A method and system for engineering collaborative design based on BIM technology

Abstract

The application discloses a kind of engineering collaborative design method and system based on BIM technology, it is related to construction collaborative management technical field, including extracting associated geological parameters from initial BIM model, and in combination with high-risk component, with structural safety and construction cost as constraint condition, parameter adjustment instruction set is generated by multi-objective optimization algorithm;Parameter adjustment instruction set is stored in block chain, and encrypted instruction block is generated;Lightweight algorithm is used to compress the encrypted instruction block in stages, and output lightweight collaborative instruction set;Lightweight collaborative instruction set is distributed to AR terminal through 5G-UWB network, and geological risk area and component change scheme are superimposed and displayed in construction scene.By solving surrounding rock-supporting structure multi-physical field coupling equation in real time on the basis of initial BIM model and outputting risk state tensor, the timeliness and scientificity of risk early warning are significantly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of construction collaborative management, in particular to an engineering collaborative design method and system based on BIM technology. BACKGROUND

[0002] With the in-depth application of building information modeling (BIM) technology in the field of civil and construction engineering, the integration, visualization and intelligentization based on digital collaborative design have become the mainstream trend of industry development. BIM technology realizes information integration and sharing of the whole life cycle of design, construction and operation by constructing a three-dimensional digital model containing geometric information and non-geometric attributes. BIM has begun to integrate geological survey data, structural monitoring information and construction progress plan, forming preliminary multi-source data collaborative analysis capability. Data middleware or general interface standards (such as IFC, CityGML) are used to realize the format conversion and model integration of heterogeneous data, thereby supporting cross-stage and cross-professional design collaboration. Some research attempts to embed finite element analysis module into BIM platform to realize preliminary simulation evaluation of structural performance and provide certain basis for design optimization.

[0003] In the process of engineering collaborative design under complex geological environment, in underground engineering with variable geological conditions, there is often a semantic gap and spatial matching deviation between survey data, monitoring information and design model, which makes it difficult for geological attribute parameters to be effectively identified and utilized by BIM model, thereby affecting the accuracy of structural safety evaluation. At present, there is generally a lack of real-time solving capability of multi-physical field coupling of surrounding rock-supporting system, which cannot dynamically identify potential high-risk components in the design stage, resulting in time delay between risk warning and design adjustment. The traceability and safety guarantee mechanism of design change instruction in the multi-party collaboration environment is not perfect, which is easy to cause information distortion or difficulty in responsibility definition. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides an engineering collaborative design method and system based on BIM technology, which solves the problems of insufficient fusion of multi-source heterogeneous geological data and dynamic response lag of high-risk components.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides a BIM technology-based engineering collaborative design method, which comprises: collecting multi-source heterogeneous data and converting the multi-source heterogeneous data into standardized geological attribute parameters; generating an initial BIM model based on a spatial topology matching principle; solving a surrounding rock-supporting structure multi-physical field coupling equation based on the initial BIM model in real time, and outputting a risk state tensor; when any component in the risk state tensor exceeds a dynamic risk threshold set, marking a high-risk component; extracting associated geological parameters from the initial BIM model, and combining the high-risk component to generate a parameter adjustment instruction set through a multi-objective optimization algorithm with structural safety and construction cost as constraint conditions; performing blockchain notarization on the parameter adjustment instruction set to generate an encrypted instruction block; performing hierarchical compression on the encrypted instruction block using a lightweight algorithm to output a lightweight collaborative instruction set; and distributing the lightweight collaborative instruction set to an AR terminal through a 5G-UWB network to superimpose and display a geological risk area and a component change scheme in a construction scene.

[0008] As a preferred scheme of the BIM technology-based engineering collaborative design method, the conversion of the multi-source heterogeneous data into the standardized geological attribute parameters comprises the following specific steps:

[0009] Obtaining surrounding rock strain values, rock mass fracture energy and rock mass score values to obtain multi-source heterogeneous data;

[0010] Aligning time stamps of the multi-source heterogeneous data and performing spatial position registration, calculating structural complexity weights of the surrounding rock strain values, the rock mass fracture energy and the rock mass score values through a Hausdorff dimension algorithm, and fusing to generate standardized geological attribute parameters.

[0011] As a preferred scheme of the BIM technology-based engineering collaborative design method, the generation of the initial BIM model based on the spatial topology matching principle comprises the following specific steps:

[0012] Analyzing anchor rod end points and lining connection points in a roadway design drawing and defining them as topology nodes, establishing a connection line between adjacent topology nodes and labeling attributes, and composing a spatial topology graph from the topology nodes and the connection line;

[0013] Taking the topology nodes in the spatial topology graph as target positions, mapping the standardized geological attribute parameters to the target positions through a Kriging spatial interpolation algorithm, calculating adjacent topology node weights by analyzing spatial correlation of adjacent geological points, and obtaining horizontal principal stresses and damage factors;

[0014] Taking the horizontal principal stresses and the damage factors as attribute values, assigning the attribute values to corresponding topology nodes in the spatial topology graph, and obtaining a geological attribute topology graph;

[0015] Based on the geological attribute topology graph, a three-dimensional anchor rod entity is created, and a roadway lining surface is generated along the topology node, so that the anchor rod end point is seamlessly connected with the lining surface, and an initial BIM model is formed.

[0016] As a preferred scheme of the engineering collaborative design method based on the BIM technology, the output risk state tensor is specifically as follows:

[0017] Geometric parameters of supporting members and real-time data of surrounding rock are extracted from the initial BIM model, and a stress field equation is obtained by describing the stress balance between the surrounding rock strain value and the supporting structure;

[0018] A seepage field equation is obtained by the impact effect of groundwater pressure on the supporting structure, and a damage field equation is obtained by quantifying the rock mass crack propagation and supporting fatigue accumulation; the stress field equation, the seepage field equation and the damage field equation are integrated to obtain a surrounding rock-supporting structure multi-physical field coupling equation;

[0019] A roadway grid is generated with the roadway lining surface in the initial BIM model as a boundary, a damage field equation is solved by quantum annealing, a stress field equation and a seepage field equation are solved by GPU, and a risk state tensor is output.

[0020] As a preferred scheme of the engineering collaborative design method based on the BIM technology, the high-risk component is marked, and the specific steps are as follows:

[0021] The rock mass creep rate, the roadway exposure time, the rock mass score value and the microseismic event frequency are collected;

[0022] Based on the rock mass creep rate and the roadway exposure time, a stress threshold value is calculated by a nonlinear function, a damage threshold value is obtained by using a hierarchical down-regulation mechanism according to the microseismic event frequency, and a seepage threshold value is calculated based on the rock mass score value and the pore water pressure, and the stress threshold value, the damage threshold value and the seepage threshold value are integrated into a dynamic risk threshold value set;

[0023] When any component of the risk state tensor exceeds the corresponding dynamic risk threshold value set, the spatial coordinates of the corresponding roadway grid are located, and a risk type is attached, to obtain a high-risk component.

[0024] As a preferred scheme of the engineering collaborative design method based on the BIM technology, the parameter adjustment instruction set is generated, and the specific steps are as follows:

[0025] Based on the high-risk component and the initial BIM model, a spatial range is defined with the geometric center of the high-risk component as the center and the engineering influence as the radius in the initial BIM model according to the spatial proximity principle, all geological attribute parameters in the spatial range generated by the Kriging interpolation are extracted and associated, and a risk-parameter association graph is formed;

[0026] The risk-parameter association graph is input into a multi-objective optimization algorithm, and a collaborative decision is made to obtain a high-risk component parameter adjustment set, which is structured and bound in the initial BIM model to form a parameter adjustment instruction set.

[0027] As a preferred scheme of the engineering collaborative design method based on the BIM technology, the encrypted instruction block is generated, and the specific steps are as follows:

[0028] Based on the parameter adjustment instruction set, a hash value is calculated, and the hash value is digitally signed using a private key, the parameter adjustment instruction set is uploaded to a distributed storage network, and a unique content identifier is obtained;

[0029] Based on the parameter adjustment instruction set and the dynamic risk threshold set, a compliance proof is generated through a privacy protection verification logic, and the hash value, the digital signature, the unique content identifier and the compliance proof are assembled into a blockchain transaction and verified by consensus to generate an encrypted instruction block.

[0030] As a preferred scheme of the engineering collaborative design method based on the BIM technology, the lightweight collaborative instruction set is output, and the specific steps are as follows:

[0031] Based on the content identifier in the encrypted instruction block, the parameter adjustment instruction set is retrieved and parsed from the distributed storage network, the parameter adjustment instruction set is converted into a semantic association graph structure representing the association relationship between components, and a low-dimensional semantic vector is generated through a neural network encoder;

[0032] According to the terminal performance and network state, a corresponding hierarchical compression strategy is selected to quantize and encode the low-dimensional semantic vector to obtain a discrete symbol sequence;

[0033] Based on the hierarchical compression strategy, the corresponding decoder identifier and codebook identifier are extracted from the decoder database, and the discrete symbol sequence, the decoder identifier and the codebook identifier are encapsulated to generate a lightweight collaborative instruction set.

[0034] As a preferred scheme of the engineering collaborative design method based on the BIM technology, the geological risk area and the component change scheme are superimposed and displayed in the construction scene, and the specific steps are as follows:

[0035] The lightweight collaborative instruction set is transmitted to the target AR terminal through the 5G-UWB heterogeneous network, and the discrete symbol sequence is neural decoded to reconstruct the parameter adjustment instruction information to obtain the geological risk area and the component change scheme;

[0036] The real-time high-precision pose of the AR terminal is calculated through the tight coupling sensor fusion algorithm, and the geological risk area and the component change scheme are superimposed and displayed in the field of view of the terminal in combination with the parameter adjustment instruction information.

[0037] In a second aspect, the present application provides a BIM technology-based engineering collaborative design system, comprising: a BIM generation module for collecting multi-source heterogeneous data and converting the multi-source heterogeneous data into standardized geological attribute parameters, generating an initial BIM model based on a spatial topology matching principle; a monitoring module for solving a surrounding rock-supporting structure multi-physical field coupling equation based on the initial BIM model in real time, outputting a risk state tensor, and marking a high-risk component when any component in the risk state tensor exceeds a dynamic risk threshold set; an adjustment module for extracting associated geological parameters from the initial BIM model, combining the high-risk component, and generating a parameter adjustment instruction set through a multi-objective optimization algorithm with structural safety and construction cost as constraint conditions; a block module for block chain notarization of the parameter adjustment instruction set to generate an encrypted instruction block; a collaborative module for hierarchical compression of the encrypted instruction block using a lightweight algorithm to output a lightweight collaborative instruction set; and a display module for distributing the lightweight collaborative instruction set to an AR terminal through a 5G-UWB network to superimpose and display a geological risk area and a component change scheme in a construction scene.

[0038] The present application has the following advantages: by converting multi-source heterogeneous data into standardized geological attribute parameters and generating an initial BIM model based on a spatial topology matching principle, accurate integration and structured modeling of geological information are achieved, and the uniformity of basic data and the accuracy of model construction are improved; by solving a surrounding rock-supporting structure multi-physical field coupling equation based on the initial BIM model in real time and outputting a risk state tensor, high-precision and real-time simulation analysis of dynamic coupling of multi-physical fields in complex underground engineering is achieved, high-risk components can be identified in time, and the timeliness and scientificity of risk warning are significantly improved, not only enhancing the mapping ability of the BIM model to the real geological environment, but also realizing the deep integration and systematic improvement of intelligentization, safety and traceability of engineering collaborative design as a whole. BRIEF DESCRIPTION OF DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0040] Figure 1 The flowchart of the BIM technology-based engineering collaborative design method.

[0041] Figure 2 The schematic diagram of the BIM technology-based engineering collaborative design system.

[0042] Figure 3 The flowchart for obtaining high-risk components.

[0043] Figure 4 A flow chart for generating a parameter adjustment instruction set. DETAILED DESCRIPTION

[0044] In order to make the above objectives, features and advantages of the present application more apparent, specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0045] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, it will be apparent to one skilled in the art that the present application can be practiced without the specific details given herein. In other instances, well-known methods have not been described in detail in order to avoid unnecessarily obscuring the present application. Therefore, the specific embodiments given herein are not to be interpreted as limiting the scope of the application.

[0046] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an embodiment that is independent or alternative to other embodiments.

[0047] Reference Figure 1 For one embodiment of the present application, the embodiment provides a BIM technology-based engineering collaborative design method, comprising the following steps:

[0048] S1, collect multi-source heterogeneous data and convert the multi-source heterogeneous data into standardized geological attribute parameters.

[0049] S1.1, obtain the surrounding rock strain value, rock mass fracture energy and rock mass score value to obtain the multi-source heterogeneous data.

[0050] Specifically, the surrounding rock strain value is measured by a resistance strain gauge installed on the surface of the surrounding rock. The resistance strain gauge converts the micro-deformation of the surrounding rock into a resistance change signal, and a data acquisition instrument records the resistance change at a sampling frequency of 100 times per second and converts it into a micro-strain value to obtain the surrounding rock strain value.

[0051] The rock mass fracture energy is collected by a microseismic monitoring sensor array arranged inside the rock mass. The sensor records the elastic wave vibration signal generated by the rock mass fracture, and calculates the energy value of each fracture event according to the amplitude and frequency integral of the vibration signal to obtain the rock mass fracture energy.

[0052] The rock mass score value is obtained by field geological logging. The rock mass structure surface spacing, rock block strength and groundwater conditions and other parameters are measured using a geological compass and a rock mass quality measuring gauge, and the values are assigned and summed according to the rock mass quality rating table to obtain the rock mass score value. The surrounding rock strain value, rock mass fracture energy and rock mass score value jointly constitute the multi-source heterogeneous data.

[0053] It should be noted that the energy value of each rupture event is expressed as:

[0054] ;

[0055] wherein, represents the energy value of each rupture event, represents the ratio of the circumference of a circle to its diameter, represents the density of the rock mass, represents the propagation speed of elastic waves in the rock mass, represents the distance from the source to the sensor, represents the signal duration, represents the particle, represents the particle propagation speed in the rock mass, represents the time, is a differential symbol, representing "infinitesimal increment of time".

[0056] S1.2, align the timestamps of multi-source heterogeneous data, and perform spatial position registration, calculate the structural complexity weight of rock mass strain value, rock mass rupture energy and rock mass score value through Hausdorff dimension algorithm, and fuse to generate standardized geological attribute parameters.

[0057] Specifically, the precise clock synchronization protocol uses IEEE 1588 precision time protocol to align the collection timestamps of rock mass strain value, rock mass rupture energy and rock mass score value, so that all data can achieve microsecond-level time synchronization.

[0058] Spatial position registration: collect the three-dimensional coordinates of each sensor installation point measured by the total station, establish a unified roadway coordinate system, and unify the measurement positions of rock mass strain value, rock mass rupture energy and rock mass score value to the roadway coordinate system.

[0059] Hausdorff dimension algorithm calculates the fractal dimension of rock mass strain value time series, rock mass rupture energy time series and rock mass score value spatial distribution, and obtains the structural complexity weight of rock mass strain value, the structural complexity weight of rock mass rupture energy and the structural complexity weight of rock mass score value.

[0060] The Hausdorff dimension algorithm calculates the fractal dimension of the surrounding rock strain time series, the rock mass fracture energy time series and the spatial distribution of the rock mass score value: the surrounding rock strain time series is divided according to different time intervals (for example: 1, 2, 4, …, 1024 seconds), the minimum box number of the coverage curve is counted, and the fractal dimension of the surrounding rock strain value is obtained by fitting the slope of the double logarithmic coordinates; the cumulative energy curve is constructed based on the rock mass fracture energy time series, the coverage box number is counted according to the time interval, and the fractal dimension of the rock mass fracture energy is obtained by fitting the slope of the double logarithmic coordinates; the grid is divided in the projection plane of the roadway, the number of grids containing sampling points is counted, and the fractal dimension of the spatial distribution of the rock mass score value is obtained by fitting the slope of the double logarithmic coordinates.

[0061] The surrounding rock strain value structure complexity weight, the rock mass fracture energy structure complexity weight and the rock mass score value structure complexity weight are obtained: a weighted fusion method is adopted, the surrounding rock strain value, the rock mass fracture energy and the rock mass score value are respectively weighted and data integrated according to the surrounding rock strain value structure complexity weight, the rock mass fracture energy structure complexity weight and the rock mass score value structure complexity weight, and the standardized geological attribute parameters are obtained.

[0062] S2, based on the spatial topological matching principle, an initial BIM model is generated.

[0063] S2.1, the anchor rod endpoints and the lining connection points in the roadway design drawing are analyzed and defined as topological nodes, connection lines are established between adjacent topological nodes and attributes are labeled, and a spatial topological graph is composed of the topological nodes and the connection lines.

[0064] Specifically, the anchor rod endpoints and the lining connection points in the roadway design drawing are analyzed, each anchor rod endpoint and lining connection point is defined as a topological node, a connection line is established between adjacent topological nodes and the material type and cross-sectional size attributes of each connection line are labeled, and a spatial topological graph is composed of all topological nodes and all connection lines with labeled attributes.

[0065] S2.2, the topological nodes in the spatial topological graph are taken as target positions, the standardized geological attribute parameters are mapped to the target positions through the Kriging spatial interpolation algorithm, the spatial correlation of adjacent geological points is analyzed, the weight of adjacent topological nodes is calculated, and the horizontal principal stress and the damage factor are obtained.

[0066] Specifically, the topological nodes in the spatial topological graph are taken as target positions, the standardized geological attribute parameters are mapped to the target positions through the Kriging spatial interpolation algorithm, the spatial correlation between the standardized geological attribute parameter sampling points is obtained through the semi-variogram function, the weight coefficient of each topological node and the adjacent standardized geological attribute parameter sampling points around it is obtained, the standardized geological attribute parameters are weighted based on the weight coefficient, and the horizontal principal stress and the damage factor corresponding to each topological node are generated.

[0067] It should be noted that the spatial correlation between the standardized geological attribute parameter sampling points includes the mathematical relationship between the degree of change of the geological attribute parameter and the distance between the sampling points, the difference in the change of the geological attribute parameter in different directions, and the change pattern of the geological attribute in the spatial scale.

[0068] S2.3, the horizontal principal stress and the damage factor are taken as attribute values, and are assigned to the corresponding topological nodes in the spatial topological graph to obtain a geological attribute topological graph.

[0069] Specifically, the horizontal principal stress value and the damage factor value are taken as attribute values and are assigned to the corresponding topological nodes in the spatial topological graph: each topological node in the spatial topological graph is traversed, the node horizontal principal stress obtained by the Kriging spatial interpolation algorithm is written into the node attribute field “horizontal principal stress”, and the damage factor is written into the node attribute field “damage factor”; at the same time, the original topological node spatial coordinate information and the connection line attributes between the nodes in the spatial topological graph are retained; and a geological attribute topological graph is formed.

[0070] S2.4, a three-dimensional anchor rod entity is created based on the topological nodes in the geological attribute topological graph, and a roadway lining surface is generated along the topological nodes to seamlessly connect the anchor rod end points and the lining surface, thereby forming an initial BIM model.

[0071] Specifically, a three-dimensional anchor rod entity is created based on the topological nodes in the geological attribute topological graph, and the three-dimensional anchor rod entity is generated through a stretching command of the BIM software. The stretching command takes the topological node as a starting point and extends to a designed length along the anchor rod design direction to form an entity.

[0072] At the same time, a roadway lining surface is generated along the topological nodes, and the roadway lining surface is generated through a lofting command of the BIM software. The lofting command takes the topological node as a control point and forms a continuous surface according to the roadway design contour to seamlessly connect the anchor rod end points and the lining surface. The seamless connection is realized through a geometry trimming command of the BIM software, and the geometry trimming command accurately trims the intersection part of the anchor rod entity and the lining surface, so that the anchor rod end points are completely embedded in the lining surface to form a complete connection, thereby forming an initial BIM model.

[0073] S3, based on the initial BIM model, a surrounding rock-supporting structure multi-physical field coupling equation is solved in real time, and a risk state tensor is output.

[0074] S3.1, the supporting member geometric parameters and the real-time data of the surrounding rock are extracted from the initial BIM model, and a stress field equation is obtained by describing the stress balance between the surrounding rock strain value and the supporting structure.

[0075] Specifically, the support member geometric parameters and real-time data of surrounding rock are extracted from the initial BIM model, the support member geometric parameters include anchor rod length, anchor rod diameter and lining thickness, and the real-time data of surrounding rock include surrounding rock strain value and rock mass fracture energy.

[0076] Hooke's law provides the material properties of the rock mass itself, and the balance equation provides the physical law that must be followed. By combining the two and introducing the force of the support structure as an external force into the balance equation, the stress field equation is established.

[0077] S3.2, the seepage field equation is obtained by the impact effect of groundwater pressure on the support structure, and the damage field equation is obtained by quantifying the rock mass crack propagation and support fatigue accumulation; the stress field equation, the seepage field equation and the damage field equation are integrated to obtain the surrounding rock-support structure multi-physical field coupling equation.

[0078] Specifically, the seepage field equation is obtained by the impact effect of groundwater pressure on the support structure, the seepage field equation is established based on Darcy's law and the law of conservation of mass, and the damage field equation is obtained by quantifying the rock mass crack propagation and support fatigue accumulation; the damage field equation adopts the continuous medium damage mechanics theory, and the support fatigue accumulation adopts the Miner linear cumulative damage theory; through the simultaneous solution of the stress field equation, the seepage field equation and the damage field equation, the surrounding rock-support structure multi-physical field coupling equation is formed.

[0079] S3.3, taking the roadway lining surface in the initial BIM model as the boundary, the roadway grid is generated, the damage field equation is solved by quantum annealing, the stress field equation and the seepage field equation are solved by GPU, and the risk state tensor is output.

[0080] Specifically, taking the roadway lining surface in the initial BIM model as the boundary, the roadway grid is generated, the roadway grid is discretized in space by tetrahedron, and the roadway grid size is determined according to the geometric characteristics of the roadway, for example, the roadway grid size is 5cm in the example in the area with large curvature, and the roadway grid size is 10cm in the example in the flat area.

[0081] The damage field equation is solved by quantum annealing, which converts the solution of the damage field equation into a quadratic unconstrained binary optimization problem, and uses the superposition and tunneling effect of quantum bits to find the global optimal solution to obtain the damage variable; the stress field equation and the seepage field equation are solved by GPU in parallel, and the GPU uses the CUDA architecture for parallel computing, and the finite element discrete equation set of the stress field equation and the seepage field equation is decomposed into multiple parallel threads for simultaneous solution; the stress field equation is solved by the preconditioned conjugate gradient method to obtain the node displacement vector, and the seepage field equation is solved by Darcy's law to calculate the seepage velocity field; based on the displacement solution, the stress tensor components of each roadway grid are calculated, combined with the seepage velocity components and the independently solved damage variables, and the risk state tensor is assembled and output according to the roadway grid number.

[0082] S4. When any component of the risk state tensor exceeds the dynamic risk threshold set, mark the high-risk component, see details in Figure 3 .

[0083] S4.1, collect the rock creep rate, roadway exposure time, rock mass score value and microseismic event frequency.

[0084] Specifically, the rock creep rate, roadway exposure time and microseismic event frequency are collected, the rock creep rate is recorded by a creep measuring instrument installed on the surface of the roadway surrounding rock in millimeters per hour, the roadway exposure time is recorded by the construction log for the number of hours from the completion of roadway excavation to the current time, and the microseismic event frequency is counted by a microseismic monitoring sensor array in the number of events per day; the rock mass score value is obtained through field geological recording.

[0085] S4.2, based on the rock creep rate and roadway exposure time, calculate the stress threshold value through a nonlinear function, obtain the damage threshold value according to the microseismic event frequency using a hierarchical down-regulation mechanism, calculate the seepage threshold value based on the rock mass score value and pore water pressure, and integrate the stress threshold value, damage threshold value and seepage threshold value into the dynamic risk threshold set.

[0086] Specifically, the stress threshold value is set: based on the rock creep rate and roadway exposure time, the stress threshold value is calculated through a nonlinear function, and the nonlinear function adopts an exponential decay form;

[0087] The damage threshold value is set: according to the microseismic event frequency, the damage threshold value is obtained using a hierarchical down-regulation mechanism, and the hierarchical down-regulation mechanism sets three microseismic event frequency intervals: 0-5 times / day corresponds to a damage threshold value of 0.8, 5-10 times / day corresponds to a damage threshold value of 0.6, and 10 times / day or more corresponds to a damage threshold value of 0.4;

[0088] The seepage threshold value is set: the seepage threshold value is calculated based on the rock mass score value and pore water pressure; and the stress threshold value, damage threshold value and seepage threshold value are sequentially integrated into the dynamic risk threshold set.

[0089] It should be noted that the expression for calculating the stress threshold value is:

[0090] ;

[0091] wherein, represents the stress threshold value, represents the strength reference coefficient, represents the natural exponential, represents the creep sensitivity coefficient, represents the rock creep rate, represents the time-dependent strengthening coefficient, represents the roadway exposure time, represents the reference time, which is a reference time for dimensionless normalization, and is taken as , and is a dimensionless quantity, ensuring the mathematical legitimacy of the logarithmic function.

[0092] The seepage threshold expression is calculated as:

[0093] ;

[0094] wherein, represents the seepage threshold, represents the RMR influence coefficient, wherein , represents the rock mass quality score, represents the reference seepage pressure.

[0095] S4.3, when any component of the risk state tensor exceeds the corresponding set of dynamic risk thresholds, locate the spatial coordinates of the corresponding roadway grid and attach the risk type to obtain the high-risk component.

[0096] Specifically, traverse the risk state tensor of all roadway grids, the risk state tensor contains the stress component, seepage velocity component and damage variable of each roadway grid; when the stress component in the risk state tensor exceeds the stress threshold component in the set of dynamic risk thresholds, or the seepage velocity component exceeds the seepage threshold component in the set of dynamic risk thresholds, or the damage variable exceeds the damage threshold component in the set of dynamic risk thresholds, locate the spatial coordinates of the corresponding roadway grid through the quantum search algorithm, the quantum search algorithm uses Grover search algorithm to search for the roadway grid number that meets the risk overrun condition in the roadway grid database, and obtains the spatial coordinates of the roadway grid according to the roadway grid number index to obtain the high-risk component.

[0097] According to the overrunning risk state tensor component, determine the high-risk component: the stress component exceeding the stress threshold is marked as a stress risk type, the seepage velocity component exceeding the seepage threshold is marked as a seepage risk type, and the damage variable exceeding the damage threshold is marked as a damage risk type.

[0098] S5, extract the associated geological parameters from the initial BIM model, and combine the high-risk components to generate a parameter adjustment instruction set through a multi-objective optimization algorithm with structural safety and construction cost as constraint conditions, please refer to Figure 4 .

[0099] S5.1, based on the high-risk components and the initial BIM model, through the principle of spatial proximity, the spatial range is determined in the initial BIM model with the geometric center of the high-risk component as the center and the engineering influence as the radius, all geological attribute parameters within the spatial range generated by Kriging interpolation are extracted and associated to form a risk-parameter association diagram.

[0100] Specifically, based on the high-risk components and the initial BIM model, by the principle of spatial proximity, the geometric center of the high-risk components is taken as the center, and the engineering influence radius is taken as an example of 5 meters to determine the spatial range, and all geological attribute parameters generated by the Kriging interpolation within the spatial range are extracted, including the horizontal principal stress value and the damage factor value.

[0101] The high-risk components are associated with the extracted horizontal principal stress value and damage factor value, the high-risk components are taken as nodes, the node attributes include the type, risk level of the high-risk components, and the associated horizontal principal stress value and damage factor value, the edges are established between the high-risk component nodes that exist physical connection or spatial adjacency, the edge attributes include the connection type and the influence coefficient, and the risk-parameter association graph is formed.

[0102] It should be noted that the geological attribute parameter extraction process: taking the geometric center of the high-risk components as the center, and taking the engineering influence radius as the search radius, all the horizontal principal stress values and damage factor values generated by the Kriging interpolation within the spatial range are extracted, and the average value of the geological attribute parameters is taken as the associated parameters of the high-risk components.

[0103] S5.2, input the risk-parameter association graph into the multi-objective optimization algorithm, and perform collaborative decision-making to obtain the high-risk component parameter adjustment set, and perform structured binding in the initial BIM model to form the parameter adjustment instruction set.

[0104] Specifically, the risk-parameter association graph is input into the multi-objective optimization algorithm, and the risk-parameter association graph is collaboratively decided based on the deep reinforcement learning algorithm, the deep reinforcement learning algorithm adopts a multi-agent collaborative optimization framework, each high-risk component is taken as an independent agent, and the parameter adjustment scheme is output through the strategy network to obtain the high-risk component parameter adjustment set, which includes the design parameter adjustment value of each high-risk component.

[0105] The structured binding is performed in the initial BIM model, and the structured binding is performed by associating and mapping each adjustment value in the high-risk component parameter adjustment set with the unique identifier of the corresponding component in the initial BIM model to form the parameter adjustment instruction set.

[0106] It should be noted that the deep reinforcement learning algorithm execution process: each agent observes the state of the adjacent nodes in the risk-parameter association graph, generates actions (parameter adjustment amounts) through the strategy network, and all agents collaboratively maximize the long-term reward function, which includes the safety performance improvement and the cost control index.

[0107] The structured binding process: traverse each entry in the high-risk component parameter adjustment set, locate the corresponding component in the initial BIM model according to the component unique identifier, and establish the mapping relationship between the adjustment value and the component object.

[0108] Parameter adjustment instruction set generation: serialize the identifier, type, pre-adjustment parameter value, and post-adjustment parameter value of each component in JSON format to form a parameter adjustment instruction set.

[0109] S6, the parameter adjustment instruction set is stored in the blockchain, and an encrypted instruction block is generated.

[0110] S6.1, based on the parameter adjustment instruction set, calculate the hash value, and use the private key to digitally sign the hash value, upload the parameter adjustment instruction set to the distributed storage network, and obtain a unique content identifier.

[0111] Specifically, based on the parameter adjustment instruction set, the SHA-256 algorithm is used to calculate the 256-bit hash value of the JSON string of the parameter adjustment instruction set.

[0112] The hash value is digitally signed using a private key, and the digital signature uses the ECDSA elliptic curve digital signature algorithm. The private key of the construction party is used to sign the hash value to generate a digital signature; the parameter adjustment instruction set is uploaded to the distributed storage network, and the distributed storage network uses the IPFS protocol. The parameter adjustment instruction set is uploaded to the IPFS network in the form of a file to obtain a unique content identifier.

[0113] It should be noted that the hash value calculation process: the JSON string of the parameter adjustment instruction set is UTF-8 encoded, and the SHA-256 hash function is iterated to output a fixed-length 256-bit hash value.

[0114] The digital signature generation process: using the elliptic curve digital signature algorithm, taking the private key of the construction party as the input, performing encryption operation on the hash value to generate a digital signature.

[0115] IPFS upload process: The parameter adjustment instruction set file is divided into data blocks with a size of 256KB, and each data block is hashed to build a Merkle DAG structure to obtain a root hash as a content identifier.

[0116] S6.2, based on the parameter adjustment instruction set and the dynamic risk threshold set, through the privacy protection verification logic, generate a compliance proof, assemble the hash value, digital signature, unique content identifier and compliance proof into a blockchain transaction, and generate an encrypted instruction block after consensus verification.

[0117] Specifically, based on the parameter adjustment instruction set and the dynamic risk threshold set, compliance proof is generated through privacy protection verification logic. The privacy protection verification logic adopts a zero-knowledge proof algorithm to verify that the risk state tensor components corresponding to all adjustment schemes in the parameter adjustment instruction set are lower than the dynamic risk threshold set. The hash value, digital signature, unique content identifier and compliance proof are assembled into a blockchain transaction. The blockchain transaction contains the structured fields of these data. After consensus verification, an encrypted instruction block is generated.

[0118] Hash values ​​are generated using SHA-256 and digitally signed using the construction party's private key (ECDSA algorithm) to ensure data integrity and identity trustworthiness. The instruction set file is uploaded to the IPFS distributed network to obtain a unique Content Identifier (CID). In current engineering construction information management, construction instructions are usually transmitted and recorded through paper documents, emails, or centralized project management platforms, which are prone to tampering, deletion of operation records, unclear responsibilities when collaborating with multiple parties, and lack of effective anti-counterfeiting verification mechanisms. In contrast, this invention uses zero-knowledge proof technology to generate compliance proofs, verifying that the adjustment scheme meets security threshold requirements without disclosing sensitive information. The hash value, signature, CID, and compliance proof are encapsulated into a blockchain transaction, and an encrypted instruction block is generated after consensus, realizing trusted storage and tamper-proof distribution of instructions.

[0119] S7 uses a lightweight algorithm to perform hierarchical compression of encrypted instruction blocks and outputs a lightweight cooperative instruction set.

[0120] S7.1, based on the content identifier in the encrypted instruction block, retrieves and parses the parameter adjustment instruction set from the distributed storage network, converts the parameter adjustment instruction set into a semantic association graph structure representing the relationship between components, and generates a low-dimensional semantic vector through a neural network encoder.

[0121] Specifically, based on the content identifier in the encrypted instruction block, the parameter adjustment instruction set is retrieved and parsed from the distributed storage network. The content identifier is used as an index to locate and obtain the parameter adjustment instruction set file in the IPFS protocol network. The JSON format file is parsed into structured data containing component identifier, component type, original parameter value and adjusted parameter value.

[0122] The parameter adjustment instruction set is converted into a semantic association graph structure that represents the relationship between components. The semantic association graph structure uses each component in the parameter adjustment instruction set as a node. The node attributes include component type, original parameter value and adjusted parameter value. According to the spatial topology relationship in the initial BIM model, edges are established between component nodes that have physical connection or spatial distance less than 2 meters of the example value. The edge attributes include connection type and spatial distance.

[0123] Generating low-dimensional semantic vectors by a neural network encoder: the neural network encoder adopts a graph autoencoder architecture, and the graph autoencoder part includes a three-layer graph convolutional network, each layer using a ReLU activation function, and the output layer compresses the graph features into a low-dimensional semantic vector.

[0124] S7.2, according to the terminal performance and network state, selecting a corresponding hierarchical compression strategy to quantize and encode the low-dimensional semantic vector to obtain a discrete symbol sequence.

[0125] Specifically, based on the hierarchical terminal performance, the low-dimensional semantic vector is quantized and encoded: the components of the low-dimensional semantic vector are mapped to the nearest discrete values of the selected codebook level (example: a 64-level codebook maps the value 0.73 to the 47th discrete value), a binary symbol sequence is generated, and then compressed according to the hierarchical network state, outputting a discrete symbol sequence.

[0126] It should be noted that according to the combination of engineering experience criteria and communication industry standards, different codebook levels are selected according to the hierarchical terminal performance: for example, a 256-level codebook quantization is selected when GPU memory is ≥8GB and CPU core is ≥8; a 128-level codebook quantization is selected when GPU memory is 4-8GB and CPU core is 4-8; and a 64-level codebook quantization is selected when GPU memory is <4GB and CPU core is <4.

[0127] Different compression modes are selected according to the hierarchical network state: for example, lossless compression (LZ77 algorithm) is enabled when the bandwidth is ≥100Mbps; lossy compression (compression ratio 0.7) is enabled when the bandwidth is 10-100Mbps; and lossy compression (compression ratio 0.5) is enabled when the bandwidth is <10Mbps.

[0128] S7.3, based on the hierarchical compression strategy, extracting the corresponding decoder identifier and codebook identifier from the decoding element database, and packaging the discrete symbol sequence, the decoder identifier and the codebook identifier to generate a lightweight collaborative instruction set.

[0129] Specifically, based on the hierarchical compression strategy, the corresponding decoder identifier and codebook identifier are extracted from the decoding element database: according to the codebook level (example: 64-level codebook) and compression mode selected by the hierarchical compression strategy, the matching decoder version identifier and codebook identifier are queried in the decoding element database; the discrete symbol sequence, the decoder identifier and the codebook identifier are packaged into a JSON format data structure, the JSON structure includes three fields: symbol_sequence: stores the discrete symbol sequence; decoder_id: stores the decoder identifier; codebook_id: stores the codebook identifier; and generates a lightweight collaborative instruction set.

[0130] It should be noted that based on the initial BIM model and the parameter adjustment instruction set data, different versions of neural network decoders are trained, and a multi-level codebook is generated through K-means clustering, and a fixed mapping relationship is established between the compression strategy and the corresponding decoder identifier and codebook identifier, and a decoding element database is obtained.

[0131] S8, distribute the lightweight collaborative instruction set to the AR terminal through the 5G-UWB network, and superimpose and display the geological risk area and the component change scheme in the construction scene.

[0132] S8.1, transmit the lightweight collaborative instruction set to the target AR terminal through the 5G-UWB heterogeneous network, and perform neural decoding on the discrete symbol sequence to reconstruct the parameter adjustment instruction information, and obtain the geological risk area and the component change scheme.

[0133] Specifically, based on the lightweight collaborative instruction set, the downlink is dynamically selected through the 5G-UWB heterogeneous network to the target AR terminal (UWB link is enabled when the terminal distance from the UWB base station is ≤10 meters, otherwise 5G millimeter wave link is enabled); the decoder identifier and the codebook identifier in the lightweight collaborative instruction set are parsed in the AR terminal, and the neural network decoder and the corresponding codebook are added, the neural network decoder reconstructs the discrete symbol sequence into a semantic associated graph structure through a three-layer graph deconvolution network, and outputs the reconstructed parameter adjustment instruction information.

[0134] From the reconstructed parameter adjustment instruction information, the component spatial coordinate set with the risk type of "stress risk type" or "seepage risk type" is extracted as the geological risk area, and the adjusted parameter values of all components are extracted to generate the component change scheme.

[0135] S8.2, calculate the real-time high-precision pose of the AR terminal through the tightly coupled sensor fusion algorithm, and combine the parameter adjustment instruction information to superimpose and display the geological risk area and the component change scheme in the terminal field of view.

[0136] Specifically, the real-time high-precision pose of the AR terminal is calculated through the tightly coupled sensor fusion algorithm: the UWB positioning signal and the visual-inertial data are combined, the UWB positioning signal calculates the absolute coordinates of the terminal through the TDOA time difference algorithm, the visual-inertial data extracts feature points and tracks the pose changes through the ORB-SLAM3 algorithm, and the UWB absolute coordinates and the visual-inertial relative pose are fused using an extended Kalman filter, and a 6-degree-of-freedom pose is output.

[0137] In combination with the parameter adjustment instruction information, the geological risk area and the component change scheme are superimposed and displayed in the terminal field of view: geological risk area rendering, positioning the component space coordinates of the risk type as "stress risk type" or "seepage risk type" in the reconstructed parameter adjustment instruction information, generating a semi-transparent warning area centered on the component space coordinates (example: stress risk with red pulse cover, seepage risk with blue ripple cover), and the warning area radius is dynamically adjusted according to the risk level.

[0138] Component change scheme rendering: superimpose a green semi-transparent BIM model (such as the adjusted anchor rod entity) at the corresponding component position, and suspend the parameter change value; the rendering process uses perspective projection transformation to map three-dimensional coordinates to terminal screen coordinates, and uses OpenGL ES 3.0 to realize real-time rendering.

[0139] Through 5G-UWB heterogeneous network dynamic selection of the optimal link, the lightweight instruction set is transmitted to the AR terminal, and the neural network decoder is used to reconstruct the semantic information; then the UWB positioning and visual-inertial data are tightly coupled and fused to calculate the high-precision terminal pose, and finally the geological risk area and the component adjustment scheme are accurately visualized in the AR field of view in the form of dynamic color and model superposition, guiding the on-site construction. The prior art usually adopts relatively independent steps: design coordination through BIM model, offline or near real-time safety monitoring relying on sensors, and warning information in the form of two-dimensional charts or screen alarms. In contrast, the present scheme ensures lossless and real-time information through dynamic heterogeneous network and intelligent decoding; through UWB, visual-inertial tight coupling fusion, the industry problem of accurate positioning in large space is solved, and the AR information is registered with centimeter-level precision; finally, the abstract risk data and drawing scheme are converted into immersive, visual three-dimensional guidance, directly superimposed on the real environment, fundamentally eliminating information misreading delay, and realizing truly intelligent human-machine cooperation and safe and controllable intelligent construction.

[0140] Please refer to Figure 2The embodiment also provides an engineering collaborative design system based on BIM technology, comprising a BIM generation module, a monitoring module, an adjustment module, a block module, a collaboration module and a display module; the BIM generation module is used for collecting multi-source heterogeneous data and converting the multi-source heterogeneous data into standardized geological attribute parameters, generating an initial BIM model based on a spatial topology matching principle; the monitoring module is used for solving a surrounding rock-supporting structure multi-physical field coupling equation based on the initial BIM model in real time, outputting a risk state tensor, and marking a high-risk component when any component in the risk state tensor exceeds a dynamic risk threshold set; the adjustment module is used for extracting associated geological parameters from the initial BIM model, combining the high-risk component, and generating a parameter adjustment instruction set by taking a multi-objective optimization algorithm and taking structure safety and construction cost as constraint conditions; the block module is used for block chain notarization on the parameter adjustment instruction set, and generating an encrypted instruction block; the collaboration module is used for hierarchical compression on the encrypted instruction block by using a lightweight algorithm, and outputting a lightweight collaborative instruction set; and the display module is used for distributing the lightweight collaborative instruction set to an AR terminal through a 5G-UWB network, and superimposedly displaying a geological risk area and a component change scheme in a construction scene.

[0141] The embodiment also provides a computer device, comprising a memory and a processor; the memory is used for storing computer executable instructions, and the processor is used for executing the computer executable instructions to implement the engineering collaborative design method based on BIM technology provided in the above embodiment.

[0142] The computer device can be a terminal, and the computer device comprises a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. The processor of the computer device is used for providing computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used for wired or wireless communication with external terminals. The wireless communication can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device. The input device of the computer device can also be an external keyboard, touchpad or mouse, etc.

[0143] The embodiment also provides a storage medium on which a computer program is stored, the program being executed by a processor to implement the engineering collaborative design method based on the BIM technology proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a magnetic disk, or an optical disk.

[0144] To sum up, the application realizes accurate integration and structured modeling of geological information, improves the uniformity of basic data and the accuracy of model construction by converting multi-source heterogeneous data into standardized geological attribute parameters and generating an initial BIM model based on the spatial topological matching principle; the application realizes high-precision and real-time simulation analysis of the dynamic coupling of multiple physical fields in complex underground engineering by solving the surrounding rock-supporting structure multi-physical field coupling equation and outputting the risk state tensor in real time on the basis of the initial BIM model, can identify high-risk components in time, and significantly improves the timeliness and scientificity of risk warning, not only enhances the mapping capability of the BIM model to the real geological environment, but also realizes the deep integration and systematic improvement of intelligentization, safety and traceability of engineering collaborative design as a whole.

[0145] It should be noted that the above embodiments are only used to illustrate the technical solutions of the application but not limit the application, although the application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the application, and all should be covered in the scope of the claims of the application.

Claims

1. A collaborative engineering design method based on BIM technology, characterized in that: include: Collect multi-source heterogeneous data and transform it into standardized geological attribute parameters. Based on the principle of spatial topology matching, generate an initial BIM model. Based on the initial BIM model, the multiphysics coupling equation of the surrounding rock-support structure is solved in real time, and the risk state tensor is output. When any component of the risk state tensor exceeds the dynamic risk threshold set, the high-risk component is marked. Relevant geological parameters are extracted from the initial BIM model and combined with high-risk components. A multi-objective optimization algorithm is used to generate a parameter adjustment instruction set under the constraints of structural safety and construction cost. The parameter adjustment instruction set is stored on the blockchain to generate encrypted instruction blocks. A lightweight algorithm is used to perform hierarchical compression on the encrypted instruction block, and output a lightweight cooperative instruction set. The lightweight collaborative instruction set is distributed to AR terminals through the 5G-UWB network, and geological risk areas and component change plans are overlaid and displayed in the construction scene. The specific steps for outputting the risk state tensor are as follows: Geometric parameters of support components and real-time data of surrounding rock are extracted from the initial BIM model. Stress field equations are obtained by establishing the stress balance between the strain value of surrounding rock and the force balance of the support structure. The seepage field equation is obtained by analyzing the impact effect of groundwater pressure on the support structure. The damage field equation is obtained by quantifying the rock mass fracture propagation and support fatigue accumulation. The stress field equation, seepage field equation, and damage field equation are integrated to obtain the multi-physics field coupling equation of the surrounding rock-support structure. Using the tunnel lining surface in the initial BIM model as the boundary, a tunnel mesh is generated. The damage field equation is solved by quantum annealing, and the stress field equation and seepage field equation are solved by GPU. The risk state tensor is output. The specific steps for marking high-risk components are as follows: Collect rock mass creep rate, tunnel exposure time, rock mass score, and microseismic event frequency; Based on the rock mass creep rate and the roadway exposure time, the stress threshold is calculated by nonlinear function. The damage threshold is obtained by a graded down-adjustment mechanism according to the frequency of microseismic events. The seepage threshold is calculated based on the rock mass score and pore water pressure. The stress threshold, damage threshold and seepage threshold are integrated into a dynamic risk threshold set. When any component of the risk state tensor exceeds the corresponding dynamic risk threshold set, locate the spatial coordinates of the corresponding roadway grid and attach the risk type to obtain the high-risk component. The specific steps for generating the parameter adjustment instruction set are as follows: Based on high-risk components and the initial BIM model, and through the principle of spatial proximity, the spatial range is defined in the initial BIM model with the geometric center of the high-risk component as the center and the engineering impact as the radius. All geological attribute parameters generated by Kriging interpolation within the spatial range are extracted and associated to form a risk-parameter association map. The risk-parameter correlation diagram is input into a multi-objective optimization algorithm and collaborative decision-making is performed to obtain a set of parameter adjustment amounts for high-risk components. This set is then structurally bound in the initial BIM model to form a parameter adjustment instruction set. The damage field equation is solved by quantum annealing to obtain the damage variables. The stress field equation is solved iteratively by the preconditional conjugate gradient method to obtain the nodal displacement vector. The seepage field equation is calculated by Darcy's law to obtain the seepage velocity field. The stress tensor components of each roadway grid are calculated based on the displacement solution. The risk state tensor is assembled and output according to the roadway grid number by combining the seepage velocity components and the independently solved damage variables.

2. The engineering collaborative design method based on BIM technology as described in claim 1, characterized in that: The specific steps for converting multi-source heterogeneous data into standardized geological attribute parameters are as follows: Obtain surrounding rock strain values, rock mass fracture energy, and rock mass score values ​​to obtain multi-source heterogeneous data; The timestamps of multi-source heterogeneous data are aligned and spatially registered. The structural complexity weights of surrounding rock strain, rock mass fracture energy, and rock mass score are calculated using the Hausdorff dimension algorithm and then fused to generate standardized geological attribute parameters.

3. The engineering collaborative design method based on BIM technology as described in claim 2, characterized in that: The initial BIM model is generated based on the spatial topology matching principle. The specific steps are as follows: Analyze the anchor bolt endpoints and lining connection points in the tunnel design drawings and define them as topological nodes. Establish connection lines between adjacent topological nodes and label their attributes. The topological nodes and connection lines form a spatial topology map. Using the topological nodes in the spatial topology map as the target locations, the standardized geological attribute parameters are mapped to the target locations through the Kriging spatial interpolation algorithm. By analyzing the spatial correlation of neighboring geological points, the weights of neighboring topological nodes are calculated to obtain the horizontal principal stress and damage factor. By assigning the horizontal principal stress and damage factor as attribute values ​​to the corresponding topological nodes in the spatial topology map, a geological attribute topology map is obtained. A three-dimensional anchor bolt entity is created based on the topological nodes in the geological attribute topology map. At the same time, a tunnel lining surface is generated along the topological nodes, and the anchor bolt endpoints are seamlessly connected to the lining surface to form an initial BIM model.

4. The engineering collaborative design method based on BIM technology as described in claim 1, characterized in that: The specific steps for generating the encrypted instruction block are as follows: Based on the parameter tuning instruction set, calculate the hash value and digitally sign the hash value using the private key; upload the parameter tuning instruction set to the distributed storage network to obtain a unique content identifier; Based on the parameter adjustment instruction set and dynamic risk threshold set, compliance proof is generated through privacy protection verification logic. The hash value, digital signature, unique content identifier and compliance proof are assembled into a blockchain transaction and generated into an encrypted instruction block after consensus verification.

5. The engineering collaborative design method based on BIM technology as described in claim 1, characterized in that: The specific steps for outputting the lightweight collaborative instruction set are as follows: Based on the content identifier in the encrypted instruction block, the parameter adjustment instruction set is retrieved and parsed from the distributed storage network, and the parameter adjustment instruction set is converted into a semantic association graph structure representing the relationship between components. Then, a low-dimensional semantic vector is generated through a neural network encoder. Based on terminal performance and network status, select the corresponding hierarchical compression strategy to quantize and encode the low-dimensional semantic vector to obtain a discrete symbol sequence; Based on a hierarchical compression strategy, the corresponding decoder identifier and codebook identifier are extracted from the decoding metadata database. The discrete symbol sequence, decoder identifier, and codebook identifier are encapsulated to generate a lightweight cooperative instruction set.

6. The engineering collaborative design method based on BIM technology as described in claim 1, characterized in that: The specific steps for overlaying and displaying geological risk areas and component modification plans in the construction scenario are as follows: The lightweight collaborative instruction set is transmitted to the target AR terminal through a 5G-UWB heterogeneous network, and the discrete symbol sequence is neurally decoded to reconstruct the parameter adjustment instruction information, thereby obtaining the geological risk area and component change plan. The AR terminal's real-time high-precision pose is calculated using a tightly coupled sensor fusion algorithm, and combined with parameter adjustment command information, the geological risk area and component change plan are overlaid and displayed in the terminal's field of view.

7. A BIM-based collaborative engineering design system, based on the BIM-based collaborative engineering design method according to any one of claims 1 to 6, characterized in that: include: The BIM generation module is used to collect multi-source heterogeneous data and transform it into standardized geological attribute parameters. Based on the principle of spatial topology matching, it generates an initial BIM model. The monitoring module is used to solve the multiphysics coupling equation of the surrounding rock-support structure in real time based on the initial BIM model and output the risk state tensor. When any component of the risk state tensor exceeds the dynamic risk threshold set, the high-risk component is marked. The adjustment module is used to extract relevant geological parameters from the initial BIM model, combine them with high-risk components, and generate a set of parameter adjustment instructions through a multi-objective optimization algorithm, with structural safety and construction cost as constraints. The block module is used to store parameter adjustment instruction sets on the blockchain and generate encrypted instruction blocks. The collaboration module is used to perform hierarchical compression of encrypted instruction blocks using a lightweight algorithm, and output a lightweight collaboration instruction set. The display module is used to distribute a lightweight collaborative instruction set to the AR terminal via the 5G-UWB network, and overlay the geological risk area and component change plan in the construction scene.

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