A prefabricated beam steel bar intelligent processing system based on BIM

Abstract

The application discloses a prefabricated beam steel bar intelligent processing system based on BIM. In the application, the layout algorithm submodule plays a crucial role in the prefabricated beam steel bar intelligent processing system based on BIM. Through optimization algorithms such as genetic algorithm, it can generate the best steel bar layout scheme, maximize material utilization, and reduce waste. This means that the system can more efficiently utilize raw materials, reduce production costs, and reduce environmental impact. In addition, optimizing the layout scheme can also improve production efficiency and shorten the production cycle, thereby faster meeting customer demand. Finally, by reducing waste and improving material utilization, the system can also reduce resource waste and improve resource utilization efficiency. The cutting path planning submodule also plays an indispensable role in the system. Through the shortest path algorithm, the cutting path can be optimized, the idle stroke of the cutting machine can be reduced, and the material utilization rate can be further improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of steel bar processing, and particularly relates to a prefabricated beam steel bar intelligent processing system based on BIM. BACKGROUND

[0002] The prefabricated beam steel bar is a steel bar that is manufactured in advance in a factory or a construction site according to design requirements and is used for the manufacturing of a prefabricated beam of reinforced concrete. The prefabricated beam steel bar processing refers to processing raw material steel bars through a series of processing technologies to manufacture steel bar components that meet the requirements of the prefabricated beam structure. The prefabricated beam steel bar processing mainly includes the following steps: 1. raw material processing: first, the raw material steel bars are subjected to rust removal, straightening and cutting and other treatments to ensure that the steel bar surface is smooth and damage-free and is cut into appropriate lengths according to the design requirements; 2. welding or mechanical connection: according to the design requirements, the treated steel bars are manufactured into corresponding steel bar skeletons or steel bar meshes through welding or mechanical connection. The welding process requires that the welds are full and free of defects, and the mechanical connection requires that the connecting pieces are firm and reliable; 3. processing and forming: according to the shape and size of the prefabricated beam, the steel bars are subjected to cold bending, hot bending and other forming processes to manufacture the required shape and size. The bending radius, angle and size are strictly controlled during the processing to ensure the processing quality; 4. quality inspection: the prefabricated beam steel bars after processing are subjected to quality inspection, including appearance inspection, size measurement, mechanical property testing and the like to ensure that the steel bar processing quality meets the relevant standards and design requirements; 5. marking and transportation: the prefabricated beam steel bars that pass the inspection are marked with information such as model, specification, length and the like and are transported and stored by taking appropriate measures to prevent the steel bars from being deformed or damaged during the transportation and storage.

[0003] However, the arrangement of the steel bars in the prior art is mainly achieved manually without algorithm optimization, thereby causing material waste and reducing the utilization rate of the materials. SUMMARY

[0004] The application aims to solve the above problems and provides a prefabricated beam steel bar intelligent processing system based on BIM.

[0005] The technical scheme adopted by the application is as follows: a prefabricated beam steel bar intelligent processing system based on BIM, comprising:

[0006] a BIM model data processing module, an order management module, an intelligent nesting and cutting module, an automatic processing module, a quality detection module, a logistics and warehouse management module, a signal processing module, a system integration and control module and a communication interface module;

[0007] The intelligent nesting and cutting module is internally provided with an input data processing sub-module, a nesting algorithm sub-module, a cutting path planning sub-module and a cutting instruction generation sub-module;

[0008] The BIM model data processing module transmits the parsed steel information to the order management module for creating and updating order information.

[0009] The order management module sends processing instructions and steel demand information to the intelligent nesting and cutting module according to order status and demand.

[0010] The intelligent nesting and cutting module generates cutting schemes and nesting diagrams and sends mechanical operation instructions to the automated processing module.

[0011] After processing is completed, the automated processing module delivers finished products to the quality detection module for quality control and acceptance.

[0012] The quality detection module sends products that pass quality detection to the logistics and warehouse management module for storage and dispatch.

[0013] The logistics and warehouse management module provides inventory and logistics data to the data feedback and optimization module for analyzing material flow and production efficiency.

[0014] The data feedback and optimization module feeds back analysis results to the system integration and control module for adjusting production plans and optimizing processing procedures; the system integration and control module exchanges and integrates data with other external systems through the communication interface module.

[0015] The communication interface module receives updated BIM models from the building design end and sends them to the BIM model data processing module for a new round of data processing.

[0016] Further, the BIM model data processing module receives IM model files and uses BIM parsing tools to extract steel-related information, including diameter, length, shape, and position; the related information is cleaned and verified; and the cleaned and verified related information is standardized.

[0017] Further, the order management module manages precast beam steel order information, including receiving new orders, tracking order status, and updating production progress; the order management module automatically creates and updates order records based on data provided by the BIM model data processing module and assigns production batch numbers; the order management module automatically assigns production tasks, optimizes production plans, and provides real-time order tracking functions.

[0018] Further, the data processing sub-module receives steel data from the BIM model data processing module and performs data cleaning and verification; the input data of the data processing sub-module includes the diameter, length, shape, and quantity of steel.

[0019] Further: the nesting algorithm submodule uses a genetic algorithm to generate the best reinforcement nesting scheme, specifically: randomly generate a set of initial solutions, each individual represents a nesting scheme; calculate the fitness of each individual, the fitness function is usually designed to maximize material utilization or minimize waste; select good individuals as parents according to fitness for generating the next generation; randomly select parents for crossover to produce new individuals; randomly mutate the new individuals to increase the diversity of the population; replace part or all of the current population with the new individuals; repeat steps b to f until the termination condition is met; the fitness function formula of the nesting algorithm submodule is:

[0020] F(x)=f1(x)+λf2(x) ;

[0021] Where f1(x) is the objective function, including material utilization; f2(x) is the penalty term, used to punish the solution that does not meet the constraints; λ is the penalty coefficient, used to balance the influence of the objective function and the penalty term; material utilization: U = total length of raw materials / total length of reinforcement; waste: W = total length of raw materials - total length of reinforcement.

[0022] Further: the cutting path planning submodule receives the reinforcement nesting scheme generated by the nesting algorithm submodule, including the position and length information of each reinforcement; generates the initial cutting path according to the nesting scheme; applies the shortest path algorithm to optimize the initial path; converts the optimized path into cutting instructions; verifies the feasibility of the cutting path; the formula for calculating the shortest path between two nodes is:

[0023] ;

[0024] Where Path(i,j) is the shortest path from node i to node j, Path(i,k) is the path from node i to node k, and Path(k,j) is the path from node k to node j; in cutting path planning, each reinforcement is regarded as a node; the straight line connection between adjacent reinforcements is regarded as an edge; distance represents the length of the edge, i.e. the distance between adjacent reinforcements.

[0025] Further: the cutting instruction generation submodule receives the results from the nesting algorithm submodule and the cutting path planning submodule, including the position, length, and distance between adjacent reinforcements of each reinforcement; the cutting instruction generation submodule analyzes the nesting results to determine the cutting position and cutting order of each reinforcement; analyzes the path planning results to determine the movement path and cutting action of the cutting machine; based on the results of nesting and path planning, the cutting instruction generation submodule will generate cutting instructions, including the type, coordinate position, and cutting parameters of the cutting instructions.

[0026] Further, the automatic processing module receives the cutting instructions generated by the intelligent nesting and cutting module and processes the steel bars using automatic cutting and bending equipment, including cutting and bending; monitors the equipment status and processing progress in real time, and after processing is completed, the module records the actual processing data of each steel bar; the quality detection module detects the quality of the steel bars processed by the automatic processing module, including size, shape, and welding points; uses high-precision measuring tools and visual detection systems for detection, automatically compares the detection results with the design requirements in the BIM model, marks unqualified products, and sends them back to the automatic processing module for reprocessing or disposal.

[0027] Further, the logistics and warehouse management module manages the storage and transportation of qualified steel bars; uses RFID tags to identify the steel bars; generates a pick-up list and a logistics plan automatically according to the shipping instructions of the order management module; the data feedback and optimization module collects various data in the production process, including production efficiency, material utilization rate, and equipment operating status; identifies bottlenecks and improvement points in the production process and proposes improvement suggestions.

[0028] Further, the system integration and control module is responsible for the integration and coordination of the entire system; provides a user interface to allow operators to monitor production status, manage orders, and adjust production plans; the communication interface module exchanges data with external systems; uses standard API communication protocols to ensure safe data transmission.

[0029] The beneficial effects of the present application are:

[0030] 1. In the present application, the nesting algorithm sub-module plays a crucial role in the BIM-based prefabricated beam steel bar intelligent processing system. Through optimization algorithms such as genetic algorithms, it can generate the best steel bar nesting scheme, maximize material utilization, and reduce waste. This means that the system can more efficiently utilize raw materials, reduce production costs, and reduce environmental impact. In addition, optimizing the nesting scheme can also improve production efficiency and shorten the production cycle, thereby meeting customer demand more quickly. Finally, by reducing waste and improving material utilization, the system can also reduce resource waste and improve resource utilization efficiency.

[0031] 2. In the present application, the cutting path planning sub-module also plays an indispensable role in the system. Through the shortest path algorithm, it can optimize the cutting path, reduce the idle travel of the cutting machine, and further improve material utilization. This means that the system can more efficiently utilize raw materials, reduce production costs, and reduce environmental impact. In addition, optimizing the cutting path can also improve production efficiency and shorten the production cycle, thereby meeting customer demand more quickly. Finally, by reducing the idle travel of the cutting machine and improving material utilization, the system can also reduce resource waste and improve resource utilization efficiency. Attached Figure Description

[0032] Fig. 1 This is an overall system block diagram of the present invention;

[0033] Fig. 2 This is a system block diagram of the intelligent nesting and cutting module in this invention. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0035] Reference Figs. 1-2 ,

[0036] A BIM-based intelligent processing system for precast beam reinforcement includes a BIM model data processing module, an order management module, an intelligent nesting and cutting module, an automated processing module, a quality inspection module, a logistics and warehousing management module, a signal processing module, a system integration and control module, and a communication interface module.

[0037] The intelligent nesting and cutting module is internally configured with an input data processing submodule, a nesting algorithm submodule, a cutting path planning submodule, and a cutting instruction generation submodule.

[0038] The BIM model data processing module transmits the parsed rebar information to the order management module for creating and updating order information.

[0039] The order management module sends processing instructions and rebar requirement information to the intelligent nesting and cutting module based on the order status and requirements.

[0040] The intelligent nesting and cutting module generates cutting plans and nesting diagrams, directly guiding the mechanical operation of the automated processing module.

[0041] After processing is completed, the automated processing module will transport the finished product to the quality inspection module for quality control and acceptance.

[0042] Products that pass quality inspection will be sent to the logistics and warehousing management module for storage and scheduling, awaiting shipment.

[0043] The logistics and warehousing management module provides inventory and logistics data to the data feedback and optimization module for analyzing material flow and production efficiency.

[0044] The analysis results of the data feedback and optimization module are fed back to the system integration and control module for adjusting production plans and optimizing processing flows. The system integration and control module exchanges and integrates data with other external systems through the communication interface module.

[0045] The communication interface module can receive an updated BIM model from the architectural design end and pass it to the BIM model data processing module for a new round of data processing.

[0046] BIM Model Data Processing Module: As the vanguard of the system, this module is responsible for receiving BIM model files from architects and using professional BIM parsing tools to extract detailed information about steel bars, such as diameter, length, shape, position, etc. After cleaning and verification to ensure data accuracy and integrity, these information is standardized into formats that manufacturing systems can recognize, such as XML, JSON, or directly input into databases, so that other modules can easily access and call.

[0047] Order Management Module: This module is the core of the system, responsible for managing precast beam steel order information, including receiving new orders, tracking order status, updating production progress, etc. The module will automatically create and update order records based on data provided by the BIM model data processing module, and assign production batch numbers. At the same time, the module will automatically allocate production tasks, optimize production plans to ensure timely delivery of orders, and provide real-time order tracking functions so that customers can always know the order progress.

[0048] Data processing sub-module receives steel data from BIM model data processing module, cleans and verifies data to ensure accuracy and integrity.

[0049] The input data of the data processing sub-module includes the diameter, length, shape, quantity of steel bars, and needs to define the data format and standardized conversion rules.

[0050] The nesting algorithm sub-module uses genetic algorithm to generate the best steel bar nesting scheme;

[0051] The algorithm flow of the nesting algorithm sub-module is as follows:

[0052] a. Initialization: Randomly generate a set of initial solutions (individuals), each individual representing a nesting scheme.

[0053] b. Evaluation: Calculate the fitness of each individual, the fitness function is usually designed to maximize material utilization or minimize waste.

[0054] c. Selection: Select good individuals as parents according to fitness to generate the next generation.

[0055] d. Crossover: Randomly select parents to cross (mate) to produce new individuals.

[0056] e. Mutation: Randomly mutate the new individuals to increase the diversity of the population.

[0057] f. Replacement: Replace some or all of the poorer individuals in the current population with newly generated individuals.

[0058] g. Iteration: Repeat steps b to f until a termination condition is met (e.g., maximum number of iterations reached or fitness no longer improving).

[0059] The fitness function formula for the nesting algorithm submodule is:

[0060] F(x) = f1(x) + λf2(x); where f1(x) is the objective function, including material utilization; f2(x) is the penalty term, used to penalize solutions that do not meet the constraints; λ is the penalty coefficient, used to balance the influence of the objective function and the penalty term.

[0061] Material utilization: U = total length of raw materials / total length of reinforcement;

[0062] Waste: W = total length of raw materials - total length of reinforcement.

[0063] The algorithm flow of the cutting path planning submodule is:

[0064] Receive the nesting scheme: Receive the reinforcement nesting scheme generated by the nesting algorithm submodule, including the position, length, etc. of each reinforcement.

[0065] Generate initial path: Generate the initial cutting path according to the nesting scheme, usually using straight lines to connect adjacent reinforcements.

[0066] Path optimization: Apply the shortest path algorithm to optimize the initial path.

[0067] Generate cutting instructions: Convert the optimized path into instructions that the cutting machine can understand, such as G code.

[0068] Path verification: Verify the feasibility of the cutting path to ensure that no collision or overcutting occurs.

[0069] Output cutting instructions: Send the verified cutting instructions to the cutting machine to start the cutting process.

[0070] The formula for calculating the shortest path between two nodes using the shortest path algorithm is:

[0071] ;

[0072] where Path(i,j) is the shortest path from node i to node j, Path(i,k) is the path from node i to node k, and Path(k,j) is the path from node k to node j; in the cutting path planning, each steel bar can be regarded as a node. The straight line connection between adjacent steel bars can be regarded as an edge. The distance represents the length of the edge, that is, the distance between adjacent steel bars.

[0073] The running process of the cutting instruction generation submodule includes:

[0074] a. Receive the layout and path planning results: the cutting instruction generation submodule first receives the results from the layout algorithm submodule and the cutting path planning submodule. These results include the position, length, and distance between adjacent steel bars of each steel bar.

[0075] b. Analyze the layout and path planning results: next, the cutting instruction generation submodule will analyze the layout results to determine the cutting position and cutting order of each steel bar. At the same time, analyze the path planning results to determine the movement path and cutting action of the cutting machine.

[0076] c. Generate cutting instructions: based on the results of layout and path planning, the cutting instruction generation submodule will generate cutting instructions. These instructions include the type of cutting instruction (such as straight cutting, circular cutting, etc.), coordinate position, and cutting parameters (such as cutting speed, cutting depth, etc.).

[0077] d. Verify the cutting instructions: after generating the cutting instructions, the submodule will verify them to ensure their correctness and effectiveness. The verification content includes the type of cutting instruction, coordinate position, and cutting parameter.

[0078] e. Convert to G code: the verified cutting instructions will be converted to G code format that can be understood by the cutting machine. G code is a programming language used for numerical control machine tools to control the movement and processing of the machine.

[0079] f. Output and execute the cutting instructions: the converted G code will be sent to the cutting machine to guide the cutting process. After cutting is completed, the cutting machine will feedback the cutting results for subsequent quality detection and processing. Through the cutting instruction generation submodule, precise control of the cutting process can be achieved to ensure cutting quality and efficiency.

[0080] Automatic processing module: this module receives the cutting instructions generated by the intelligent layout and cutting module and uses automatic cutting and bending equipment to process the steel bars, including cutting, bending, etc. The module will monitor the equipment status and processing progress in real time to ensure the accuracy of the processing size and shape. After processing is completed, the module will record the actual processing data of each steel bar, such as cutting time, material consumption, etc., for subsequent analysis and optimization;

[0081] Quality Inspection Module: This module performs quality inspections on the reinforcing bars processed by the automated processing module, including dimensions, shape, and weld points. The module uses high-precision measuring tools and a vision inspection system to automatically compare the inspection results with the design requirements in the BIM model, marking non-conforming products and sending them back to the automated processing module for reprocessing or disposal. This ensures that every reinforcing bar meets quality standards, reducing rework and waste.

[0082] Logistics and Warehousing Management Module: This module manages the storage and transportation of qualified steel bars, ensuring timely delivery to the construction site. The module uses RFID tags or other tracking technologies to identify the steel bars, enabling real-time inventory management. Based on the delivery instructions from the order management module, the module automatically generates delivery notes and logistics plans, and interfaces with external logistics systems to ensure on-time delivery. This improves inventory management efficiency and reduces human error.

[0083] Data Feedback and Optimization Module: This module collects various data from the production process, including production efficiency, material utilization, and equipment operating status. It analyzes this data to identify bottlenecks and areas for improvement, and proposes suggestions such as optimizing the nesting algorithm and adjusting production plans to support closed-loop management for continuous improvement. Through data analysis and feedback, the efficiency and quality of the production process are continuously improved.

[0084] System Integration and Control Module: This module is responsible for the integration and coordination of the entire system, ensuring seamless connectivity and data flow between modules. It provides a user interface, allowing operators to monitor production status, manage orders, and adjust production plans. Simultaneously, the module also implements anomaly handling and emergency shutdown functions to ensure production safety. Through integration and automation, it improves the stability and reliability of the system.

[0085] Communication Interface Module: This module is responsible for data exchange with external systems (such as construction project management software, supply chain management systems, etc.). The module uses standard APIs, web services, or other communication protocols to ensure secure data transmission. Furthermore, the module supports system scalability, allowing for future integration with other information systems. Through seamless integration with other systems, information sharing and flow are achieved, improving overall collaborative work efficiency.

[0086] In the present application, the nesting algorithm sub-module plays a crucial role in the BIM-based prefabricated beam steel bar intelligent processing system. Through optimization algorithms such as genetic algorithm, it can generate the best steel bar nesting scheme, maximize material utilization, and reduce waste. This means that the system can more efficiently utilize raw materials, reduce production costs, and reduce environmental impact. In addition, optimizing the nesting scheme can also improve production efficiency, shorten production cycles, and thus meet customer demand more quickly. Finally, by reducing waste and improving material utilization, the system can also reduce resource waste and improve resource utilization efficiency.

[0087] In the present application, the cutting path planning sub-module also plays an indispensable role in the system. Through the shortest path algorithm, it can optimize the cutting path, reduce the idle travel of the cutting machine, and further improve material utilization. This means that the system can more efficiently utilize raw materials, reduce production costs, and reduce environmental impact. In addition, optimizing the cutting path can also improve production efficiency, shorten production cycles, and thus meet customer demand more quickly. Finally, by reducing the idle travel of the cutting machine and improving material utilization, the system can also reduce resource waste and improve resource utilization efficiency.

[0088] It should be noted that in this paper, relational terms such as first and second are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or other elements inherent in such a process, method, article or device. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article or device including the element.

[0089] The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A BIM-based intelligent processing system for precast beam reinforcement, characterized in that, include: The system includes a BIM model data processing module, an order management module, an intelligent nesting and cutting module, an automated processing module, a quality inspection module, a logistics and warehousing management module, a signal processing module, a system integration and control module, and a communication interface module. The intelligent nesting and cutting module is internally equipped with an input data processing submodule, a nesting algorithm submodule, a cutting path planning submodule, and a cutting instruction generation submodule; the BIM model data processing module transmits the parsed rebar information to the order management module for creating and updating order information; The order management module sends processing instructions and rebar requirement information to the intelligent nesting and cutting module based on the order status and requirements; the intelligent nesting and cutting module generates a cutting plan and nesting diagram, and sends mechanical operation instructions to the automated processing module. After processing is completed, the automated processing module will transport the finished product to the quality inspection module for quality control and acceptance. The quality inspection module sends products that pass the quality inspection to the logistics and warehousing management module for storage and scheduling. The logistics and warehousing management module provides inventory and logistics data to the data feedback and optimization module for analyzing material flow and production efficiency. The data feedback and optimization module feeds back the analysis results to the system integration and control module for adjusting production plans and optimizing processing flows; the system integration and control module exchanges and integrates data with other external systems through the communication interface module. The communication interface module receives the updated BIM model from the architectural design end and sends it to the BIM model data processing module for a new round of data processing. The nesting algorithm submodule uses a genetic algorithm to generate the optimal rebar nesting scheme, specifically: a. Initialization: Randomly generate a set of initial solutions, each individual representing a sorting scheme; b. Evaluation: Calculate the fitness of each individual, with the fitness function designed to maximize material utilization or minimize waste; c. Selection: Select superior individuals based on fitness to serve as parents for generating the next generation; d. Crossover: Randomly select parents for crossover to produce new individuals; e. Mutation: Randomly mutating newborn individuals to increase population diversity; f. Replacement: Replacing some or all of the weaker individuals in the current population with newborn individuals; g. Iteration: Repeat steps b to f until the termination condition is met; the fitness function calculation formula of the sorting algorithm submodule is: F(x) = f1(x) + λf2(x); Where f1(x) is the objective function, including material utilization rate; f2(x) is the penalty term, used to penalize solutions that do not meet the constraints; λ is the penalty coefficient, used to balance the influence of the objective function and the penalty term; material utilization rate: U = total length of raw materials / total length of steel bars; waste: W = total length of raw materials − total length of steel bars; The cutting path planning submodule receives the rebar layout scheme generated by the layout algorithm submodule, including the position and length information of each rebar; generates an initial cutting path based on the layout scheme; optimizes the initial path using a shortest path algorithm; converts the optimized path into a cutting command; and verifies the feasibility of the cutting path. The formula for calculating the shortest path between two nodes using the shortest path algorithm is: ； Where Path(i,j) is the shortest path from node i to node j, Path(i,k) is the path from node i to node k, and Path(k,j) is the path from node k to node j; in the cutting path planning, each rebar is regarded as a node; the straight line connection between adjacent rebars is regarded as an edge; distance represents the length of the edge, that is, the distance between adjacent rebars. The cutting instruction generation submodule receives results from the nesting algorithm submodule and the cutting path planning submodule, including the position, length, and distance between adjacent reinforcing bars of each bar. The cutting instruction generation submodule analyzes the nesting results to determine the cutting position and cutting sequence of each reinforcing bar. It also analyzes the path planning results to determine the movement path and cutting action of the cutting machine. Based on the nesting and path planning results, the cutting instruction generation submodule generates cutting instructions, including the type, coordinate position, and cutting parameters of the cutting instructions.

2. The BIM-based intelligent processing system for precast beam reinforcement as described in claim 1, characterized in that: The BIM model data processing module receives the BIM model file and uses the BIM parsing tool to extract relevant information about the reinforcing bars, including diameter, length, shape, and location; it then cleans and verifies the relevant information and standardizes the cleaned and verified information.

3. The BIM-based intelligent processing system for precast beam reinforcement as described in claim 2, characterized in that: The order management module manages order information for precast beam reinforcement, including receiving new orders, tracking order status, and updating production progress; the order management module automatically creates and updates order records and assigns production batch numbers based on data provided by the BIM model data processing module. The order management module automatically assigns production tasks, optimizes production plans, and provides real-time order tracking.

4. The BIM-based intelligent processing system for precast beam reinforcement as described in claim 3, characterized in that: The data processing submodule receives reinforcement data from the BIM model data processing module and performs data cleaning and verification; the input data of the data processing submodule includes the diameter, length, shape and quantity of the reinforcement.

5. The BIM-based intelligent processing system for precast beam reinforcement as described in claim 4, characterized in that: The automated processing module receives cutting instructions generated by the intelligent nesting and cutting module and uses automated cutting and bending equipment to process the reinforcing bars, including cutting and bending. It monitors the equipment status and processing progress in real time. After processing is completed, the automated processing module records the actual processing data of each reinforcing bar. The quality inspection module performs quality inspection on the reinforcing bars completed by the automated processing module, including size, shape, and welding points. It uses high-precision measuring tools and a vision inspection system to perform inspection, automatically compares the inspection results with the design requirements in the BIM model, marks unqualified products, and sends them back to the automated processing module for reprocessing or disposal.

6. The BIM-based intelligent processing system for precast beam reinforcement as described in claim 5, characterized in that: The logistics and warehousing management module manages the storage and transportation of qualified steel bars; it uses RFID tags to identify the steel bars; based on the delivery instructions from the order management module, the module automatically generates delivery notes and logistics plans; the data feedback and optimization module collects various data during the production process, including production efficiency, material utilization rate, and equipment operating status; it identifies bottlenecks and improvement points in the production process and proposes improvement suggestions.

7. The BIM-based intelligent processing system for precast beam reinforcement as described in claim 6, characterized in that: The system integration and control module is responsible for the integration and coordination of the entire system; it provides a user interface that allows operators to monitor production status, manage orders, and adjust production plans. The communication interface module exchanges data with external systems; Use standard API communication protocols to ensure secure data transmission.

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