A steel bar material management and control system and method based on three-dimensional digital model

Abstract

The present invention discloses a steel bar material management and control system and method based on a three-dimensional digital model, and relates to the technical field of engineering construction management. The technical key points of the scheme are: a steel bar ingredient list or a steel bar model is imported into a platform, processed by a model lightweight system, a lightweight steel bar three-dimensional model is obtained, and an AI intelligent model review is performed. When the AI ​​intelligent review fails, a review mechanism based on remote collaboration is triggered, and the processing sequence scheme is optimally solved by optimizing the material cutting calculation, a steel bar processing list is generated, and material tracking is coded. Construction documents are distributed to corresponding users according to the user's role and authority, and daily construction tasks are dispatched according to the construction schedule. Through steel bar material management, materials are captured and tracked in real time, operation data and abnormal value records are collected, and analysis and early warning are performed, thereby avoiding the problems of information dispersion and difficulty in coordination in traditional management methods and significantly improving management efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering construction management, and in particular to a steel bar material management and control system and method based on a three-dimensional digital model. Background Art

[0002] In the construction industry, steel bars are an important building material that is used in large quantities and in a wide variety of types. Traditional management methods rely heavily on manual records and paper documents, which are subject to problems such as recording errors, information loss, untimely updates, and low management efficiency. These problems not only affect the construction progress, but may also lead to material waste and quality risks. With the advancement of information technology, advanced technologies such as Building Information Modeling (BIM) have been gradually applied to the construction industry, providing possibilities for the precise management of steel bar materials.

[0003] In the Chinese invention application with application publication number CN109785436A, a management method for the entire life cycle of steel bar information based on BIM technology is disclosed, which belongs to the field of BIM technology application and includes the following steps: Step 1, importing structural design software on the BIM platform to establish a BIM three-dimensional information model; Step 2, importing analysis software on the BIM platform to form a component flat reinforcement information result package. By establishing a BIM database platform, the steel bar information can be turned into an inheritable and traceable database file. The owner, designer, constructor and supervisor are also included as participants in the BIM system application. The comprehensive project management system is operated through a Web browser and a mobile client to improve construction transparency. The owner unit, supervisor unit, construction unit, etc. can timely and accurately understand the quality, safety and progress of the construction site to ensure the smooth progress of the project and provide information for the full life cycle information management of steel bars.

[0004] In view of the above invention application, the prior art still has the following deficiencies:

[0005] Traditional management methods are unable to respond quickly to construction needs, resulting in mismatches in materials and progress, unclear material flow, and thus affecting overall construction efficiency. It is difficult to achieve collaborative work among all parties involved in the project, resulting in poor information transmission and inefficient decision-making. Poor management may lead to unnecessary waste of steel materials, while increasing quality risks and affecting the safety and durability of building structures. Summary of the invention

[0006] 1. Technical issues to be resolved

[0007] In view of the shortcomings of the prior art, the present invention provides a steel bar material management and control system and method based on a three-dimensional digital model. The steel bar ingredient list or steel bar model is imported into the platform, processed by the model lightweight system to obtain a lightweight steel bar three-dimensional model, and AI intelligent model review and model review based on remote collaboration are performed. The processing sequence plan is optimally solved by optimizing the material cutting calculation, and a steel bar processing list is generated. Material tracking is coded, and construction documents are distributed to corresponding users according to the user's role and authority. Daily construction tasks are dispatched according to the construction schedule. Through steel bar material management, materials are captured and tracked in real time, operation data and abnormal value records are collected, and analysis and early warning are performed, thereby solving the problems mentioned in the background technology.

[0008] (II) Technical solution

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions: A steel bar material control method based on a three-dimensional digital model, comprising the following steps:

[0010] Collect the steel bar ingredient list and steel bar model required for the construction project, generate a lightweight steel bar 3D model after lightweight processing of the model, use preset rules and machine learning algorithms to conduct AI intelligent review of the lightweight steel bar 3D model, and generate a review classification report;

[0011] When AI intelligent review fails, a review mechanism based on remote collaboration is triggered;

[0012] The dimensions of residual waste, site occupancy and processing speed are preset, and each optimization dimension of the lightweight steel bar three-dimensional model is calculated by sub-dimensions. A multi-attribute decision algorithm is used to assign weights to each optimization dimension. The weights of each optimization dimension and the results of sub-dimension calculation are weighted to obtain a comprehensive score of different cutting schemes, and the cutting scheme with the highest score is selected as the final optimization solution.

[0013] Generate a steel bar processing list based on the optimized cutting plan, generate a unique tracking code for each steel bar in the lightweight steel bar 3D model, and associate the tracking code with the corresponding item in the steel bar processing list;

[0014] Authentication is performed on users, and corresponding roles are assigned based on their basic information. The final authority weight of the user is calculated based on the user's part-time job status. When the authority weight is less than or equal to the user's final authority weight, the authority is associated with the user, and construction documents are distributed to the corresponding users based on their roles and authorities.

[0015] The construction schedule is broken down into several construction tasks, and the construction tasks are connected with the lightweight steel bar 3D model data. The steel bar items in the steel bar processing list are searched through the tracking code. If the processing status of the steel bar item is pending processing, a daily task is generated and dispatched;

[0016] Use smart cameras to capture and track material images in real time, analyze images through image recognition algorithms, extract material feature information, and compare the analyzed data with the feedback data completed daily on site. If the comparison passes, the consumption record is updated; if not, an early warning is automatically issued;

[0017] The operation data and abnormal value records are collected on a daily basis, classified according to the abnormal type of the abnormal value, and the abnormal trend value of each abnormal type is calculated. When the abnormal trend value of the abnormal type exceeds the deviation threshold, an early warning is automatically issued.

[0018] Furthermore, for the steel bar ingredient list, a third-party library is used to parse the steel bar ingredient list, and a three-dimensional model is reversely constructed through a three-dimensional reconstruction algorithm to generate a lightweight steel bar three-dimensional model;

[0019] For the steel bar model, the analysis tools in the BIM software are used to scan and identify the third-party model, identify the various components and hierarchical structures in the model, and perform geometric simplification, texture optimization, and model segmentation and layering to generate a lightweight steel bar three-dimensional model.

[0020] Furthermore, all the steel bar elements in the lightweight steel bar 3D model are traversed using the function provided by the 3D modeling software API, and the previously defined preset rules are applied to each element to check whether its properties meet the requirements. For each violation, its detailed information is recorded, including the violation type, the ID of the violation element, and the severity of the violation;

[0021] Label the violations found in the preliminary review, use the labeled data to train the machine learning model, and optimize the machine learning model by adjusting model parameters and adding training data;

[0022] The optimized machine learning model is enabled to make autonomous judgments, and the newly uploaded lightweight steel bar 3D model is reviewed once according to preset rules, and then reviewed again by the machine learning model.

[0023] Furthermore, the review mechanism based on remote collaboration includes: when the AI ​​intelligent review fails, a notification of review failure is sent in real time, and a review classification report is attached, and the type of reviewer is determined according to the violation type in the review classification report; reviewers of the corresponding type are screened from the preset reviewer list, assigned according to the polling method, and review task notifications are sent; if the review passes, the review results are submitted; if not, adjustment suggestions are returned to the model submitter.

[0024] Furthermore, according to the preset cutting path and mode, the cutting process under different cutting schemes is simulated, and the raw material utilization rate is calculated. The calculation formula is as follows:

[0025] ;

[0026] in, Indicates the utilization rate of raw materials, represents the total area of ​​raw materials, represents the area of ​​the i-th steel bar, represents the number of the i-th type of steel bars;

[0027] According to different material cutting plans, simulate the stacking of materials on the site and calculate the space utilization rate. The calculation formula is as follows:

[0028] ;

[0029] in, Indicates the space utilization rate. Indicates the actual used space. Indicates the total space.

[0030] Furthermore, the processing time of each steel bar under different cutting schemes is simulated, and the factors of equipment startup time, processing time and tool change time are combined to predict the processing time of each scheme and calculate the processing speed. The calculation formula is as follows:

[0031] ;

[0032] in, Indicates the processing speed, represents the number of the i-th type of steel bars, It represents the time required to process one type i steel bar, C represents the tool change time, and S represents the equipment startup time.

[0033] Furthermore, a basic weight is set for each role, and a part-time bonus coefficient is set for each part-time role. When a user only plays one role, his final authority weight is equal to the basic weight of the role; when a user plays several roles, his final authority weight is:

[0034] ;

[0035] in, Indicates the final authority weight, represents the basic weight of the kth role, represents the multi-time bonus coefficient of the k-th character, , K represents the number of roles held by the user;

[0036] Set a permission set containing several permissions for each role, and assign a fixed weight value to each permission. Traverse the permission set and compare the user's final permission weight with the weight of each permission. When the weight of a permission is less than or equal to the user's final permission weight, associate the permission with the user account. Otherwise, do nothing.

[0037] Furthermore, the construction tasks and lightweight steel bar three-dimensional model data are connected, and the task number, construction area, construction content, required steel bar model and quantity of the construction tasks are matched one by one with the steel bar elements in the lightweight steel bar three-dimensional model. The tracking code of each steel bar element in the lightweight steel bar three-dimensional model is used to find the steel bar items that have been processed or are to be processed in the steel bar processing details table.

[0038] Furthermore, a trend analysis is performed on the classified outliers, and the outlier trend value of each outlier type is calculated. A deviation threshold is pre-set. When the outlier trend value of an outlier type exceeds the deviation threshold, the outlier value data of the outlier type is listed and an early warning is issued. Otherwise, no operation is performed. The calculation formula of the outlier trend value is as follows:

[0039] ;

[0040] in, Indicates Monthly abnormal trend value, Indicates the month number, , T is a positive integer, Indicates The number of outliers per month.

[0041] A steel bar material management and control system based on a three-dimensional digital model, comprising:

[0042] The model lightweight module collects the steel bar ingredient list and steel bar model required for the construction project, and generates a lightweight steel bar 3D model after model lightweight processing;

[0043] Intelligent review module, which uses preset rules and machine learning algorithms to conduct AI intelligent review of lightweight steel bar 3D models and generate review classification reports;

[0044] The review module based on remote collaboration triggers the review mechanism based on remote collaboration when the AI ​​intelligent review fails;

[0045] The optimized cutting algorithm module presets the dimensions of residual waste, site occupancy, and processing speed, performs dimensional calculations on each optimization dimension of the lightweight steel bar three-dimensional model, uses a multi-attribute decision algorithm to assign weights to each optimization dimension, performs weighted calculations on the weights of each optimization dimension and the results of dimensional calculations, obtains a comprehensive score for different cutting schemes, and selects the cutting scheme with the highest score as the final optimization solution;

[0046] The processing details module generates a steel bar processing details list based on the optimized cutting plan, generates a unique tracking code for each steel bar in the lightweight steel bar 3D model, and associates the tracking code with the corresponding entry in the steel bar processing details list;

[0047] The personnel management module authenticates the user, assigns the corresponding role according to the user's basic information, calculates the user's final authority weight according to the user's part-time job status, associates the authority with the user when the authority weight is less than or equal to the user's final authority weight, and distributes the construction documents to the corresponding user according to the user's role and authority;

[0048] The construction plan management module breaks down the construction schedule into several construction tasks, connects the construction tasks with the lightweight steel bar 3D model data, and searches for steel bar items in the steel bar processing list through tracking codes. If the processing status of the steel bar item is pending processing, a daily task is generated and dispatched;

[0049] The steel bar material management module uses smart cameras to capture and track material images in real time, analyzes the images through image recognition algorithms, extracts material feature information, compares the analyzed data with the daily feedback data from the site, and takes corresponding measures based on the comparison results;

[0050] The analysis and early warning module collects operation data and abnormal value records on a daily basis, classifies them according to the abnormal type of the abnormal value, calculates the abnormal trend value of each abnormal type, and automatically issues an early warning when the abnormal trend value of the abnormal type exceeds the deviation threshold.

[0051] (III) Beneficial effects

[0052] The present invention provides a steel bar material management and control system and method based on a three-dimensional digital model, which has the following beneficial effects:

[0053] (1) Based on the violation types in the AI ​​review classification report, the model is assigned to the reviewer who is good at handling this type of violation, thus realizing the professional division of labor among the reviewers. This not only improves the review efficiency, but also enables the reviewers to play the greatest role in their respective areas of expertise, thereby optimizing the allocation of human resources.

[0054] (2) Dimensional calculation allows for separate evaluation of each key aspect, thus ensuring that the evaluation process covers all important aspects and avoiding the one-sidedness that may be caused by single-dimensional evaluation. Each dimension has a specific calculation method and standard, which makes the evaluation results more objective and accurate. At the same time, by weighted calculation of the scores of each dimension, the importance of different dimensions can be comprehensively considered, further improving the accuracy of the evaluation.

[0055] (3) The steel bar items in the steel bar processing list can be found through the tracking code to ensure that the steel bars required for each construction task can be accurately allocated, reducing rework and delays caused by material mismatch. The source, processing process and usage of the steel bars can be traced through the tracking code, which helps to trace the source when problems arise and ensure construction quality.

[0056] (4) Real-time capture and tracking of material images ensures immediate monitoring of steel materials. By comparing the data obtained from image analysis with the feedback data completed daily on site, data differences can be discovered in a timely manner, improving data accuracy and reliability, and helping to quickly identify and resolve problems in material management.

[0057] (5) By integrating lightweight three-dimensional steel bar models, optimized cutting, processing details, construction progress, and steel bar materials, the problems of information dispersion and difficulty in coordination in traditional management methods are avoided, management efficiency is significantly improved, recording errors and information loss in manual operations are reduced, and data accuracy and consistency are ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a schematic diagram of the process of the steel bar material control method based on the three-dimensional digital model of the present invention;

[0059] Figure 2 This is a schematic diagram of the lightweight process of the model of the present invention;

[0060] Figure 3 It is a schematic diagram of the review process based on remote collaboration of the present invention;

[0061] Figure 4 This is a schematic diagram of the process flow of the optimized material cutting algorithm of the present invention;

[0062] Figure 5 It is a schematic diagram of the personnel management process of the present invention;

[0063] Figure 6 It is a schematic diagram of the construction plan management process of the present invention;

[0064] Figure 7 It is a schematic diagram of the steel bar material management process of the present invention. DETAILED DESCRIPTION

[0065] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0066] See also Figure 1-Figure 7 The present invention provides a steel bar material management and control method based on a three-dimensional digital model, comprising the following steps:

[0067] Step 1: Collect the steel bar ingredient list and steel bar model required for the construction project, generate a lightweight steel bar 3D model through model lightweight processing, use preset rules and machine learning algorithms to conduct AI intelligent review of the lightweight steel bar 3D model, and generate a review classification report;

[0068] The step 1 includes the following contents:

[0069] Step 101: Collect the steel bar material list (Excel material list) and steel bar model (third-party model) required for the construction project. For the Excel material list, use a third-party library or tool (such as an API interface in a Java library) to parse the Excel material list, extract information such as steel bar specifications, quantity, and location, and use a 3D reconstruction algorithm to reversely build a 3D model to generate a lightweight steel bar 3D model. Align the constructed lightweight steel bar 3D model information with the information in the original Excel material list in detail to ensure the accuracy and consistency of the model.

[0070] It should be noted that the Excel material list comes from a variety of software, including but not limited to E-Rebar, Glodon, Luban, and specific processing data documents obtained by manual sample drawing. Manual sample drawing refers to the bending, cutting, docking and other processing of steel bars according to the requirements of the design drawings through manual operation or auxiliary tools to form steel bar components that meet the structural needs. The sources of third-party models include but are not limited to BIM modeling software such as Revit, Tekla, BIMmake, etc.

[0071] Step 102: For the third-party model, use the analysis tools in professional software (such as CAD, BIM software, etc.) to scan and identify the third-party model, identify the various components and hierarchical structures in the model, and perform geometric simplification (such as deleting redundant faces and merging similar vertices), texture optimization (such as reducing texture resolution), and model segmentation and layering (splitting a complex model into multiple simple parts and dividing the model into layers) to generate a lightweight steel bar three-dimensional model;

[0072] Step 103: Compare the specifications and quantity of the steel bars in the lightweight model with the data in the original Excel bill of materials item by item to ensure that there are no omissions or errors, use a quantization algorithm to compress the model data, and further optimize the model, such as removing useless components and optimizing the structure of the model;

[0073] Step 104: Use preset rules and machine learning algorithms to intelligently review the lightweight steel bar three-dimensional model and generate a review classification report, including:

[0074] Step 1041: Select a suitable cloud service platform such as AWS, Azure, Alibaba Cloud, etc. according to the requirements, configure necessary computing resources, storage resources and software environment, and upload the lightweight steel bar 3D model data to the cloud platform;

[0075] Step 1042: Establishing preset rules based on industry standards and safety specifications, including the diameter, spacing, bending angle, and connection method of the steel bars;

[0076] Step 1043: Use a programming language to write and execute an automated review script to comprehensively review the lightweight steel reinforcement three-dimensional model, identify violations, and record the review results in a classification log;

[0077] The automated review script uses the functions provided by the 3D modeling software API to traverse all the steel bar elements in the lightweight steel bar 3D model, applies the previously defined preset rules to each element, checks whether its properties (such as diameter, position, bending angle, etc.) meet the requirements, and records detailed information for each violation, including the violation type, the ID of the violation element, and the severity of the violation.

[0078] Step 1044: Label the violations found in the preliminary review to provide training data for machine learning. Use the labeled data to train the machine learning model so that it can identify similar violations. Continuously optimize the accuracy and efficiency of the machine learning model by adjusting model parameters, increasing training data, etc.

[0079] Step 1045: When the data volume reaches the preset threshold and the model performance is stable, the optimized machine learning model is enabled to make autonomous judgments. The newly uploaded lightweight steel bar 3D model will first be reviewed by the preset rules, and then the machine learning model will conduct a secondary review to find potential violations;

[0080] Step 1046: After the review is completed, a review classification report is generated, including the violation type, the ID of the violation element, and the severity of the violation;

[0081] When using, combine the contents of step 101 to step 104:

[0082] Through AI intelligent review of lightweight steel bar three-dimensional models, it is possible to automatically and quickly identify content that does not conform to preset rules, such as whether the specifications, positions, quantities, and connection methods of the steel bars meet the design requirements or building specifications. Compared with traditional manual review, this automated review method not only greatly improves the review efficiency, but also reduces the possibility of human errors and improves the accuracy of the review.

[0083] Step 2: When the AI ​​intelligent review fails, the review mechanism based on remote collaboration is triggered;

[0084] The step 2 includes the following contents:

[0085] Step 201: When the AI ​​intelligent review fails, the cloud platform sends a notification of the review failure to the remote collaborative platform in real time, and attaches a detailed review classification report. The reviewer type is determined based on the violation type in the review classification report. The violation type includes unreasonable structural design, violation of construction specifications, improper use of materials, etc. The reviewer type includes structural engineers, construction experts, and material experts. The violation type and the reviewer type are in correspondence:

[0086] Structural engineers: for violations in structural design, such as insufficient bearing capacity, unreasonable reinforcement, etc. Construction experts: responsible for reviewing violations in construction specifications, such as insufficient lap length of steel bars, wrong anchoring methods, etc. Material experts: for violations in material selection or performance, such as inconsistent steel bar models, substandard quality, etc.;

[0087] Step 202: Filter the corresponding type of reviewers from the preset reviewer list, and assign the reviewers according to the round-robin method (reviewers are assigned in turn within a certain period to ensure that each reviewer has a relatively balanced workload), and send the review task notification. The reviewer logs in to the remote collaboration platform, receives and views the review task, conducts a detailed manual review, and fills in the review opinion;

[0088] Step 203: If the manual review passes, the review result is submitted; if not, adjustment suggestions are returned to the model submitter, and the model submitter adjusts the model according to the adjustment suggestions and resubmits it for review. The adjusted model enters the review process again until it passes the AI ​​intelligent review or manual review;

[0089] When used, combine the contents of step 201 to step 203:

[0090] Based on the violation types in the AI ​​review classification report, the model is assigned to reviewers who are good at handling such violations, thus achieving professional division of labor among reviewers. This not only improves review efficiency, but also enables reviewers to play their greatest role in their respective areas of expertise, thereby optimizing the allocation of human resources.

[0091] Step 3: Preset the dimensions of residual waste, site occupancy, and processing speed, perform dimensional calculations on each optimization dimension of the lightweight steel bar 3D model, use a multi-attribute decision-making algorithm to assign weights to each optimization dimension, perform weighted calculations on the weights of each optimization dimension and the results of dimensional calculations, obtain a comprehensive score for different cutting schemes, and select the cutting scheme with the highest score as the final optimization solution;

[0092] The step three includes the following contents:

[0093] Step 301: Obtain the approved lightweight steel bar three-dimensional model, preset optimization dimensions such as residual waste dimension, site occupancy dimension and processing speed dimension, and allow users to add custom dimensions according to actual needs, such as cost dimension (considering material cost, processing cost, etc.) and environmental dimension (considering energy consumption, carbon emission, etc.). These dimensions are added and calculated according to actual needs;

[0094] Step 302: Perform dimensional calculations on each optimization dimension, specifically including:

[0095] Step 3021: Obtain basic data of the lightweight steel bar three-dimensional model, including the size, shape, and weight of the raw material (before steel bar cutting) and the size and weight of the required material (after steel bar cutting). According to the preset cutting path and mode, simulate the cutting process under different material cutting schemes, and calculate the raw material utilization rate. The calculation formula is as follows:

[0096] ;

[0097] in, Indicates the utilization rate of raw materials, represents the total area of ​​raw materials, represents the area of ​​the i-th steel bar, represents the number of the i-th type of steel bars;

[0098] Total raw material area: refers to the total area of ​​uncut raw materials (such as steel plates, billets, etc.) on a two-dimensional plane. This area is usually fixed and represents the amount of raw materials available;

[0099] Area of ​​the i-th type of steel bar: refers to the area of ​​a certain type of steel bar (steel bars of different sizes, shapes or specifications) obtained after cutting on a two-dimensional plane. This area is calculated based on the cutting path and mode, and the area of ​​each steel bar may be different;

[0100] Step 3022: According to the actual situation of the production site, define the layout and size of the production site, simulate the stacking of materials on the site according to different material cutting plans, and calculate the space utilization rate. The calculation formula is as follows:

[0101] ;

[0102] in, Indicates the space utilization rate. Indicates the actual used space. represents the total space;

[0103] Step 3033: Collect equipment data, including the model and performance parameters of the processing equipment, and simulate the processing time of each steel bar under different cutting schemes according to the size, shape and quantity of the steel bars. Combined with factors such as the equipment's startup time, processing time and tool change time, predict the processing time of each scheme and calculate the processing speed. The calculation formula is as follows:

[0104] ;

[0105] in, Indicates the processing speed, represents the number of the i-th type of steel bars, represents the time required to process one type i steel bar, C represents the tool change time, and S represents the equipment startup time. Since the total amount of processing for different cutting schemes is the same, the total amount of processing is set to 1 here;

[0106] It should be noted that the simulation process of the above steps can be performed by computer-aided design and computer-aided manufacturing software. For example, the intelligent algorithm of CAM software is used to automatically plan the optimal cutting path according to the three-dimensional model, or the preset cutting path and mode, and the cutting process is simulated in the CAM software, and the key information such as the movement trajectory, cutting speed, and cutting force of the tool are displayed in the form of three-dimensional animation. During the simulation process, the software will provide real-time feedback on the cutting effect, including key indicators such as cutting accuracy and surface quality. After the simulation is completed, the software will generate a detailed data report, including key data such as cutting time, material utilization, and energy consumption;

[0107] Step 3034: Allow the user to add custom dimensions based on actual needs and perform evaluation based on the calculation method and standards set by the user, for example:

[0108] Cost dimension: define cost calculation standards (such as material cost, processing cost, labor cost, etc.), collect relevant data, and perform cost calculation. The calculation formula is: total cost = material cost + processing cost + labor cost + ... (other related costs);

[0109] Environmental dimension: define environmental assessment standards (such as energy consumption, carbon emissions, etc.), collect relevant data, and conduct environmental assessments. Assessment standards: may involve the calculation and comparison of indicators such as energy consumption per unit of output and carbon emissions;

[0110] Step 304: Use a multi-attribute decision-making algorithm (such as analytic hierarchy process, TOPSIS method, etc.) to assign weights to each optimization dimension, perform weighted calculation based on the weights of each optimization dimension and the results of sub-dimensional calculation, obtain comprehensive scores of different cutting schemes, and select the cutting scheme with the highest score as the final optimization solution;

[0111] When used, combine the contents of step 301 to step 304:

[0112] Dimensional calculation allows for separate evaluation of each key aspect, thus ensuring that the evaluation process covers all important aspects and avoiding the one-sidedness that may be caused by single-dimensional evaluation. Each dimension has a specific calculation method and standard, which makes the evaluation results more objective and accurate. At the same time, by weighted calculation of the scores of each dimension, the importance of different dimensions can be comprehensively considered to further improve the accuracy of the evaluation.

[0113] Step 4: Generate a steel bar processing list based on the optimized cutting plan, generate a unique tracking code for each steel bar in the lightweight steel bar 3D model, and associate the tracking code with the corresponding entry in the steel bar processing list;

[0114] The step 4 includes the following contents:

[0115] Step 401: Generate a steel bar processing list according to the optimized cutting plan, which lists in detail the processing information of each steel bar in the lightweight steel bar three-dimensional model, including serial number, steel bar model, position, length, weight, processing requirements, processing status, estimated completion time, and remarks, etc.;

[0116] Step 402: Generate a unique tracking code for each steel bar in the lightweight steel bar three-dimensional model. The tracking code includes but is not limited to a QR code, a barcode, a digital ID or a watermark, etc., which is selected according to actual needs, and the tracking code is associated with the corresponding entry in the steel bar processing list to ensure that each steel bar has a unique traceable identifier;

[0117] Step 5: Authenticate the user, assign the corresponding role according to the user's basic information, calculate the user's final authority weight according to the user's part-time job status, and associate the authority with the user when the authority weight is less than or equal to the user's final authority weight. Distribute construction documents according to the user's role and authority;

[0118] The step five includes the following contents:

[0119] Step 501: authenticate the user through ID card information, face recognition or other biometric technology to obtain the user's basic information, including the user's identity information (such as name, work number), department, position and possible part-time job information;

[0120] Step 502: Set a basic weight for each role, set a part-time bonus coefficient for each part-time role, and assign corresponding roles according to the basic information of the user. For example, establish a mapping relationship between the user's department, position and role, and assign corresponding roles according to the mapping relationship;

[0121] Step 503: Calculate the final authority weight of the user according to the part-time job status of the user, including:

[0122] When a user only holds one role, his / her final permission weight is equal to the basic weight of the role;

[0123] When a user assumes several roles, the final authority weight is:

[0124] ;

[0125] in, Indicates the final authority weight, represents the basic weight of the kth role, represents the multi-time bonus coefficient of the k-th character, , K represents the number of roles held by the user;

[0126] Step 504: Set a permission set containing several permissions for each role, and assign a fixed weight value to each permission. Traverse the permission set and compare the user's final permission weight with the weight of each permission. When the weight of the permission is less than or equal to the user's final permission weight, associate the permission with the user account. Otherwise, do nothing.

[0127] Step 505: Generate corresponding construction documents according to the processing requirements and estimated completion time in the steel bar processing details table. Each construction document includes but is not limited to the steel bar model, length, weight, processing requirements, tracking code, and estimated completion time. The construction documents are distributed to corresponding users according to their roles and permissions. For example, a project manager can receive an overview of the construction documents for all projects, while specific construction teams only receive the documents for the part they are responsible for.

[0128] When used, combine the contents of step 501 to step 504:

[0129] Automating the permission allocation process reduces manual intervention and improves work efficiency. Taking into account the user's part-time situation, permission management is further refined by calculating the user's final permission weight. This can more flexibly respond to the actual situation of users' multiple responsibilities and ensure that users can exercise their permissions within an appropriate scope.

[0130] Step 6: Decompose the construction schedule into several construction tasks, connect the construction tasks with the lightweight steel bar 3D model data, and use the tracking code to find the steel bar item in the steel bar processing list. If the processing status of the steel bar item is to be processed, generate daily tasks and dispatch them;

[0131] The step six includes the following contents:

[0132] Step 601: Obtain construction schedule data, and decompose the construction schedule into several construction tasks, including task number, construction area, construction content, required steel bar model and quantity, start date, end date, and responsible person;

[0133] Step 602: Connect the construction task with the lightweight steel bar three-dimensional model data, and use the task number, construction area, construction content, required steel bar model and quantity and other data of the construction task to correspond to the steel bar elements in the lightweight steel bar three-dimensional model one by one, and use the tracking code of each steel bar element in the lightweight steel bar three-dimensional model to find the steel bar items that have been processed or to be processed in the steel bar processing details table;

[0134] Step 603: Check the processing status of the steel bar item. If the processing status of the steel bar item is to be processed, calculate the number of steel bar items that need to be processed each day according to the start date and end date of the construction task, create a task list for each day, including specific steel bar requirements (model, quantity, tracking code), pick-up location and pick-up time, and send the daily task list to the corresponding processing personnel or team;

[0135] When used, combine the contents of step 601 to step 603:

[0136] By using the tracking code to find the steel bar items in the steel bar processing list, we can ensure that the steel bars required for each construction task can be accurately allocated, reducing rework and delays caused by material mismatches. By using the tracking code to trace the source, processing process and usage of the steel bars, it is helpful to trace the source when problems arise and ensure construction quality.

[0137] Step 7: Use smart cameras to capture and track material images in real time, analyze the images through image recognition algorithms, extract material feature information, and compare the analyzed data with the feedback data completed daily on site. If the comparison passes, the consumption record is updated; if not, an early warning is automatically issued;

[0138] The step seven includes the following contents:

[0139] Step 701: Generate a material plan based on the lightweight model data and the construction schedule data, that is, the specifications and quantity of the steel bars required for each construction stage. After the material plan is generated, it is sent to the material procurement department to guide subsequent procurement work;

[0140] It should be noted that the generation of the above material plan is to automatically access the lightweight model data and construction schedule data, but the access is not mandatory, and the input method is not limited to automatic access, and manual input is also possible;

[0141] Step 702: After the materials arrive at the site, authorized personnel will register the materials, including the type, quantity, arrival time and other information of the materials. In the material collection stage, authorized personnel will also record the collection to ensure that each collection has a clear record. Waste material treatment and consumption management are carried out simultaneously, recording the generation and treatment of waste materials, as well as daily material consumption data;

[0142] Step 703: deploy smart cameras in key areas of the construction site (such as material storage areas, processing areas, transportation channels, etc.), capture and track material images in real time, analyze the images through image recognition algorithms (such as target detection, feature extraction algorithms, and tracking algorithms, etc.), extract feature information of the materials, including type, quantity, location, and status, and dynamically track the movement trajectory of the materials to achieve accurate tracking of materials on the construction site;

[0143] Step 704: Compare the analyzed data with the feedback data completed daily on site, for example, compare the type and quantity of materials in the image with the data in the construction log and material collection record, and take corresponding measures according to the comparison results, including: if the comparison passes, update the consumption record; if it fails, automatically issue an early warning to prompt the management personnel to check and handle;

[0144] When used, combine the contents of step 701 to step 704:

[0145] Real-time capture and tracking of material images ensures immediate monitoring of steel materials. Comparing the data obtained from image analysis with the feedback data completed daily on site can promptly discover data differences, improve data accuracy and reliability, and help quickly identify and resolve problems in material management.

[0146] Step 8: Collect operation data and abnormal value records on a daily basis, classify them according to the abnormal type of the abnormal value, calculate the abnormal trend value of each abnormal type, and automatically issue an early warning when the abnormal trend value of the abnormal type exceeds the deviation threshold.

[0147] The step eight includes the following contents:

[0148] Step 801: collect operation data and abnormal value records on a daily basis, including but not limited to abnormal values ​​fed back in the above steps of model lightweighting, AI intelligent review or manual review, optimized material breaking calculation, user authority calculation, construction plan management, and steel bar material management, and classify them according to the abnormal type of abnormal value (such as model deviation, AI intelligent review or manual review error, material breaking calculation error, user authority calculation error, construction plan conflict, material management abnormality, etc.);

[0149] Step 803: Use statistical analysis tools to perform trend analysis on the classified abnormal values, calculate the abnormal trend value of each abnormal type, and pre-set the deviation threshold. When the abnormal trend value of the abnormal type exceeds the deviation threshold, the abnormal value data of the abnormal type is listed, and an early warning is automatically issued to prompt relevant personnel to calibrate the abnormal value. Otherwise, no operation is performed. The calculation formula of the abnormal trend value is as follows:

[0150] ;

[0151] in, Indicates Monthly abnormal trend value, Indicates the month number, , T is a positive integer, Indicates The number of outliers in a month;

[0152] When used, combine the contents of steps 801 to 803:

[0153] By collecting data on a daily basis, we can understand the operating status of steel materials in real time and promptly discover potential problems or anomalies. Once an anomaly is discovered, it can be immediately classified and processed according to the type of anomaly, improving the efficiency and accuracy of problem solving.

[0154] By integrating lightweight three-dimensional steel bar models, optimized cutting, processing details, construction progress and steel bar materials, the problems of information dispersion and difficulty in coordination in traditional management methods are avoided, management efficiency is significantly improved, recording errors and information loss in manual operations are reduced, and data accuracy and consistency are ensured.

[0155] The present invention also provides a steel bar material management and control system based on a three-dimensional digital model, comprising: a model lightweight module (corresponding to Figure 1 , 2 Model lightweight system), intelligent review module, review module based on remote collaboration (corresponding to Figure 1 , 3 Manual review system based on remote collaboration), optimized material cutting algorithm module (corresponding to Figure 1 , 4Optimize the material cutting algorithm system), processing details module, personnel management module (corresponding to Figure 1 , 5 Personnel management system), construction plan management module (corresponding to Figure 1 , 6 Construction plan management system), steel bar material management module (corresponding to Figure 1 , 7 Steel bar material management system) and analysis and early warning modules; among them,

[0156] The model lightweight module collects the steel bar ingredient list and steel bar model required for the construction project, and generates a lightweight steel bar 3D model after model lightweight processing;

[0157] Intelligent review module, which uses preset rules and machine learning algorithms to conduct AI intelligent review of lightweight steel bar 3D models and generate review classification reports;

[0158] The review module based on remote collaboration triggers the review mechanism based on remote collaboration when the AI ​​intelligent review fails;

[0159] The optimized cutting algorithm module presets the dimensions of residual waste, site occupancy, and processing speed, performs dimensional calculations on each optimization dimension of the lightweight steel bar three-dimensional model, uses a multi-attribute decision algorithm to assign weights to each optimization dimension, performs weighted calculations on the weights of each optimization dimension and the results of dimensional calculations, obtains a comprehensive score for different cutting schemes, and selects the cutting scheme with the highest score as the final optimization solution;

[0160] The processing details module generates a steel bar processing details list based on the optimized cutting plan, generates a unique tracking code for each steel bar in the lightweight steel bar 3D model, and associates the tracking code with the corresponding entry in the steel bar processing details list;

[0161] The personnel management module authenticates the user, assigns the corresponding role according to the user's basic information, calculates the user's final authority weight according to the user's part-time job status, associates the authority with the user when the authority weight is less than or equal to the user's final authority weight, and distributes the construction documents to the corresponding user according to the user's role and authority;

[0162] The construction plan management module breaks down the construction schedule into several construction tasks, connects the construction tasks with the lightweight steel bar 3D model data, and searches for steel bar items in the steel bar processing list through tracking codes. If the processing status of the steel bar item is pending processing, a daily task is generated and dispatched;

[0163] The steel bar material management module uses smart cameras to capture and track material images in real time, analyzes the images through image recognition algorithms, extracts material feature information, compares the analyzed data with the daily feedback data from the site, and takes corresponding measures based on the comparison results;

[0164] The analysis and early warning module collects operation data and abnormal value records on a daily basis, classifies them according to the abnormal type of the abnormal value, calculates the abnormal trend value of each abnormal type, and automatically issues an early warning when the abnormal trend value of the abnormal type exceeds the deviation threshold.

[0165] In the application, the several formulas involved are all calculated by removing dimensions and taking their numerical values. The formula is a formula of the most recent real situation obtained by collecting a large amount of data and performing software simulation. The coefficients in the formula are set by technicians in this field according to actual conditions.

[0166] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in combination with electronic hardware, computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0167] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0168] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for controlling steel bar materials based on a three-dimensional digital model, characterized in that: The following steps are involved: Collect the steel bar ingredient list and steel bar model required for the construction project, generate a lightweight steel bar 3D model after lightweight processing of the model, use preset rules and machine learning algorithms to conduct AI intelligent review of the lightweight steel bar 3D model, and generate a review classification report; For the steel bar ingredient list, a third-party library is used to parse the steel bar ingredient list, and a 3D model is generated through a 3D reconstruction algorithm to generate a lightweight steel bar 3D model. For the steel bar model, the analysis tools in the BIM software are used to scan and identify the third-party model, identify the various components and hierarchical structures in the model, and perform geometric simplification, texture optimization, and model segmentation and layering to generate a lightweight steel bar 3D model; Use the functions provided by the 3D modeling software API to traverse all the steel bar elements in the lightweight steel bar 3D model, apply the previously defined preset rules to each element, check whether its properties meet the requirements, and for each violation, record its detailed information, including the violation type, the ID of the violation element, and the severity of the violation; Label the violations found in the preliminary review, use the labeled data to train the machine learning model, and optimize the machine learning model by adjusting model parameters and adding training data; The optimized machine learning model is enabled to make independent judgments, and the newly uploaded lightweight steel bar 3D model is reviewed once according to preset rules, and the machine learning model performs a second review; When AI intelligent review fails, the review mechanism based on remote collaboration is triggered: Send a notification of review failure in real time, attach a review classification report, and determine the type of reviewer based on the violation type in the review classification report; select the reviewer of the corresponding type from the preset reviewer list, assign them according to the polling method, and send a review task notification; If the review is passed, the review result will be submitted; if it is not passed, the adjustment suggestions will be returned to the model submitter; The dimensions of residual waste, site occupation and processing speed are preset, and the optimization dimensions of the lightweight steel bar 3D model are calculated in different dimensions: According to the preset cutting path and mode, the cutting process under different cutting schemes is simulated, and the raw material utilization rate is calculated. The calculation formula is as follows: Among them, Sr represents the raw material utilization rate, A raw Represents the total area of ​​raw materials, A i represents the area of ​​the i-th steel bar, N i represents the number of the i-th type of steel bars; According to different material cutting plans, simulate the stacking of materials on the site and calculate the space utilization rate. The calculation formula is as follows: Among them, Ur represents space utilization, As represents actual used space, and Ts represents total space; The processing time of each steel bar under different cutting schemes is simulated. The processing time of each scheme is predicted by combining the equipment startup time, processing time and tool change time factors. The processing speed is calculated. The calculation formula is as follows: Among them, Pv represents the processing speed, Q i represents the number of the i-th type of steel bars, t i represents the time required to process one type i steel bar, C represents the tool change time, and S represents the equipment startup time; A multi-attribute decision-making algorithm is used to assign weights to each optimization dimension. The weights of each optimization dimension and the results of the sub-dimensional calculation are weighted to obtain the comprehensive scores of different cutting schemes, and the cutting scheme with the highest score is selected as the final optimization solution. Generate a steel bar processing list based on the optimized cutting plan, generate a unique tracking code for each steel bar in the lightweight steel bar 3D model, and associate the tracking code with the corresponding item in the steel bar processing list; Authentication is performed on users, and corresponding roles are assigned based on their basic information. The final authority weight of the user is calculated based on the user's part-time job status. When the authority weight is less than or equal to the user's final authority weight, the authority is associated with the user, and construction documents are distributed to the corresponding users based on their roles and authorities. Set a basic weight for each role and a part-time bonus coefficient for each part-time role. When a user only plays one role, his final authority weight is equal to the basic weight of the role; when a user plays several roles, his final authority weight is: Among them, W final Represents the final authority weight, W base,k represents the basic weight of the kth role, C part,k represents the multi-role bonus coefficient of the kth role, k = 1, 2, ..., K, K represents the number of roles held by the user; Set a permission set containing several permissions for each role, and assign a fixed weight value to each permission. Traverse the permission set and compare the user's final permission weight with the weight of each permission. When the weight of a permission is less than or equal to the user's final permission weight, associate the permission with the user account. Otherwise, do nothing. The construction schedule is broken down into several construction tasks, and the construction tasks are connected with the lightweight steel bar 3D model data. The steel bar items in the steel bar processing list are searched through the tracking code. If the processing status of the steel bar item is pending processing, a daily task is generated and dispatched; Use smart cameras to capture and track material images in real time, analyze images through image recognition algorithms, extract material feature information, and compare the analyzed data with the feedback data completed daily on site. If the comparison passes, the consumption record is updated; if not, an early warning is automatically issued; The operation data and abnormal value records are collected on a daily basis, classified according to the abnormal type of the abnormal value, and the abnormal trend value of each abnormal type is calculated. When the abnormal trend value of the abnormal type exceeds the deviation threshold, an early warning is automatically issued.

2. A steel bar material management and control method based on a three-dimensional digital model according to claim 1, characterized in that: Connect the construction task with the lightweight steel bar three-dimensional model data, and correspond the task number, construction area, construction content, required steel bar model and quantity of the construction task to the steel bar elements in the lightweight steel bar three-dimensional model one by one. Use the tracking code of each steel bar element in the lightweight steel bar three-dimensional model to find the steel bar items that have been processed or are to be processed in the steel bar processing details table.

3. The method for controlling steel bar materials based on a three-dimensional digital model according to claim 1, characterized in that: Perform trend analysis on the classified outliers, calculate the outlier trend value of each outlier type, and pre-set the deviation threshold. When the outlier trend value of an outlier type exceeds the deviation threshold, list the outlier value data of the outlier type and issue an early warning. Otherwise, no operation is performed. The calculation formula of the outlier trend value is as follows: Among them, Q t Indicates the abnormal trend value of the tth month, t represents the month number, t=2,3,…,T, T is a positive integer, N t represents the number of outliers in month t.

4. A steel bar material management and control system based on a three-dimensional digital model, used to implement the method described in any one of claims 1 to 3, characterized in that: include: The model lightweight module collects the steel bar ingredient list and steel bar model required for the construction project, and generates a lightweight steel bar 3D model after model lightweight processing; Intelligent review module, which uses preset rules and machine learning algorithms to conduct AI intelligent review of lightweight steel bar 3D models and generate review classification reports; The review module based on remote collaboration triggers the review mechanism based on remote collaboration when the AI ​​intelligent review fails; The optimized cutting algorithm module presets the dimensions of residual waste, site occupancy, and processing speed, performs dimensional calculations on each optimization dimension of the lightweight steel bar three-dimensional model, uses a multi-attribute decision algorithm to assign weights to each optimization dimension, performs weighted calculations on the weights of each optimization dimension and the results of dimensional calculations, obtains a comprehensive score for different cutting schemes, and selects the cutting scheme with the highest score as the final optimization solution; The processing details module generates a steel bar processing details list based on the optimized cutting plan, generates a unique tracking code for each steel bar in the lightweight steel bar 3D model, and associates the tracking code with the corresponding entry in the steel bar processing details list; The personnel management module authenticates the user, assigns the corresponding role according to the user's basic information, calculates the user's final authority weight according to the user's part-time job status, associates the authority with the user when the authority weight is less than or equal to the user's final authority weight, and distributes the construction documents to the corresponding user according to the user's role and authority; The construction plan management module breaks down the construction schedule into several construction tasks, connects the construction tasks with the lightweight steel bar 3D model data, and searches for steel bar items in the steel bar processing list through tracking codes. If the processing status of the steel bar item is pending processing, a daily task is generated and dispatched; The steel bar material management module uses smart cameras to capture and track material images in real time, analyzes the images through image recognition algorithms, extracts material feature information, compares the analyzed data with the daily feedback data from the site, and takes corresponding measures based on the comparison results; The analysis and early warning module collects operation data and abnormal value records on a daily basis, classifies them according to the abnormal type of the abnormal value, calculates the abnormal trend value of each abnormal type, and automatically issues an early warning when the abnormal trend value of the abnormal type exceeds the deviation threshold.

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