Intelligent management system for building construction based on BIM (Building Information Modeling)

Abstract

The invention provides an intelligent management system for building construction based on BIM, and relates to the technical field of building design, and the system comprises a data collection module which is used for obtaining various data in a building construction process in real time, and the data comprises construction progress, personnel information, material information and equipment state; the BIM model construction module is used for constructing a virtual three-dimensional model of the target building according to various data in the building construction process, and realizing management and simulation analysis of the building construction process; and the ontology knowledge base construction module is used for constructing a building construction safety risk knowledge base, the knowledge base comprises concepts, attributes, relationships and instances related to safety risks, and knowledge representation and inference rule formulation are carried out. According to the invention, the construction process can be comprehensively monitored in real time, the safety risk is effectively early warned, the resource allocation is optimized, and the management efficiency and safety of building construction are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of building design, and in particular to an intelligent management system for building construction based on BIM. Background Art

[0002] Traditional construction management relies on paper documents and manual communication. For example, key information such as construction progress reports, staffing tables, and bills of materials need to be filled out, printed, distributed, and archived manually. This process is not only time-consuming and labor-intensive, but also prone to human errors, such as data entry errors, missing information, or delayed delivery.

[0003] In a large-scale construction project, due to the large number of participants (such as designers, construction teams, suppliers, etc.), the chain of information transmission is long, which makes it easy for information to be distorted or lost during the transmission process. This may affect the accuracy of decision-making and even lead to construction delays or cost overruns.

[0004] Traditional construction management relies on experience and manual operations in resource allocation and scheduling. For example, the formulation of construction plans is based on the experience of project managers or construction workers, while the procurement and deployment of materials and equipment may rely on the intuitive judgment of the site manager. This approach may be effective in small or simple projects, but it is difficult to ensure the allocation of resources in large or complex projects.

[0005] For example, in a high-rise building project, due to changes in construction phases and the dynamic nature of personnel and material requirements, manual resource allocation may lead to material waste or idle equipment, thereby increasing construction costs. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide an intelligent management system for building construction based on BIM, thereby realizing information management of the entire life cycle of the building.

[0007] In order to solve the above technical problems, the technical solution of the present invention is as follows: First, the intelligent management system of building construction based on BIM includes: Data acquisition module, used to obtain various data in the construction process in real time, including construction progress, personnel information, material information, and equipment status; BIM model building module, which is used to build a virtual 3D model of the target building based on various data in the construction process, and realize the management and simulation analysis of the construction process; The ontology knowledge base construction module is used to construct a construction safety risk knowledge base, which contains concepts, attributes, relations and instances related to safety risks, and formulates knowledge representation and reasoning rules; The safety risk reasoning module is used to analyze the construction data in the virtual 3D model of the target building in real time according to the rules and logic in the ontology knowledge base, identify potential safety risks, and obtain corresponding early warning information and solutions; The construction resource optimization configuration module is used to automatically predict resource demand, optimize allocation and schedule management based on the construction plan in the BIM model, real-time personnel information, material information and equipment status; The user interaction module is used to query, browse, edit and manage various types of information and data, including construction progress information, personnel information, material information, equipment status information and safety risk warning information.

[0008] Furthermore, based on various data during the construction process, a virtual 3D model of the target building is constructed to achieve management and simulation analysis of the construction process, including: Determine the 3D modeling software and set up the modeling environment, including selecting the template type, setting the elevation and axis grid reference elements; Construct a virtual 3D model of the target building in the 3D modeling software, and use the tools and functions of the modeling software, including stretching, rotation, and Boolean operations, to create the main structure of the building, such as beams, slabs, columns, and walls; The constructed 3D model can be used to monitor and manage the construction progress, perform resource allocation and conflict detection, and realize the management and simulation analysis of the construction process.

[0009] Furthermore, a knowledge base of construction safety risks is constructed, which contains concepts, attributes, relations and instances related to safety risks, and formulates knowledge representation and reasoning rules, including: Acquire safety risk knowledge in the field of construction, extract key information related to safety risks, including risk types, occurrence conditions, and consequences, and obtain a safety risk knowledge dataset; Based on the safety risk knowledge dataset, a construction safety risk knowledge base is constructed. Each safety risk knowledge entry in the knowledge base contains concept, attribute, relationship and instance elements. According to the content and structure of the security risk knowledge base, reasoning rules are formulated, including the logic of risk matching, the criteria for determining risk levels, the setting of warning conditions and the generation rules of warning information.

[0010] Furthermore, according to the rules and logic in the ontology knowledge base, the construction data in the virtual 3D model of the target building is analyzed in real time to identify potential safety risks and obtain corresponding warning information and solutions, including: Extract real-time construction data from the virtual 3D model of the target building, including component status, construction progress, resource usage, and personnel activity trajectory; Treat each element in the real-time construction data, including components, tasks, and resources, as nodes in the network diagram. Construct the edges between nodes to form a construction network diagram; According to the connection relationship between nodes and the weight of the edge, the importance of each node is calculated to obtain the importance score of each node; According to historical data, an importance threshold is set, and the importance score of each node is compared with the threshold, and nodes with importance scores greater than the threshold are identified and regarded as high-risk nodes; If the importance score is greater than the set threshold of the node, and the security risk reaches the warning level, the corresponding warning information is generated according to the information in the ontology knowledge base, including the description of the risk, the scope of impact, and the recommended solutions.

[0011] Furthermore, the calculation formula for the importance score of each node is: ; in, Representation Node Importance score of , , , represents the weight coefficient; , Represents a node in the network; Representation Node The set of all in-degree nodes of ; Represents a slave node arrive The weight of the edge; Representation and Node The number of all connected edges; Representation Node Numerical value of importance in the network; Representation and Node The number of all connected edges; Representation and Node The number of all connected edges; Representation Node The clustering coefficient of Represents the total number of nodes in the network; Representation Node The clustering coefficient of Representation Node A numerical value of importance in the network.

[0012] Furthermore, based on the construction plan in the BIM model, the real-time acquired personnel information, material information and equipment status, resource demand forecasting, allocation optimization and scheduling management are automatically performed, including: Define decision variables, including the number of personnel, materials, and equipment usage time required for each construction task; Construct the objective function and set constraints; input the objective function and constraints into the linear programming solver to determine the final solution, i.e. the number of personnel, materials, and equipment usage time required for each construction task; Based on the final solution, a resource allocation and scheduling plan is generated, including the execution time of each construction task, the number of personnel required, the amount of materials, and the equipment usage time; During the construction process, the use of personnel, materials, and equipment resources is monitored in real time. If any deviation is found between the resource allocation or scheduling plan and the actual situation, the plan will be automatically adjusted to achieve automatic resource demand forecasting, allocation optimization, and scheduling management.

[0013] Furthermore, the calculation formula of the objective function is: ; in, represents the value of the objective function; Indicates the total number of construction tasks; Represents an index variable; Indicates Personnel unit cost for each construction task; Indicates The number of personnel required for each construction task; Indicates The growth rate of personnel costs per construction task; Indicates the unit cost of the material; Indicates The quantity of materials required for each construction task; Indicates The material cost growth rate of each construction task; Indicates the unit usage cost of the equipment; Indicates Equipment usage time required for each construction task; Indicates The growth rate of equipment cost per construction task; , represents the weight coefficient; Indicates The execution time of each construction task; Indicates The priority coefficient of each construction task; , , Indicates the total quantity limit of personnel, materials and equipment.

[0014] Second, the intelligent management method of building construction based on BIM includes: Obtain all kinds of data in the construction process in real time, including construction progress, personnel information, material information, and equipment status; According to various data in the construction process, a virtual 3D model of the target building is constructed to achieve management and simulation analysis of the construction process; Construct a knowledge base of construction safety risks, which includes concepts, attributes, relations and instances related to safety risks, and formulate knowledge representation and reasoning rules; According to the rules and logic in the ontology knowledge base, the construction data is analyzed in real time to identify potential safety risks and obtain corresponding early warning information and solutions; Automatically forecast resource demand based on construction plans, real-time personnel information, material information, and equipment status, and optimize and manage resource allocation based on safety risk warning information; Query, browse, edit and manage various types of information and data through the user interactive interface, including construction progress information, personnel information, material information, equipment status information and safety risk warning information.

[0015] The above solution of the present invention includes at least the following beneficial effects: The data acquisition module acquires all kinds of data in the construction process in real time, ensuring the timeliness and accuracy of the information. Through systematic data management, it avoids the information transmission delays and errors caused by traditional paper documents and manual communication, allowing construction managers to quickly grasp the real-time status of the construction site.

[0016] The BIM model building module builds a virtual 3D model of the target building based on the data during the construction process. This model not only provides construction managers with an intuitive and three-dimensional view of the building, but also supports simulation analysis of the construction process. By simulating the effects of different construction plans, managers can optimize the construction plan and reduce uncertainty and risks during the construction process.

[0017] The ontology knowledge base construction module and the safety risk reasoning module together constitute the safety risk early warning mechanism of the system. The ontology knowledge base contains a wealth of knowledge about construction safety risks, while the safety risk reasoning module uses this knowledge to conduct real-time analysis of the construction data in the BIM model to identify potential safety risks. This enables construction managers to take measures in advance to effectively prevent safety accidents and ensure the safety of the construction site.

[0018] The construction resource optimization configuration module automatically predicts resource demand, optimizes allocation, and manages scheduling based on the construction plan in the BIM model and the real-time personnel information, material information, and equipment status. This function enables construction resources to be used more reasonably and efficiently, reduces resource waste and idleness, and reduces construction costs.

[0019] The user interaction module provides a friendly user interface, allowing construction managers to easily query, browse, edit and manage various information and data. Through this module, managers can quickly obtain the required information and make more accurate decisions, thus improving the efficiency and quality of construction management. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic diagram of an intelligent management system for building construction based on BIM provided by an embodiment of the present invention.

[0021] Figure 2 It is a flow chart of an intelligent management system for building construction based on BIM provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0023] like Figure 1 As shown, an embodiment of the present invention proposes an intelligent management system for building construction based on BIM, comprising the following steps: The data acquisition module 11 is used to obtain various data in the construction process in real time, including construction progress, personnel information, material information, and equipment status; The BIM model building module 12 is used to build a virtual three-dimensional model of the target building according to various data in the building construction process, so as to realize the management and simulation analysis of the building construction process; The ontology knowledge base construction module 13 is used to construct a construction safety risk knowledge base, which contains concepts, attributes, relationships and instances related to safety risks, and performs knowledge representation and reasoning rule formulation; The safety risk reasoning module 14 is used to analyze the construction data in the virtual three-dimensional model of the target building in real time according to the rules and logic in the ontology knowledge base, identify potential safety risks, and obtain corresponding early warning information and solutions; The construction resource optimization configuration module 15 is used to automatically perform resource demand forecasting, allocation optimization and scheduling management based on the construction plan in the BIM model, the personnel information, material information and equipment status obtained in real time; The user interaction module 16 is used to query, browse, edit and manage various types of information and data, including construction progress information, personnel information, material information, equipment status information and safety risk warning information.

[0024] In the embodiment of the present invention, various data in the construction process are obtained in real time, including construction progress, personnel information, material information, and equipment status, ensuring the timeliness and accuracy of the information. Through automated data collection, errors and omissions in manual input are reduced, and the reliability and consistency of data are improved.

[0025] Based on various data in the construction process, a virtual 3D model of the target building is constructed to achieve visual management of the construction process. The BIM model supports simulation analysis of the construction process, helping construction managers to discover potential problems in advance and optimize construction plans. As a platform for information sharing, the BIM model promotes collaboration and communication between various links such as design, construction, and operation.

[0026] A knowledge base of construction safety risks was constructed, which contains a wealth of safety risk-related concepts, attributes, relationships, and instances. The knowledge representation and reasoning rules in the knowledge base provide a basis for the safety risk reasoning module, improving the accuracy and efficiency of safety risk identification. The ontology knowledge base is scalable and can be continuously updated and improved according to actual needs to meet the needs of different construction projects.

[0027] According to the rules and logic in the ontology knowledge base, the construction data in the BIM model is analyzed in real time to identify potential safety risks. Early warning information and solutions are generated in a timely manner to help construction managers take measures in advance to prevent safety accidents, improve the safety of the construction site, and protect the lives and property of construction workers.

[0028] According to the construction plan in the BIM model and the real-time personnel information, material information and equipment status, resource demand forecasting, allocation optimization and scheduling management are automatically carried out, which improves the utilization efficiency of construction resources, reduces resource waste and idleness, and reduces construction costs. By optimizing resource allocation, the smooth progress of the construction process is ensured, and the construction efficiency and quality are improved.

[0029] It provides a friendly user interface, allowing construction managers to easily query, browse, edit and manage various information and data, improving the user interaction experience, allowing construction managers to obtain the required information more quickly and accurately and make decisions. Through the user interaction module, construction managers can understand the status of the construction site in real time, and keep abreast of construction progress, safety risks and other information, providing strong support for construction management.

[0030] In a preferred embodiment of the present invention, various data in the construction process are obtained in real time, including construction progress, personnel information, material information, and equipment status, which may include: In the embodiment of the present invention, the data types that need to be collected are clearly defined: construction progress (such as the completion time of each process, the remaining workload, etc.), personnel information (such as the name of the worker, the type of work, attendance, etc.), material information (such as the name of the material, the quantity, the consumption, etc.), and the equipment status (such as the equipment name, the operating status, the maintenance record, etc.). Design the data acquisition layer, the data transmission layer, the data processing layer, the data storage layer, and the application layer. Install cameras at the construction site, obtain construction progress information through video recognition or location tracking technology, equip each worker with an RFID wristband or smart card, and record attendance through the access control system or attendance machine. Install weight sensors or RFID tags at the material warehouse or construction site to record the entry and exit of materials in real time, and use a barcode scanner to quickly obtain material information. Install sensors on the equipment to monitor the equipment's operating status, energy consumption, temperature and other parameters, and record the equipment's maintenance records and fault alarm information.

[0031] In a preferred embodiment of the present invention, a virtual three-dimensional model of a target building is constructed based on various data in the construction process to achieve management and simulation analysis of the construction process, which may include: Determine the 3D modeling software and set up the modeling environment, including selecting the template type, setting the elevation and axis grid reference elements; Construct a virtual 3D model of the target building in the 3D modeling software, and use the tools and functions of the modeling software, including stretching, rotation, and Boolean operations, to create the main structure of the building, such as beams, slabs, columns, and walls; The constructed 3D model can be used to monitor and manage the construction progress, perform resource allocation and conflict detection, and realize the management and simulation analysis of the construction process.

[0032] In the embodiment of the present invention, the mainstream 3D modeling software on the market, such as Autodesk Revit, is analyzed by algorithms, and the most suitable 3D modeling software for building construction process management is selected based on factors such as software compatibility, function richness, and user evaluation. A new project is created in the selected software, and the template type, such as building template, structural template, etc., is selected according to project requirements. The elevation and axis grid reference elements are set, and the vertical height and horizontal layout of the building are defined in three-dimensional space by entering precise values ​​or using the tools provided by the software.

[0033] Use the modeling software's stretching tools to stretch out building components such as beams, plates, and columns in three-dimensional space based on the dimensional information in the design drawings. Use the rotation tool to rotate certain components (such as cylinders, cones, etc.) to a specified angle based on the design requirements. Apply Boolean operations to perform union, difference, or intersection operations on components to create complex geometric shapes, such as door and window openings in walls. Continue to add architectural details such as stairs, handrails, decorative lines, etc. to ensure the accuracy and authenticity of the model, and use the software's material and texture tools to give the model a realistic appearance.

[0034] Import the construction schedule into the 3D model and associate each construction process with the components in the model through the software's timeline function. Update the progress status in the 3D model in real time based on the construction progress data collected on site, such as marking completed, ongoing, or pending processes. Use the 3D model to simulate resource allocation and optimize the configuration of manpower, materials, and equipment. Use collision detection algorithms to identify potential conflicts in the construction process, such as collisions between components and conflicts in construction sequence, and provide solutions.

[0035] Suppose you want to build a 3D model of a high-rise residential building and manage the construction process. You choose Autodesk Revit as the modeling software and create a building model project. Set the elevation of the first floor to ±0.000, the height of each floor to 3 meters, and set a total of 20 floors. Define the axis grid base element, and divide the horizontal and vertical axes at intervals of 10 meters. Use the stretch tool to create beams, plates, and columns to form the basic framework of the residential building. Use the rotation tool to create circular columns in the stairwell. Apply Boolean operations to open door and window openings on the wall, add detailed elements such as balconies and bay windows, and give them materials and textures. Import the construction schedule into the model and associate each process with the floor and components. Monitor the construction progress on site in real time and update the progress status in the model. Through collision detection, a conflict between the steel bars and the water and electricity pipelines was found, and an adjustment plan was provided.

[0036] The 3D model provides managers with an intuitive and comprehensive view of the construction, helping them to quickly understand the construction progress and resource allocation, so as to make more efficient decisions. Through functions such as collision detection, potential conflicts and risks in the construction process can be discovered and resolved in a timely manner, reducing rework and delays, and reducing construction costs. The 3D model can accurately reflect the design intent and construction requirements, helping construction personnel to accurately understand and execute construction drawings and improve construction quality. The 3D model can be conveniently used to communicate with owners, design units, construction units and other parties, helping all parties to better understand the construction plan and requirements and reduce misunderstandings and disputes. By simulating and analyzing resource consumption and environmental impact during the construction process, the construction plan can be optimized, waste and pollution can be reduced, and sustainable development can be supported.

[0037] In a preferred embodiment of the present invention, a construction safety risk knowledge base is constructed, which contains concepts, attributes, relationships and instances related to safety risks, and performs knowledge representation and reasoning rules formulation, which may include: Acquire safety risk knowledge in the field of construction, extract key information related to safety risks, including risk types, occurrence conditions, and consequences, and obtain a safety risk knowledge dataset; Based on the safety risk knowledge dataset, a construction safety risk knowledge base is constructed. Each safety risk knowledge entry in the knowledge base contains concept, attribute, relationship and instance elements. According to the content and structure of the security risk knowledge base, reasoning rules are formulated, including the logic of risk matching, the criteria for determining risk levels, the setting of warning conditions and the generation rules of warning information.

[0038] In an embodiment of the present invention, text data related to safety risks are collected from professional websites, paper databases, industry reports and other sources in the field of construction. At the same time, safety risk event reports can also be collected from actual construction projects as instance data. Natural language processing technology (such as named entity recognition and relationship extraction) is used to parse the collected text data to extract key information related to safety risks. The key information includes risk types (such as falling from heights, being hit by objects, electric shock, etc.), occurrence conditions (such as weather conditions, construction environment, personnel operations, etc.), and consequences (such as casualties, property losses, etc.). The extracted key information is cleaned and sorted to remove duplicate, erroneous or irrelevant information to form a safety risk knowledge data set.

[0039] According to the content of the security risk knowledge data set, each security risk knowledge entry in the knowledge base is designed. The knowledge entry contains concepts (such as risk type, occurrence conditions, consequences, etc.), attributes (such as description of risk type, detailed list of occurrence conditions, severity of consequences, etc.), relationships (such as the association between risk type and occurrence conditions, causal relationship between consequences and risk type, etc.) and instances (such as specific security risk events). Using database or knowledge graph technology, the designed knowledge entries are stored in the knowledge base, which supports knowledge addition, deletion, modification and query operations.

[0040] According to the risk types and occurrence conditions in the knowledge base, logical rules for risk matching are formulated. When a new safety risk event occurs, the relevant safety risk knowledge items can be quickly located by matching logical rules. According to the severity of the consequences in the knowledge base, the criteria for determining the risk level are formulated. The risk level can be divided into three levels: high, medium, and low. According to the risk types and occurrence conditions in the knowledge base, combined with the actual situation of the construction site, early warning conditions are set. When the construction site meets the early warning conditions, the early warning mechanism is triggered. According to the early warning conditions and risk levels, rules for generating early warning information are formulated. The early warning information includes key information such as risk type, occurrence conditions, consequences, risk level, and corresponding response measures and suggestions.

[0041] Suppose you want to build a knowledge base containing the safety risks of falling from heights and formulate corresponding inference rules.

[0042] Concept: Falling from a height; Attributes: Described as an accident involving an unexpected fall from a height; conditions for occurrence include lack of protective facilities, improper operation, etc.; consequences include casualties, disability, etc.

[0043] Relationship: There is a correlation between falling from height and the lack of protective facilities and improper operation; casualties and disability are the consequences of falling from height.

[0044] Example: A height fall accident occurred at a construction site because a worker did not wear a safety belt and fell from the fifth floor, resulting in serious injuries.

[0045] Reasoning rule formulation: Risk matching logic: When there are no protective facilities or workers operate improperly at the construction site, the risk of falling from height is matched. According to the casualties, the risk of falling from height is divided into three levels: high, medium and low. When there are no protective facilities at the construction site and workers are working at height, the early warning mechanism is triggered to generate early warning information including risk type (falling from height), occurrence conditions (no protective facilities, workers are working at height), consequences (possible casualties), risk level (determined according to the specific situation) and response measures (strengthening protective facilities, improving workers' safety awareness, etc.).

[0046] By building a knowledge base and formulating reasoning rules, we can quickly identify, evaluate and warn of construction safety risks and improve safety management efficiency. By timely warning and taking corresponding countermeasures, we can reduce the probability and severity of safety accidents and protect the lives of construction workers. The knowledge base can easily store and share knowledge and experience related to safety risks, and promote the sharing and inheritance of knowledge among construction workers. The knowledge base and reasoning rules can provide decision-making support for construction managers, helping them to formulate more scientific and reasonable safety management measures and emergency plans.

[0047] In a preferred embodiment of the present invention, according to the rules and logic in the ontology knowledge base, the construction data in the virtual 3D model of the target building is analyzed in real time to identify potential safety risks and obtain corresponding warning information and solutions, which may include: Extract real-time construction data from the virtual 3D model of the target building, including component status, construction progress, resource usage, and personnel activity trajectory; Treat each element in the real-time construction data, including components, tasks, and resources, as nodes in the network diagram. Construct the edges between nodes to form a construction network diagram; According to the connection relationship between nodes and the weight of the edge, the importance of each node is calculated to obtain the importance score of each node; According to historical data, an importance threshold is set, and the importance score of each node is compared with the threshold, and nodes with importance scores greater than the threshold are identified and regarded as high-risk nodes; If the importance score is greater than the set threshold of the node, and the security risk reaches the warning level, the corresponding warning information is generated according to the information in the ontology knowledge base, including the description of the risk, the scope of impact, and the recommended solutions.

[0048] In an embodiment of the present invention, a data extraction tool is used to obtain real-time construction data from a model, including component status (such as location, integrity), construction progress (such as completed part, remaining part), resource usage (such as material consumption, equipment usage status) and personnel activity trajectory (such as worker location, movement path). According to the type of real-time construction data, a node type is defined, such as a component node, a task node, a resource node, etc. A corresponding node is created for each real-time construction data element, and a unique identifier and attributes (such as name, type, status, etc.) are given to the node. The created node is stored in a data structure (such as a list, a dictionary, or a database).

[0049] Define the type of edge based on the relationship between nodes, such as dependency, resource usage, personnel activity, etc. Create edges based on the actual relationship between nodes, and give them unique identifiers and attributes (such as weight, direction, etc.). Combine nodes and edges to build a construction network diagram. You can use graph theory algorithm libraries (such as NetworkX) to build and visualize the network diagram. Select a suitable importance calculation method, such as PageRank, based on the characteristics and requirements of the network diagram. Use the selected importance calculation method to calculate the importance score of each node, and store the calculated importance score in the node's attributes.

[0050] By analyzing the accidents and dangerous situations in the historical construction data, the importance threshold is determined. According to the analysis results, a reasonable importance threshold is set. The importance score of each node is compared with the threshold, and the nodes with importance scores > the threshold are identified as high-risk nodes. According to the attributes of the high-risk nodes and the real-time construction data, it is judged whether the safety risk reaches the warning level. If the safety risk reaches the warning level, the corresponding warning information is generated according to the information in the ontology knowledge base, including the description of the risk, the scope of impact, etc.

[0051] Generate solutions for early warning information based on the rules and logic in the ontology knowledge base.

[0052] Assume that the target building is an office building under construction, and its virtual 3D model contains real-time construction data: A floor slab of a certain floor has been poured, but there are quality problems with the steel bar binding. 50% of the construction tasks of this floor have been completed. 80% of the steel bars and 60% of the concrete have been consumed on this floor, and two workers are doing the steel bar binding work on this floor. Component node (floor slab), task node (steel bar binding, concrete pouring), resource node (steel bar, concrete), personnel node (worker). Dependency relationship (concrete pouring can only be done after steel bar binding is completed), resource usage relationship (steel bar and concrete are used for floor slab construction), personnel activity relationship (workers are doing steel bar binding on the floor slab). Combine nodes and edges to form a construction network diagram. Use the PageRank algorithm to calculate the importance score of each node. Assume that the importance score of the floor slab node is 0.9, and the importance score of the steel bar binding task node is 0.8, and both are greater than the set threshold of 0.7. Nodes with importance scores greater than the threshold are identified as high-risk nodes, namely, the floor slab node and the steel bar binding task node. There are quality problems with the steel bar binding of the floor slab, which may lead to safety hazards of the floor slab structure, and the construction of the floor slab and the upper floor slab may be affected. Immediately stop the subsequent construction of the floor slab, rework the steel bar binding, and strengthen quality inspection.

[0053] By analyzing the construction data in the virtual 3D model in real time, potential safety risks can be discovered in time and corresponding solutions can be taken to improve construction safety. By building a construction network diagram and calculating the importance of nodes, resources can be allocated more reasonably to avoid resource waste and shortage. By identifying high-risk nodes and giving priority to them, delays and rework in the construction process can be reduced, and construction efficiency can be improved. The generated early warning information and solutions can provide powerful decision-making support for construction managers, helping them better deal with challenges and problems in the construction process.

[0054] In a preferred embodiment of the present invention, the calculation formula for the importance score of each node is: ; in, Representation Node Importance score of , , , represents the weight coefficient; , Represents a node in the network; Representation Node The set of all in-degree nodes of ; Represents a slave node arrive The weight of the edge; Representation and Node The number of all connected edges; Representation Node Numerical value of importance in the network; Representation and Node The number of all connected edges; Representation and Node The number of all connected edges; Representation Node The clustering coefficient of Represents the total number of nodes in the network; Representation Node The clustering coefficient of Representation Node A numerical value of importance in the network.

[0055] In the embodiment of the present invention, the weight coefficient is set to , , , . Traverse the network graph and determine the in-degree nodes of each node (i.e. the nodes pointing to the node) , and record these nodes. For each edge , get its weight (e.g., by edge type or flow setting).

[0056] Assume there are nodes: , , , , ; side , Type: Normal connection; Traffic / importance: Medium; Weight .

[0057] side , Type: Critical Path; Traffic / Importance: High; Weight .

[0058] side , Type: Secondary Connection; Traffic / Importance: Low; Weight .

[0059] side , Type: Normal connection; Traffic / importance: Medium; Weight .

[0060] side , Type: Critical Path; Traffic / Importance: Very High; Weight .

[0061] side , Type: Auxiliary connection; Traffic / importance: Low; Weight .

[0062] Calculate each node Degree , that is, with the node The number of all connected edges. For each node , calculate its clustering coefficient For example, if the node has 3 neighbors, and there are 2 edges between these neighbors, then For each node , calculate its importance score according to the formula ;in, Representation and Node The number of all connected edges; It can be determined based on other characteristics of the node (such as resource usage, personnel activity frequency, etc.). For example, if the node Is a key device, then Can be set to a higher value.

[0063] Assume there are nodes: , , , , ; node , Feature description: ordinary office equipment; Resource usage: medium (such as CPU usage, memory usage, etc.); Personnel activity frequency: medium (such as employee access frequency); Feature value

[0064] node ,Feature description: auxiliary storage device; resource usage: low (small amount of stored data, low access frequency); personnel activity frequency: low (a few people need to access); feature value

[0065] node ,Feature description: Critical server; Resource usage: High (processing a large number of requests, high CPU and memory usage); Personnel activity frequency: High (multi-department dependence, frequent access); Feature value

[0066] node ,Feature description: Secondary network device; Resource usage: Low (not much network traffic); Personnel activity frequency: Medium (some personnel need to access through network devices); Feature value

[0067] node ,Feature description: Core database server; Resource usage: Very high (storing a large amount of critical data, high I / O operations); Personnel activity frequency: Very high (frequent access by all company personnel); Feature value

[0068] calculate , which is the average of all node degrees and clustering coefficients in the network. Use an iterative method (such as the iterative process of the PageRank algorithm) to update the importance score of each node until convergence is reached. The convergence condition is set as the change in the importance score of the node in two consecutive iterations is less than a certain threshold (for example, 0.01). According to the calculated importance score , set an importance threshold (for example, 2). Compare the importance score of each node with the threshold, and identify nodes with importance scores greater than the threshold as high-risk nodes. For the identified high-risk nodes, determine whether the safety risk has reached the warning level based on the node attributes and real-time construction data. If the safety risk reaches the warning level, generate corresponding warning information based on the information in the ontology knowledge base, including a description of the risk, the scope of impact, etc. Generate solutions for the warning information based on the rules and logic in the ontology knowledge base. For example, if the high-risk node is the network interface of a key device, the solution may include strengthening network monitoring, adding backup equipment, etc.

[0069] Through the in-degree, degree, and clustering coefficient of the node, the formula can more accurately evaluate the importance of the node in the network, thereby improving the accuracy of risk identification. Real-time calculation of the importance score of the node and timely identification of high-risk nodes can help to discover potential safety risks in advance and enhance the timeliness of early warning. By identifying high-risk nodes and giving priority to them, resources can be allocated more reasonably, ensuring the safety of key nodes and avoiding waste and shortage of resources. The generated early warning information and solutions can provide strong decision-making support for construction managers, helping them better cope with challenges and problems in the construction process and improve the scientificity and effectiveness of decision-making. By continuously monitoring and evaluating the importance scores of nodes, safety hazards in the construction process can be discovered and corrected in a timely manner, promoting the improvement of construction safety management level.

[0070] In a preferred embodiment of the present invention, according to the construction plan in the BIM model, the personnel information, material information and equipment status acquired in real time, the resource demand forecasting, allocation optimization and scheduling management are automatically performed, which may include: Define decision variables, including the number of personnel, materials, and equipment usage time required for each construction task; Construct the objective function and set constraints; input the objective function and constraints into the linear programming solver to determine the final solution, i.e. the number of personnel, materials, and equipment usage time required for each construction task; Based on the final solution, a resource allocation and scheduling plan is generated, including the execution time of each construction task, the number of personnel required, the amount of materials, and the equipment usage time; During the construction process, the use of personnel, materials, and equipment resources is monitored in real time. If any deviation is found between the resource allocation or scheduling plan and the actual situation, the plan will be automatically adjusted to achieve automatic resource demand forecasting, allocation optimization, and scheduling management.

[0071] In the embodiment of the present invention, the construction plan in the BIM model is first read to identify all construction tasks and their related information. For each construction task, three decision variables are defined: the number of personnel, the number of materials, and the equipment usage time. These decision variables are initialized or set in range according to the specific requirements of the construction task (such as process complexity, engineering quantity, etc.).

[0072] Construct an objective function based on the construction goals (such as minimizing costs, minimizing construction time, etc.). The objective function is a mathematical expression used to calculate the construction cost or construction time for a given combination of decision variables. Set up constraints, such as the number of personnel cannot exceed the number of available personnel, the number of materials cannot exceed the inventory, and the equipment usage time cannot exceed the equipment availability time. Constraints are expressed in the form of mathematical inequalities and are used to limit the range of values ​​of decision variables. Input the constructed objective function and constraints into the linear programming solver. The linear programming solver is a mathematical optimization tool that can automatically find the final solution of the objective function that meets the constraints. Wait for the solver to return the final solution, that is, the number of personnel, the number of materials, and the equipment usage time required for each construction task.

[0073] According to the final solution returned by the solver, a resource allocation and scheduling plan is generated. The plan includes detailed information such as the execution time of each construction task, the number of personnel required, the number of materials, and the equipment usage time. The plan is output in a visual or textual form to facilitate management personnel to understand and execute. The use of personnel, materials, and equipment resources is monitored in real time through sensors, cameras, RFID and other technologies. Real-time data is collected and stored in the database. The actual resource usage is compared with the planned resource allocation in real time. If deviations are found, the resource allocation and scheduling plan are automatically adjusted according to the degree of deviation and the construction goals. The adjusted plan is input into the linear programming solver again for optimization to ensure the feasibility and optimality of the plan.

[0074] Assume that there is a construction task, including two subtasks: wall building and painting. According to the construction plan in the BIM model, define the following decision variables: The number of personnel required for the wall-building task (e.g., bricklayers, laborers); The quantity of materials (e.g. bricks, cement) required for the wall-building task; The time required to use equipment for the wall-building task (e.g., mixer); The number of personnel (e.g. painters) required for the painting task; The quantity of materials (e.g. paint) required for the painting task; The time that equipment (e.g., sprayer) is used for painting tasks.

[0075] Construct an objective function, such as minimizing construction costs, and set constraints, such as the number of personnel cannot exceed the available number of people, the number of materials cannot exceed the inventory, etc. Then, input the objective function and constraints into the linear programming solver to solve the final solution.

[0076] Based on the final solution, a resource allocation and scheduling plan is generated. For example, if the wall-building task is carried out in the morning, it requires 3 bricklayers, 2 laborers, 100 bricks, 50 bags of cement, and the mixer is used for 2 hours; the painting task is carried out in the afternoon, it requires 2 painters, 50 liters of paint, and the sprayer is used for 1 hour.

[0077] During the construction process, the use of personnel, materials, and equipment resources is monitored in real time. If it is found that the actual number of personnel used in the wall-building task is more than the planned number, the machine will automatically adjust the plan, such as postponing the painting task to the next day, or increasing the material supply for the wall-building task to meet the personnel needs.

[0078] By automatically forecasting resource demand, optimizing allocation, and managing scheduling, we can ensure that resources are used in the most reasonable way and avoid waste and idleness. By optimizing resource allocation and scheduling plans, we can reduce construction costs, such as reducing idle time for personnel and reducing material inventory costs. We can monitor resource usage in real time and automatically adjust plans based on actual conditions to ensure that construction tasks are completed on time and shorten construction periods.

[0079] Through precise resource allocation and scheduling management, machines can ensure that construction tasks are adequately resourced and supported, thereby improving construction quality. They can monitor the use of personnel and equipment in real time, promptly identify and address potential safety hazards, and thus enhance construction safety. Managing construction resources in an automated and intelligent way can improve construction management and reduce the workload of managers.

[0080] In a preferred embodiment of the present invention, the calculation formula of the objective function is: ; in, represents the value of the objective function; Indicates the total number of construction tasks; Represents an index variable; Indicates Personnel unit cost for each construction task; Indicates The number of personnel required for each construction task; Indicates The growth rate of personnel costs per construction task; Indicates the unit cost of the material; Indicates The quantity of materials required for each construction task; Indicates The material cost growth rate of each construction task; Indicates the unit usage cost of the equipment; Indicates Equipment usage time required for each construction task; Indicates The growth rate of equipment cost per construction task; , represents the weight coefficient; Indicates The execution time of each construction task; Indicates The priority coefficient of each construction task; , , Indicates the total quantity limit of personnel, materials and equipment.

[0081] In this embodiment of the present invention, all variables are initialized, including the total number of construction tasks. . Personnel unit cost for each construction task (For example, the unit cost of personnel for Task 1 is RMB 100 per person.) (For example, Task 1 requires 5 people, and other tasks are set according to actual needs.) Personnel cost growth rate (For example, the personnel cost growth rate of Task 1 is 5%, which is 0.05). Quantity of materials required (assuming the material unit cost is the same for all tasks). (For example, Task 1 requires 20 pieces of material.) Material cost growth rate (For example, the material cost growth rate of Task 1 is 3%, which is 0.03). RMB / hour (assuming that the unit cost of equipment for all tasks is the same). Required equipment usage time (For example, Task 1 requires 10 hours of equipment time, and other tasks are set according to actual needs.) Equipment cost growth rate (For example, the equipment cost growth rate for Task 1 is 2%, which is 0.02). , . Execution time of each construction task (For example, the execution time of Task 1 is 2 days, which is 48 hours). Priority coefficient (For example, the priority coefficient of Task 1 is 1.2, indicating a higher priority.) Person. Material quantity limit Total equipment limit (by time) Hour.

[0082] Read construction plans, personnel information, material information, equipment status and other related data from the BIM model. (from 1 to 10), calculate its corresponding cost item: The personnel cost is ; The material cost is ; The equipment cost is ; The time cost is ; The resource utilization cost is .

[0083] Add up the cost items of all construction tasks to get the value of the objective function .

[0084] The total construction cost can be reduced by accurately calculating various costs (personnel cost, material cost, equipment cost, time cost, resource utilization cost) and seeking the final solution. The total resource limit and resource utilization cost can ensure that resources are used in the most reasonable way to avoid waste and idleness. By considering the execution time and priority coefficient of construction tasks, the construction sequence and progress can be reasonably arranged to shorten the construction period. Real-time monitoring and adjustment of resource allocation and scheduling plans during the construction process can cope with unforeseen situations and changes and improve the flexibility of construction plans. The objective function calculation and constraint condition checking process provide decision support for managers to help them make more informed decisions. By managing construction resources in an automated and intelligent way, the level of construction management can be improved, and the efficiency and quality of construction can be improved.

[0085] like Figure 2 As shown, the embodiment of the present invention also provides an intelligent management method for building construction based on BIM, including: Obtain all kinds of data in the construction process in real time, including construction progress, personnel information, material information, and equipment status; According to various data in the construction process, a virtual 3D model of the target building is constructed to achieve management and simulation analysis of the construction process; Construct a knowledge base of construction safety risks, which includes concepts, attributes, relations and instances related to safety risks, and formulate knowledge representation and reasoning rules; According to the rules and logic in the ontology knowledge base, the construction data is analyzed in real time to identify potential safety risks and obtain corresponding early warning information and solutions; Automatically forecast resource demand based on construction plans, real-time personnel information, material information, and equipment status, and optimize and manage resource allocation based on safety risk warning information; Query, browse, edit and manage various types of information and data through the user interactive interface, including construction progress information, personnel information, material information, equipment status information and safety risk warning information.

[0086] It should be noted that this method is a method corresponding to the above-mentioned system, and all implementation methods in the above-mentioned system embodiment are applicable to this embodiment and can achieve the same technical effect.

[0087] The embodiment of the present invention further provides a computing device, including: a processor, a memory storing a computer program, and when the computer program is executed by the processor, the system described above is executed. All implementation methods in the above system embodiment are applicable to this embodiment and can achieve the same technical effect.

[0088] The embodiment of the present invention further provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the system described above. All implementations in the above system embodiment are applicable to this embodiment and can achieve the same technical effect.

[0089] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. Intelligent management system for building construction based on BIM, characterized by: include: Data acquisition module, used to obtain various data in the construction process in real time, including construction progress, personnel information, material information, and equipment status; BIM model building module, which is used to build a virtual 3D model of the target building based on various data in the construction process, and realize the management and simulation analysis of the construction process; The ontology knowledge base construction module is used to construct a construction safety risk knowledge base, which contains concepts, attributes, relations and instances related to safety risks, and formulates knowledge representation and reasoning rules; The safety risk reasoning module is used to analyze the construction data in the virtual 3D model of the target building in real time according to the rules and logic in the ontology knowledge base, identify potential safety risks, and obtain corresponding early warning information and solutions; The construction resource optimization configuration module is used to automatically predict resource demand, optimize allocation and schedule management based on the construction plan in the BIM model, real-time personnel information, material information and equipment status; The user interaction module is used to query, browse, edit and manage various types of information and data, including construction progress information, personnel information, material information, equipment status information and safety risk warning information.

2. The intelligent management system for building construction based on BIM according to claim 1 is characterized in that: According to various data in the construction process, a virtual 3D model of the target building is constructed to achieve the management and simulation analysis of the construction process, including: Determine the 3D modeling software and set up the modeling environment, including selecting the template type, setting the elevation and axis grid reference elements; Construct a virtual 3D model of the target building in the 3D modeling software, and use the tools and functions of the modeling software, including stretching, rotation, and Boolean operations, to create the main structure of the building, such as beams, slabs, columns, and walls; The constructed 3D model can be used to monitor and manage the construction progress, perform resource allocation and conflict detection, and realize the management and simulation analysis of the construction process.

3. The intelligent management system for building construction based on BIM according to claim 2 is characterized in that: Construct a construction safety risk knowledge base, which contains concepts, attributes, relationships and instances related to safety risks, and formulates knowledge representation and reasoning rules, including: Acquire safety risk knowledge in the field of construction, extract key information related to safety risks, including risk types, occurrence conditions, and consequences, and obtain a safety risk knowledge dataset; Based on the safety risk knowledge dataset, a construction safety risk knowledge base is constructed. Each safety risk knowledge entry in the knowledge base contains concept, attribute, relationship and instance elements. According to the content and structure of the security risk knowledge base, reasoning rules are formulated, including the logic of risk matching, the criteria for determining risk levels, the setting of warning conditions and the generation rules of warning information.

4. The intelligent management system for building construction based on BIM according to claim 3 is characterized in that: According to the rules and logic in the ontology knowledge base, the construction data in the virtual 3D model of the target building is analyzed in real time to identify potential safety risks and obtain corresponding warning information and solutions, including: Extract real-time construction data from the virtual 3D model of the target building, including component status, construction progress, resource usage, and personnel activity trajectory; Treat each element in the real-time construction data, including components, tasks, and resources, as nodes in the network diagram. Construct the edges between nodes to form a construction network diagram; According to the connection relationship between nodes and the weight of the edge, the importance of each node is calculated to obtain the importance score of each node; According to historical data, an importance threshold is set, and the importance score of each node is compared with the threshold, and nodes with importance scores greater than the threshold are identified and regarded as high-risk nodes; If the importance score is greater than the set threshold of the node, and the security risk reaches the warning level, the corresponding warning information is generated according to the information in the ontology knowledge base, including the description of the risk, the scope of impact, and the recommended solutions.

5. The intelligent management system for building construction based on BIM according to claim 4 is characterized in that: The importance score of each node is calculated as: ; in, Representation Node Importance score of , , , represents the weight coefficient; , Represents a node in the network; Representation Node The set of all in-degree nodes of ; Represents a slave node arrive The weight of the edge; Representation and Node The number of all connected edges; Representation Node Numerical value of importance in the network; Representation and Node The number of all connected edges; Representation and Node The number of all connected edges; Representation Node The clustering coefficient of Represents the total number of nodes in the network; Representation Node The clustering coefficient of Representation Node A numerical value of importance in the network.

6. The intelligent management system for building construction based on BIM according to claim 5 is characterized in that: According to the construction plan in the BIM model, real-time personnel information, material information and equipment status, resource demand forecasting, allocation optimization and scheduling management are automatically performed, including: Define decision variables, including the number of personnel, materials, and equipment usage time required for each construction task; Construct the objective function and set constraints; input the objective function and constraints into the linear programming solver to determine the final solution, i.e. the number of personnel, materials, and equipment usage time required for each construction task; Based on the final solution, a resource allocation and scheduling plan is generated, including the execution time of each construction task, the number of personnel required, the amount of materials, and the equipment usage time; During the construction process, the use of personnel, materials, and equipment resources is monitored in real time. If any deviation is found between the resource allocation or scheduling plan and the actual situation, the plan will be automatically adjusted to achieve automatic resource demand forecasting, allocation optimization, and scheduling management.

7. The intelligent management system for building construction based on BIM according to claim 6 is characterized in that: The calculation formula of the objective function is: ; in, represents the value of the objective function; Indicates the total number of construction tasks; Represents an index variable; Indicates Personnel unit cost for each construction task; Indicates The number of personnel required for each construction task; Indicates The growth rate of personnel costs per construction task; Indicates the unit cost of the material; Indicates The quantity of materials required for each construction task; Indicates The material cost growth rate of each construction task; Indicates the unit usage cost of the equipment; Indicates Equipment usage time required for each construction task; Indicates The growth rate of equipment cost per construction task; , represents the weight coefficient; Indicates The execution time of each construction task; Indicates The priority coefficient of each construction task; , , Indicates the total quantity limit of personnel, materials and equipment.

8. An intelligent management method for building construction based on BIM, the method realizing the system as claimed in any one of claims 1 to 7, characterized in that: include: Obtain all kinds of data in the construction process in real time, including construction progress, personnel information, material information, and equipment status; According to various data in the construction process, a virtual 3D model of the target building is constructed to achieve management and simulation analysis of the construction process; Construct a knowledge base of construction safety risks, which includes concepts, attributes, relations and instances related to safety risks, and formulate knowledge representation and reasoning rules; According to the rules and logic in the ontology knowledge base, the construction data is analyzed in real time to identify potential safety risks and obtain corresponding early warning information and solutions; Automatically forecast resource demand based on construction plans, real-time personnel information, material information, and equipment status, and optimize and manage resource allocation based on safety risk warning information; Query, browse, edit and manage various types of information and data through the user interactive interface, including construction progress information, personnel information, material information, equipment status information and safety risk warning information.

9. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the system according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, and when the program is executed by a processor, the system according to any one of claims 1 to 7 is implemented.

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