the sequence and iteration status of BIM component data and physical parameters during the acquisition and updating process.

4. The data collaboration platform management method integrating all project participants as described in claim 1, characterized in that: The process involves simplifying the lightweight 3D model using triangular facets and compressing textures to generate a lightweight 3D scene. The time-weighted index set is then matched with the spatial topology of the lightweight 3D scene and the sensor coordinates. A comprehensive risk probability value is calculated using a risk probability equation, followed by hazard level classification to generate a high-risk spatial coordinate set. The steps are as follows: Perform topology correction on the lightweight 3D model to generate a lightweight 3D scene file with complete spatial topology; Parse the component IDs in the lightweight 3D scene file to associate them with device types, and accurately bind the spatial location with the weighted physical parameter set to establish a spatial location mapping table; Based on the deviation between the physical parameter weights and safety benchmark values ​​in the lightweight 3D scene, the risk factor of the physical parameter weights is calculated, and the BIM confidence in the BIM component data is superimposed to generate a comprehensive risk probability value. Then, the risk level is divided according to the preset threshold to generate a component risk level list. Based on the preset risk and safety level threshold, spatial coordinate data and equipment type information are filtered, and the hazard characteristics of the equipment type information are classified into levels to generate a set of high-risk spatial coordinates.

5. The data collaboration platform management method integrating all project participants as described in claim 4, characterized in that: The preset risk safety level threshold is set based on the following factors: industry safety norms and standards, actual engineering operation data and historical risk cases, as well as risk assessment methods and risk management strategies.

6. The data collaboration platform management method integrating all project participants as described in claim 1, characterized in that: The steps for mapping the high-risk spatial coordinate set onto a lightweight 3D scene for shading pulses and generating a three-way collaborative operation interface are as follows: The high-risk coordinates in the high-risk spatial coordinate set are spatially matched with the equipment components in the lightweight 3D scene to construct a device-coordinate binding table. Based on the device type and hazard level in the device-coordinate binding table, the shading rule library is called to shading the lightweight 3D scene, generating a lightweight 3D scene file after shading pulse rendering; Based on the lightweight 3D scene file rendered by shading pulses, we perform three-party differentiated data encapsulation and hierarchical permission control to generate a three-party collaborative operation interface.

7. The data collaboration platform management method integrating all project participants as described in claim 6, characterized in that: The coloring rule base refers to a collection used to store the visual coloring schemes corresponding to device types and their corresponding hazard levels.

8. The data collaboration platform management method integrating all project participants as described in claim 1, characterized in that: The process involves using a multi-objective optimization algorithm based on a three-party collaborative operation interface to select an optimized treatment plan, adjusting the parameters of the data acquisition device according to the optimized treatment plan, monitoring the risk decay rate in the data acquisition device parameters in real time, and tracking it against preset stability criteria to generate a risk treatment audit report. The steps are as follows: Based on a three-party collaborative operation interface, a multi-objective optimization algorithm is adopted to calculate a multi-objective weighted decision score. The highest decision score scheme is automatically selected based on the multi-objective weighted decision score to generate smart contract execution instructions. Finally, the optimized disposal scheme execution instruction set is integrated and obtained. The edge controller executes the instruction set of the optimized treatment plan to adjust parameters and obtains the equipment parameter adjustment records. Based on the equipment parameter adjustment records, obtain the risk value before the equipment parameters were adjusted, calculate the current risk value based on the collected equipment parameters, and finally integrate them to obtain a risk decay rate report; Integrate risk decay rate reports and equipment parameter adjustment records, and generate a risk disposal audit report using the six-element method.

9. The data collaboration platform management method integrating all project participants as described in claim 8, characterized in that: The six-element method refers to classifying and expressing the comprehensive data set from six dimensions—time element, location element, object element, event element, behavior element, and result element—when generating a risk disposal audit report.

10. A data collaboration platform management system integrating all project participants, based on the data collaboration platform management method integrating all project participants as described in any one of claims 1 to 9, characterized in that: include, The data acquisition module collects BIM component data and physical parameters in real time through the OpenBIM standard interface, parses the BIM component data to generate a lightweight 3D model, and then performs weighted calculation and weight reduction processing on the physical parameters to generate a time-weighted index set. The model component module simplifies the triangular facets and compresses the textures of the lightweight 3D model to generate a lightweight 3D scene. It matches the time-weighted index set with the spatial topology of the lightweight 3D scene and the sensor coordinates, calculates the comprehensive risk probability value through the risk probability equation, and then classifies the comprehensive risk probability value into hazard levels to generate a set of high-risk spatial coordinates. The risk analysis module maps a set of high-risk spatial coordinates onto a lightweight 3D scene to generate color pulses and a three-party collaborative operation interface. The optimization and control module, based on a three-party collaborative operation interface, uses a multi-objective optimization algorithm to select an optimized treatment plan, adjusts the parameters of the acquisition equipment according to the optimized treatment plan, monitors the risk decay rate in the acquisition equipment parameters in real time, tracks and processes it against preset stability criteria, and generates a risk treatment audit report.

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