A processing control method and system for steel bridge plate units

Abstract

The present invention discloses a processing control method and system for steel bridge plate units, relating to the field of combined processing. The method includes: obtaining a first plate unit; obtaining the first production forming record of the first plate unit, and obtaining the first production accuracy and the first forming quality; inputting data into an assembly priority prediction model to obtain a first prediction priority index, and generating a first assembly order of the plurality of plate units; optimizing to determine a target assembly order; and performing assembly processing control on the plurality of plate units according to the target assembly order. By adopting this method, the technical problems in the prior art that due to insufficient positioning accuracy and a single assembly order, the positioning of the plate units is inaccurate, affecting the subsequent welding and the stability of the overall structure are solved. It realizes optimizing the assembly order, obtaining a target assembly order, and performing assembly processing control on the plate units, achieving the technical effects of accurate positioning during the assembly process, improving production efficiency and quality stability.

Description

Technical Field

[0001] The present application relates to the technical field of combined processing, and particularly to a processing control method and system for steel bridge deck units. Background Art

[0002] The processing control method for steel bridge deck units mainly stems from the needs of modern bridge construction, especially large and complex steel structure bridges. During the design, construction, and operation of these bridges, extremely high requirements are placed on the processing accuracy and quality control of the deck units. With the progress of technology and the accumulation of engineering practice experience, a relatively complete set of processing control methods for steel bridge deck units has gradually taken shape. However, there are still some defects in the existing processing control methods for steel bridge deck units. Among them, the problems in the assembly link are particularly prominent.

[0003] In summary, in the prior art, there are technical problems that due to insufficient positioning accuracy and a single assembly sequence, the positioning of the deck units is inaccurate, affecting the subsequent welding and the stability of the overall structure. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide a processing control method and system for steel bridge deck units that can optimize the assembly sequence, obtain the target assembly sequence, control the assembly and processing of the deck units, achieve accurate positioning during the assembly process, and improve production efficiency and quality stability.

[0005] In a first aspect, a processing control method for steel bridge deck units is provided, including: obtaining a first deck unit among multiple deck units in a set of steel bridge deck units, where the first deck unit has an identifier of first importance; obtaining a first production forming record of the first deck unit, where the first production forming record includes a first production record and a first forming record; sequentially analyzing the first production record and the first forming record, and respectively obtaining a first production accuracy and a first forming quality; inputting the first importance, the first production accuracy, and the first forming quality into an assembly priority prediction model to obtain a first predicted priority index; generating a first assembly sequence of the multiple deck units based on the first predicted priority index; analyzing first simulation information obtained by performing simulated assembly on the first assembly sequence, and optimizing to determine a target assembly sequence; and controlling the assembly and processing of the multiple deck units according to the target assembly sequence.

[0006] Second aspect, a processing control system for steel bridge plate units is provided, including: a plate unit acquisition module, which is used to acquire a first plate unit among multiple plate units in a set of steel bridge plate units, and the first plate unit has an identifier of first importance; a production and forming record acquisition module, which is used to acquire a first production and forming record of the first plate unit, and the first production and forming record includes a first production record and a first forming record; a record analysis module, which is used to sequentially analyze the first production record and the first forming record, and respectively obtain a first production accuracy and a first forming quality; a predicted priority index acquisition module, which is used to input the first importance, the first production accuracy, and the first forming quality into an assembly priority prediction model to obtain a first predicted priority index; an assembly sequence generation module, which is used to generate a first assembly sequence of the multiple plate units based on the first predicted priority index; an assembly sequence optimization module, which is used to analyze first simulation information obtained by simulating the assembly of the first assembly sequence and optimize and determine a target assembly sequence; a processing control module, which is used to perform assembly processing control on the multiple plate units according to the target assembly sequence.

[0007] The above-mentioned processing control method and system for steel bridge plate units solve the technical problem in the prior art that due to insufficient positioning accuracy and a single assembly sequence, the positioning of the plate units is inaccurate, affecting the subsequent welding and the stability of the overall structure. By optimizing the assembly sequence and obtaining the target assembly sequence, assembly processing control is performed on the plate units, achieving the technical effect of accurate positioning during the assembly process, improving production efficiency and quality stability.

[0008] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of this application more obvious and understandable, the specific embodiments of this application are specifically given below. Brief Description of the Drawings

[0009] Figure 1 It is a flowchart of a processing control method for steel bridge plate units in an embodiment;

[0010] Figure 2 It is a flowchart of obtaining a target assembly sequence of a processing control method for steel bridge plate units in an embodiment;

[0011] Figure 3 It is a structural block diagram of a processing control system for steel bridge plate units in an embodiment.

[0012] Explanation of the reference numerals: plate unit acquisition module 11, production molding record acquisition module 12, record analysis module 13, prediction priority index acquisition module 14, assembly sequence generation module 15, assembly sequence optimization module 16, processing control module 17. DETAILED DESCRIPTION

[0013] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0014] like Figure 1 As shown, the present application provides a processing control method for a steel bridge plate unit, comprising:

[0015] Acquire a first plate unit of a plurality of plate units in a steel bridge plate unit set, wherein the first plate unit has a first importance identifier;

[0016] Steel bridge plate units are an important component of bridge structures, and their design, manufacturing and installation processes all require high precision and rigor. The shape of the plate unit can be rectangular, triangular, etc., and is usually used to simulate plane structures, such as the deck and wall of a bridge. These plate units are mainly composed of multiple nodes, each of which has a specific degree of freedom. The response of the structure under different working conditions is predicted by applying loads and boundary conditions. Processing control is the precise and efficient regulation of the processing of raw materials or semi-finished products to ensure that the products meet the predetermined quality standards and performance requirements. In this application, precise and efficient regulation is performed on the assembly link in the processing. The present application provides a processing control method for steel bridge plate units. By precisely regulating the assembly link, the production efficiency is improved, the production cost is reduced, and the stability and reliability of the product are ensured.

[0017] The steel bridge plate unit set includes different plate units, each of which has a specific function and position, such as large webs, diaphragms, top and bottom plates, etc. Among them, the large web is usually an important part of the main girder of the steel bridge, located below the bridge, and bears the main vertical load of the bridge; the diaphragm is used to separate the internal space of the bridge and enhance the overall stability of the bridge, and the top and bottom plates are located at the upper and lower parts of the bridge respectively, forming the basic outline of the main girder of the bridge. The plate units play a crucial role in the design and manufacturing process of the steel bridge, jointly constituting the main structure of the bridge and ensuring the safety, stability and durability of the bridge. The first plate unit among multiple plate units refers to any plate unit among different types of plate units in the steel bridge plate unit set, denoted as the first plate unit, and the importance corresponding to the first plate unit refers to the priority degree of the first plate unit during the assembly of the steel bridge, denoted as the first importance, and the first plate unit is marked with the first importance. By obtaining the first plate units of multiple plate units in the steel bridge plate unit set, it provides basic information for the optimization of subsequent processing control.

[0018] Collect the first plate unit characteristic parameters of the first plate unit based on the predetermined plate unit characteristics;

[0019] Obtain the first criticality estimation value of the first plate unit based on the expert decision principle;

[0020] Weight the first plate unit characteristic parameters and the first criticality estimation value based on the coefficient of variation principle to obtain the first importance;

[0021] Among them, the predetermined plate unit characteristics include plate support force and plate volume ratio.

[0022] The predetermined plate unit features include plate support force and plate volume ratio. Among them, the plate support force refers to the ability of the steel bridge plate unit to bear its own weight and external loads. It reflects the mechanical properties and stability of the plate unit in the bridge structure; the plate volume ratio is the ratio of the volume of the steel bridge plate unit to the total volume of the structure it is in. This parameter is used to describe the space occupied by the plate unit in the overall structure. The characteristic parameters of the first plate unit are collected according to the predetermined plate unit features, obtained through actual measurement and calculation analysis. For example, the plate support force of the first plate unit can be determined by performing mechanical analysis or experimental testing on the plate unit; the plate volume ratio can be calculated by comparing the volume of the plate unit with the ratio of the total volume of the structure it is in. The principle of expert decision-making is used to obtain the first critical estimate value of the first plate unit, that is, an expert system is used to score or rate the criticality of the first plate unit through the analysis and understanding of big data. The above scoring or rating can be further converted into a numerical estimate value for subsequent calculation and analysis. The principle of the coefficient of variation is used to weight the characteristic parameters of the first plate unit and the first critical estimate value, thereby obtaining the first importance of the first plate unit. The coefficient of variation is an index that measures the degree of data dispersion and can determine the relative importance of different characteristic parameters and estimate values in determining the first importance. The first importance refers to the part that has the greatest impact on structural stability, such as the main beam and the support structure. The correct assembly of the structure is the basis for subsequent work. Calculate the coefficient of variation of each characteristic parameter and estimate value, and assign weights to each characteristic parameter and estimate value according to the size of the coefficient of variation. The parameter or estimate value with a larger coefficient of variation will obtain a larger weight, and vice versa. Multiply each characteristic parameter and estimate value by its corresponding weight to obtain the weighted value, and add up all the weighted values to obtain the first importance of the first plate unit. Through the above method, the characteristic parameters of the first plate unit and the results of expert decision-making are comprehensively considered, so as to more accurately evaluate its importance.

[0023] Obtain the first production and forming record of the first plate unit, where the first production and forming record includes the first production record and the first forming record;

[0024] Obtain the first production and forming record of the first plate unit, where the first production and forming record includes the first production record and the first forming record. The first production record generally covers the processing process of the plate unit from raw materials to semi-finished products, including information on material procurement, cutting, welding, pre-treatment, etc. The first forming record involves the processing and forming process of the plate unit from semi-finished products to final products, such as straightening, grinding, assembly, etc.; by obtaining the first production and forming record of the first plate unit, the detailed situation of the plate unit during the production process can be understood, including the materials, equipment, processes used and possible problems, etc. This is of great significance for subsequent quality analysis and process improvement.

[0025] Analyze the first production record and the first forming record in sequence, and obtain the first production accuracy and the first forming quality respectively;

[0026] The analysis of the first production record includes raw material analysis and processing process analysis. Check various inspection and test reports carried out during the production process, such as dimensional measurement, non-destructive testing, etc. Analyze the inspection data to obtain the first production accuracy corresponding to the first production record. The first production accuracy refers to a comprehensive evaluation of raw materials, processing processes, inspection and testing, and personnel operations. That is, during the production process of steel bridge plate units, it refers to the accuracy achieved from raw material preparation to the completion of semi-finished product processing. It reflects the control level of various process parameters and standards during the production process, as well as the capabilities of the equipment, technology, and operators used. The analysis of the first forming record includes forming process analysis, appearance and dimension inspection, performance and function testing, etc., and obtains the evaluation result of the first forming quality, which covers evaluations in multiple aspects such as forming process, appearance dimensions, performance functions, and problem handling. Integrate the evaluation results of the first production accuracy and the first forming quality to provide a basis for subsequent decision-making and improvement. Use the first production accuracy and the first forming quality to optimize production processes, improve production quality, reduce failure rates, etc., thereby improving the overall performance and reliability of steel bridge plate units.

[0027] Based on the principle of neural network, screen and train the data in the plate unit production and forming database to obtain an intelligent prediction model. The intelligent prediction model includes a processing accuracy predictor and a welding quality predictor;

[0028] Analyze the first production cutting parameters in the first production record extracted based on predetermined cutting characteristics through the processing accuracy predictor to obtain the first production accuracy, and analyze the first welding control parameters in the first production record extracted based on predetermined welding characteristics through the welding quality predictor to obtain the first forming quality;

[0029] Among them, the predetermined cutting characteristics include cutting power, cutting speed, and cutting gas, and the predetermined welding characteristics include welding heat, welding speed, and welding gas.

[0030] Based on the principle of neural network, the data in the production and forming database of plate units is screened and trained to construct an intelligent prediction model. Relevant data related to production accuracy and forming quality are extracted from the production and forming database of plate units, including various production parameters, process conditions, equipment status, etc. Based on the relevant data, an initial intelligent prediction model is built, and the initial intelligent prediction model is supervised and trained according to the relevant data, that is, the accuracy of the initial intelligent prediction model is improved. When the model accuracy of the initial intelligent prediction model reaches the standard, an intelligent prediction model is obtained. The intelligent prediction model includes a processing accuracy predictor and a welding quality predictor. According to the requirements of the processing accuracy predictor and the welding quality predictor, predetermined cutting features and predetermined welding features are respectively extracted. The predetermined cutting features include cutting power, cutting speed, cutting gas, etc., and these features reflect the key parameters in the cutting process. The predetermined welding features include welding heat, welding speed, welding gas, etc., and these features affect the quality and performance of the welded joint. The first production and forming record of the first plate unit is obtained, and by extracting the corresponding cutting features and welding features, it is input into the intelligent prediction model for analysis. The processing accuracy predictor will predict the first production accuracy according to the first production cutting parameters, while the welding quality predictor will predict the first forming quality according to the first welding control parameters. It can provide strong support for the monitoring, optimization and decision-making of the production process. Through the above method, the production and forming data of plate units are deeply mined and analyzed using the principle of neural network to realize the intelligent prediction of processing accuracy and welding quality, thereby improving production efficiency and product quality.

[0031] Input the first importance, the first production accuracy, and the first forming quality into the assembly priority prediction model to obtain the first prediction priority index;

[0032] The assembly priority prediction model is a model constructed based on data analysis and machine learning principles. It is used to predict the priority order of different plate units during the bridge assembly process. This model considers multiple dimensions such as the importance of plate units, production accuracy, and forming quality, and determines the assembly priority by comprehensively evaluating these factors. The first importance represents the relative importance of the first plate unit in the overall structure; the first production accuracy is obtained by analyzing the cutting parameters in the first production record through a processing accuracy predictor, reflecting the accuracy of the first plate unit during the production process; the first forming quality is obtained by analyzing the welding control parameters in the first production record through a welding quality predictor, measuring the quality performance of the plate unit during the forming process. Input the first importance, the first production accuracy, and the first forming quality into the assembly priority prediction model to calculate the first predicted priority index. The first predicted priority index is a numerical result that reflects the priority degree of the first plate unit during the assembly process. The higher the index, the higher the priority the plate unit should have during assembly; conversely, the lower the index, the relatively lower the priority. This result can provide a basis for decision-making during the bridge assembly process, help optimize the assembly sequence, and improve construction efficiency and structural quality. Through the above method, by comprehensively considering multiple factors such as the importance of plate units, production accuracy, and forming quality, intelligent prediction of assembly priority is achieved. This helps reduce the subjectivity and uncertainty of human decision-making and improve the scientificity and accuracy of bridge construction.

[0033] Extract the first assembly control data group from the assembly control database of plate units;

[0034] Obtain the first importance deviation between the first importance and the importance of the first plate unit in the first assembly control data group;

[0035] Obtain the first production accuracy deviation between the first production accuracy and the production accuracy of the first plate unit in the first assembly control data group;

[0036] Obtain the first forming quality deviation between the first forming quality and the forming quality of the first plate unit in the first assembly control data group;

[0037] When the first importance deviation, the first production accuracy deviation, and the first forming quality deviation are all within the predetermined deviation threshold, weight the first importance deviation, the first production accuracy deviation, and the first forming quality deviation to obtain the first comprehensive deviation;

[0038] The assembly priority prediction model obtains the first comprehensive similarity based on the first comprehensive deviation and generates an assembly control descending sequence based on the first comprehensive similarity;

[0039] Use the target assembly priority index of the target assembly control data group in the extracted assembly control descending sequence as the first prediction priority index.

[0040] Extract the first assembly control data group related to the current analysis object from the board unit assembly control database, which contains various control parameters and indicators for the first board unit during the assembly process. The board unit assembly control database refers to a database system specifically used to store and manage the relevant control data and information during the assembly process of bridge board units, recording the key parameters, indicators, and related control requirements at each stage from production to assembly of the board units. Compare the first importance with the importance of the first board unit in the first assembly control data group. Calculate the deviation between the two, that is, the first importance deviation, which reflects the difference in importance of the current analysis object. Compare the first production accuracy with the production accuracy of the first board unit in the first assembly control data group. Calculate the deviation between the two, that is, the first production accuracy deviation. This deviation quantifies the difference in production accuracy. Compare the first forming quality with the forming quality of the first board unit in the first assembly control data group. Calculate the deviation between the two, that is, the first forming quality deviation. This deviation reflects the difference in forming quality. After obtaining the above three deviations, determine whether all three deviations are within the predetermined deviation threshold range. The deviation threshold is set in advance and is used to determine the acceptable range of the deviation. When all deviations meet the threshold conditions, further process these deviations. If all deviations are within the predetermined threshold, the first importance deviation, the first production accuracy deviation, and the first forming quality deviation will be weighted. The purpose of the weighting process is to comprehensively evaluate the impact of these deviations on the assembly priority according to the importance degree of each factor. The weighted result is the first comprehensive deviation. Based on the calculated first comprehensive deviation, the assembly priority prediction model will further analyze and output the first comprehensive similarity, and the first comprehensive similarity reflects the similarity degree between the current analysis object and other board units in the assembly control data group. As can be seen from the above, the deviation and the similarity are inversely proportional, that is, the greater the deviation, the smaller the similarity. Based on the first comprehensive similarity, generate an assembly control descending sequence. The assembly control descending sequence is arranged in descending order of similarity, that is, the board unit with higher similarity is ranked higher in the sequence. Since the greater the similarity, the higher the reference value of its assembly order, extract the target assembly priority index of the target assembly control data group from the generated assembly control descending sequence. For the target board unit in the target assembly control data group, during historical assembly, the ratio of its assembly serial number to the total number of assembled board units in the historical steel bridge assembly is the first prediction priority index. For example, if a total of 10 board units are assembled and the target board unit is the 4th to be assembled, then the priority index is 4 / 10. The first prediction priority index represents the priority level of the target board unit during the assembly process under the current analysis conditions. Through the above method, comprehensively considering multiple factors such as the importance, production accuracy, and forming quality of the board unit, the first prediction priority index is obtained using the assembly priority prediction model, providing scientific and accurate guidance for the bridge assembly process.

[0041] As Figure 2 shown, a first actual three-dimensional model is constructed according to the first actual feature information of the first board unit collected, wherein the first actual feature information includes the first actual dimensional structure parameters and the first actual welding feature parameters of the first board unit;

[0042] The computer-aided design platform performs simulated assembly on the first actual three-dimensional model based on the first assembly sequence to obtain the first simulation information;

[0043] Judge whether there is a predetermined assembly conflict in the first assembly stage simulation data in the extracted first simulation information;

[0044] If not, use the first assembly sequence as the target assembly sequence.

[0045] Based on the first actual feature information of the first plate unit collected, a first actual 3D model can be constructed. The first actual feature information mainly includes the first actual dimensional structure parameters and the first actual welding feature parameters of the first plate unit. The first actual dimensional structure parameters describe the specific dimensions, shapes, and structural characteristics of the plate unit, such as length, width, thickness, hole positions, etc.; the first actual welding feature parameters involve the welding methods, weld types, welding positions, etc. between the plate units. Using the actual feature information, the actual 3D model of the first plate unit, that is, the 3D model of the actually produced plate unit, is constructed on a computer-aided design platform as the basis for subsequent simulated assembly. Among them, the computer-aided design platform is a platform that uses computer technology and related software tools for product design and analysis. In this application, 3D modeling is carried out using the computer-aided design platform. The computer-aided design platform will perform simulated assembly on the first actual 3D model based on a pre-determined first assembly sequence. The simulated assembly process will consider factors such as the relative positions, connection methods, and assembly constraints between the plate units to simulate the actual assembly process. Through simulated assembly, first simulation information can be obtained, including but not limited to the positions, postures, connection states, etc. of the plate units during the assembly process. This is crucial for evaluating the feasibility of the assembly sequence and optimizing the assembly process. After obtaining the first simulation information, the first assembly stage simulation data therein is carefully analyzed to determine whether there are any pre-determined assembly conflicts. The first assembly stage simulation data refers to the assembly process between any one plate unit and the semi-assembled product. Assembly conflicts include problems such as interference between plate units, inability to align, and unreasonable welding positions. The methods for judging assembly conflicts include collision detection algorithms, assembly constraint analysis, welding path planning, etc., which can effectively identify possible assembly conflicts in the simulated assembly process. If after judgment, there are no pre-determined assembly conflicts in the first assembly stage simulation data in the first simulation information, then the first assembly sequence can be considered feasible and used as the target assembly sequence. That is, in the actual assembly process, assembling according to the first assembly sequence can be expected to obtain a structure without assembly conflicts. Through the above method, using the computer-aided design platform, through simulated assembly and assembly conflict judgment, a reasonable target assembly sequence is determined, providing strong support for actual bridge construction.

[0046] The pre-determined assembly conflicts at least include size mismatch, interface mismatch, and unreasonable clearance.

[0047] The predetermined assembly conflicts at least include size mismatches, interface mismatches, and unreasonable clearances. When there are differences in the expected assembly sizes of two or more components, for example, the hole size of one component may not match the shaft of another component, resulting in difficulties in smooth insertion or fixation. Interface mismatches usually refer to the incompatibility of the connection surfaces or interface forms between two components. This may be due to incorrect connection method design, improper selection of interface types, or incorrect setting of the mating relationship between components. For example, the specifications of bolts and nuts do not match, or the interface shapes of two components are inconsistent, which will lead to incorrect connection. Unreasonable clearances mean that the assembly clearances between components are too large or too small and do not meet the design requirements. Excessive clearances may cause structural looseness, instability, or noise; while too small clearances may cause friction, jamming, or assembly difficulties between components. In addition to the above three situations, assembly conflicts also include problems such as interference between board units.

[0048] Generate the first assembly sequence of the multiple board units based on the first predicted priority index;

[0049] Sort the multiple board units based on the first predicted priority index. The board unit with a higher predicted priority index usually has a higher assembly priority. According to the sorted list of board units, generate the first assembly sequence, which serves as a reference for subsequent simulated assembly and actual assembly, and includes information such as the assembly sequence, assembly position, and assembly method of each board unit. By generating the first assembly sequence based on the first predicted priority index, the assembly sequence of multiple board units can be determined more scientifically and efficiently, improving the assembly efficiency and quality, and reducing production costs and risks.

[0050] Analyze the first simulation information obtained from the simulated assembly of the first assembly sequence, and optimize to determine the target assembly sequence;

[0051] Conduct a simulated assembly of the first assembly sequence on a computer-aided design platform. During the simulated assembly process, the platform will dynamically simulate the assembly process of each board unit according to the preset assembly rules and parameters. During the simulation process, the platform will record various key information, such as assembly time, assembly force, assembly accuracy, etc., to form the first simulation information. Analyze the first simulation information, including evaluating various situations that occur during the assembly process, such as assembly conflicts, assembly efficiency, and assembly quality. Based on the analysis of the simulation information, the target assembly sequence can be optimized and determined. For example, optimization algorithms can be used to automatically compare and select multiple possible assembly sequences; or through expert review and empirical judgment, qualitative analysis and evaluation of the simulation results can be carried out. Optimizing and determining the target assembly sequence aims to continuously improve the assembly efficiency and quality of the product.

[0052] Obtain a steel bridge simulation assembly model obtained by controlling the assembly and processing of the multiple plate units according to the target assembly sequence;

[0053] Compare the steel bridge simulation assembly model with a predetermined steel bridge design assembly model to obtain assembly comparison information;

[0054] Analyze the assembly comparison information to obtain the target assembly quality of the target assembly sequence;

[0055] Evaluate the target assembly sequence according to the target assembly quality and the target assembly efficiency of the obtained target assembly sequence.

[0056] Obtain a steel bridge simulation assembly model obtained by controlling the assembly and processing of the multiple plate units according to the target assembly sequence. According to the target assembly sequence, adjust the control program for assembly processing. The control program guides the assembly equipment to simulate the entire assembly process on a computer-aided design platform, assemble the plate units in the target order, and generate a steel bridge simulation assembly model, which can intuitively display the bridge structure after assembly. Obtain a predetermined steel bridge design assembly model, which is established based on the ideal state in the design stage and represents the expected assembly effect. Compare the steel bridge simulation assembly model with the predetermined steel bridge design assembly model to check the differences between the two, including the consistency of the overall structure, the accuracy of the plate unit positions, the quality of the joints, etc. The comparison results will form assembly comparison information, including detailed information such as the amount of difference and the position of the difference between the two. According to the assembly comparison information, a set of indicators related to the target assembly quality can be determined, such as position deviation, connection strength, overall stability, etc. Analyze each indicator in detail to determine whether it is within the acceptable range or exceeds the allowable range, and evaluate whether the overall assembly quality meets the design requirements and usage requirements. During the simulation assembly process, the time used in each stage can be recorded, and the total time consumed in the entire assembly process can be calculated as an indicator of the target assembly efficiency. Combine the target assembly quality and the target assembly efficiency to comprehensively evaluate the target assembly sequence and obtain an evaluation result. The evaluation result will determine whether this assembly sequence can be adopted or whether further optimization is required. Through the above method, obtain a steel bridge simulation assembly model, compare its differences with the design model, analyze the target assembly quality, and finally evaluate the target assembly sequence. This helps to ensure that the assembly process of the steel bridge meets the design requirements and is efficient and feasible.

[0057] Obtain the first design feature information of the first plate unit, where the first design feature information includes the first design dimension structure parameters and the first design welding feature parameters of the first plate unit;

[0058] Construct a first designed 3D model based on the first designed dimensional structure parameters and the first designed welding feature parameters;

[0059] Perform simulated assembly on the first designed 3D model to obtain the designed assembly model of the predetermined steel bridge.

[0060] Obtain the first designed feature information of the first plate element, where the first designed feature information includes the specific attributes and characteristics of the first plate element in the steel bridge design. The first designed feature information includes the first designed dimensional structure parameters and the first designed welding feature parameters of the first plate element. The first designed dimensional structure parameters describe the basic dimensions and structure of the plate element, such as length, width, thickness, hole positions, bending angles, etc., which are the basis for constructing the 3D model and determine the shape and position of the plate element in space. The first designed welding feature parameters are related to the connection method between the plate elements and include welding type, welding position, welding dimensions, etc. The first designed welding feature parameters directly affect the connection strength and stability between the plate elements. After obtaining the first designed feature information, use 3D modeling software to construct the 3D model of the first plate element, including creating the basic shape. According to the dimensional structure parameters, create the basic shape of the plate element in the modeling software, such as a cuboid, cylinder, etc.; add detailed features, add detailed features such as hole positions, bending, chamfers, etc. to the basic shape to make the model closer to the actual plate element; set the welding features. According to the welding feature parameters, mark the welding positions on the model and set the corresponding welding attributes; through the above steps, obtain the first designed 3D model. Perform simulated assembly on the first designed 3D model to obtain the designed assembly model of the predetermined steel bridge. Position and align each plate element in space to ensure the correct relative position and angle between the plate elements. According to the previously set welding features, simulate the welding process between each plate element and check the welding quality and connection strength. After completing the simulated assembly, combine all the plate elements into a whole to generate the designed assembly model of the predetermined steel bridge. Through the above method, visually understand the overall structure and assembly effect of the steel bridge, and provide guidance and reference for the subsequent actual assembly process. By obtaining the designed feature information of the first plate element, constructing the 3D model and performing simulated assembly, the designed assembly model of the predetermined steel bridge can be obtained, providing a basis for the subsequent evaluation and optimization work.

[0061] Control the assembly and processing of the multiple plate elements according to the target assembly sequence.

[0062] Define the target assembly sequence, and formulate a detailed assembly process flow according to the target assembly sequence, including specific information such as the assembly positions, directions, and connection methods of the multiple board units. Control the assembly and processing of the multiple board units according to the target assembly sequence. Through the above method, conduct an overall inspection and test on the assembled bridge structure, and adjust and optimize the assembly process flow in a timely manner according to the actual situation to improve the assembly efficiency and quality. The present method solves the technical problems in the prior art that due to insufficient positioning accuracy and a single assembly sequence, the positioning of the board units is inaccurate, affecting the subsequent welding and the stability of the overall structure. It realizes the optimization of the assembly sequence, obtains the target assembly sequence, controls the assembly and processing of the board units, and achieves the technical effects of accurate positioning during the assembly process, improving production efficiency and quality stability.

[0063] In summary, the beneficial effects of the present method include:

[0064] 1. By strictly following the target assembly sequence for the processing control of the board units, it can ensure that the assembly process of the bridge is more orderly and efficient, reduce the chaos and errors that may occur during the assembly process, and thus improve the overall work efficiency.

[0065] 2. Through the overall inspection and test, it can verify whether the dimensions, shapes, stability, and load-bearing capacity of the bridge structure meet the expected goals. This ensures that the quality and performance of the bridge meet the design requirements and provides a strong guarantee for the safe operation of the bridge;

[0066] 3. Through precise measurement and inspection, potential quality problems can be discovered and corrected in a timely manner, thus avoiding potential structural hazards that may occur later.

[0067] As Figure 3 shown, an embodiment of the present application includes a processing control system for steel bridge board units, including:

[0068] A board unit acquisition module 11, where the board unit acquisition module 11 is used to acquire a first board unit from a set of steel bridge board units, and the first board unit has an identifier of first importance;

[0069] A production and forming record acquisition module 12, where the production and forming record acquisition module 12 is used to acquire a first production and forming record of the first board unit, and the first production and forming record includes a first production record and a first forming record;

[0070] A record analysis module 13, where the record analysis module 13 is used to sequentially analyze the first production record and the first forming record, and respectively obtain a first production accuracy and a first forming quality;

[0071] A predicted priority index obtaining module 14, which is configured to input the first importance, the first production precision, and the first forming quality into an assembly priority prediction model to obtain a first predicted priority index;

[0072] An assembly sequence generating module 15, which is configured to generate a first assembly sequence of the multiple board units based on the first predicted priority index;

[0073] An assembly sequence optimization module 16, which is configured to analyze first simulation information obtained by performing simulated assembly on the first assembly sequence and optimize and determine a target assembly sequence;

[0074] A processing control module 17, which is configured to perform assembly processing control on the multiple board units according to the target assembly sequence.

[0075] Further, the embodiment of the present application further includes:

[0076] A feature parameter obtaining module, which is configured to collect first board unit feature parameters of the first board unit based on predetermined board unit features;

[0077] A criticality estimation value obtaining module, which is configured to obtain a first criticality estimation value of the first board unit based on the principle of expert decision-making;

[0078] An importance obtaining module, which is configured to weight the first board unit feature parameters and the first criticality estimation value based on the coefficient of variation principle to obtain the first importance;

[0079] A predetermined board unit feature inclusion module, wherein the predetermined board unit features include board support force and board volume ratio.

[0080] Further, the embodiment of the present application further includes:

[0081] A screening and training module, which is configured to screen and train data in a board unit production and forming database based on the principle of neural network to obtain an intelligent prediction model, and the intelligent prediction model includes a processing precision predictor and a welding quality predictor;

[0082] A production precision obtaining module, which is configured to analyze first production cutting parameters in the first production record extracted based on predetermined cutting features through the processing precision predictor to obtain the first production precision, and analyze first welding control parameters in the first production record extracted based on predetermined welding features through the welding quality predictor to obtain the first forming quality;

[0083] The feature includes a module. The feature inclusion module is used for this purpose. The predetermined cutting features include cutting power, cutting speed, and cutting gas, and the predetermined welding features include welding heat, welding speed, and welding gas.

[0084] Furthermore, the embodiments of the present application further include:

[0085] An assembly control data group extraction module, which is used to extract the first assembly control data group from the board unit assembly control database;

[0086] An importance deviation module, which is used to obtain the first importance deviation between the first importance and the importance of the first board unit in the first assembly control data group;

[0087] A production precision deviation acquisition module, which is used to obtain the first production precision deviation between the first production precision and the production precision of the first board unit in the first assembly control data group;

[0088] A forming quality deviation acquisition module, which is used to obtain the first forming quality deviation between the first forming quality and the forming quality of the first board unit in the first assembly control data group;

[0089] A comprehensive deviation acquisition module, which is used to weight the first importance deviation, the first production precision deviation, and the first forming quality deviation to obtain a first comprehensive deviation when the first importance deviation, the first production precision deviation, and the first forming quality deviation are all within a predetermined deviation threshold;

[0090] An assembly control descending sequence generation module, which is used for the assembly priority prediction model to obtain a first comprehensive similarity based on the first comprehensive deviation and generate an assembly control descending sequence based on the first comprehensive similarity;

[0091] An assembly priority index acquisition module, which is used to use the target assembly priority index of the target assembly control data group in the extracted assembly control descending sequence as the first prediction priority index.

[0092] Furthermore, the embodiments of the present application further include:

[0093] An actual three-dimensional model construction module, which is used to construct a first actual three-dimensional model according to the first actual feature information of the first board unit collected, where the first actual feature information includes the first actual dimensional structure parameters and the first actual welding feature parameters of the first board unit;

[0094] A simulation assembly module, which is used for a computer-aided design platform to perform simulation assembly on the first actual 3D model based on the first assembly sequence to obtain the first simulation information;

[0095] A predetermined assembly conflict module, which is used to determine whether there is a predetermined assembly conflict in the first assembly stage simulation data in the extracted first simulation information;

[0096] A target assembly sequence acquisition module, which is used to, if not, use the first assembly sequence as the target assembly sequence.

[0097] Furthermore, the embodiment of the present application further includes:

[0098] A predetermined assembly conflict inclusion module, which is used to include that the predetermined assembly conflict at least includes size mismatch, interface mismatch, and unreasonable gap.

[0099] Furthermore, the embodiment of the present application further includes:

[0100] A steel bridge simulation assembly module, which is used to obtain a steel bridge simulation assembly model obtained by controlling the assembly and processing of the plurality of plate units according to the target assembly sequence;

[0101] An assembly comparison information acquisition module, which is used to compare the steel bridge simulation assembly model with a predetermined steel bridge design assembly model to obtain assembly comparison information;

[0102] A target assembly quality acquisition module, which is used to analyze the assembly comparison information to obtain the target assembly quality of the target assembly sequence;

[0103] A target assembly sequence evaluation module, which is used to evaluate the target assembly sequence according to the target assembly quality and the target assembly efficiency of the obtained target assembly sequence.

[0104] Furthermore, the embodiment of the present application further includes:

[0105] A design feature information inclusion module, which is used to obtain the first design feature information of the first plate unit, and the first design feature information includes the first design dimension structure parameters and the first design welding feature parameters of the first plate unit;

[0106] A 3D model construction module, which is used to construct a first design 3D model based on the first design dimension structure parameters and the first design welding feature parameters;

[0107] A predetermined steel bridge design and assembly model acquisition module, which is used to perform simulated assembly on the first design 3D model to obtain the predetermined steel bridge design and assembly model.

[0108] For a specific embodiment of a processing control system for a steel bridge plate unit, reference can be made to the above-described embodiment of the processing control method for a steel bridge plate unit, which will not be elaborated here. Each of the above modules can be embedded in the processor of a computer device in hardware form or be independent of it, or can be stored in the memory of the computer device in software form, so as to facilitate the processor to call and execute the operations corresponding to each of the above modules.

[0109] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0110] The above-described embodiments merely represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application.

Claims

1. A processing control method for a steel bridge plate unit, characterized in that: include: Obtaining a first plate unit of a plurality of plate units in a steel bridge plate unit set, wherein the first plate unit has a mark of first importance, and the mark of the first plate unit having first importance means that the first plate unit is marked with the first importance, and the importance corresponding to the first plate unit means the priority of the first plate unit when assembling the steel bridge, which is recorded as the first importance; Acquire a first production and molding record of the first panel unit, wherein the first production and molding record includes a first production record and a first molding record; Analyzing the first production record and the first molding record in sequence, and obtaining a first production accuracy and a first molding quality respectively; Inputting the first importance, the first production accuracy and the first molding quality into an assembly priority prediction model to obtain a first prediction priority index; generating a first assembly order of the plurality of panel units based on the first predicted priority index; Analyze first simulation information obtained by simulating the first assembly sequence, and optimize and determine a target assembly sequence; The assembly process of the plurality of panel units is controlled according to the target assembly order.

2. The processing control method according to claim 1, characterized in that: Obtaining a first plate element of a plurality of plate elements in a steel bridge plate element set, wherein the first plate element has an identifier of a first importance, includes: Acquire a first plate unit characteristic parameter of the first plate unit based on a predetermined plate unit characteristic; Obtaining a first critical estimated value of the first plate element based on an expert decision principle; The first importance is obtained by weighting the first plate unit characteristic parameter and the first critical estimated value based on the coefficient of variation principle; Wherein, the predetermined plate unit characteristics include plate supporting force and plate volume ratio.

3. The processing control method according to claim 1, characterized in that: Analyzing the first production record and the first molding record in sequence, and obtaining a first production accuracy and a first molding quality respectively, includes: Based on the principle of neural network, the data in the plate unit production and forming database are screened and trained to obtain an intelligent prediction model, which includes a processing accuracy predictor and a welding quality predictor; The first production cutting parameter in the first production record extracted based on the predetermined cutting feature is analyzed by the processing accuracy predictor to obtain the first production accuracy, and the first welding control parameter in the first production record extracted based on the predetermined welding feature is analyzed by the welding quality predictor to obtain the first molding quality; The predetermined cutting characteristics include cutting power, cutting speed and cutting gas, and the predetermined welding characteristics include welding heat, welding speed and welding gas.

4. The processing control method according to claim 1, characterized in that: include: Extracting a first assembly control data group from a board unit assembly control database; obtaining a first importance deviation between the first importance and the importance of a first panel unit in the first assembly control data set; Obtaining a first production precision deviation between the first production precision and the production precision of the first panel unit in the first assembly control data group; Acquire a first molding quality deviation between the first molding quality and a first panel unit molding quality in the first assembly control data group; When the first importance deviation, the first production precision deviation and the first molding quality deviation are all within a predetermined deviation threshold, weighting the first importance deviation, the first production precision deviation and the first molding quality deviation to obtain a first comprehensive deviation; The assembly priority prediction model obtains a first comprehensive similarity based on the first comprehensive deviation, and generates an assembly control descending sequence based on the first comprehensive similarity; The target assembly priority index of the target assembly control data group extracted in the assembly control descending sequence is used as the first predicted priority index.

5. The processing control method according to claim 1, characterized in that: Get the first prediction priority index, including: Constructing a first actual three-dimensional model according to the collected first actual characteristic information of the first plate unit, wherein the first actual characteristic information includes first actual size structure parameters and first actual welding characteristic parameters of the first plate unit; The computer-aided design platform simulates and assembles the first actual three-dimensional model based on the first assembly sequence to obtain the first simulation information; Determining whether the first assembly stage simulation data in the extracted first simulation information has a predetermined assembly conflict; If it does not exist, the first assembly sequence is used as the target assembly sequence.

6. The processing control method according to claim 5, characterized in that: The predetermined assembly conflict at least includes size mismatch, interface mismatch, and unreasonable gap.

7. The processing control method according to claim 1, characterized in that: The method also includes: Acquire a steel bridge simulation assembly model obtained by assembling and processing the plurality of plate units according to the target assembly sequence; Comparing the steel bridge simulation assembly model with a predetermined steel bridge design assembly model to obtain assembly comparison information; Analyzing the assembly comparison information to obtain a target assembly quality of the target assembly sequence; The target assembly sequence is evaluated according to the target assembly quality and the acquired target assembly efficiency of the target assembly sequence.

8. The processing control method according to claim 7, characterized in that: Comparing the steel bridge simulation assembly model with the predetermined steel bridge design assembly model to obtain assembly comparison information includes: Acquire first design feature information of the first plate unit, where the first design feature information includes first design size structure parameters and first design welding feature parameters of the first plate unit; Constructing a first designed three-dimensional model based on the first designed size and structure parameters and the first designed welding characteristic parameters; The first designed three-dimensional model is simulated and assembled to obtain the predetermined steel bridge designed assembly model.

9. A processing control system for a steel bridge plate unit, characterized in that: include: A plate unit acquisition module, the plate unit acquisition module is used to acquire a first plate unit of a plurality of plate units in a steel bridge plate unit set, the first plate unit having a mark of first importance, the mark of the first plate unit having first importance means that the first plate unit is marked with the first importance, and the importance corresponding to the first plate unit means the priority of the first plate unit when assembling the steel bridge, recorded as the first importance; A production molding record acquisition module, the production molding record acquisition module is used to acquire a first production molding record of the first panel unit, the first production molding record including a first production record and a first molding record; A record analysis module, the record analysis module is used to analyze the first production record and the first molding record in sequence, and obtain a first production accuracy and a first molding quality respectively; A prediction priority index obtaining module, wherein the prediction priority index obtaining module is used to input the first importance, the first production accuracy and the first molding quality into an assembly priority prediction model to obtain a first prediction priority index; an assembly sequence generating module, the assembly sequence generating module being used to generate a first assembly sequence of the plurality of panel units based on the first predicted priority index; An assembly sequence optimization module, the assembly sequence optimization module is used to analyze first simulation information obtained by simulating the first assembly sequence, and optimize and determine a target assembly sequence; A processing control module is used to control the assembly processing of the plurality of panel units according to the target assembly sequence.

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