A computational guidance method and device for mathematical logic recognition

Abstract

The present invention discloses a calculation guidance method and device for mathematical logic identification, which relates to the technical field related to data processing. The method includes: obtaining user submitted information for a target bridge; performing mathematical logic identification on a three-dimensional design model of the bridge and establishing a mathematical logic rule base; discretizing each sheet metal part to be processed into a finite number of sheet metal units and configuring target parameters for each sheet metal unit; constructing a mathematical model and target function for optimizing sheet metal blanking; inputting the mathematical model into a calculation guidance module for solving and obtaining an optimal blanking solution; controlling sheet metal blanking equipment for processing and obtaining a plurality of sheet metal parts required for the target bridge. The method solves the technical problem that the existing sheet metal processing relies on manual arrangement according to sheet metal bridge drawings, resulting in insufficient sheet metal processing precision, low processing efficiency and material utilization, realizes intelligent control of sheet metal blanking, and achieves the technical effect of improving sheet metal blanking efficiency and precision and saving materials.

Description

Technical Field

[0001] The present application relates to the technical field related to data processing, and specifically to a computational guidance method and device for mathematical logic recognition. Background Art

[0002] In the current field of bridge manufacturing engineering, the processing of sheet metal parts is a complex and time-consuming process. How to efficiently and economically process sheet metal parts that meet design requirements has become a key issue that needs to be urgently solved in the field of bridge manufacturing. Sheet metal bridge cutting refers to the cutting, hole-punching and other processing procedures of metal sheets during the manufacturing process of steel structure bridges. Traditional sheet metal processing methods mainly rely on manual experience and manual nesting. This method is not only time-consuming and labor-intensive, but also prone to errors, resulting in low processing efficiency and unable to meet the high requirements of modern bridge engineering for precision and efficiency. Manual nesting often cannot make full use of raw materials, resulting in waste of raw materials. At the same time, existing sheet metal processing technology is difficult to ensure the processing accuracy of sheet metal, thus affecting the stability and safety of the entire bridge structure.

[0003] Therefore, in the current sheet metal bridge cutting technology, there is a technical problem of relying on manual material arrangement according to sheet metal bridge drawings, which leads to insufficient sheet metal processing accuracy, low processing efficiency and material utilization. Summary of the invention

[0004] The present application provides a calculation guidance method and device for mathematical logic recognition, which solves the technical problems existing in the existing sheet metal processing, that is, relying on manual arrangement according to sheet metal bridge drawings, resulting in insufficient sheet metal processing precision, low processing efficiency and material utilization rate, and realizes intelligent control of sheet metal cutting, achieving the technical effect of improving the efficiency and precision of sheet metal cutting and saving materials.

[0005] The present application provides a calculation guidance method for mathematical logic identification, the method comprising: obtaining user submitted information for a target bridge, the user submitted information comprising a three-dimensional design model of the bridge and target bridge requirements; based on the target bridge requirements, performing mathematical logic identification on the three-dimensional design model of the bridge, and establishing a mathematical logic rule library for the target bridge; extracting a plurality of sheet metal parts to be processed from the three-dimensional design model of the bridge, discretizing each sheet metal part to be processed into a finite number of sheet metal units, and configuring target parameters for each sheet metal unit based on the mathematical logic rule library; constructing a mathematical model and an objective function for sheet metal blanking optimization based on sheet metal raw material information and the target parameters, the objective function being constructed based on sheet metal blanking cost and sheet metal blanking efficiency; inputting the mathematical model into a calculation guidance module for solving, and obtaining an optimal blanking plan based on the objective function; generating a CNC machining program based on the optimal blanking plan, controlling the sheet metal blanking equipment for processing, and obtaining a plurality of sheet metal parts required for the target bridge.

[0006] In a possible implementation, based on the target bridge requirements, mathematical logic identification is performed on the three-dimensional design model of the bridge, a mathematical logic rule base of the target bridge is established, and the following processing is performed: the geometric features and topological relationships of the three-dimensional design model of the bridge are extracted, and multiple structural components of the bridge are identified; based on the target bridge requirements, the mechanical performance indicators and design specification constraints of the multiple structural components are obtained; the mechanical performance indicators and the design specification constraints are converted into mathematical expressions, and a mathematical evaluation model for the performance of multiple structural components is established; the logical relationship between the geometric entities in the three-dimensional design model of the bridge is analyzed, and the assembly relationship and connection method between the multiple structural components are extracted; the assembly relationship and the connection method are converted into mathematical expressions, and a mathematical logic model for the assembly of multiple structural components is established; the mathematical evaluation model and the mathematical logic model are integrated to form a complete mathematical logic rule base for the target bridge.

[0007] In a possible implementation, the mechanical performance index and the design specification constraint are converted into mathematical expressions, a mathematical evaluation model for the performance of multiple structural components is established, and the following processing is performed: traverse multiple structural components, obtain a first structural component, extract a first mechanical performance index and a first design specification constraint; based on the first mechanical performance index, determine a first strength performance index, a first stiffness performance index and a first stability performance index of the first structural component, and establish a first strength expression, a first stiffness expression and a first stability expression; according to the first design specification constraint, establish multiple constraint expressions; combine the first strength expression, the first stiffness expression, the first stability expression and the multiple constraint expressions to generate a mathematical evaluation model.

[0008] In a possible implementation, the assembly relationship and the connection method are converted into mathematical expressions, a mathematical logic model for the assembly of multiple structural components is established, and the following processing is performed: traverse the geometric entities in the three-dimensional design model of the bridge to extract multiple assembly component pairs and multiple connection component groups; obtain the first assembly component pair, extract the first assembly feature parameter, compare the first assembly feature parameter with the tolerance matching requirements in the design specification constraints, and establish a first assembly gap judgment inequality; obtain the first connection component group, extract the first connection process information, obtain the first connection position coordinates and the first connection size parameter, and establish a first connection geometry mathematical equation; traverse multiple assembly component pairs and multiple connection component groups, establish multiple assembly gap judgment inequalities and multiple connection geometry mathematical equations; analyze the relative positions of multiple structural components in three-dimensional space, and establish a spatial relationship matrix of multiple structural components; based on the spatial relationship matrix, merge the multiple assembly gap judgment inequalities and the multiple connection geometry mathematical equations to construct a mathematical logic model.

[0009] In a possible implementation, the target parameters of each sheet metal unit are configured based on the mathematical logic rule base, and the following processing is also performed: for the target sheet metal unit, based on the mathematical logic rule base, the target performance parameter requirements and the target size parameter requirements are extracted; based on the target performance parameter requirements and the target size parameter requirements, a target search is performed in a historical sheet metal processing database to obtain multiple historical sheet metal units; distribution characteristics of the performance parameters and size parameters of the multiple historical sheet metal units are analyzed to extract the optimal distribution value of each parameter; based on the optimal distribution value of each parameter, a parameter optimization configuration sequence is constructed as the target parameter of the target sheet metal unit.

[0010] In a possible implementation, the performance parameters and size parameters of the multiple historical sheet metal units are analyzed for distribution characteristics, and the optimal distribution value of each parameter is extracted. The following processing is also performed: for a first performance parameter, the corresponding performance parameter values ​​are extracted from the multiple historical sheet metal units to construct a first performance parameter sequence; for a first size parameter, the corresponding size parameter values ​​are extracted from the multiple historical sheet metal units to construct a first size parameter sequence; numerical distribution statistics are performed on the first performance parameter sequence to obtain a first distribution interval and a first frequency distribution; numerical distribution statistics are performed on the first size parameter sequence to obtain a second distribution interval and a second frequency distribution; based on the first distribution interval and the first frequency distribution, a kurtosis analysis is performed to determine the central tendency and dispersion degree of the first performance parameter distribution; based on the second distribution interval and the second frequency distribution, a kurtosis analysis is performed to determine the central tendency and dispersion degree of the first size parameter distribution; based on the central tendency and dispersion degree of the first performance parameter distribution, the optimal distribution value of the first performance parameter is determined, and based on the central tendency and dispersion degree of the first size parameter distribution, the optimal distribution value of the first size parameter is determined.

[0011] In a possible implementation, the mathematical model is input into a calculation guidance module for solving, and the following processing is also performed: the calculation guidance module includes multiple optimization solution sub-modules, and the multiple optimization solution sub-modules are established based on multiple optimization algorithms; the mathematical model is solved based on the multiple optimization solution sub-modules to obtain multiple sheet metal cutting schemes; through the objective function, the objective function values ​​of the multiple sheet metal cutting schemes are respectively obtained, and the sheet metal cutting scheme with the largest objective function value is selected as the optimal cutting scheme.

[0012] The present application also provides a computational guidance device for mathematical logic recognition, comprising:

[0013] A user submitted information acquisition module is used to acquire user submitted information for a target bridge, wherein the user submitted information includes a three-dimensional bridge design model and target bridge requirements; a mathematical logic rule base establishment module is used to perform mathematical logic identification on the three-dimensional bridge design model based on the target bridge requirements, and establish a mathematical logic rule base for the target bridge; a target parameter configuration module is used to extract a plurality of sheet metal parts to be processed from the three-dimensional bridge design model, discretize each sheet metal part to be processed into a finite number of sheet metal units, and configure the target parameters of each sheet metal unit based on the mathematical logic rule base; an objective function construction module is used to construct a mathematical model and an objective function for sheet metal blanking optimization according to sheet metal raw material information and the target parameters, wherein the objective function is constructed based on sheet metal blanking cost and sheet metal blanking efficiency; an optimal blanking scheme acquisition module is used to input the mathematical model into a calculation guidance module for solving, and obtain an optimal blanking scheme according to the objective function; a CNC machining program generation module is used to generate a CNC machining program according to the optimal blanking scheme, control the sheet metal blanking equipment for processing, and obtain a plurality of sheet metal parts required for the target bridge.

[0014] Through the mathematical logic recognition calculation guidance method and device proposed in this application, it is intended to obtain user-submitted information for the target bridge; perform mathematical logic recognition on the three-dimensional design model of the bridge and establish a mathematical logic rule base; discretize each sheet metal part to be processed into a finite number of sheet metal units, and configure the target parameters of each sheet metal unit; construct a mathematical model and objective function for sheet metal cutting optimization; input the mathematical model into the calculation guidance module for solution to obtain the optimal cutting plan; control the sheet metal cutting equipment for processing to obtain multiple sheet metal parts required for the target bridge. It solves the technical problems existing in the existing sheet metal processing, which relies on manual arrangement according to the sheet metal bridge drawings, resulting in insufficient sheet metal processing precision, low processing efficiency and material utilization, and realizes intelligent control of sheet metal cutting, achieving the technical effect of improving sheet metal cutting efficiency and precision and saving materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the device according to the embodiment of the present application. It should be understood that the previous or following operations are not necessarily performed precisely in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0016] Figure 1 A schematic diagram of a flow chart of a calculation guidance method for mathematical logic recognition provided in an embodiment of the present application;

[0017] Figure 2A schematic diagram of the structure of a calculation guidance device for mathematical logic recognition provided in an embodiment of the present application.

[0018] Explanation of the reference numerals: user submitted information acquisition module 10, mathematical logic rule base establishment module 20, target parameter configuration module 30, target function construction module 40, optimal cutting plan acquisition module 50, CNC machining program generation module 60. DETAILED DESCRIPTION

[0019] 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.

[0020] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0021] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, device, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0022] The present application embodiment provides a computational guidance method for mathematical logic recognition, such as Figure 1 As shown, the method includes:

[0023] First, the three-dimensional design model of the bridge and related design requirements are obtained. Through mathematical logic recognition, these requirements are converted into mathematical rules to form a logical rule base. Then, the sheet metal parts to be processed are automatically extracted according to the logical rule base, discretized into multiple sheet metal units and the corresponding processing parameters are configured. On this basis, the sheet metal raw material information is considered to establish a mathematical model for sheet metal cutting optimization. Before sheet metal cutting, the construction calculator is used to optimize it according to the calculation-guided technology, and the optimal cutting plan is searched and obtained. Finally, the corresponding processing program is generated and the sheet metal cutting equipment is guided to perform sheet metal processing.

[0024] The Construction Calculator is an all-around practical calculation and data query tool designed for scenarios such as project cost, construction site, sheet metal bridge, etc. It is suitable for all types of construction workers, such as cost engineers, construction workers, surveyors, supervisors, etc., as well as related personnel who need to perform engineering calculations. The Construction Calculator has built-in a large number of professional formulas in the construction industry, including hardware profiles commonly used in cost and construction (such as commonly used steel, various pipes, steel bar calculations, hardware accessories), project site management calculations (such as earthwork pavement, decorative masonry, stair railings, laboratories, HVAC electrical, coordinate analysis, etc.), bridge sheet metal pipe cutting and setting out, etc. Users only need to enter parameters to get the calculation results instantly; it supports user-defined formulas to meet the formula editing requirements of multiple industries and help engineering personnel greatly improve work efficiency; it can meet the results of various scenarios Output requirements, including but not limited to various total areas, internal areas, side areas, volumes, total weights, total lengths, total quantities, total prices, unfolded line distances, etc.; it has built-in industry-related data information and data diagram references, with clear and traceable sources, to solve the problem of users constantly searching for data and finding sources in different business scenarios. In addition, the construction calculator has a variety of built-in calculation formulas, which basically cover all work needs in the industry, including cost, construction site, bridge sheet metal, water and electricity and other fields. It is rich in functions and practical, and users do not need to check or measure by themselves.

[0025] Assuming that a steel structure bridge design has specific requirements for the strength and stiffness of sheet metal parts, these requirements are automatically identified and converted into mathematical rules, for example: "The thickness of the web of the main beam is not less than 10mm, the width of the flange is not less than 200mm, and the welding length is not less than 100mm". Then all main beam parts are automatically extracted and divided into sheet metal units such as webs, upper flanges, and lower flanges. The corresponding thickness, width and other processing parameters are configured according to the rule library. Then, an optimization model is established in combination with information such as steel inventory. The goal is to minimize material waste and meet all processing requirements. Through calculation-guided search, a cutting plan is obtained: on a steel plate 6 meters long and 2 meters wide, 8 webs are cut along the length direction and 16 flanges are cut along the width direction. The scrap rate is only 2%, which is much lower than the traditional manual layout and cutting. Finally, the cutting plan is converted into a CNC cutting program to realize automated processing, and the entire process does not require human intervention.

[0026] Step S100: obtaining user submitted information for a target bridge, wherein the user submitted information includes a three-dimensional design model of the bridge and requirements of the target bridge.

[0027] Preferably, user-submitted information of the target bridge is obtained, including the three-dimensional design model of the bridge and the requirements of the target bridge. Specifically, the three-dimensional design model of the bridge is a virtual model of the bridge created by the user through professional three-dimensional modeling software (such as AutoCAD, Revit, SketchUp, etc.), which displays in detail the structure, shape, size, material and other information of the bridge, such as the overall structure of the bridge, including the superstructure of the bridge (such as the bridge span structure, support system), the lower structure (such as piers, abutments, foundations) and ancillary structures (such as bridge head cladding, conical slope protection, diversion projects, etc.); detailed size and proportion, the size of each part in the model should be consistent with the actual bridge, and the proportion should be accurate; material information, the model should contain the material information used in each part of the bridge, such as steel, concrete, wood, etc., as well as the specifications and performance parameters of the materials; connection and construction details, the model should clearly display the connection method between the various parts of the bridge, construction details and process requirements such as welding and bolting. Target bridge requirements refer to the specific requirements of users for bridge design, construction and manufacturing, usually including bridge type and function, bridge type (such as highway bridge, railway bridge, pedestrian bridge, etc.) and functional requirements (such as traffic capacity, bearing capacity, seismic resistance, etc.); design parameters and standards, parameters and data required for bridge design, such as bridge span, bridge deck width, load requirements, etc.; construction conditions and environment, environmental conditions during bridge construction (such as geological conditions, climatic conditions, etc.) and construction conditions (such as construction equipment, construction methods, etc.); economy, factors such as bridge cost, construction period and later maintenance cost.

[0028] Step S200: Based on the requirements of the target bridge, mathematical logic recognition is performed on the three-dimensional design model of the bridge to establish a mathematical logic rule base of the target bridge.

[0029] Preferably, based on the target bridge requirements, mathematical logic recognition is performed on the three-dimensional design model of the bridge, that is, an in-depth analysis of the bridge design model, and extraction and establishment of corresponding mathematical logic rules according to the specific requirements of the bridge. Specifically, mathematical logic recognition refers to the use of the principles and methods of mathematics and logic to conduct in-depth analysis and recognition of an object (such as a three-dimensional design model of a bridge). First, the three-dimensional design model of the bridge is deeply analyzed, including the geometric shape, structural characteristics, material properties, etc. of the model. On the basis of model analysis, key data related to bridge design are extracted, such as the span, height, width, material strength, etc. of the bridge. Using the principles of mathematics and logic, Analyze and process the extracted data to find out the internal connections and rules between the data; then, according to the requirements and objectives of bridge design, design the structure and content of the mathematical logic rule library, which includes various mathematical logic rules related to bridge design, such as structural stability rules, material strength rules, construction process rules, etc., while fully considering the specific requirements and actual conditions of the bridge. For example, different types of bridges (such as suspension bridges, arch bridges, beam bridges, etc.) need to be formulated, and the impact of the environmental conditions and construction conditions of the bridge on the rules must also be considered. The rules are verified and tested to ensure the accuracy and reliability of the rules, as well as the effectiveness and practicality of the rule library.

[0030] Furthermore, step S200 also includes step S210, extracting geometric features and topological relationships of the three-dimensional design model of the bridge, and identifying multiple structural components of the bridge; step S220, based on the requirements of the target bridge, obtaining mechanical performance indicators and design specification constraints of multiple structural components; step S230, converting the mechanical performance indicators and the design specification constraints into mathematical expressions, and establishing a mathematical evaluation model for the performance of multiple structural components; step S240, analyzing the logical relationship between geometric entities in the three-dimensional design model of the bridge, and extracting the assembly relationship and connection method between multiple structural components; step S250, converting the assembly relationship and the connection method into mathematical expressions, and establishing a mathematical logic model for the assembly of multiple structural components; step S260, fusing the mathematical evaluation model and the mathematical logic model to form a complete mathematical logic rule base for the target bridge.

[0031] Preferably, the geometric features and topological relationships of the bridge are extracted from the three-dimensional design model of the bridge, wherein the geometric features include the size, shape, etc. of each part of the bridge, and the topological relationship describes the connection mode and spatial position relationship between the various parts of the bridge, thereby identifying multiple structural components of the bridge, such as piers, bridge decks, beams, supports, etc., and obtaining the mechanical performance indicators of each structural component according to the specific requirements of the target bridge, such as strength, stiffness, stability, etc., and understanding the relevant design specification constraints, such as bridge design specifications, seismic design specifications, etc., and then converting the mechanical performance indicators and design specification constraints into mathematical expressions, quantitatively evaluating the structural components of the bridge, calculating and analyzing the performance of the structural components under different working conditions through mathematical expressions, and establishing a mathematical evaluation model for the performance of multiple structural components to evaluate the overall performance of the bridge; then analyzing the three-dimensional design model of the bridge. The logical relationships between the geometric entities in the model describe the assembly relationships and connection methods between the structural components, that is, extracting information such as the assembly sequence, assembly method and connection method between the structural components for the subsequent assembly and construction of the bridge; then the assembly relationship and connection method are converted into mathematical expressions to quantitatively describe the assembly process of the bridge, and then a mathematical logic model for the assembly of multiple structural components is established to guide the assembly and construction process of the bridge; finally, the two models are merged to form a complete mathematical logic rule library for the target bridge. This logic rule library contains information on the performance evaluation and assembly logic of the bridge structural components, which can be used in the design, analysis, optimization and various links of the construction process of the bridge. Through this logic rule library, a comprehensive evaluation and precise control of the bridge performance can be achieved to ensure the safety and reliability of the bridge.

[0032] Furthermore, step S230 also includes step S231, traversing multiple structural components, obtaining a first structural component, and extracting a first mechanical performance index and a first design specification constraint; step S232, based on the first mechanical performance index, determining a first strength performance index, a first stiffness performance index, and a first stability performance index of the first structural component, and establishing a first strength expression, a first stiffness expression, and a first stability expression; step S233, establishing multiple constraint expressions according to the first design specification constraint; step S234, combining the first strength expression, the first stiffness expression, the first stability expression, and the multiple constraint expressions to generate a mathematical evaluation model.

[0033] Preferably, one is randomly selected from multiple structural components (such as beams, columns, plates, etc.) as an analysis object, as the first structural component, and its first mechanical performance index and design specification constraints are extracted, wherein the mechanical performance index generally includes strength performance index (such as tensile strength, compressive strength, etc.), stiffness performance index (such as elastic modulus, deflection, etc.) and stability performance index (such as critical load, buckling factor, etc.), and the design specification constraints refer to the conditions and restrictions that must be met by the structural components during the design, construction and use according to relevant engineering specifications and standards; then, the first strength performance index, the first stiffness performance index and the first stability performance index of the first structural component are determined according to the first mechanical performance index, and the strength expression, stiffness expression and stability expression are respectively established for the first structural component to describe the behavior of the structural component under stress. For the strength performance index of each structural component, its mathematical expression can be: σ≤[σ], where σ represents the actual stress of the structural component, and [σ] represents the allowable stress of the structural component; for the stiffness performance index of each structural component, its mathematical expression can be: Δ≤[Δ], where Δ represents the actual deformation of the structural component, and [Δ] represents the allowable deformation of the structural component; for the stability performance index of each structural component, its mathematical expression can be: λ≤[λ], where λ represents the actual stability coefficient of the structural component, and [λ] represents the critical stability coefficient of the structural component; according to the first design code constraint, establish corresponding multiple constraint expressions, such as L / 250≤Δ≤L / 1000, Where L represents the span of the structural component and other constraints; finally, the first strength expression, the first stiffness expression, the first stability expression and multiple constraint expressions are combined to form a complete mathematical evaluation model for quantitative analysis and evaluation of the performance of the structural component to determine whether it meets the design requirements. Specifically, the mathematical evaluation model may be an optimization problem containing multiple variables and constraints, where the variables may include the size and material properties of the structural component, while the constraints may include strength, stiffness, stability requirements and design specification constraints. By solving this optimization problem, a structural component design solution that meets all constraints and has the best performance can be found.

[0034] Furthermore, step S250 also includes step S251, traversing the geometric entities in the three-dimensional design model of the bridge, extracting multiple assembly component pairs and multiple connection component groups; step S252, obtaining the first assembly component pair, extracting the first assembly feature parameter, comparing the first assembly feature parameter with the tolerance matching requirements in the design specification constraint, and establishing a first assembly gap determination inequality; step S253, obtaining the first connection component group, extracting the first connection process information, obtaining the first connection position coordinates and the first connection size parameters, and establishing a first connection geometric mathematical equation; step S254, traversing multiple assembly component pairs and multiple connection component groups, establishing multiple assembly gap determination inequalities and multiple connection geometric mathematical equations; step S255, analyzing the relative positions of multiple structural components in three-dimensional space, and establishing a spatial relationship matrix of multiple structural components; step S256, based on the spatial relationship matrix, integrating the multiple assembly gap determination inequalities and the multiple connection geometric mathematical equations to construct a mathematical logic model.

[0035] Preferably, the three-dimensional design model of the entire bridge is scanned or traversed to identify all geometric entities, such as beams, columns, connecting plates, etc., and from the traversed geometric entities, component pairs that need to be assembled together (such as two beam sections) and components connecting these components (such as bolts, welds, etc.) are identified, and a pair of assembly components is randomly selected as the first assembly component pair, and its assembly feature parameters, i.e., the first assembly feature parameters, such as the size, shape, and position of the mating surface, are extracted, and then the extracted assembly feature parameters are compared with the tolerances and fitting requirements specified in the design specifications, and the judgment conditions of the assembly gap are expressed in the form of mathematical inequalities; then a connection component group is randomly selected as the first connection component group, and its connection process information, i.e., the first connection process information, is extracted, such as the connection method (welding, bolt connection, etc.), connection position, connection size, etc., and then a mathematical equation describing the connection geometric relationship is established based on the connection process information and the connection position coordinates, such as the position relationship of the bolt holes, the size of the weld, etc.

[0036] Preferably, assembly feature parameters are extracted for all assembly component pairs and connection component groups, assembly gap determination inequalities and connection process information are extracted, and connection geometry mathematical equations are established, thereby establishing multiple assembly gap determination inequalities and multiple connection geometry mathematical equations, analyzing the position of each structural component in three-dimensional space, and establishing a spatial relationship matrix between them, describing the relative position and direction between the components, and finally integrating all the established inequalities and equations into a unified mathematical logic model. This mathematical logic model not only includes the determination conditions of the assembly gap (i.e., whether the assembly meets the design requirements), but also includes a description of the connection geometry relationship (i.e., whether the connection is implemented as designed). Specifically, the mathematical logic model may be a set of equations or inequalities containing multiple constraints. By solving or verifying this set of equations or inequalities, it can be determined whether the three-dimensional design model of the bridge meets all the requirements of the design specifications.

[0037] Step S300: extract multiple sheet metal parts to be processed from the three-dimensional design model of the bridge, discretize each sheet metal part to be processed into a finite number of sheet metal units, and configure target parameters of each sheet metal unit based on the mathematical logic rule library.

[0038] Preferably, the three-dimensional design model of the bridge usually includes many components made of sheet metal materials, such as the guardrails, support frames, connecting plates, etc. of the bridge. Specifically, multiple sheet metal parts to be processed are extracted, and each sheet metal part to be processed is discretized into a finite number of sheet metal units. Each sheet metal part to be processed is usually a complex three-dimensional shape and cannot be directly processed. These sheet metal parts need to be further discretized into a finite number of simpler sheet metal units, such as plane shapes or simple curved surface shapes. Then, target parameters are configured for each sheet metal unit based on the mathematical logic rule library. The target parameters usually include the size, shape, material, thickness, bending radius, expansion coefficient, and material thickness compensation of the sheet metal unit. The configuration of the target parameters needs to follow the rules in the mathematical logic rule library to ensure that the processing and manufacturing of the sheet metal unit meet the requirements of the bridge. The overall requirements of the design, including configuring the corresponding size and shape parameters for each sheet metal unit according to the shape and size of the sheet metal parts in the three-dimensional design model of the bridge; selecting appropriate materials and thickness for each sheet metal unit according to the bridge design requirements, considering the material strength, toughness, etc.; for sheet metal units that need to be bent, appropriate bending radius parameters should be configured to ensure that the sheet metal units will not produce cracks or deformation during the bending process; when unfolding the three-dimensional sheet metal unit into a two-dimensional plane, it is necessary to configure appropriate expansion coefficient parameters to ensure that the unfolded plane is completely matched with the size and shape of the actual part; in the sheet metal processing process, due to the bending and deformation of the material, size adjustment is often required, and appropriate material thickness compensation parameters are configured for each sheet metal unit to ensure the dimensional accuracy of the processed sheet metal parts. Through precise mathematical logic identification and parameter configuration, the processing accuracy and manufacturing quality of each sheet metal unit can be ensured, thereby improving the stability and safety of the entire bridge structure.

[0039] Furthermore, step S300 also includes step S310, extracting target performance parameter requirements and target size parameter requirements for the target sheet metal unit based on the mathematical logic rule library; step S320, performing a target search in a historical sheet metal processing database based on the target performance parameter requirements and the target size parameter requirements to obtain multiple historical sheet metal units; step S330, performing distribution characteristic analysis on the performance parameters and size parameters of the multiple historical sheet metal units to extract the optimal distribution value of each parameter; step S340, constructing a parameter optimization configuration sequence based on the optimal distribution value of each parameter as the target parameter of the target sheet metal unit.

[0040] Preferably, according to the mathematical logic rule library, the target performance parameter requirements and the target size parameter requirements are extracted, that is, the performance and size requirements that the target sheet metal unit needs to meet are determined, which may include reasonable value ranges of strength, stiffness, corrosion resistance, weight, dimensional accuracy, etc. Assuming that the parameters are set according to the design specifications and engineering experience, there is a lack of consideration of the actual processing capabilities, which leads to some parameter settings being too idealized, which may exceed the actual processing level, resulting in problems such as high processing difficulty and high rework rate; based on these parameter requirements, a target search is performed in the historical sheet metal processing database, that is, historical sheet metal unit information similar to the target sheet metal unit in performance and size requirements is searched in the database, and multiple historical sheet metal units are extracted; the performance parameters and size parameters of the multiple historical sheet metal units obtained are statistically analyzed to find out their distribution characteristics, such as mean, Standard deviation, maximum value, minimum value, etc., and then according to these distribution characteristics and the requirements of the target sheet metal unit, the optimal distribution values ​​of each parameter are extracted. These optimal distribution values ​​may be parameter values ​​under conditions of optimal performance, lowest cost, and lowest processing difficulty; the extracted optimal distribution values ​​of each parameter are integrated into a parameter optimization configuration sequence, which includes all the key parameter values ​​that need to be followed in the design and manufacturing process of the target sheet metal unit, such as material type, thickness, shape, dimensional accuracy, processing method, etc., which together constitute the target parameters of the target sheet metal unit, that is, the parameter standards that need to be followed in the design and manufacturing process of the target sheet metal unit, to ensure that the target sheet metal unit can meet the predetermined performance and size requirements, while optimizing its manufacturing cost and processing difficulty, and by fully considering the actual processing level, thereby improving the rationality of the target sheet metal parameter configuration.

[0041] Further, step S330 also includes step S331, for the first performance parameter, extracting corresponding performance parameter values ​​from the multiple historical sheet metal units to construct a first performance parameter sequence; step S332, for the first size parameter, extracting corresponding size parameter values ​​from the multiple historical sheet metal units to construct a first size parameter sequence; step S333, performing numerical distribution statistics on the first performance parameter sequence to obtain a first distribution interval and a first frequency distribution; step S334, performing numerical distribution statistics on the first size parameter sequence to obtain a second distribution interval and a second frequency distribution; step S335, performing kurtosis analysis based on the first distribution interval and the first frequency distribution to determine the central tendency and dispersion degree of the first performance parameter distribution; step S336, performing kurtosis analysis based on the second distribution interval and the second frequency distribution to determine the central tendency and dispersion degree of the first size parameter distribution; step S337, determining the optimal distribution value of the first performance parameter based on the central tendency and dispersion degree of the first performance parameter distribution, and determining the optimal distribution value of the first size parameter based on the central tendency and dispersion degree of the first size parameter distribution.

[0042] Preferably, from multiple historical sheet metal units, for the first performance parameter and the first size parameter, the corresponding performance parameter values ​​and size parameter values ​​are respectively extracted to form a first performance parameter sequence and a first size parameter sequence, and then the first performance parameter sequence is subjected to numerical distribution statistics to obtain a first distribution interval and a first frequency distribution, which represent the distribution of the first performance parameter in different numerical ranges, and the first size parameter sequence is subjected to numerical distribution statistics to obtain a second distribution interval and a second frequency distribution, which represent the distribution of the first size parameter in different numerical ranges; then, based on the first distribution interval and the first frequency distribution, a kurtosis analysis is performed, that is, through the kurtosis analysis, the central trend (such as mean) and dispersion (such as standard deviation) of the first performance parameter distribution can be determined, wherein the kurtosis It is a statistic that measures the shape of data distribution and is used to describe the sharpness or flatness of data distribution. Based on the second distribution interval and the second frequency distribution, kurtosis analysis is performed to determine the central trend and dispersion of the first size parameter distribution. Finally, the optimal distribution value of the first performance parameter is determined based on the central trend and dispersion of the first performance parameter distribution. This optimal distribution value may be obtained after comprehensive consideration of factors such as performance requirements, manufacturing costs, and processing difficulty. It represents the value that the first performance parameter should take on the premise of meeting performance requirements. Similarly, the optimal distribution value of the first size parameter is determined based on the central trend and dispersion of the first size parameter distribution. This optimal distribution value may be obtained under the conditions of meeting size requirements, material utilization, and processing accuracy. Through actual application requirements and constraints (such as performance requirements, size requirements, manufacturing costs, etc.), the optimal value range of these two parameters should be obtained after comprehensive consideration.

[0043] Step S400, constructing a mathematical model and an objective function for optimizing sheet metal blanking according to sheet metal raw material information and the target parameters, wherein the objective function is constructed based on sheet metal blanking cost and sheet metal blanking efficiency.

[0044] Preferably, sheet metal raw material information includes material type, specification, performance, etc., which is the basis for constructing a mathematical model. For example, material type may include stainless steel, carbon steel, aluminum alloy, etc.; specifications may involve plate thickness, width, length, etc.; performance may include tensile strength, yield strength, elongation, etc.; target parameters determine specific requirements for sheet metal cutting, such as cutting method, cutting path, cutting sequence, etc. Specifically, after obtaining sheet metal raw material information and target parameters, a mathematical model is constructed to describe the sheet metal cutting process, reflecting the relationship between sheet metal raw materials and sheet metal parts and various constraints in the cutting process, such as sheet metal cutting efficiency, material utilization, sheet metal cutting cost, etc., wherein the mathematical model is generally It often includes a series of mathematical formulas, constraints and variables to describe the various relationships and restrictions in the sheet metal cutting process; the objective function is constructed based on the sheet metal cutting cost and sheet metal cutting efficiency to evaluate the advantages and disadvantages of different cutting schemes. The cost function reflects the cost in the sheet metal cutting process, including sheet metal material cost (specifications and quantity of sheet metal raw materials), cutting cost (cutting method, cutting path, cutting speed, etc.), transportation cost, etc. The efficiency function reflects the efficiency of the sheet metal cutting process, including cutting speed (the number and area of ​​sheet metal parts cut per unit time), material utilization rate (the ratio of actual sheet metal raw materials to total sheet metal raw materials), etc. The objective function can be expressed as a combination of cost function and efficiency function, such as weighted sum. By optimizing the objective function, a cutting scheme that can reduce cost and improve efficiency can be found, which can meet the needs of sheet metal parts and has the lowest cost and highest efficiency.

[0045] Step S500, input the mathematical model into the calculation guidance module for solution, and obtain the optimal material cutting plan according to the objective function.

[0046] Preferably, a mathematical model constructed according to sheet metal raw material information and target parameters is input into a calculation guidance module, wherein the calculation guidance module is a calculation unit for solving complex mathematical models and optimization problems, that is, a variety of mathematical algorithms and calculation techniques can be used according to the input mathematical model to perform efficient and accurate solutions. Specifically, the solution parameters are first initialized, such as the number of iterations, convergence conditions, etc., and the values ​​of the variables are continuously updated through iteration to gradually approach the optimal solution. During the solution process, various constraints need to be processed, such as the size limit of sheet metal parts, the requirements for material utilization, etc. After each iteration, the current solution is evaluated according to the objective function to determine its advantages and disadvantages, and finally a solution that satisfies all constraints and optimizes the objective function is obtained, that is, the optimal cutting plan, which specifies in detail the size, shape, quantity, cutting method, cutting path and cutting order of sheet metal parts, and is used to guide the actual sheet metal cutting process to ensure the highest material utilization, the lowest cost and the highest cutting efficiency.

[0047] Furthermore, step S500 also includes step S510, wherein the calculation guidance module includes multiple optimization solution sub-modules, and the multiple optimization solution sub-modules are established based on multiple optimization algorithms; step S520, solving the mathematical model based on multiple optimization solution sub-modules to obtain multiple sheet metal cutting schemes; step S530, through the objective function, respectively obtaining the objective function values ​​of multiple sheet metal cutting schemes, and selecting the sheet metal cutting scheme with the largest objective function value as the optimal cutting scheme.

[0048] Preferably, the mathematical model is solved by multiple optimization solving submodules of the calculation guidance module, and the optimal sheet metal cutting scheme is selected through the objective function. Specifically, the calculation guidance module is a calculation unit including multiple optimization solving submodules, and these optimization solving submodules are established based on multiple optimization algorithms (such as genetic algorithm, particle swarm algorithm, simulated annealing algorithm, etc.) to solve different types of optimization problems. The mathematical model is a mathematical expression that describes the sheet metal cutting problem, which may include multiple variables (such as the size, shape, quantity, etc. of the sheet metal plate) and constraints (such as material utilization, processing cost, cutting accuracy, etc.). The calculation guidance module uses multiple An optimization solving sub-module solves this mathematical model, and each sub-module will try to find multiple sheet metal cutting schemes that meet the constraints. By solving the mathematical model, the calculation guidance module will obtain multiple possible sheet metal cutting schemes, that is, candidate solutions that meet the constraints found in the search space. Finally, the objective function value of each sheet metal cutting scheme is obtained through the objective function, which represents the comprehensive performance of the scheme in terms of sheet metal cutting cost and sheet metal cutting efficiency. The scheme with the largest objective function value is then selected as the optimal cutting scheme. The larger the objective function value, the better the scheme performance, because the objective function is designed to maximize material utilization and minimize material cost.

[0049] Step S600, generating a numerical control machining program according to the optimal blanking solution, controlling the sheet metal blanking equipment to perform machining, and obtaining a plurality of sheet metal parts required for the target bridge.

[0050] Preferably, a corresponding CNC machining program is generated according to the optimal cutting plan, which is used to control the sheet metal cutting equipment for processing, wherein the CNC machining program usually includes a program header, which contains basic information of the program, such as program name, date, operator, etc.; workpiece setting, which defines the coordinate system, origin, size and other parameters of the workpiece; tool selection, which selects a suitable tool for cutting according to the material and thickness of the sheet metal; a cutting path, which defines the movement trajectory of the tool on the sheet metal raw material according to the cutting path in the optimal cutting plan; cutting parameters, including cutting speed, feed speed, spindle speed, etc., which directly affect the cutting quality and efficiency; and a program tail, which includes the end instruction of the program and cleanup work, etc. After the CNC machining program is generated, it is input into the control system of the sheet metal blanking equipment. The control system controls the various components of the equipment to work together according to the instructions in the program to complete the sheet metal cutting task. It should be noted that before processing, the equipment needs to be calibrated to ensure the accuracy and stability of the equipment; the sheet metal raw materials are fixed on the workbench of the equipment to prevent movement or deformation during the processing; during the processing, the cutting situation is monitored in real time to ensure the cutting quality and efficiency; safety protection measures are strengthened to prevent sparks and spatters generated during the cutting process from causing harm to the human body. Finally, multiple sheet metal parts required for the target bridge are obtained for the assembly and construction of the bridge, ensuring the structural stability and safety of the bridge, and realizing accurate cutting and efficient production of sheet metal parts.

[0051] In the above, refer to Figure 1 A computational guidance method for mathematical logic recognition according to an embodiment of the present invention is described in detail. Figure 2 A computational guidance device for mathematical logic recognition according to an embodiment of the present invention is described.

[0052] A mathematical logic recognition calculation guidance device according to an embodiment of the present invention is used to solve the technical problems existing in the existing sheet metal processing, which relies on manual material arrangement according to sheet metal bridge drawings, resulting in insufficient sheet metal processing precision, low processing efficiency and material utilization, and realizes intelligent control of sheet metal cutting, achieving the technical effect of improving sheet metal cutting efficiency and precision and saving materials. A mathematical logic recognition calculation guidance device includes: a user submission information acquisition module 10, a mathematical logic rule library establishment module 20, a target parameter configuration module 30, an objective function construction module 40, an optimal cutting plan acquisition module 50, and a CNC machining program generation module 60.

[0053] A user submitted information acquisition module 10 is used to acquire user submitted information for a target bridge, wherein the user submitted information includes a three-dimensional bridge design model and target bridge requirements; a mathematical logic rule base establishment module 20 is used to perform mathematical logic identification on the three-dimensional bridge design model based on the target bridge requirements, and establish a mathematical logic rule base for the target bridge; a target parameter configuration module 30 is used to extract a plurality of sheet metal parts to be processed from the three-dimensional bridge design model, discretize each sheet metal part to be processed into a finite number of sheet metal units, and configure the target parameters of each sheet metal unit based on the mathematical logic rule base; an objective function construction module 40 is used to construct a mathematical model and an objective function for sheet metal blanking optimization according to sheet metal raw material information and the target parameters, wherein the objective function is constructed based on sheet metal blanking cost and sheet metal blanking efficiency; an optimal blanking solution acquisition module 50 is used to input the mathematical model into a calculation guidance module for solving, and obtain an optimal blanking solution according to the objective function; a CNC machining program generation module 60 is used to generate a CNC machining program according to the optimal blanking solution, control the sheet metal blanking equipment for processing, and obtain a plurality of sheet metal parts required for the target bridge.

[0054] The specific configuration of the mathematical logic rule base establishment module 20 will be described in detail below. The mathematical logic rule base establishment module 20 may further include: extracting the geometric features and topological relationships of the three-dimensional design model of the bridge, identifying multiple structural components of the bridge; based on the requirements of the target bridge, obtaining the mechanical performance indicators and design specification constraints of multiple structural components; converting the mechanical performance indicators and the design specification constraints into mathematical expressions, and establishing a mathematical evaluation model for the performance of multiple structural components; analyzing the logical relationships between the geometric entities in the three-dimensional design model of the bridge, extracting the assembly relationships and connection methods between multiple structural components; converting the assembly relationships and the connection methods into mathematical expressions, and establishing a mathematical logic model for the assembly of multiple structural components; integrating the mathematical evaluation model and the mathematical logic model to form a complete mathematical logic rule base for the target bridge.

[0055] The specific configuration of the mathematical logic rule base establishment module 20 will be described in detail below. The mathematical logic rule base establishment module 20 may further include: traversing multiple structural components, obtaining a first structural component, extracting a first mechanical performance index and a first design specification constraint; based on the first mechanical performance index, determining a first strength performance index, a first stiffness performance index and a first stability performance index of the first structural component, and establishing a first strength expression, a first stiffness expression and a first stability expression; according to the first design specification constraint, establishing multiple constraint expressions; combining the first strength expression, the first stiffness expression, the first stability expression and the multiple constraint expressions to generate a mathematical evaluation model.

[0056] The specific configuration of the mathematical logic rule base establishment module 20 will be described in detail below. The mathematical logic rule base establishment module 20 may further include: traversing the geometric entities in the three-dimensional design model of the bridge, extracting multiple assembly component pairs and multiple connection component groups; obtaining the first assembly component pair, extracting the first assembly feature parameter, comparing the first assembly feature parameter with the tolerance matching requirements in the design specification constraint, and establishing the first assembly gap determination inequality; obtaining the first connection component group, extracting the first connection process information, obtaining the first connection position coordinates and the first connection size parameter, and establishing the first connection geometry mathematical equation; traversing multiple assembly component pairs and multiple connection component groups, establishing multiple assembly gap determination inequalities and multiple connection geometry mathematical equations; analyzing the relative positions of multiple structural components in three-dimensional space, and establishing a spatial relationship matrix of multiple structural components; based on the spatial relationship matrix, integrating the multiple assembly gap determination inequalities and the multiple connection geometry mathematical equations, and constructing a mathematical logic model.

[0057] The specific configuration of the target parameter configuration module 30 will be described in detail below. The target parameter configuration module 30 may further include: for the target sheet metal unit, based on the mathematical logic rule library, extracting the target performance parameter requirements and the target size parameter requirements; based on the target performance parameter requirements and the target size parameter requirements, performing a target search in the historical sheet metal processing database to obtain multiple historical sheet metal units; performing distribution characteristic analysis on the performance parameters and size parameters of the multiple historical sheet metal units to extract the optimal distribution value of each parameter; based on the optimal distribution value of each parameter, constructing a parameter optimization configuration sequence as the target parameter of the target sheet metal unit.

[0058] The specific configuration of the target parameter configuration module 30 will be described in detail below. The target parameter configuration module 30 may further include: for the first performance parameter, extracting the corresponding performance parameter value from the multiple historical sheet metal units to construct a first performance parameter sequence; for the first size parameter, extracting the corresponding size parameter value from the multiple historical sheet metal units to construct a first size parameter sequence; performing numerical distribution statistics on the first performance parameter sequence to obtain a first distribution interval and a first frequency distribution; performing numerical distribution statistics on the first size parameter sequence to obtain a second distribution interval and a second frequency distribution; performing kurtosis analysis based on the first distribution interval and the first frequency distribution to determine the central tendency and dispersion degree of the first performance parameter distribution; performing kurtosis analysis based on the second distribution interval and the second frequency distribution to determine the central tendency and dispersion degree of the first size parameter distribution; determining the optimal distribution value of the first performance parameter based on the central tendency and dispersion degree of the first performance parameter distribution, and determining the optimal distribution value of the first size parameter based on the central tendency and dispersion degree of the first size parameter distribution.

[0059] The specific configuration of the optimal blanking scheme obtaining module 50 will be described in detail below. The optimal blanking scheme obtaining module 50 further includes: the calculation guidance module includes multiple optimization solution submodules, and the multiple optimization solution submodules are established based on multiple optimization algorithms; the mathematical model is solved based on the multiple optimization solution submodules to obtain multiple sheet metal blanking schemes; through the objective function, the objective function values ​​of multiple sheet metal blanking schemes are respectively obtained, and the sheet metal blanking scheme with the largest objective function value is selected as the optimal blanking scheme.

[0060] A computational guidance device for mathematical logic identification provided in an embodiment of the present invention can execute a computational guidance method for mathematical logic identification provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0061] Although the present application makes various references to certain modules in the device according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0062] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A computational guidance method for mathematical logic recognition, characterized in that: include: Acquire user submitted information for a target bridge, wherein the user submitted information includes a three-dimensional design model of the bridge and requirements of the target bridge; Based on the requirements of the target bridge, mathematical logic recognition is performed on the three-dimensional design model of the bridge to establish a mathematical logic rule library of the target bridge; Extracting a plurality of sheet metal parts to be processed from the three-dimensional design model of the bridge, discretizing each sheet metal part to be processed into a finite number of sheet metal units, and configuring target parameters of each sheet metal unit based on the mathematical logic rule library; According to the sheet metal raw material information and the target parameters, a mathematical model and an objective function for optimizing sheet metal blanking are constructed, wherein the objective function is constructed based on the sheet metal blanking cost and the sheet metal blanking efficiency; The mathematical model is input into the calculation guidance module for solving, and the optimal material cutting plan is obtained according to the objective function; According to the optimal blanking scheme, a numerical control machining program is generated to control the sheet metal blanking equipment to perform machining to obtain a plurality of sheet metal parts required for the target bridge; Based on the target bridge requirements, mathematical logic recognition is performed on the three-dimensional design model of the bridge to establish a mathematical logic rule base of the target bridge, including: Extract the geometric features and topological relationships of the 3D bridge design model and identify multiple structural components of the bridge; Based on the target bridge requirements, obtain mechanical performance indicators and design specification constraints of multiple structural components; Convert the mechanical performance index and the design specification constraints into mathematical expressions, and establish a mathematical evaluation model for the performance of multiple structural components; Analyze the logical relationship between geometric entities in the three-dimensional design model of the bridge, and extract the assembly relationship and connection mode between multiple structural components; Converting the assembly relationship and the connection mode into mathematical expressions to establish a mathematical logic model for assembling multiple structural components; The mathematical evaluation model and the mathematical logic model are integrated to form a complete mathematical logic rule base of the target bridge.

2. A mathematical logic recognition calculation guidance method according to claim 1, characterized in that: The mechanical performance index and the design specification constraint are converted into mathematical expressions, and a mathematical evaluation model for the performance of multiple structural components is established, including: Traversing multiple structural components, obtaining a first structural component, and extracting a first mechanical performance index and a first design specification constraint; Based on the first mechanical performance index, determine a first strength performance index, a first stiffness performance index, and a first stability performance index of the first structural component, and establish a first strength expression, a first stiffness expression, and a first stability expression; Establishing a plurality of constraint expressions according to the first design specification constraint; A mathematical evaluation model is generated by combining the first strength expression, the first stiffness expression, the first stability expression and a plurality of constraint expressions.

3. The method for calculating and guiding mathematical logic recognition according to claim 1, characterized in that: The assembly relationship and the connection mode are converted into mathematical expressions, and a mathematical logic model for the assembly of multiple structural components is established, including: Traversing the geometric entities in the three-dimensional design model of the bridge, extracting a plurality of assembly component pairs and a plurality of connection component groups; Acquire a first assembly component pair, extract a first assembly feature parameter, compare the first assembly feature parameter with a tolerance matching requirement in a design specification constraint, and establish a first assembly clearance determination inequality; Acquire a first connection component group, extract first connection process information, acquire first connection position coordinates and first connection size parameters, and establish a first connection geometric mathematical equation; Traversing multiple assembly component pairs and multiple connection component groups, establishing multiple assembly gap determination inequalities and multiple connection geometry mathematical equations; Analyze the relative positions of multiple structural components in three-dimensional space and establish the spatial relationship matrix of multiple structural components; Based on the spatial relationship matrix, the multiple assembly gap determination inequalities and the multiple connection geometry mathematical equations are integrated to construct a mathematical logic model.

4. The method for calculating and guiding mathematical logic recognition according to claim 1, characterized in that: Based on the mathematical logic rule base, the target parameters of each sheet metal unit are configured, including: For the target sheet metal unit, based on the mathematical logic rule base, extract the target performance parameter requirements and the target size parameter requirements; Based on the target performance parameter requirements and the target size parameter requirements, performing a target search in a historical sheet metal processing database to obtain a plurality of historical sheet metal units; Performing distribution characteristic analysis on the performance parameters and size parameters of the plurality of historical sheet metal units to extract the optimal distribution value of each parameter; Based on the optimal distribution value of each parameter, a parameter optimization configuration sequence is constructed as the target parameter of the target sheet metal unit.

5. A computational guidance method for mathematical logic recognition according to claim 4, characterized in that: Performing distribution characteristic analysis on the performance parameters and size parameters of the plurality of historical sheet metal units to extract the optimal distribution value of each parameter includes: For a first performance parameter, extract corresponding performance parameter values ​​from the plurality of historical sheet metal units to construct a first performance parameter sequence; For a first size parameter, extract corresponding size parameter values ​​from the plurality of historical sheet metal units to construct a first size parameter sequence; Performing numerical distribution statistics on the first performance parameter sequence to obtain a first distribution interval and a first frequency distribution; Performing numerical distribution statistics on the first size parameter sequence to obtain a second distribution interval and a second frequency distribution; Performing a kurtosis analysis based on the first distribution interval and the first frequency distribution to determine a central tendency and a dispersion degree of a first performance parameter distribution; Performing a kurtosis analysis based on the second distribution interval and the second frequency distribution to determine a central tendency and a dispersion degree of the first size parameter distribution; The optimal distribution value of the first performance parameter is determined based on the central tendency and the degree of dispersion of the distribution of the first performance parameter, and the optimal distribution value of the first size parameter is determined based on the central tendency and the degree of dispersion of the distribution of the first size parameter.

6. The method for calculating and guiding mathematical logic recognition according to claim 1, characterized in that: The mathematical model is input into the calculation guidance module for solving, and the optimal material cutting scheme is obtained according to the objective function, including: The calculation guidance module includes a plurality of optimization solution submodules, and the plurality of optimization solution submodules are established based on a plurality of optimization algorithms; Solving the mathematical model based on multiple optimization solving submodules to obtain multiple sheet metal cutting solutions; Through the objective function, objective function values ​​of multiple sheet metal cutting solutions are obtained respectively, and the sheet metal cutting solution with the largest objective function value is selected as the optimal cutting solution.

7. A computational guidance device for mathematical logic recognition, characterized in that: The device is used to implement a computational guidance method for mathematical logic recognition as described in any one of claims 1 to 6, and the device comprises: A user submitted information acquisition module, used to acquire user submitted information for a target bridge, wherein the user submitted information includes a three-dimensional design model of the bridge and requirements of the target bridge; A mathematical logic rule base building module is used to perform mathematical logic recognition on the three-dimensional design model of the bridge based on the requirements of the target bridge, and to build a mathematical logic rule base for the target bridge; A target parameter configuration module is used to extract a plurality of sheet metal parts to be processed from the three-dimensional design model of the bridge, discretize each sheet metal part to be processed into a finite number of sheet metal units, and configure the target parameters of each sheet metal unit based on the mathematical logic rule library; An objective function construction module is used to construct a mathematical model and an objective function for optimizing sheet metal blanking according to sheet metal raw material information and the target parameters, wherein the objective function is constructed based on sheet metal blanking cost and sheet metal blanking efficiency; An optimal material cutting scheme obtaining module is used to input the mathematical model into the calculation guidance module for solving, and obtain the optimal material cutting scheme according to the objective function; The NC machining program generation module is used to generate a NC machining program according to the optimal blanking plan, control the sheet metal blanking equipment to perform processing, and obtain multiple sheet metal parts required for the target bridge.

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