BIM-based intelligent steel component machining modular drive method

Abstract

This disclosure relates to a BIM-based intelligent modular-driven method for steel component fabrication. The method includes: acquiring a concrete BIM model of a tunnel or bridge and its bound design parameters; generating a BIM model of the steel component based on the concrete BIM model and design parameters, using a parametric rule set matching the steel component type corresponding to the concrete BIM model; binding fabrication parameters to the steel component BIM model, and calculating fabrication layout data of the steel component based on a preset fabrication rule library and fabrication parameters; storing the fabrication layout data and fabrication parameters in a database; and generating a steel component fabrication order in response to an order generation instruction for a target steel component, based on the fabrication layout data and fabrication parameters corresponding to the target steel component in the database. This disclosure improves the level of intelligence in steel component production.

Description

Technical Field

[0001] This disclosure relates to the field of intelligent manufacturing technology, and in particular to a BIM-based intelligent processing module-driven method for steel components. Background Technology

[0002] In the traditional steel component processing field, the work process heavily relies on manual drawing interpretation, manual quantity calculation, and two-dimensional layout, which has drawbacks such as low efficiency, easy error, and a long information transmission chain.

[0003] Currently, while BIM (Building Information Modeling) technology has achieved 3D visualization design and clash detection to some extent, the models are mostly limited to geometric representation and construction simulation. A significant gap remains between the design information they contain and the precise process parameters required for downstream CNC machining (such as accurate cutting length, bending angle, and cutting coordinates). Existing methods merely utilize BIM software for parametric modeling or simple data export, failing to achieve deep and structured integration of design attributes and manufacturing information. Furthermore, they lack an automatic conversion and driving mechanism from model to production order, thus failing to meet the demands of modern intelligent construction for data continuity and automated production. Summary of the Invention

[0004] To overcome the problems existing in related technologies, this disclosure provides a BIM-based intelligent processing module-driven method for steel components.

[0005] According to a first aspect of the present disclosure, a BIM-based intelligent processing modular driving method for steel components is provided, comprising:

[0006] Obtain the concrete BIM model of the tunnel or bridge and its associated design parameters; Based on the concrete BIM model and design parameters, a BIM model of the steel component is generated using a set of parametric rules that matches the steel component type corresponding to the concrete BIM model. Processing parameters are bound to the BIM model of the steel component, and processing layout data of the steel component is calculated based on the preset processing rule library and processing parameters; the processing layout data is used to drive CNC equipment to produce the steel component; The processing layout data and the processing parameters are stored in the database; In response to an order generation instruction for a target steel component, a steel component processing order is generated based on the processing layout data and processing parameters corresponding to the target steel component in the database.

[0007] According to a second aspect of the present disclosure, a BIM-based intelligent processing modular driving device for steel components is provided, comprising: Acquisition unit, used to acquire the concrete BIM model of a tunnel or bridge and its associated design parameters; The model generation unit is used to generate a BIM model of a steel component based on the concrete BIM model and design parameters, using a set of parametric rules that matches the steel component type corresponding to the concrete BIM model. The calculation unit is used to bind processing parameters to the BIM model of the steel component, and calculate the processing layout data of the steel component based on the preset processing rule library and processing parameters; the processing layout data is used to drive the CNC equipment to produce the steel component; Storage unit, used to store the processing layout data and the processing parameters into a database; The order generation unit is used to generate a steel component processing order in response to an order generation instruction for a target steel component, based on the processing layout data and processing parameters corresponding to the target steel component in the database.

[0008] According to a third aspect of the present disclosure, an electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as described in any one of the first aspects.

[0009] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method as described in any one of the first aspects.

[0010] According to a fifth aspect of the present disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the method as described in any one of the first aspects.

[0011] The technical solutions provided by the embodiments of this disclosure can include the following beneficial effects: By deeply binding the BIM design model with structured processing parameters and automatically converting it into processing layout data that can directly drive CNC equipment, a fully automatic and high-precision conversion from three-dimensional design to two-dimensional manufacturing instructions is realized, replacing the traditional manual drawing reading, segmented quantity calculation and manual material cutting process, improving the processing preparation efficiency by an order of magnitude, while eliminating human error and ensuring the consistency from design data to manufacturing data. In addition, by generating structured orders and directly connecting them to the processing platform, a data-driven integrated design and manufacturing closed loop is constructed, which significantly improves the intelligence level and overall collaborative efficiency of steel component production.

[0012] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0013] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0014] Figure 1 This is a flowchart illustrating a BIM-based intelligent processing modular driving method for steel components, according to an exemplary embodiment.

[0015] Figure 2 This is a block diagram illustrating a BIM-based intelligent processing modular drive device for steel components, according to an exemplary embodiment.

[0016] Figure 3 This is a block diagram illustrating an apparatus for a BIM-based intelligent machining module-driven method for steel components, according to an exemplary embodiment.

[0017] Figure Labels 201 - Acquisition Unit; 202 - Model Generation Unit; 203 - Calculation Unit; 204 - Storage Unit; 205 - Order Generation Unit; 300 - Device; 302 - Processing Component; 304 - Memory; 306 - Power Component; 308 - Multimedia Component; 310 - Audio Component; 312 - I / O Interface; 314 - Sensor Component; 316 - Communication Component; 320 - Processor. Detailed Implementation

[0018] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure.

[0019] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to limit the scope of this disclosure.

[0020] It should be understood that although the terms first, second, third, etc., may be used to describe various information in embodiments of this disclosure, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first information may also be referred to as second information without departing from the scope of embodiments of this disclosure, and similarly, second information may also be referred to as first information. Depending on the context, the words “if” and “suppose” as used herein may be interpreted as “when”, “when”, or “in response to a determination”.

[0021] Furthermore, various forms of processes shown in the embodiments of this disclosure can be used to reorder, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and no limitation is imposed herein.

[0022] It should be noted that the collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution disclosed herein all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0023] Figure 1 This is a flowchart illustrating a BIM-based intelligent processing modular driving method for steel components, according to an exemplary embodiment. Figure 1 As shown, it should be noted that the BIM-based intelligent processing modular driving method for steel components in this embodiment is applied to a BIM-based intelligent processing modular driving device for steel components. Figure 1 As shown, the method may include the following steps: Step 101: Obtain the BIM model of the tunnel or bridge concrete and its associated design parameters.

[0024] In one embodiment, a pre-built BIM 3D model of a concrete structure such as a tunnel lining or bridge pier, beam, etc., can be located and loaded, and the pre-associated set of structural design parameters in the BIM model can be automatically identified and read.

[0025] As an example, the design parameter set can vary depending on the component type: for tunnels, it can include cross-sectional profiles, mileage information, and lining type; for bridges, it can include control parameters such as key section geometry, beam height variation curves, and prestressed tendon positioning. These parameters can be associated with specific component elements in the BIM model as attribute sets, ensuring a one-to-one correspondence between geometric information and engineering attributes, forming a digital design foundation that can be directly accessed and supports automated decision-making.

[0026] Step 102: Based on the concrete BIM model and design parameters, generate the BIM model of the steel component using a set of parametric rules that match the steel component type corresponding to the concrete BIM model.

[0027] In this embodiment of the disclosure, the attributes and design parameters of the concrete BIM model can be automatically parsed first to identify the structural type (such as tunnel lining, bridge piers or beams), and the corresponding steel component type and its exclusive parametric rule set can be intelligently matched accordingly.

[0028] For example, for tunnel lining, the steel mesh rule set is called, and a three-dimensional arrangement model of circumferential and longitudinal reinforcement is automatically calculated and generated based on the cross-sectional profile, protective layer thickness and steel bar spacing parameters; for bridge piers and abutments, the reinforcement rule set is called, and steel bar guide lines are generated based on the structural edge offset, and the model is completed according to the main reinforcement and stirrup parameters.

[0029] As an example of a possible implementation, steel component types can be automatically identified by parsing the design attributes bound to the concrete BIM model. For instance, if the model is a tunnel lining and its attributes include "cross-section ID" and "surrounding rock grade," it can be determined that "tunnel lining reinforcement" needs to be created, and the corresponding parametric rule set for the reinforcement mesh can be matched. If the model is a bridge pier and its attributes include "section type: rectangular pier" and "pier height" parameters, it can be determined that "bridge pier main reinforcement and stirrups" need to be created, and the corresponding pier reinforcement rule set can be invoked. This identification process can be based on the existing structured parameters of the model without manual specification, thus achieving an intelligent and accurate mapping from the concrete structure to the associated steel component type.

[0030] In this embodiment of the disclosure, the entire generation process can be driven by the geometric logic and engineering algorithms encapsulated within the rule set, automatically completing the precise modeling of steel components from spatial positioning to geometric creation, and simultaneously assigning them identity codes, positioning information and design attributes, thereby generating a complete steel component BIM model that can be directly used in subsequent processing.

[0031] In some embodiments of this disclosure, step 102 may specifically include the following steps: Step a1: For the reinforcement of the tunnel lining structure, obtain the target mileage range and reinforcement design parameters based on the concrete BIM model.

[0032] The design parameters for the reinforcing bars include the thickness of the protective layer, the spacing and diameter of the circumferential and longitudinal reinforcing bars.

[0033] In one embodiment, the target start and end mileage range can be determined based on the tunnel line segment selected by the user. The design parameters of the reinforcing steel for the lining structure of that mileage segment can be received through the interactive interface of the terminal device or called from the preset template. These parameters clearly specify the thickness of the protective layer of the reinforcing steel relative to the concrete surface, as well as the arrangement spacing and specification diameter of the circumferential and longitudinal reinforcing steel.

[0034] As an example, some of the reinforcement design parameters are derived from the design attributes already bound in the concrete BIM model (such as the standard reinforcement parameters corresponding to the lining type), while others are input or adjusted by the user according to specific project requirements. Together, they constitute the precise input conditions that drive the subsequent automated and parametric generation of the reinforcement model.

[0035] Step a2: Read the cross-sectional information of the lining structure from the corresponding mileage section of the concrete BIM model.

[0036] In this embodiment, based on a determined target mileage range, the lining structural units of the corresponding mileage section can be precisely located in the concrete BIM model. The attribute sets associated with these structural units can be accessed, and pre-generated and bound cross-sectional information can be read. This information can be stored in a specific data format (such as a vector dataset containing parameters like contour coordinate sequences, contour radii, and key dimensions, or a standardized cross-sectional identification code). This information can be completely extracted into memory and converted into a standardized data structure that can be directly called by subsequent geometric calculation modules, thus providing an indispensable geometric reference for the precise spatial positioning of the reinforcing bars.

[0037] Step a3: Based on cross-sectional information, target mileage range, and reinforcement design parameters, determine the spatial placement of circumferential reinforcement, longitudinal reinforcement, and stirrups in the tunnel lining structure through geometric calculation using a parametric rule set.

[0038] In this embodiment, the cross-sectional profile information, target mileage range, and specific rebar design parameters (protective layer thickness, spacing, and diameter) can be used as inputs to call a dedicated parameterized rule set for tunnel lining rebar. Based on the cross-sectional profile, the mileage is divided along the tunnel axis according to the longitudinal rebar spacing. At each division point, the inner profile of the lining is offset inward according to the protective layer thickness to generate the layout space for circumferential rebar. The path of the longitudinal rebar can be automatically generated by connecting consecutive layout points at the same angle. The position of the stirrups can be determined at the intersection of the circumferential and longitudinal rebars according to their design spacing. The spatial coordinate calculation of all rebars is automatically completed by the mathematical algorithm encapsulated in the rule set, and a precise layout scheme including the three-dimensional spatial position and direction vector of each rebar is output.

[0039] Step a4: Generate the corresponding steel reinforcement BIM model based on the spatial placement location, and bind the model with identity, location and design attributes.

[0040] In this embodiment, based on the calculated spatial placement coordinates and direction vectors of each rebar, the BIM platform's graphics generation interface can be invoked to create corresponding 3D rebar model elements (e.g., using cylinders to represent main bars and specific curves to represent stirrups). Each generated rebar model is assigned a globally unique identification code as its identity attribute. The calculated precise 3D coordinates and direction vectors are written into the model as fixed attributes. Rebar design parameters (e.g., diameter, grade, type) and related processing reference information (e.g., mileage, lining block number) are structurally bound to the model elements as design attributes. Finally, a digital rebar BIM model unit integrating complete geometric information and engineering attributes is generated, which can be directly extracted and called in subsequent processes.

[0041] In other embodiments of this disclosure, step 102 may specifically include the following steps: Step b1: For the reinforcement of the bridge pier structure, obtain the concrete model identifier, protective layer thickness and reinforcement parameters of the target bridge pier from the design parameters.

[0042] The reinforcement parameters include those for main bars, stirrups, and reinforcing bars.

[0043] In this embodiment of the disclosure, a specific pier component can be located from the overall model based on the target pier concrete model identifier specified by the user. The standard value of the protective layer thickness applicable to the component can be obtained from the design attribute library or the associated design specification database, and detailed reinforcement parameters can be extracted simultaneously. These parameters specify the number, diameter, and arrangement of the main reinforcement bars, the type, spacing, and diameter of the stirrups, and the setting position and geometric specifications of the reinforcing bars in a machine-readable structured format (such as a list or JSON object). This provides complete and unambiguous input instructions for subsequent parametric modeling.

[0044] Step b2: Determine the pier concrete model corresponding to the target pier concrete model identifier from the concrete BIM model, and extract the structural edge lines of the pier concrete model.

[0045] In this embodiment, the target pier concrete model can be matched and located in the component library of the concrete BIM model based on the acquired target pier concrete model identifier, thereby determining the target pier concrete 3D model. The solid geometric data of the model can be parsed, and its key external structural edges (e.g., closed polygons or spatial curves) can be extracted using boundary representation methods or similar algorithms. This process is completed in a procedural manner, and the extracted edges serve as the precise geometric reference necessary for the subsequent generation of reinforcement layout guide lines, ensuring a strict spatial fit between the reinforcement model and the main concrete structure.

[0046] Step b3: Based on the thickness of the protective layer, offset the structural edge lines to generate guide lines for the reinforcement layout.

[0047] In this embodiment, the structural edge line can be translated inward along the normal direction by a distance equal to the thickness of a protective layer. For complex contours, the translated edge line can undergo topology checking and smoothing to ensure that the generated guide line is a continuous, closed spatial curve that meets engineering requirements. This guide line precisely defines the theoretical center position of the reinforcement model within the concrete.

[0048] Step b4: Based on the guide lines and reinforcement parameters, generate the corresponding BIM models of main reinforcement, stirrups and reinforcing bars through the parametric rule set.

[0049] In this embodiment of the disclosure, the guide line and detailed reinforcement parameters can be input together into the parameterized rule set for the reinforcement of bridge piers and abutments.

[0050] In one embodiment, the parameter rule set can be pre-set with differentiated generation logic for main bars, stirrups, and reinforcing bars: For main bars, based on the guide line, the number of main bars, diameter, and arrangement parameters, the bars are automatically arrayed along the guide line to generate a three-dimensional curve model representing the center path of each main bar; For stirrups, the guide line is segmented according to the stirrup spacing parameters, and at each segment point, a closed ring or spiral three-dimensional curve model is generated based on the stirrup type and size parameters; For reinforcing bars, the corresponding steel reinforcement component model is inserted at its specified setting position according to its geometric specifications.

[0051] Step 103: Bind processing parameters to the BIM model of the steel component, and calculate the processing layout data of the steel component based on the preset processing rule library and processing parameters.

[0052] Among them, the machining layout data is used to drive CNC equipment to produce steel components.

[0053] In one embodiment, users can input specific processing parameters for an existing steel component BIM model (such as a specific rebar or steel arch) through an interactive interface, and bind these processing parameters as new attributes to the original model in a structured data format (such as JSON). Subsequently, a built-in processing rule library can be invoked. This library integrates knowledge of material properties, processing specifications (such as bending adjustment value calculation formulas), and equipment capability parameters. Combined with the bound processing parameters and the model's geometric attributes (such as length and angle), an automated calculation engine precisely calculates the processing layout data required to drive the CNC equipment (e.g., 2D unfolded dimensions, precise cutting / bending point coordinate sequences, processing order, and machine instructions that can be directly imported into the CNC system). This results in a precise set of data instructions that can directly drive the processing equipment for automated production.

[0054] For example, when selecting a longitudinal reinforcement model for a tunnel, the program extracts the bound unit names, start and end mileage, and reinforcement diameter from the model, and calculates the number of reinforcements required for creation. The program then inputs the reinforcement grade and diameter (based on this diameter) for processing, sets the reinforcement cutting length, and automatically cuts the reinforcement according to the mileage range, displaying the length and quantity of the cut reinforcement. Finally, processing parameters are bound to the reinforcement model, including the reinforcement grade and diameter, and the processing data is bound in JSON string format.

[0055] For example, when selecting a steel arch frame for a tunnel, the program extracts the pre-bound unit name, start and end mileage, and generated arch frame ring information from the model. After entering the processing parameters of the I-beam (model, name, code, raw material name, and raw material model) and setting the segment parameters for a single arch frame ring, the program automatically calculates the total number of segments corresponding to each steel arch frame unit. Finally, the entire set of processing parameters is bound to the target steel arch frame model in JSON string format.

[0056] For example, stirrups are bound to parameters such as bending angle and bend length. Steel mesh is bound to parameters such as mesh width, mesh area, number of mesh sheets, mesh length, transverse rebar taps, transverse rebar tails, longitudinal rebar taps, and longitudinal rebar tails.

[0057] In one embodiment, attributes can be extracted from the identity information, location information, and processing information bound to the steel component BIM model. This information is then categorized and displayed on the interface. The user inputs the project name, and based on the steel component type and processing parameters, enlarged sample data (i.e., processing layout data) is calculated. Finally, this information is written into the steel component processing table in the database.

[0058] In some embodiments of this disclosure, step 103, which involves binding processing parameters to the BIM model of the steel component, may specifically include the following steps: In response to the selection of the target steel component model in the BIM model of all steel components, the pre-bound attribute information of the target steel component model is obtained; Send the attribute information to the terminal device so that the terminal device can display the attribute information; Receive processing parameter values ​​sent by the terminal device; the processing parameter values ​​are input by the user based on attribute information; The processing parameter values ​​are bound to the target steel component model in JSON format.

[0059] In this embodiment, the interactive parameter binding process intuitively pushes the attribute information embedded in the BIM model to the user terminal, enabling the input of processing parameters to be guided by a full understanding of the original design attributes. This greatly reduces the risk of human input errors and improves the accuracy and rationality of parameter settings. Simultaneously, binding processing parameters in a standardized JSON format allows for a structured and deep integration of manufacturing information with the design model. This not only facilitates subsequent automatic extraction and parsing but also provides compatibility guarantees for data exchange and integration between different systems. While ensuring data rigor, it optimizes the user experience and achieves an efficient and reliable conversion from design information to manufacturing parameters.

[0060] In some embodiments of this disclosure, step 103, which calculates the machining layout data of the steel component based on a preset machining rule library and machining parameters, may specifically include the following steps: Based on the preset processing rules in the processing rule library that match the steel components, and combined with the bound processing parameters, the two-dimensional blanking length, bending angle sequence, and key point coordinates used for positioning bending or cutting of the steel components are calculated. Based on the two-dimensional cutting length, bending angle sequence, and key point coordinates, processing layout data for driving CNC steel bar processing equipment is generated; the processing layout data includes machine instructions or two-dimensional layout diagrams.

[0061] In this embodiment of the disclosure, the corresponding preset processing rules (such as material properties, process standards and equipment constraints) can be matched in the processing rule library according to the type of steel component (such as steel bars, steel arches), and combined with the processing parameters already bound to the model, the integrated geometric algorithm engine performs automated calculations: for steel bars, the two-dimensional accurate cutting length after unfolding is calculated based on its three-dimensional path, bound bending radius and hook type, and the angle sequence of each bending point and its coordinates on the two-dimensional lofting plane are calculated; for steel arches, the two-dimensional unfolded dimensions of each segment and the coordinates of the connection points can be calculated based on its three-dimensional curve and segmentation parameters.

[0062] In this embodiment, the calculated precise geometric data (two-dimensional blanking length, bending angle sequence, and key point coordinates) can be compiled into machine instructions that can be directly loaded and executed, such as a CNC program file containing G-code and M-code, according to the instruction specifications of the target CNC equipment. This file can instruct the equipment to automatically complete all actions such as straightening, length setting, bending, and cutting. Simultaneously, based on the same data, a two-dimensional lofting diagram containing dimensions, angles, and coordinates is automatically drawn and annotated, providing intuitive visual guidance for manual verification or specific processing scenarios. This process achieves a seamless and accurate conversion from general geometric parameters to executable instructions or readable construction drawings.

[0063] Step 104: Store the processing layout data and processing parameters in the database.

[0064] In this embodiment of the disclosure, the processing layout data and its associated processing parameters can be stored in a server database.

[0065] Step 105: In response to the order generation instruction for the target steel component, generate a steel component processing order based on the processing layout data and processing parameters corresponding to the target steel component in the database.

[0066] In other embodiments of this disclosure, step 105 may specifically include the following steps: Based on the filtering conditions in the order generation instruction, the target processing layout data and target processing parameters that match the target steel component are retrieved from the database. Send the target processing layout data and target processing parameters to the terminal device; Receive order management information and confirmation instructions from users based on the target processing layout data and target processing parameters displayed on the terminal device; The target processing layout data, target processing parameters, and order management information are linked to generate and store steel component processing orders.

[0067] In some embodiments of this disclosure, tunnel and bridge concrete models based on a BIM platform can be pre-built. The element type of the model is entity, and cross-sectional information can be bound to the model. A server is prepared, and a database is established on the server. The database may include an account table, a tunnel steel component processing table, a bridge steel component processing table, a tunnel order table, a bridge order table, and a log table.

[0068] S2: For tunnel steel components, different construction methods are designed according to the different types of steel components.

[0069] In this embodiment, target processing data can be located from the database based on filtering criteria and pushed to the user terminal for visualization. After the user supplements necessary management information based on the intuitive data display and confirms it, the core processing data and supplementary information are structurally associated and encapsulated to ultimately generate a standardized processing order that can directly drive the downstream production process. This process effectively integrates the advantages of automated processing and manual decision-making review while ensuring data accuracy, significantly improving the efficiency and accuracy of order creation.

[0070] As an example, matching processing data can be retrieved from the steel component processing table in the server's value database. Additional order information can be added, an order can be created, and the data saved on the server. Taking a tunnel as an example, first, select the project name, start mileage, end mileage, surrounding rock grade, construction location, and component type. Then, matching processing data will be searched from the server and displayed in the interface list. If the data has not been ordered before, it will be automatically selected. Confirm the selected data row and fill in the planned usage date, category, construction team leader, work area technical supervisor, work area manager, and processing plant manager, etc. Then, order (materials list) data is created and saved in the order table in the database.

[0071] In other embodiments of this disclosure, the method further includes performing at least one of the following operations on the generated steel component processing order: Submit for approval, execute the approval process, modify order details, delete orders, and export order data to a local file.

[0072] In this embodiment of the disclosure, orders can be retrieved from the order table in the server database, and operations such as viewing, submitting for approval, modifying, deleting, exporting, and sending orders to the processing platform can be performed. The main operations may include: Enter search criteria, which support multiple criteria. After selecting different fields and setting values ​​and relationships, you can retrieve orders that meet the criteria from the database and display them in the interface list. Select an order and view the processing details to see the processing data for each type of steel bar in that order; Select an order and choose an approver; the program will then submit the order to the approver. Approval is available to users with approval privileges. It displays all orders awaiting approval and those already approved. For orders awaiting approval, users can fill in their comments and choose whether to approve. For approved orders, users can view the order status. Orders that have not been submitted for approval or have failed approval can be modified. Orders that have already been approved cannot be modified. Export orders: Select a template and export the orders to a local file; Send the order to the processing platform. Convert the order data into JSON format that meets the processing plant's requirements, and call the data submission POST interface provided by the processing plant platform to send the order data to the processing platform; The management platform and the rebar equipment interact with each other, calling the Get interface provided by the processing plant platform to obtain the processing status of the sent orders and fill the information into the order data table.

[0073] The BIM-based intelligent processing modular driving method for steel components proposed in this disclosure deeply binds the BIM design model with structured processing parameters and automatically converts them into processing layout data that can directly drive CNC equipment. This achieves fully automatic and high-precision conversion from 3D design to 2D manufacturing instructions, replacing the traditional manual drawing interpretation, segmented quantity calculation, and manual material cutting process. This improves processing preparation efficiency by an order of magnitude, eliminates human error, and ensures consistency from design data to manufacturing data. Furthermore, by generating structured orders and directly connecting them to the processing platform, a data-driven integrated design and manufacturing closed loop is constructed, significantly improving the intelligence level and overall collaborative efficiency of steel component production.

[0074] In this embodiment, by reading the geometric information of the concrete structure and inputting unified steel component parameters in the interface, a three-dimensional model of the steel component is created with one click, and corresponding attribute information is bound. This disclosure does not require high software skills from users, greatly simplifying the steel component modeling process. It is easy to operate and learn, and easy to promote among engineering designers. In addition, the deep binding and integrated management of steel component processing parameters based on BIM not only includes geometric information in the BIM steel component model, but also deeply binds and stores specific process parameters for automated processing, forming a complete "machinable digital steel component unit," realizing the deep integration of design information and manufacturing information. Furthermore, the "one-click" automatic conversion and issuance mechanism from BIM model to CNC equipment instructions: through the integrated data conversion and drive module, the BIM steel component data containing complete processing information is automatically and directly converted into executable code for various brands of CNC equipment, realizing seamless intelligent docking between design and production equipment. This is a key technical path for achieving automated processing. Furthermore, a closed-loop management process centered on "digital processing orders" has been established: a digital order process with screening, generation, issuance, and tracking as its core has been constructed, transforming discrete steel component processing tasks into structured data flows, realizing a paradigm shift from "drawing management" to "data order management," and improving the collaboration and traceability of production organization.

[0075] Figure 2 This is a block diagram of a BIM-based intelligent processing modular drive device for steel components, according to an exemplary embodiment. (Refer to...) Figure 2 The device includes an acquisition unit 201, a model generation unit 202, a calculation unit 203, a storage unit 204, and an order generation unit 205.

[0076] Among them, the acquisition unit 201 is used to acquire the BIM model of the tunnel or bridge concrete and its bound design parameters; Model generation unit 202 is used to generate a BIM model of steel components based on the concrete BIM model and design parameters, using a set of parametric rules that match the steel component type corresponding to the concrete BIM model. The calculation unit 203 is used to bind processing parameters to the BIM model of the steel component, and calculate the processing layout data of the steel component based on the preset processing rule library and processing parameters; the processing layout data is used to drive the CNC equipment to produce the steel component; Storage unit 204 is used to store machining layout data and machining parameters to a database; The order generation unit 205 is used to generate a steel component processing order in response to an order generation instruction for the target steel component, based on the processing layout data and processing parameters corresponding to the target steel component in the database.

[0077] In some embodiments of this disclosure, the computing unit 203 may specifically be used for: In response to the selection of the target steel component model in the BIM model of all steel components, the pre-bound attribute information of the target steel component model is obtained; Send the attribute information to the terminal device so that the terminal device can display the attribute information; Receive processing parameter values ​​sent by the terminal device; the processing parameter values ​​are input by the user based on attribute information; The processing parameter values ​​are bound to the target steel component model in JSON format.

[0078] In some embodiments of this disclosure, the computing unit 203 may specifically be used for: Based on the preset processing rules in the processing rule library that match the steel components, and combined with the bound processing parameters, the two-dimensional blanking length, bending angle sequence, and key point coordinates used for positioning bending or cutting of the steel components are calculated. Based on the two-dimensional cutting length, bending angle sequence, and key point coordinates, processing layout data for driving CNC steel bar processing equipment is generated; the processing layout data includes machine instructions or two-dimensional layout diagrams.

[0079] In some embodiments of this disclosure, the model generation unit 202 may specifically be used for: For the reinforcement of tunnel lining structures, based on the concrete BIM model, the target mileage range and reinforcement design parameters are obtained; the reinforcement design parameters include the protective layer thickness, the spacing and diameter of the circumferential and longitudinal reinforcements; Read the cross-sectional information of the lining structure from the corresponding mileage section of the concrete BIM model. Based on cross-sectional information, target mileage range, and reinforcement design parameters, the spatial placement of circumferential reinforcement, longitudinal reinforcement, and stirrups in the tunnel lining structure is determined through geometric calculation using a parameterized rule set. Based on the spatial placement, a corresponding BIM model of the steel reinforcement is generated, and the model is bound with identity, location, and design attributes.

[0080] In some embodiments of this disclosure, the model generation unit 202 may specifically be used for: In some embodiments of this disclosure, the steel reinforcement of bridge piers and abutments is obtained from design parameters, including the concrete model identifier of the target pier, the thickness of the protective layer, and the reinforcement parameters; the reinforcement parameters include the parameters of the main reinforcement, stirrups, and reinforcing bars. Identify the pier concrete model corresponding to the target pier concrete model identifier from the concrete BIM model, and extract the structural boundary lines of the pier concrete model. Based on the thickness of the protective layer, the structural edge lines are offset to generate guide lines for the reinforcement layout; Based on the guide lines and reinforcement parameters, the corresponding BIM models of main reinforcement, stirrups and reinforcing bars are generated through a parametric rule set.

[0081] In some embodiments of this disclosure, the order generation unit 205 may specifically be used for: Based on the filtering conditions in the order generation instruction, the target processing layout data and target processing parameters that match the target steel component are retrieved from the database. Send the target processing layout data and target processing parameters to the terminal device; Receive order management information and confirmation instructions from users based on the target processing layout data and target processing parameters displayed on the terminal device; The target processing layout data, target processing parameters, and order management information are linked to generate and store steel component processing orders.

[0082] In some embodiments of this disclosure, the method further includes performing at least one of the following operations on the generated steel component processing order: Submit for approval, execute the approval process, modify order details, delete orders, and export order data to a local file.

[0083] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0084] The BIM-based intelligent processing modular drive device for steel components proposed in this disclosure deeply binds the BIM design model with structured processing parameters and automatically converts it into processing layout data that can directly drive CNC equipment. This achieves fully automatic and high-precision conversion from 3D design to 2D manufacturing instructions, replacing the traditional manual drawing interpretation, segmented quantity calculation, and manual material cutting process. This improves processing preparation efficiency by an order of magnitude, avoids human error, and ensures consistency from design data to manufacturing data. Furthermore, by generating structured orders and directly connecting them to the processing platform, a data-driven integrated design and manufacturing closed loop is constructed, significantly improving the intelligence level and overall collaborative efficiency of steel component production.

[0085] Figure 3 This is a block diagram illustrating an apparatus for a BIM-based intelligent fabrication modular driving method for steel components, according to an exemplary embodiment. For example, apparatus 300 may be an electronic device, such as a mobile phone, computer, digital broadcasting terminal, messaging device, tablet device, personal digital assistant, etc.

[0086] Reference Figure 3 The device 300 may include one or more of the following components: processing component 302, memory 304, power component 306, multimedia component 308, audio component 310, input / output I / O interface 312, sensor component 314, and communication component 316.

[0087] Processing component 302 typically controls the overall operation of device 300, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 302 may include one or more processors 320 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 302 may include one or more modules to facilitate interaction between processing component 302 and other components. For example, processing component 302 may include a multimedia module to facilitate interaction between multimedia component 308 and processing component 302.

[0088] Memory 304 is configured to store various types of data to support the operation of device 300. Examples of such data include instructions for any application or method operating on device 300, contact data, phonebook data, messages, pictures, videos, etc. Memory 304 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0089] The power supply component 306 provides power to the various components of the device 300. The power supply component 306 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power to the device 300.

[0090] Multimedia component 308 includes a screen that provides an output interface between the device 300 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 308 includes a front-facing camera and / or a rear-facing camera. When the device 300 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0091] Audio component 310 is configured to output and / or input audio signals. For example, audio component 310 includes a microphone (MIC) configured to receive external audio signals when device 300 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 304 or transmitted via communication component 316. In some embodiments, audio component 310 also includes a speaker for outputting audio signals.

[0092] I / O interface 312 provides an interface between processing component 302 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, start buttons, and lock buttons.

[0093] Sensor assembly 314 includes one or more sensors for providing status assessments of various aspects of device 300. For example, sensor assembly 314 may detect the on / off state of device 300, the relative positioning of components such as the display and keypad of device 300, changes in the position of device 300 or a component of device 300, the presence or absence of user contact with device 300, the orientation or acceleration / deceleration of device 300, and temperature changes of device 300. Sensor assembly 314 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 314 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 314 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.

[0094] Communication component 316 is configured to facilitate wired or wireless communication between device 300 and other devices. Device 300 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 316 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 316 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0095] In an exemplary embodiment, the apparatus 300 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0096] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 304 including instructions, which can be executed by a processor 320 of the device 300 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0097] In an exemplary embodiment, a computer program product is also provided, including a computer program that implements the above-described method when executed by the processor 320 of the device 300.

[0098] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein.

[0099] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A BIM-based intelligent processing modular driving method for steel components, characterized in that, include: Obtain the concrete BIM model of the tunnel or bridge and its associated design parameters; Based on the concrete BIM model and design parameters, a BIM model of the steel component is generated using a set of parametric rules that matches the steel component type corresponding to the concrete BIM model. Processing parameters are bound to the BIM model of the steel component, and processing layout data of the steel component is calculated based on the preset processing rule library and processing parameters; The machining layout data is used to drive the CNC equipment to produce the steel components; The processing layout data and the processing parameters are stored in the database; In response to an order generation instruction for a target steel component, a steel component processing order is generated based on the processing layout data and processing parameters corresponding to the target steel component in the database.

2. The BIM-based intelligent processing modular driving method for steel components according to claim 1, characterized in that, The process of binding processing parameters to the BIM model of the steel component includes: In response to the selection of a target steel component model in all steel component BIM models, the pre-bound attribute information of the target steel component model is obtained; The attribute information is sent to the terminal device so that the terminal device displays the attribute information. The system receives processing parameter values ​​sent by the terminal device; these processing parameter values ​​are input by the user based on the attribute information. The processing parameter values ​​are bound to the target steel component model in JSON format.

3. The BIM-based intelligent processing modular driving method for steel components according to claim 1, characterized in that, Based on a preset processing rule base and processing parameters, the processing layout data of the steel component is calculated, including: Based on the preset processing rules in the processing rule library that match the steel component, and combined with the bound processing parameters, the two-dimensional blanking length, bending angle sequence, and key point coordinates for positioning bending or cutting of the steel component are calculated. Based on the two-dimensional cutting length, bending angle sequence, and key point coordinates, processing layout data for driving CNC rebar processing equipment is generated; the processing layout data includes machine instructions or two-dimensional layout diagrams.

4. The BIM-based intelligent processing modular driving method for steel components according to claim 1, characterized in that, The step of generating a BIM model of steel components based on the concrete BIM model and design parameters, using a set of parametric rules matching the steel component type corresponding to the concrete BIM model, includes: For the reinforcement of the tunnel lining structure, based on the concrete BIM model, the target mileage range and reinforcement design parameters are obtained; the reinforcement design parameters include the protective layer thickness, the spacing and diameter of the circumferential and longitudinal reinforcements; Read the cross-sectional information of the lining structure from the corresponding mileage section of the concrete BIM model; Based on the cross-sectional information, the target mileage range, and the reinforcement design parameters, the spatial placement of circumferential reinforcement, longitudinal reinforcement, and stirrups in the tunnel lining structure is determined through geometric calculations using the parameterized rule set. Based on the spatial placement location, a corresponding steel reinforcement BIM model is generated, and the model is bound with identity, location, and design attributes.

5. The BIM-based intelligent processing modular driving method for steel components according to claim 1, characterized in that, The step of generating a BIM model of steel components based on the concrete BIM model and design parameters, using a set of parametric rules matching the steel component type corresponding to the concrete BIM model, includes: For the reinforcement of bridge piers and abutments, the concrete model identifier, protective layer thickness, and reinforcement parameters of the target pier are obtained from the design parameters; the reinforcement parameters include the parameters of main bars, stirrups, and reinforcing bars. The concrete model corresponding to the target pier concrete model identifier is determined from the concrete BIM model, and the structural edge lines of the pier concrete model are extracted. Based on the thickness of the protective layer, the structural edge line is offset to generate a guide line for the reinforcement arrangement; Based on the guide lines and the reinforcement parameters, the corresponding BIM models of main reinforcement, stirrups and reinforcing bars are generated through the parameterized rule set.

6. The BIM-based intelligent processing modular driving method for steel components according to claim 1, characterized in that, The step of generating a steel component processing order based on the processing layout data and processing parameters corresponding to the target steel component in the database includes: Based on the filtering conditions in the order generation instruction, the target processing layout data and target processing parameters that match the target steel component are queried from the database. The target processing layout data and target processing parameters are sent to the terminal device; Receive order management information and confirmation instructions supplemented by the user based on the target processing layout data and target processing parameters displayed on the terminal device; The target processing layout data, target processing parameters, and order management information are associated to generate and store steel component processing orders.

7. The BIM-based intelligent processing modular driving method for steel components according to claim 1, characterized in that, The method also includes performing at least one of the following operations on the generated steel component processing order: Submit for approval, execute the approval process, modify order details, delete orders, and export order data to a local file.

8. A BIM-based intelligent processing modular drive device for steel components, characterized in that, include: Acquisition unit, used to acquire the concrete BIM model of a tunnel or bridge and its associated design parameters; The model generation unit is used to generate a BIM model of a steel component based on the concrete BIM model and design parameters, using a set of parametric rules that matches the steel component type corresponding to the concrete BIM model. The calculation unit is used to bind processing parameters to the BIM model of the steel component, and calculate the processing layout data of the steel component based on the preset processing rule library and processing parameters; The machining layout data is used to drive the CNC equipment to produce the steel components; Storage unit, used to store the processing layout data and the processing parameters into a database; The order generation unit is used to generate a steel component processing order in response to an order generation instruction for a target steel component, based on the processing layout data and processing parameters corresponding to the target steel component in the database.

9. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 7.

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