Machining control method and system for steel bridge plate unit

Abstract

The invention discloses a machining control method and system for a steel bridge plate unit, and relates to the field of combined machining. The method comprises the following steps: acquiring a first plate unit; a first production forming record of the first plate unit is obtained, and first production precision and first forming quality are obtained; inputting data to an assembly priority prediction model to obtain a first prediction priority index, and generating a first assembly sequence of the plurality of board units; optimizing and determining a target assembly sequence; and carrying out assembly processing control on the plurality of plate units according to the target assembly sequence. By the adoption of the method, the technical problems that in the prior art, due to insufficient positioning precision and single assembly sequence, positioning of the plate units is inaccurate, and the stability of subsequent welding and the stability of the overall structure are affected are solved, and the purposes of optimizing the assembly sequence, obtaining the target assembly sequence and conducting assembly machining control on the plate units are achieved. And the technical effects of accurate positioning in the assembling process and improvement of the production efficiency and the quality stability are achieved.

Description

Technical Field

[0001] The present application relates to the technical field of combined processing, and in particular to a processing control method and system for a steel bridge plate unit. Background Art

[0002] The processing control method of steel bridge plate units mainly stems from the needs of modern bridge construction, especially large and complex steel structure bridges. These bridges have extremely high requirements for the processing accuracy and quality control of plate units during design, construction and operation. With the advancement of science and technology and the accumulation of engineering practice experience, a relatively complete set of processing control methods for steel bridge plate units has gradually been formed. The existing processing control methods for steel bridge plate units still have some defects. Among them, the problems in the assembly link are particularly prominent.

[0003] In summary, the prior art has technical problems such as insufficient positioning accuracy and a single assembly sequence, which lead to inaccurate positioning of the plate unit and affect the subsequent welding and stability of the overall structure. Summary of the invention

[0004] Based on this, it is necessary to provide a processing control method and system for steel bridge plate units that can optimize the assembly sequence and obtain the target assembly sequence to control the assembly processing of the plate units, thereby achieving accurate positioning during the assembly process and improving production efficiency and quality stability.

[0005] In a first aspect, a processing control method for a steel bridge plate unit is provided, comprising: obtaining a first plate unit of a plurality of plate units in a set of steel bridge plate units, the first plate unit having an identification of a first importance; obtaining a first production and forming record of the first plate unit, the first production and forming record comprising a first production record and a first forming record; analyzing the first production record and the first forming record in sequence, and obtaining a first production accuracy and a first forming quality, respectively; inputting the first importance, the first production accuracy and the first forming quality into an assembly priority prediction model, and obtaining a first prediction priority index; generating a first assembly sequence of the plurality of plate units based on the first prediction priority index; analyzing first simulation information obtained by simulating the assembly of the first assembly sequence, and optimizing and determining a target assembly sequence; and performing assembly and processing control on the plurality of plate units according to the target assembly sequence.

[0006] In a second aspect, a processing control system for a steel bridge plate unit is provided, comprising: a plate unit acquisition module, the plate unit acquisition module is used to acquire a first plate unit of multiple plate units in a steel bridge plate unit set, the first plate unit having a first importance identifier; a production and forming record acquisition module, the production and forming record acquisition module is used to acquire a first production and forming record of the first plate unit, the first production and forming record including a first production record and a first forming record; a record analysis module, the record analysis module is used to analyze the first production record and the first forming record in sequence, and obtain a first production accuracy and a first forming quality respectively; a prediction priority index acquisition module, the prediction priority index acquisition module 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 prediction priority index; an assembly sequence generation module, the assembly sequence generation module is used to generate a first assembly sequence of the multiple plate units based on the first prediction priority index; an assembly sequence optimization module, the assembly sequence optimization module 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, the processing control module 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 of the steel bridge plate unit adopts this method to solve the technical problems in the prior art that the plate unit is not accurately positioned due to insufficient positioning accuracy and a single assembly sequence, thereby affecting the subsequent welding and the stability of the overall structure. By optimizing the assembly sequence and obtaining the target assembly sequence, the plate unit is assembled and processed, achieving accurate positioning during the assembly process and improving the technical effect of production efficiency and quality stability.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A schematic flow chart of a processing control method for a steel bridge plate unit in one embodiment; Figure 2 A schematic flow chart of obtaining a target assembly sequence of a processing control method for a steel bridge plate unit in one embodiment; Figure 3 The present invention is a structural block diagram of a processing control system for a steel bridge plate unit in one embodiment.

[0010] 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

[0011] 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.

[0012] like Figure 1 As shown, the present application provides a processing control method for a steel bridge plate unit, comprising: 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; 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.

[0013] The steel bridge plate unit set contains different plate units, each of which has a specific function and position, such as a large web, a partition, a top and bottom plate, etc. Among them, the large web is usually an important part of the main beam of the steel bridge, located below the bridge, and bears the main vertical load of the bridge; the partition is used to separate the internal space of the bridge and enhance the overall stability of the bridge. The top and bottom plates are located at the upper and lower parts of the bridge, respectively, and constitute the basic outline of the main beam of the bridge. Plate units play a vital role in the design and manufacturing process of steel bridges, and together constitute the main structure of the bridge, ensuring the safety, stability and durability of the bridge. The first plate unit of multiple plate units refers to any plate unit of different types of plate units in the steel bridge plate unit set, recorded as the first plate unit, and the corresponding importance of the first plate unit refers to the priority of the first plate unit when assembling the steel bridge, recorded as the first importance, and the first plate unit is marked with the first importance. By obtaining the first plate unit of multiple plate units in the steel bridge plate unit set, basic information is provided for the optimization of subsequent processing control.

[0014] 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 unit 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.

[0015] The predetermined plate unit characteristics include plate support force and plate volume ratio, wherein 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 in which it is located. This parameter is used to describe the size of 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 characteristics, and obtained through actual measurement and calculation analysis. For example, the plate support force of the first plate unit can be determined by mechanical analysis or experimental testing of the plate unit; the plate volume ratio can be calculated by comparing the ratio of the volume of the plate unit to the total volume of the structure in which it is located, and the first critical estimation value of the first plate unit is obtained by using the expert decision principle, that is, the expert system is used to analyze and understand the big data to score or rate the criticality of the first plate unit. The above score or rating can be further converted into a numerical estimate for subsequent calculation and analysis. The first importance of the first plate unit is obtained by weighting the characteristic parameters and the first critical estimated value of the first plate unit using the coefficient of variation principle. The coefficient of variation is an indicator to measure the degree of data dispersion, which can determine the relative importance of different characteristic parameters and estimated values ​​in determining the first importance. The first importance refers to the part that has the greatest impact on the stability of the structure, such as the main beam and the supporting structure. The correct assembly of the structure is the basis for subsequent work. The coefficient of variation of each characteristic parameter and estimated value is calculated, and a weight is assigned to each characteristic parameter and estimated value according to the size of the coefficient of variation. Parameters or estimated values ​​with larger coefficients of variation will receive larger weights, and vice versa. Each characteristic parameter and estimated value is multiplied by its corresponding weight to obtain a weighted value. All weighted values ​​are added 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 to more accurately evaluate its importance.

[0016] 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; The first production and forming record of the first panel unit is obtained. The first production and forming record includes the first production record and the first forming record. The first production record usually covers the processing of the panel unit from raw materials to semi-finished products, including information on material procurement, cutting, welding, pretreatment and other steps. The first forming record involves the processing and forming process of the panel unit from semi-finished products to final products, such as correction, polishing, assembly, etc. By obtaining the first production and forming record of the first panel unit, the detailed situation of the panel unit in 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.

[0017] analyzing the first production record and the first molding record in sequence, and obtaining a first production accuracy and a first molding quality respectively; The first production record analysis includes raw material analysis and processing analysis, and checks various inspection and test reports conducted during the production process, such as dimension measurement, non-destructive testing, etc., analyzes the inspection data, and obtains the first production accuracy corresponding to the first production record. The first production accuracy refers to a comprehensive evaluation of raw materials, processing, inspection and testing, and personnel operations, that is, the accuracy achieved in the production process of the steel bridge plate unit from raw material preparation to semi-finished product processing. It reflects the control level of various process parameters and standards in the production process, as well as the equipment, technology and operator capabilities used. The first molding record analysis includes molding process analysis, appearance and size inspection, performance and function testing, etc., and obtains the evaluation results of the first molding quality, which covers the evaluation of molding process, appearance size, performance function and problem handling. The evaluation results of the first production accuracy and the first molding quality are integrated to provide a basis for subsequent decision-making and improvement. The first production accuracy and the first molding quality are used to optimize the production process, improve production quality, reduce failure rate, etc., thereby improving the overall performance and reliability of the steel bridge plate unit.

[0018] 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.

[0019] Based on the principle of neural network, the data in the plate unit production and molding database are screened and trained to build an intelligent prediction model, and data related to production accuracy and molding quality are extracted from the plate unit production and molding database, 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, the intelligent prediction model is obtained, and the intelligent prediction model includes a processing accuracy predictor and a welding quality predictor. According to the needs of the processing accuracy predictor and the welding quality predictor, predetermined cutting features and predetermined welding features are extracted respectively. The predetermined cutting features include cutting power, cutting speed, and cutting gas, etc., which reflect the key parameters in the cutting process. The predetermined welding features include welding heat, welding speed, and welding gas, etc., which affect the quality and performance of the welded joint. The first production and molding record of the first plate unit is obtained, and the corresponding cutting features and welding features are extracted and input into the intelligent prediction model for analysis. The machining accuracy predictor will predict the first production accuracy based on the first production cutting parameters, while the welding quality predictor will predict the first molding quality based on the first welding control parameters. This can provide strong support for the monitoring, optimization and decision-making of the production process. Through the above method, the neural network principle is used to deeply mine and analyze the production molding data of the plate unit, realize the intelligent prediction of machining accuracy and welding quality, thereby improving production efficiency and product quality.

[0020] 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; The assembly priority prediction model is a model built based on data analysis and machine learning principles. It is used to predict the priority of different plate units in the bridge assembly process. The model takes into account multiple dimensions such as the importance, production accuracy and molding quality of the plate unit, and determines the priority during assembly 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 by the processing accuracy predictor, reflecting the accuracy of the first plate unit in the production process; the first molding quality is obtained by analyzing the welding control parameters in the first production record by the welding quality predictor, measuring the quality performance of the plate unit in the molding process. The first importance, the first production accuracy and the first molding quality are input into the assembly priority prediction model to calculate the first prediction priority index, which is a numerical result that reflects the priority of the first plate unit in the assembly process. The higher the index, the higher the priority of the plate unit should be during assembly; conversely, the lower the index, the lower the priority. This result can provide a basis for decision-making in the bridge assembly process, help optimize the assembly sequence, and improve construction efficiency and structural quality. Through the above method, multiple factors such as the importance of plate units, production accuracy and molding quality are comprehensively considered to achieve intelligent prediction of assembly priority. This helps to reduce the subjectivity and uncertainty of human decision-making and improve the scientificity and accuracy of bridge construction.

[0021] 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.

[0022] Extract the first assembly control data group related to the current analysis object from the plate unit assembly control database, which contains various control parameters and indicators about the first plate unit in the assembly process. The plate unit assembly control database refers to a database system specifically used to store and manage relevant control data and information in the bridge plate unit assembly process, recording the key parameters, indicators and related control requirements of the plate unit from production to assembly. Compare the first importance with the importance of the first plate 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 plate 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 molding quality with the molding quality of the first plate unit in the first assembly control data group. Calculate the deviation between the two, that is, the first molding quality deviation. This deviation reflects the difference in molding quality. After obtaining the above three deviations, it is determined whether the above three deviations are all within a predetermined deviation threshold range. The deviation threshold is set in advance and is used to determine the acceptable range of deviations. When all deviations meet the threshold condition, these deviations are further processed. If all deviations are within the predetermined threshold, the first importance deviation, the first production accuracy deviation and the first molding quality deviation will be weighted. The purpose of weighted processing is to comprehensively evaluate the impact of these deviations on assembly priority according to the importance 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, which reflects the degree of similarity between the current analysis object and other plate units in the assembly control data group. From the above, it can be obtained that the deviation is inversely proportional to the similarity, that is, the larger the deviation, the smaller the similarity. Based on the first comprehensive similarity, an assembly control descending sequence is generated. The assembly control descending sequence is arranged from high to low according to the similarity, that is, the plate unit with higher similarity is located at the front of the sequence. Since the greater the similarity, the higher the reference value of its assembly order, the target assembly priority index of the target assembly control data group is extracted from the generated assembly control descending sequence. The target plate unit in the target assembly control data group, during the historical assembly, is the ratio of the assembly sequence number of the target plate unit to the total number of assembled plate units among all the plate units assembled in the historical steel bridge, that is, the first predicted priority index. For example, if a total of 10 plate units are assembled, and the target plate unit is the 4th assembled, then the priority index is 4 / 10. The first predicted priority index represents the priority level of the target plate unit in the assembly process under the current analysis conditions. Through the above method, multiple factors such as the importance of the plate unit, production accuracy and molding quality are comprehensively considered, and the assembly priority prediction model is used to obtain the first predicted priority index, providing scientific and accurate guidance for the bridge assembly process.

[0023] like Figure 2 As shown, a first actual three-dimensional model is constructed 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.

[0024] According to the first actual characteristic information of the first plate unit collected, a first actual three-dimensional model can be constructed, and the first actual characteristic information mainly includes the first actual size structural parameter and the first actual welding characteristic parameter of the first plate unit. The first actual size structural parameter describes the specific size, shape and structural characteristics of the plate unit, such as length, width, thickness, hole position, etc.; the first actual welding characteristic parameter involves the welding method, weld type, welding position, etc. between the plate units. Using the actual characteristic information, the actual three-dimensional model of the first plate unit is constructed on the computer-aided design platform, that is, the three-dimensional model of the plate unit actually produced, as the basis for subsequent simulated assembly, wherein the computer-aided design platform is a platform for product design and analysis using computer technology and related software tools. In this application, the computer-aided design platform is used for three-dimensional modeling. The computer-aided design platform will simulate the assembly of the first actual three-dimensional model based on the predetermined first assembly sequence. The simulated assembly process will consider factors such as the relative position, connection method and assembly constraints between the plate units to simulate the actual assembly process. Through the simulated assembly, the first simulation information can be obtained, including but not limited to the position, posture, connection state, etc. of the plate unit during the assembly process. It is essential to evaluate the feasibility of the assembly sequence and optimize the assembly process. After obtaining the first simulation information, the simulation data of the first assembly stage is carefully analyzed to determine whether there is a predetermined assembly conflict. The simulation data of the first assembly stage refers to the assembly process between any plate unit and the semi-assembled product. Assembly conflicts include interference between plate units, inability to align, unreasonable welding positions, etc. Methods for judging assembly conflicts include collision detection algorithms, assembly constraint analysis, welding path planning, etc., which can effectively identify assembly conflicts that may occur during the simulated assembly process. If it is judged that there is no predetermined assembly conflict in the simulation data of the first assembly stage in the first simulation information, the first assembly sequence can be considered feasible and used as the target assembly sequence. That is, in the actual assembly process, assembly is performed according to the first assembly sequence, and a structure without assembly conflicts can be expected. Through the above method, using a computer-aided design platform, a reasonable target assembly sequence is determined through simulated assembly and assembly conflict judgment, providing strong support for actual bridge construction.

[0025] The predetermined assembly conflict at least includes size mismatch, interface mismatch, and unreasonable gap.

[0026] The predetermined assembly conflicts include at least size mismatch, interface mismatch, and unreasonable clearance. Size mismatch occurs when there is a difference between the expected assembly dimensions of two or more parts. For example, the hole size of one part may not match the axis of another part, resulting in failure to insert or fix smoothly. Interface mismatch usually refers to incompatible connection surfaces or interface forms between two parts. This may be due to incorrect connection design, improper interface type selection, or incorrect setting of the matching relationship between parts. For example, the specifications of bolts and nuts do not match, or the interface shapes of two parts are inconsistent, which will lead to failure to connect correctly; unreasonable clearance means that the assembly clearance between parts is too large or too small, which does not meet the design requirements. Too large a clearance may cause the structure to be loose, unstable or noisy; while too small a clearance may cause friction, jamming or assembly difficulties between parts. In addition to the above three situations, assembly conflicts also include problems such as interference between plate units.

[0027] generating a first assembly order of the plurality of panel units based on the first predicted priority index; Based on the first prediction priority index, multiple panel units are sorted. Panel units with higher prediction priority indexes usually have higher assembly priorities. Based on the sorted panel unit list, a first assembly sequence is generated, 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 panel unit. By generating the first assembly sequence based on the first prediction priority index, the assembly sequence of multiple panel units can be determined more scientifically and efficiently, thereby improving assembly efficiency and quality and reducing production costs and risks.

[0028] Analyze first simulation information obtained by simulating the first assembly sequence, and optimize and determine a target assembly sequence; The first assembly sequence is simulated on the computer-aided design platform. During the simulated assembly process, the platform will dynamically simulate the assembly process of each panel 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. The first simulation information is analyzed, including the evaluation of various situations that occur during the assembly process, such as assembly conflicts, assembly efficiency, assembly quality, etc. Based on the analysis of the simulation information, the target assembly sequence can be optimized. For example, an optimization algorithm can be used to automatically compare and select multiple possible assembly sequences; or the simulation results can be qualitatively analyzed and evaluated through expert review and experience judgment. The goal of optimizing the target assembly sequence is to continuously improve the assembly efficiency and quality of the product.

[0029] 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.

[0030] A simulated assembly model of a steel bridge is obtained by controlling the assembly process of the multiple plate units according to the target assembly sequence. According to the target assembly sequence, the control program of the assembly process is adjusted. The control program guides the assembly equipment to simulate the entire assembly process on the computer-aided design platform, assembles the plate units according to the target sequence, and generates a simulated assembly model of a steel bridge, which can intuitively display the bridge structure after the assembly is completed. A predetermined steel bridge design assembly model is obtained, which is established based on the ideal state in the design stage and represents the expected assembly effect. The simulated assembly model of the steel bridge is compared 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 position, the quality of the connection, etc. The comparison result will form assembly comparison information, including detailed information such as the difference amount and the difference position 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. Each indicator is analyzed in detail to determine whether it is within the acceptable range or exceeds the allowable range, and to evaluate whether the overall assembly quality meets the design requirements and usage requirements. During the simulation assembly process, the time taken at each stage can be recorded, and the total time taken for the entire assembly process can be calculated. As an indicator of the target assembly efficiency, the target assembly sequence is comprehensively evaluated in combination with the target assembly quality and target assembly efficiency to obtain the evaluation results. The evaluation results will determine whether the assembly sequence can be adopted or whether further optimization is required. Through the above method, the simulation assembly model of the steel bridge is obtained, the difference between it and the design model is compared, the target assembly quality is analyzed, and finally the target assembly sequence is evaluated. This helps to ensure that the assembly process of the steel bridge meets the design requirements and is efficient and feasible.

[0031] 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.

[0032] The first design feature information of the first plate unit is obtained, wherein the first design feature information includes the specific properties and characteristics of the first plate unit in the design of the steel bridge, 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. The first design dimension structure parameters describe the basic size and structure of the plate unit, such as length, width, thickness, hole position, bending angle, etc. It is the basis for building a three-dimensional model and determines the shape and position of the plate unit in space. The first design welding feature parameters are related to the connection method between the plate units, including welding type, welding position, welding size, etc. The first design welding feature parameters directly affect the connection strength and stability between the plate units. After obtaining the first design feature information, a three-dimensional modeling software is used to construct a three-dimensional model of the first plate unit, including creating a basic shape. According to the size and structure parameters, the basic shape of the plate unit is created in the modeling software, such as a cuboid, a cylinder, etc.; adding detail features, adding hole positions, bends, chamfers and other detail features to the basic shape to make the model closer to the actual plate unit; setting welding features, marking the welding position on the model according to the welding feature parameters, and setting the corresponding welding properties; through the above steps, the first design three-dimensional model is obtained. The first design three-dimensional model is simulated and assembled to obtain the predetermined steel bridge design assembly model, and each plate unit is positioned and aligned in space to ensure that the relative position and angle between the plate units are correct. According to the previously set welding features, the welding process between each plate unit is simulated to check the welding quality and connection strength. After completing the simulated assembly, all plate units are combined into a whole to generate a predetermined steel bridge design assembly model. Through the above method, the overall structure and assembly effect of the steel bridge can be intuitively understood, providing guidance and reference for the subsequent actual assembly process. By obtaining the design feature information of the first plate unit, constructing a three-dimensional model and performing simulated assembly, a design assembly model of the predetermined steel bridge can be obtained, providing a basis for subsequent evaluation and optimization work.

[0033] The assembly process of the plurality of panel units is controlled according to the target assembly order.

[0034] The target assembly sequence is clarified, and a detailed assembly process flow is formulated according to the target assembly sequence, including specific information such as the assembly position, direction, and connection method of the multiple plate units. The multiple plate units are assembled and processed according to the target assembly sequence. Through the above method, the assembled bridge structure is inspected and tested as a whole, and the assembly process flow is adjusted and optimized in time according to actual conditions to improve assembly efficiency and quality. This method solves the technical problem in the prior art that the positioning accuracy is insufficient and the assembly sequence is single, resulting in inaccurate positioning of the plate units, which affects the subsequent welding and the stability of the overall structure. The method optimizes the assembly sequence, obtains the target assembly sequence, and controls the assembly and processing of the plate units, achieving accurate positioning during the assembly process and improving production efficiency and quality stability.

[0035] In summary, the beneficial effects of this method include: 1. By strictly following the target assembly sequence for plate unit processing control, the bridge assembly process can be ensured to be more orderly and efficient, reducing confusion and errors that may occur during the assembly process, thereby improving overall work efficiency.

[0036] 2. Through overall inspection and testing, it can be verified whether the size, shape, stability and 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; 3. Through precise measurement and inspection, potential quality problems can be discovered and corrected in time, thus avoiding structural risks that may occur later.

[0037] like Figure 3 As shown, the embodiment of the present application includes a processing control system for a steel bridge plate unit, including: A plate unit acquisition module 11, wherein the plate unit acquisition module 11 is used to 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; A production molding record acquisition module 12, wherein the production molding record acquisition module 12 is used to acquire a first production molding record of the first panel unit, wherein the first production molding record includes a first production record and a first molding record; A record analysis module 13, the record analysis module 13 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 14, wherein the prediction priority index obtaining module 14 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 15, the assembly sequence generating module 15 is used to generate a first assembly sequence of the plurality of panel units based on the first prediction priority index; An assembly sequence optimization module 16, the assembly sequence optimization module 16 is used to analyze the first simulation information obtained by simulating the first assembly sequence, and optimize and determine the target assembly sequence; A processing control module 17 is used to control the assembly processing of the plurality of panel units according to the target assembly sequence.

[0038] Furthermore, the embodiment of the present application also includes: A characteristic parameter acquisition module, the characteristic parameter acquisition module is used to acquire a first plate unit characteristic parameter of the first plate unit based on a predetermined plate unit characteristic; A critical estimated value acquisition module, the critical estimated value acquisition module is used to acquire a first critical estimated value of the first panel unit based on an expert decision principle; An importance obtaining module, the importance obtaining module is used to obtain the first importance by weighting the first plate unit characteristic parameter and the first critical estimation value based on a coefficient of variation principle; The predetermined panel unit characteristics include modules, wherein the predetermined panel unit characteristics include modules for use therein, wherein the predetermined panel unit characteristics include panel support force and panel volume ratio.

[0039] Furthermore, the embodiment of the present application also includes: A screening and training module, wherein the screening and training module is used to screen and train the data in the plate unit production and forming database based on the principle of a neural network to obtain an intelligent prediction model, wherein the intelligent prediction model includes a processing accuracy predictor and a welding quality predictor; A production accuracy acquisition module, the production accuracy acquisition module is used to analyze the first production cutting parameter in the first production record extracted based on the predetermined cutting feature through the processing accuracy predictor to obtain the first production accuracy, and analyze the first welding control parameter in the first production record extracted based on the predetermined welding feature through the welding quality predictor to obtain the first molding quality; The feature containing module is used in which 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.

[0040] Furthermore, the embodiment of the present application also includes: An assembly control data group extraction module, the assembly control data group extraction module is used to extract a first assembly control data group in a board unit assembly control database; an importance deviation module, the importance deviation module being used to obtain a first importance deviation between the first importance and the importance of the first panel unit in the first assembly control data group; a production precision deviation acquisition module, the production precision deviation acquisition module being used to acquire 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; a molding quality deviation acquisition module, the molding quality deviation acquisition module being used to acquire a first molding quality deviation between the first molding quality and the molding quality of the first panel unit in the first assembly control data group; a comprehensive deviation acquisition module, wherein when the first importance deviation, the first production precision deviation and the first molding quality deviation are all within a predetermined deviation threshold, the comprehensive deviation acquisition module is used to weight the first importance deviation, the first production precision deviation and the first molding quality deviation to obtain a first comprehensive deviation; An assembly control descending sequence generation module, wherein the assembly control descending sequence generation module is used for the assembly priority prediction model to obtain a first comprehensive similarity based on the first comprehensive deviation, and to generate an assembly control descending sequence based on the first comprehensive similarity; An assembly priority index acquisition module, wherein the assembly priority index acquisition module is used to use the target assembly priority index of the target assembly control data group extracted in the assembly control descending sequence as the first predicted priority index.

[0041] Furthermore, the embodiment of the present application also includes: An actual three-dimensional model construction module, the actual three-dimensional model construction module is used to construct 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 a first actual size structure parameter and a first actual welding characteristic parameter of the first plate unit; A simulation assembly module, wherein the simulation assembly module is used for a computer-aided design platform to simulate the assembly of the first actual three-dimensional model based on the first assembly sequence to obtain the first simulation information; A predetermined assembly conflict module, the predetermined assembly conflict module is used to determine whether the first assembly stage simulation data extracted from the first simulation information has a predetermined assembly conflict; A target assembly sequence acquisition module, wherein the target assembly sequence acquisition module is used to use the first assembly sequence as the target assembly sequence if it does not exist.

[0042] Furthermore, the embodiment of the present application also includes: The predetermined assembly conflict inclusion module is used for the predetermined assembly conflict to at least include size mismatch, interface mismatch, and unreasonable gap.

[0043] Furthermore, the embodiment of the present application also includes: A steel bridge simulation assembly module, the steel bridge simulation assembly module is used to obtain a steel bridge simulation assembly model obtained by assembling and processing the plurality of plate units according to the target assembly sequence; An assembly comparison information acquisition module, wherein the assembly comparison information acquisition module is used to compare the steel bridge simulation assembly model with a predetermined steel bridge design assembly model to obtain assembly comparison information; A target assembly quality obtaining module, the target assembly quality obtaining module is used to analyze the assembly comparison information to obtain the target assembly quality of the target assembly sequence; A target assembly sequence evaluation module is used to evaluate the target assembly sequence according to the target assembly quality and the acquired target assembly efficiency of the target assembly sequence.

[0044] Furthermore, the embodiment of the present application also includes: A design feature information inclusion module, the design feature information inclusion module is used to obtain first design feature information of the first plate unit, the first design feature information includes first design size structure parameters and first design welding feature parameters of the first plate unit; A three-dimensional model building module, wherein the three-dimensional model building module is used to build a first designed three-dimensional model based on the first designed size and structure parameters and the first designed welding feature parameters; A module for obtaining a predetermined steel bridge design assembly model is used to simulate and assemble the first designed three-dimensional model to obtain the predetermined steel bridge design assembly model.

[0045] The specific embodiment of a processing control system for a steel bridge plate unit can be referred to the embodiment of a processing control method for a steel bridge plate unit described above, which will not be described in detail here. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0046] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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, they should be considered to be within the scope of this specification.

[0047] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present application, 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: 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; 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 unit 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 first importance identifier; 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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