Reinforcement method, device and equipment based on reinforcement optimization model and storage medium

Abstract

The invention relates to the technical field of intelligent construction, and discloses a reinforcement method, device and equipment based on a reinforcement optimization model and a storage medium, which are used for improving the generation efficiency of a reinforcement scheme and giving consideration to the economical efficiency and personalized requirements of the reinforcement scheme. The reinforcement optimization model-based reinforcement method comprises the following steps of: obtaining each reinforcement constraint parameter and a to-be-selected reinforcement set of a target business scene; performing dynamic configuration on a preset reinforcement optimization model based on each reinforcement constraint parameter and the to-be-selected reinforcement set to obtain a reinforcement optimization model corresponding to each constraint matrix set; integer programming is carried out through the reinforcement optimization models corresponding to the constraint matrix sets, a reinforcement scheme alternative list is obtained, and the reinforcement scheme alternative list comprises all candidate reinforcement schemes meeting the requirement for minimization of the constraint matrix sets and the target function; and determining a target reinforcement scheme in the reinforcement scheme alternative list according to each screening condition preset by the target business scene.

Description

Technical Field

[0001] The present application relates to the field of intelligent construction technology, and in particular, to a steel bar reinforcement method, device, equipment and storage medium based on a steel bar reinforcement optimization model. Background Technique

[0002] In the fields of architecture and structural engineering, steel bars are the main materials used to withstand tension in concrete structures. Correct steel bar reinforcement design can ensure the strength, stability and durability of concrete structures. Steel bar reinforcement design, or rather steel bar reinforcement recommendation, is a crucial link.

[0003] In related technologies, traditional steel bar reinforcement methods often rely on the experience of engineers and specification requirements. For example, the reinforcement target value is determined through experience, and then the reinforcement scheme is determined through the look-up table method. Although this method can meet the basic design requirements, it will take a lot of time for trial calculation and adjustment in the case of insufficient personnel experience, and the obtained reinforcement scheme is often difficult to achieve the optimal solution in terms of economic cost. For traditional brute-force solution algorithms, they usually directly traverse all schemes by configuring the target reinforcement value. The operation of the reinforcement scheme requires a lot of computing resources and is inefficient; or multi-parameter configuration is carried out based on different components, resulting in the inability to reuse the reinforcement programs of different components, poor versatility, and usually only a single reinforcement scheme can be generated by the brute-force solution algorithm, which cannot meet the personalized needs of different users. Summary of the Invention

[0004] The present application provides a steel bar reinforcement method, device, equipment and storage medium based on a steel bar reinforcement optimization model, which is used to solve the technical problems that traditional steel bar reinforcement methods rely on experience, are time-consuming and lack economy, as well as complex parameter configuration, poor versatility and inability to meet personalized needs.

[0005] The first aspect of the present application provides a steel bar reinforcement method based on a steel bar reinforcement optimization model, including: obtaining each steel bar reinforcement constraint parameter and the set of candidate steel bars for the target business scenario;

[0006] Dynamically configuring the preset steel bar reinforcement optimization model based on each steel bar reinforcement constraint parameter and the set of candidate steel bars to obtain the steel bar reinforcement optimization model corresponding to each constraint matrix set;

[0007] Performing integer programming through the steel bar reinforcement optimization model corresponding to each constraint matrix set to obtain a list of alternative steel bar reinforcement schemes, and the list of alternative steel bar reinforcement schemes includes all candidate steel bar reinforcement schemes that meet each constraint matrix set and minimize the objective function;

[0008] Determine the target steel bar reinforcement scheme in the list of alternative steel bar reinforcement schemes according to each screening condition preset for the target business scenario.

[0009] The second aspect of the present application provides a reinforcement configuration device based on a reinforcement optimization model, including: an acquisition module for acquiring each reinforcement constraint parameter of the target business scenario and a set of candidate steel bars;

[0010] A dynamic configuration module for dynamically configuring a preset reinforcement optimization model based on each reinforcement constraint parameter and the set of candidate steel bars to obtain a reinforcement optimization model corresponding to each constraint matrix set;

[0011] A traversal module for performing integer programming through the reinforcement optimization model corresponding to each constraint matrix set to obtain a list of alternative reinforcement plans, where the list of alternative reinforcement plans includes all candidate reinforcement plans that satisfy each constraint matrix set and minimize the objective function;

[0012] A screening module for determining a target reinforcement plan in the list of alternative reinforcement plans according to each screening condition preset for the target business scenario.

[0013] The third aspect of the present application provides a reinforcement device based on a reinforcement optimization model, including: a memory and at least one processor, where instructions are stored in the memory; the at least one processor calls the instructions in the memory so that the reinforcement device based on the reinforcement optimization model executes the above-mentioned reinforcement method based on the reinforcement optimization model.

[0014] The fourth aspect of the present application provides a computer-readable storage medium, in which instructions are stored, and when it runs on a computer, it causes the computer to execute the above-mentioned reinforcement method based on the reinforcement optimization model.

[0015] In the technical solution provided by the present application, the dynamic configuration of the reinforcement optimization model is performed based on each reinforcement constraint parameter of the target business scenario and the set of candidate steel bars to ensure that as many reinforcement plans as possible are listed, solving the problem that the look-up table method can only generate a single reinforcement plan and requires a large amount of time for trial calculation and adjustment; secondly, integer programming is performed through the reinforcement optimization model corresponding to each constraint matrix set, traversing various reinforcement plans, and efficiently screening out all candidate reinforcement plans that satisfy each constraint matrix set, solving the problem that the traditional brute-force solution algorithm that only configures the target reinforcement value cannot automatically adjust the constraint conditions based on the business scenario. By using each constraint matrix set, the reinforcement combinations that need to be traversed by the reinforcement optimization model are reduced, saving the computing resources required for the operation of the reinforcement plan, and ensuring that the list of alternative reinforcement plans output by the traversal includes all candidate reinforcement plans that satisfy each constraint matrix set, ensuring that the output candidate reinforcement plan is the most economical plan in each constraint matrix set for subsequent screening. Further, determining the target reinforcement plan in the list of alternative reinforcement plans according to each screening condition preset for the target business scenario can meet the personalized needs of different users. This technical solution provides a highly versatile reinforcement method that can be applied to the reinforcement requirements of different components and various business scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 FIG. is a schematic diagram of an embodiment of the steel bar reinforcement method based on the steel bar reinforcement optimization model in the present application;

[0017] Figure 2 FIG. is a schematic diagram of another embodiment of the steel bar reinforcement method based on the steel bar reinforcement optimization model in the present application;

[0018] Figure 3 FIG. is a schematic diagram of an embodiment of the steel bar reinforcement device based on the steel bar reinforcement optimization model in the present application;

[0019] Figure 4 FIG. is a schematic diagram of another embodiment of the steel bar reinforcement device based on the steel bar reinforcement optimization model in the present application;

[0020] Figure 5 FIG. is a schematic diagram of an embodiment of the steel bar reinforcement equipment based on the steel bar reinforcement optimization model in the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] The present application provides a steel bar reinforcement method, device, equipment and storage medium based on a steel bar reinforcement optimization model, which is used to improve the generation efficiency of the steel bar reinforcement plan and take into account the economy and personalized needs of the steel bar reinforcement plan.

[0022] The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims and above-mentioned drawings of the present application are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments described here can be implemented in an order other than that shown or described here. In addition, the terms "comprising" or "having" and any deformation thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or equipment comprising a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or equipment.

[0023] For ease of understanding, the specific process of the present application is described below. Please refer to Figure 1 , an embodiment of the steel bar reinforcement method based on the steel bar reinforcement optimization model in the present application includes:

[0024] 101. Obtain each steel bar reinforcement constraint parameter and the set of candidate steel bars for the target business scenario.

[0025] It can be understood that the execution entity of this application can be a reinforcement device based on a reinforcement optimization model, or a system, terminal, or server equipped with a reinforcement optimization model. Among them, the terminal can be various types of computers, laptops, tablets, etc., and specific details are not limited here. This embodiment takes the terminal as the execution entity for illustration.

[0026] In this embodiment, the target business scenario is used to indicate the scenario that currently requires reinforcement. For example, for the reinforcement scenario of the longitudinal bars of a structural column, the reinforcement scenarios for different components such as the longitudinal bars of a beam, shear wall, and edge member.

[0027] In this embodiment, each reinforcement constraint parameter is used to indicate the constraint parameter configured according to the target business scenario. Different reinforcement constraint parameters can be set according to different business scenarios. Optionally, the reinforcement constraint parameters include but are not limited to the target reinforcement area (or target reinforcement value), the cross-sectional area of the component to be reinforced, the threshold value of the difference in steel bar diameters, and the reinforcement coefficient interval. Among them, the target reinforcement value is used to indicate the sum of the steel bar cross-sectional areas that are expected to be achieved. For example, for the reinforcement plan of a certain structural column, a reference value can be calculated based on structural mechanics to ensure the safety and economy of the structure.

[0028] The above-mentioned cross-sectional area of the component to be reinforced is used to indicate the cross-sectional area of the component that needs to be designed for reinforcement. For example, for the reinforcement plan of a certain structural column, it can be represented by the cross-sectional rectangular or circular area of the column to be reinforced. The cross-sectional area of the component to be reinforced can also be the cross-sectional area of other shapes.

[0029] The above-mentioned threshold value of the difference in steel bar diameters is used to indicate the difference between the maximum and minimum values of the selected steel bar diameters. This parameter is used to control the difference between steel bars of different diameters in a reinforcement plan and avoid construction inconvenience or unreasonable design caused by too large a diameter difference.

[0030] The above-mentioned reinforcement coefficient interval is used to indicate the interval range between the maximum and minimum values of the selected reinforcement coefficient. The selected reinforcement coefficient is used to indicate the allowable range set for the reinforcement plan. It can be understood that the smaller the limited reinforcement magnification coefficient, the better the economy of the corresponding reinforcement plan. For example, when the reinforcement coefficient is set to 1, the reinforcement plan requires that the sum of the cross-sectional areas of all steel bars in the reinforcement plan is exactly equal to the target reinforcement area; when the reinforcement coefficient is set to 1.05, all plans where the sum of the cross-sectional areas of all steel bars in the reinforcement plan is equal to the target reinforcement area or within the range of the target reinforcement area * 1.05 are acceptable.

[0031] It can be understood that this embodiment does not limit the quantity and types of reinforcement constraint parameters corresponding to the target business scenario. Users can select and / or combine reinforcement constraint parameters according to actual situations so that the output reinforcement plan can be applicable to the reinforcement scenarios of different users and different components.

[0032] In this embodiment, the set of candidate steel bars is used to indicate the set of all steel bar specifications that are currently selectable. The set of candidate steel bars may include multiple steel bar specifications, that is, the set of candidate steel bars includes steel bars of different diameters currently selected. For example, it is a set composed of various steel bars with a cross-sectional diameter ranging from 10 mm to 40 mm. The set of candidate steel bars can be constructed according to the actual situation. The set of candidate steel bars can also include a set composed of steel bar specifications / types / diameters and the quantities of various steel bars. For example, it is a set constructed with 30 steel bars of 12 mm, 25 steel bars of 20 mm, and 10 steel bars of 32 mm. This embodiment does not make specific restrictions.

[0033] 102. Dynamically configure the preset steel bar reinforcement optimization model based on each reinforcement constraint parameter and the set of candidate steel bars to obtain the steel bar reinforcement optimization model corresponding to each constraint matrix set.

[0034] It should be noted that each constraint matrix set in this embodiment is used to indicate the set composed of each constraint condition in the target business scenario.

[0035] Specifically, combine the constraint conditions based on each reinforcement constraint parameter and the set of candidate steel bars to obtain each constraint matrix set, and initialize the steel bar reinforcement optimization model based on each constraint matrix set;

[0036] Optionally, determine each scenario constraint condition according to each reinforcement constraint parameter of the target business scenario, and initialize the steel bar reinforcement optimization model through the set of candidate steel bars and each scenario constraint condition to obtain the steel bar reinforcement optimization model corresponding to each constraint matrix set.

[0037] Exemplarily, set the reinforcement value constraint condition of the steel bar reinforcement optimization model according to the preset reinforcement coefficient interval and the target reinforcement value.

[0038] The above scenario constraint conditions are used to indicate the constraint conditions restricted in this business scenario. For example, it restricts that the quantity of a certain specification of steel bar does not exceed a preset value, etc.

[0039] In this embodiment, by combining the constraint conditions of each scenario with the set of candidate steel bars, it can meet the constraint requirements of the reinforcement scheme in different business scenarios, reduce the reinforcement schemes that need to be traversed in the minimization operation of the objective function, and save the computing resources required for the operation of the reinforcement scheme.

[0040] In this embodiment, the steel bar reinforcement optimization model is used to indicate the structural mathematical optimization theory model set based on architectural engineering and mechanics. It can be any one of a linear programming model, a genetic algorithm model, or a simulated annealing algorithm model, etc., and can be selected according to the specific business scenario, and no specific restrictions are made.

[0041] The above genetic algorithm model is suitable for dealing with complex, multi-variable, and non-linear optimization problems, and can search for the global optimal solution. Exemplarily, it optimizes the steel bar configuration through population iteration, and the fitness function measures the quality of the scheme, and finally evolves various candidate reinforcement schemes that meet all the constraint conditions.

[0042] The above simulated annealing algorithm model is a stochastic search algorithm based on the physical annealing process, which can jump out of the local optimal solution to find the global optimal solution. Exemplarily, starting from an initial reinforcement configuration scheme, it gradually adjusts the quantity of steel bars and the composition types of each steel bar, and accepts better or slightly worse solutions during the process of gradually decreasing temperature until it reaches a stable state to obtain various candidate reinforcement schemes.

[0043] 103. Perform integer programming through the reinforcement optimization model corresponding to each constraint matrix set to obtain a list of alternative reinforcement schemes.

[0044] It can be understood that for all the reinforcement schemes generated by the integer programming of the reinforcement optimization model in this embodiment, such as the number of steel bars in the initial reinforcement scheme, candidate reinforcement scheme, and target reinforcement scheme, they are all integers greater than or equal to zero.

[0045] In this embodiment, the list of alternative reinforcement schemes includes all candidate reinforcement schemes that meet each constraint matrix set. The reinforcement optimization model combines each constraint matrix set to perform integer programming to obtain the optimal reinforcement scheme under different constraint combinations. Based on the list of alternative reinforcement schemes, the user can further select a scheme through screening conditions, fully meeting the personalized needs of the user.

[0046] The number of candidate reinforcement schemes included in the list of alternative reinforcement schemes is closely related to the constraint conditions set in the constraint matrix set. For example, the fewer the constraint conditions it restricts and the more types of steel bars available for selection, the more reinforcement schemes the list of alternative reinforcement schemes can include, without specific limitation.

[0047] Optionally, traverse the set of candidate steel bars based on each constraint matrix set to perform integer programming to solve, obtain the candidate reinforcement schemes corresponding to each constraint matrix set, and form a list of alternative reinforcement schemes. In this embodiment, each constraint matrix set reduces the reinforcement combinations that the reinforcement optimization model needs to traverse, saves the computing resources required for the operation of the reinforcement scheme, and the generated candidate reinforcement schemes have better economy.

[0048] Exemplarily, after traversing the set of candidate steel bars based on each constraint matrix set to perform integer programming to solve and obtain the candidate reinforcement schemes corresponding to each constraint matrix set, it further includes: removing duplicate schemes.

[0049] 104. Determine the target reinforcement scheme in the list of alternative reinforcement schemes according to each screening condition preset for the target business scenario.

[0050] In this embodiment, the screening condition is a personalized screening method set according to the target business scenario, which can be set according to the specific business scenario and the personalized needs of the user. This embodiment does not specifically limit the number and specific rules of the screening conditions. Exemplarily, the screening condition can be that the total number of steel bars in the reinforcement configuration plan is limited within a certain range, etc.

[0051] Optionally, each candidate reinforcement configuration plan in the reinforcement configuration plan alternative list is sorted according to the screening conditions preset according to the target business scenario, and the candidate reinforcement configuration plan with the highest priority is determined as the target reinforcement configuration plan. In this embodiment, the candidate reinforcement configuration plans are sorted preferentially through the screening conditions, and the priority of the candidate reinforcement configuration plan that better meets the requirements of the screening conditions and better adapts to the personalized needs of the user is increased, so that the user can intuitively select the target reinforcement configuration plan.

[0052] It can be understood that the reinforcement configuration plan alternative list can also be filtered according to each screening condition to determine the target reinforcement configuration plan, or other target reinforcement configuration plan screening logics can be adopted. This embodiment does not make specific limitations.

[0053] It should be noted that, compared with the above-mentioned constraint conditions, the screening conditions generally do not affect each reinforcement configuration plan itself. It screens the reinforcement configuration plans as a whole to obtain a target reinforcement configuration plan that better meets the personalized needs of the user, while the constraint conditions usually limit each steel bar specification and its respective quantity in each reinforcement configuration plan to improve the generation efficiency of the reinforcement configuration plan alternative list.

[0054] In this embodiment, the dynamic configuration of the reinforcement optimization model is performed based on each reinforcement constraint parameter and the set of candidate steel bars of the target business scenario to ensure that all possible reinforcement configuration plans are listed as much as possible, solving the problem that the look-up table method can only generate a single reinforcement configuration plan and requires a lot of time for trial calculation and adjustment; secondly, integer programming is performed through the reinforcement optimization model corresponding to each constraint matrix set, traversing various reinforcement configuration plans, and efficiently screening out all candidate reinforcement configuration plans that meet each constraint matrix set and minimize the objective function, solving the problem that the traditional brute-force solution algorithm that only configures the target reinforcement value cannot automatically adjust the constraint conditions based on the business scenario. By each constraint matrix set, the reinforcement combinations that need to be traversed by the reinforcement optimization model are reduced, saving the computing resources required for the operation of the reinforcement configuration plan, and ensuring that the traversed output reinforcement configuration plan alternative list includes all candidate reinforcement configuration plans that meet each constraint matrix set, ensuring that the output candidate reinforcement configuration plan is the most economical plan in each constraint matrix set for subsequent screening. Further, the target reinforcement configuration plan is determined in the reinforcement configuration plan alternative list according to the screening conditions preset according to the target business scenario, which can meet the personalized needs of different users. This technical solution provides a highly versatile reinforcement method that can be applied to the reinforcement requirements of different components and various business scenarios.

[0055] Please refer to Figure 2 , in this embodiment, taking the reinforcement optimization model as a linear programming model as an example, another embodiment of the reinforcement method based on the reinforcement optimization model in the present application is provided:

[0056] 201. Obtain each reinforcement constraint parameter of the target business scenario and the set of candidate steel bars.

[0057] Exemplarily, each reinforcement constraint parameter in this embodiment includes a reinforcement coefficient interval (including the minimum reinforcement coefficient and the maximum reinforcement coefficient), a target reinforcement value, the cross-sectional area of the member to be reinforced, and the threshold value of the difference in steel bar diameters.

[0058] 202. Set each scenario constraint condition of the reinforcement optimization model according to each reinforcement constraint parameter.

[0059] In this embodiment, each set of constraint matrices may include a set composed of each scenario constraint condition, and each set of constraint matrices may also include a set composed of each scenario constraint condition and each steel bar candidate matrix.

[0060] It can be understood that the scenario constraint condition is used to indicate the constraint conditions restricted in this business scenario. The user can set corresponding scenario constraint conditions for different business scenarios. Among them, the scenario constraint conditions include, but are not limited to, the reinforcement value constraint condition, the steel bar quantity constraint condition of at least one target steel bar, etc. This embodiment does not limit the specific rules and quantity of the scenario constraint conditions.

[0061] Optionally, if the scenario constraint condition is a reinforcement value constraint condition, then set the reinforcement value constraint condition of the reinforcement optimization model according to the target reinforcement value, the preset fixed sequence range, and the preset reinforcement coefficient interval. For example, set the fixed sequence range to 0.02, and take the target reinforcement value as the minimum reinforcement value Amin. Thus, the reinforcement value constraint conditions can be obtained in sequence as Amin*1.02, Amin*1.04,...., Amax, and each set of constraint matrices corresponding to each reinforcement value constraint condition can correspond to at least one initial reinforcement plan, and the candidate reinforcement plan with the best economy under this set of constraint matrices can be screened out through the model.

[0062] Optionally, if the scenario constraint condition is a reinforcement value constraint condition, then generate a magnification factor sequence according to the reinforcement coefficient interval, the target ratio between the target reinforcement value and the cross-sectional area of the member to be reinforced; set the reinforcement value constraint condition of the reinforcement optimization model according to the target reinforcement value and the magnification factor sequence.

[0063] Exemplarily, generating a magnification factor sequence according to the reinforcement ratio interval, the target reinforcement value, and the target ratio between the cross-sectional area of the component to be reinforced includes: determining the target reinforcement ratio range in a plurality of preset reinforcement ratio range grades according to the target ratio; generating a magnification factor sequence between the reinforcement ratio intervals according to the target reinforcement ratio range.

[0064] Specifically, generating a magnification factor sequence between the reinforcement ratio intervals according to the target reinforcement ratio range includes: generating a magnification factor sequence between the minimum reinforcement ratio and the maximum reinforcement ratio according to the target reinforcement ratio range.

[0065] Specifically, assuming that a plurality of reinforcement ratio range grades include three grades, the above-mentioned determining the target reinforcement ratio range in a plurality of preset reinforcement ratio range grades according to the target ratio includes: if the target ratio is in the preset first grade, determining the first ratio range as the target reinforcement ratio range; if the target ratio is in the preset second grade, determining the second ratio range as the target reinforcement ratio range; if the target ratio is in the preset third grade, determining the third ratio range as the target reinforcement ratio range; wherein, the first grade, the second grade, and the third grade increase in sequence, and the first ratio range, the second ratio range, and the third ratio range increase in sequence. In this embodiment, a magnification factor sequence is automatically generated according to the target ratio between the target reinforcement value and the cross-sectional area of the component to be reinforced. By setting the target reinforcement ratio ranges of multiple grades, the number of candidate reinforcement schemes generated under different target ratios can be adjusted to better balance the comprehensiveness and generation efficiency of the candidate reinforcement schemes.

[0066] It can be understood that within the same reinforcement ratio interval, the smaller the target reinforcement ratio range, the more candidate reinforcement schemes are generated. In the case of a larger target ratio, a larger target reinforcement ratio range can be set, while in the case of a smaller target ratio, a smaller target reinforcement ratio range can be set. The sensitivity of the reinforcement ratio range under different target ratios can be adjusted. In the case of a larger target ratio, the steel bars selected are usually larger. Therefore, selecting a larger target reinforcement ratio range can improve the generation efficiency. In the case of a smaller target ratio, the steel bars selected are usually smaller. At this time, selecting a smaller target reinforcement ratio range can generate more candidate reinforcement schemes and reduce the omission of reinforcement schemes.

[0067] For example, set the first grade as the target ratio less than 1.0%, and the corresponding first ratio range is set to 0.02; set the second grade as the target ratio greater than or equal to 1.0% and less than 2.5%, and the corresponding second ratio range is set to 0.05; set the third grade as the target ratio greater than or equal to 2.5% and less than 5.0%, and the corresponding second ratio range is set to 0.1;

[0068] 203. Generate each steel bar candidate matrix according to a preset steel bar diameter difference threshold and a set of candidate steel bars.

[0069] It can be understood that the list of steel bar specifications that can be used in the design process is limited, and these steel bars can be combined according to the requirements of the diameter type and the steel bar diameter difference threshold. We use a rule-based algorithm to automatically combine steel bars. The steel bar candidate matrix is a matrix composed of the selectable steel bars restricted in this scenario, that is, a subset of the set of candidate steel bars. The types of steel bars in each steel bar candidate matrix are different. To a certain extent, the steel bar candidate matrix can also be understood as a kind of scenario constraint condition, which can be called the steel bar type constraint condition.

[0070] Optionally, generate each steel bar candidate matrix according to the steel bar diameter difference threshold and the set of candidate steel bars, and the diameter difference between any two steel bars in each steel bar candidate matrix is less than or equal to the steel bar diameter difference threshold.

[0071] It can be understood that the steel bar diameter difference threshold can be represented by the steel bar range. The steel bar range is used to indicate the difference in position numbers in the steel bar sequence. For example, the complete steel bar sequence is: 12, 14, 16, 18, 20, 22, 25, 28. Then, the position serial number of the steel bar with a diameter of 14 is 2, and the position serial number of the steel bar with a diameter of 18 is 4, so the range is 4 - 2 = 2.

[0072] It should be noted that the above method for generating the steel bar candidate matrix is only a possible example, and further restrictions can be made according to the actual situation, that is, the steel bar type constraint condition can include the combination of the steel bar diameter difference threshold and other steel bar arrangement constraints. This embodiment does not make specific restrictions.

[0073] 204. Arrange and combine each scenario constraint condition and each steel bar candidate matrix, and initialize the steel bar arrangement optimization model to obtain the steel bar arrangement optimization model corresponding to each constraint matrix set.

[0074] In this embodiment, by arranging and combining each scenario constraint condition and each steel bar candidate matrix, each constraint matrix set is obtained to generate the candidate steel bar arrangement schemes corresponding to each constraint matrix set. That is, the constraint matrix sets in this embodiment correspond to various combinations of each scenario constraint condition and each steel bar candidate matrix. Different steel bar arrangement schemes are generated by adjusting the amplification factor and the steel bar diameter combination, so as to provide more optional schemes.

[0075] 205. Traverse each steel bar candidate matrix in each constraint matrix set to determine all initial steel bar arrangement schemes that meet each scenario constraint condition.

[0076] In this embodiment, the quantity of each type of steel bar in each initial reinforcement plan, candidate reinforcement plan, and target reinforcement plan is an integer greater than or equal to zero. The reinforcement optimization model can list as many reinforcement plans that meet the requirements as possible based on each set of constraint matrices, so as to achieve a better optimization effect in terms of economy for the subsequently selected candidate reinforcement plans.

[0077] For ease of understanding, a linear programming model is provided for illustration. Among them, the set of constraint matrices of the reinforcement optimization model can be expressed as:

[0078]

[0079] Among them, x i represents the number of the i-th type of steel bar, Z represents the set of integers, that is, the number of steel bars is an integer greater than or equal to zero; a i represents the cross-sectional area of the i-th type of steel bar; A represents the target reinforcement value; p represents the reinforcement coefficient;

[0080] It should be noted that the above set of constraint matrices is only an example. Under appropriate circumstances, other scenario constraint conditions can also be set. For example, the steel bar type constraint condition is set as x i ∈Z j , Z j represents the set of integers composed of the j-th steel bar candidate matrix, Z j ={a 1,j a 2,j ...a n,j}; Another example is to limit the quantity of a certain specification of steel bar, and then add the corresponding constraint condition x j =k, where k is any non-negative integer, etc.

[0081] 206. Select the initial reinforcement plan corresponding to the minimum value of the objective function as the candidate reinforcement plan corresponding to each set of constraint matrices to obtain a list of alternative reinforcement plans.

[0082] In this embodiment, the list of alternative reinforcement plans includes all candidate reinforcement plans that meet each set of constraint matrices and minimize the objective function. The linear programming model of this embodiment is solved with the goal of minimizing the objective function. Among them, the objective function is set based on economic indicators. For ease of understanding, an example of a reinforcement optimization model is provided. The objective function of the reinforcement optimization model can be expressed as:

[0083] S = a1x1 + a2x2 +... + a n x n

[0084] Among them, S represents the objective function under each set of constraint matrices, which is represented here by the sum of the cross-sectional areas of various types of steel bars in this reinforcement configuration plan. By minimizing the objective function, the optimal candidate reinforcement configuration plan under each set of constraint matrices can be solved.

[0085] It can be understood that the objective function can also be calculated in other ways, and this embodiment does not make specific limitations.

[0086] In this embodiment, the complex reinforcement planning problem is abstracted into a classical mathematical optimization model - an integer linear programming problem, which makes the result more optimized, improves the planning efficiency of the reinforcement configuration plan, takes into account the economy and personalized needs of the reinforcement configuration plan, and can better meet the reinforcement requirements of different components and various business scenarios.

[0087] It can be understood that there may be one or more initial reinforcement configuration plans that satisfy each set of constraint matrices. In this application, the plan with the best economy is selected from all the initial reinforcement configuration plans under each set of constraint matrices by minimizing the objective function as the candidate reinforcement configuration plan for this set of constraint matrices.

[0088] 207. Determine the target reinforcement configuration plan in the alternative list of reinforcement configuration plans according to each screening condition preset for the target business scenario.

[0089] In this embodiment, the screening condition is a personalized screening method set according to the target business scenario, which can be set according to the specific business scenario and the personalized needs of the user. This embodiment does not make specific limitations on the number and specific rules of the screening conditions.

[0090] Exemplarily, screen the candidate reinforcement configuration plans with the number of steel bars within the preset number range; and / or, screen the candidate reinforcement configuration plans with the number of steel bar specifications less than the preset type threshold; and / or, screen the candidate reinforcement configuration plan with the smallest reinforcement magnification factor.

[0091] Optionally, filter the alternative list of reinforcement configuration plans according to any one of the screening conditions in the target business scenario to obtain a set of preferred reinforcement configuration plans; traverse each screening condition to obtain the target reinforcement configuration plan.

[0092] In this embodiment, the dynamic configuration of the reinforcement optimization model is performed based on the reinforcement constraint parameters and the set of candidate steel bars for the target business scenario to ensure that all possible reinforcement schemes are listed as much as possible, solving the problem that the look-up table method can only generate a single reinforcement scheme and requires a large amount of time for trial calculation and adjustment. Secondly, integer programming is performed through the reinforcement optimization model corresponding to each constraint matrix set, traversing various reinforcement schemes, and efficiently screening out all candidate reinforcement schemes that meet each constraint matrix set and the minimization of the objective function, solving the problem that the traditional brute-force solution algorithm that only configures the target reinforcement value cannot automatically adjust the constraint conditions based on the business scenario. By each constraint matrix set, the reinforcement combinations that need to be traversed by the reinforcement optimization model are reduced, saving the computing resources required for the operation of the reinforcement scheme, and ensuring that the traversed output reinforcement scheme alternative list includes all candidate reinforcement schemes that meet each constraint matrix set and the minimization of the objective function, ensuring that the output candidate reinforcement scheme is the most economical scheme in each constraint matrix set for subsequent screening. Further, the target reinforcement scheme is determined in the reinforcement scheme alternative list according to each screening condition preset for the target business scenario, which can meet the personalized needs of different users. This technical solution provides a highly versatile reinforcement method that can be applied to the reinforcement requirements of different components and various business scenarios.

[0093] The above describes the reinforcement method based on the reinforcement optimization model in the present application. Next, the reinforcement device based on the reinforcement optimization model in the present application will be described. Please refer to Figure 3 , an embodiment of the reinforcement device based on the reinforcement optimization model in the present application includes:

[0094] An acquisition module 301, configured to acquire each reinforcement constraint parameter and the set of candidate steel bars for the target business scenario;

[0095] A dynamic configuration module 302, configured to perform dynamic configuration on a preset reinforcement optimization model based on each reinforcement constraint parameter and the set of candidate steel bars to obtain a reinforcement optimization model corresponding to each constraint matrix set;

[0096] A traversal module 303, configured to perform integer programming through the reinforcement optimization model corresponding to each constraint matrix set to obtain a reinforcement scheme alternative list, and the reinforcement scheme alternative list includes all candidate reinforcement schemes that meet each constraint matrix set and the minimization of the objective function;

[0097] A screening module 304, configured to determine a target reinforcement scheme in the reinforcement scheme alternative list according to each screening condition preset for the target business scenario.

[0098] In this embodiment, based on the reinforcement constraint parameters of the target business scenario and the set of candidate steel bars, the dynamic configuration of the reinforcement optimization model is carried out to ensure that as many reinforcement schemes as possible are listed, solving the problem that the look-up table method can only generate a single reinforcement scheme and requires a large amount of time for trial calculation and adjustment. Secondly, through the integer programming of the reinforcement optimization model corresponding to each constraint matrix set, various reinforcement schemes are traversed, and all candidate reinforcement schemes that meet each constraint matrix set and the minimization of the objective function are efficiently screened out, solving the problem that the traditional brute-force solution algorithm that only configures the target reinforcement value cannot automatically adjust the constraint conditions based on the business scenario. By using each constraint matrix set, the reinforcement combinations that need to be traversed by the reinforcement optimization model are reduced, saving the computing resources required for the operation of the reinforcement scheme, and ensuring that the traversed output list of alternative reinforcement schemes includes all candidate reinforcement schemes that meet each constraint matrix set, ensuring that the output candidate reinforcement schemes are the most economical schemes in each constraint matrix set for subsequent screening. Further, according to each screening condition preset for the target business scenario, the target reinforcement scheme is determined in the list of alternative reinforcement schemes, which can meet the personalized needs of different users. This technical solution provides a highly versatile reinforcement method that can be applied to the reinforcement requirements of different components and various business scenarios.

[0099] Please refer to Figure 4 , another embodiment of the reinforcement device based on the reinforcement optimization model in this application includes:

[0100] An acquisition module 301, configured to acquire each reinforcement constraint parameter of the target business scenario and the set of candidate steel bars;

[0101] A dynamic configuration module 302, configured to perform dynamic configuration on a preset reinforcement optimization model based on each reinforcement constraint parameter and the set of candidate steel bars to obtain a reinforcement optimization model corresponding to each constraint matrix set;

[0102] A traversal module 303, configured to perform integer programming through the reinforcement optimization model corresponding to each constraint matrix set to obtain a list of alternative reinforcement schemes, where the list of alternative reinforcement schemes includes all candidate reinforcement schemes that meet each constraint matrix set and the minimization of the objective function;

[0103] A screening module 304, configured to determine a target reinforcement scheme in the list of alternative reinforcement schemes according to each screening condition preset for the target business scenario.

[0104] Optionally, the dynamic configuration module 302 includes:

[0105] A scenario constraint configuration unit 3021, configured to set each scenario constraint condition of the reinforcement optimization model according to each reinforcement constraint parameter;

[0106] A steel bar matrix configuration unit 3022, configured to generate each steel bar candidate matrix according to the steel bar diameter difference threshold and the set of candidate steel bars;

[0107] Combination unit 3023 is used to permute and combine each scenario constraint condition and each steel bar candidate matrix, and initialize the reinforcement optimization model to obtain the reinforcement optimization models corresponding to each constraint matrix set.

[0108] Optionally, the scenario constraint configuration unit 3021 is specifically configured to: generate a magnification factor sequence according to the reinforcement coefficient interval, the target reinforcement value, and the target ratio between the cross-sectional area of the component to be reinforced;

[0109] Set the reinforcement value constraint condition of the reinforcement optimization model according to the target reinforcement value and the magnification factor sequence.

[0110] Optionally, the scenario constraint configuration unit 3021 is specifically configured to: determine the target reinforcement coefficient range in a plurality of preset reinforcement coefficient range grades according to the target ratio;

[0111] Generate a magnification factor sequence according to the target reinforcement coefficient range between the reinforcement coefficient intervals.

[0112] Optionally, the scenario constraint condition further includes: the steel bar quantity constraint condition of at least one target steel bar.

[0113] Optionally, the traversal module 303 includes:

[0114] The traversal unit 3031 is used to traverse each steel bar candidate matrix in each constraint matrix set to determine all initial reinforcement schemes that meet each scenario constraint condition;

[0115] The selection unit 3032 is used to select the initial reinforcement scheme corresponding to the minimum value of the objective function as the candidate reinforcement scheme corresponding to each constraint matrix set, and obtain a list of alternative reinforcement schemes.

[0116] Optionally, the screening condition of the screening module 304 is: screening the steel bar quantity within a preset root number range; and / or, screening that the type of steel bar specification is less than a preset type threshold; and / or, screening the candidate reinforcement scheme with the smallest reinforcement magnification factor.

[0117] In this embodiment, the dynamic configuration of the reinforcement optimization model is performed based on the reinforcement constraint parameters of the target business scenario and the set of candidate steel bars, so as to ensure that all reinforcement schemes are listed as much as possible, solving the problem that the look-up table method can only generate a unique reinforcement scheme and requires a large amount of time for trial calculation and adjustment. Secondly, integer programming is performed through the reinforcement optimization model corresponding to each constraint matrix set, traversing various reinforcement schemes, and efficiently screening out all candidate reinforcement schemes that meet each constraint matrix set and the minimization of the objective function, solving the problem that the traditional brute-force solution algorithm that only configures the target reinforcement value cannot automatically adjust the constraint conditions based on the business scenario. By each constraint matrix set, the reinforcement combinations that need to be traversed by the reinforcement optimization model are reduced, saving the computing resources required for the operation of the reinforcement scheme, and ensuring that the traversed output list of alternative reinforcement schemes includes all candidate reinforcement schemes that meet each constraint matrix set and the minimization of the objective function, ensuring that the output candidate reinforcement scheme is the most economical scheme in each constraint matrix set for subsequent screening. Further, the target reinforcement scheme is determined in the list of alternative reinforcement schemes according to each screening condition preset by the target business scenario, which can meet the personalized needs of different users. This technical solution provides a highly versatile reinforcement method that can be applied to the reinforcement requirements of different components and various business scenarios.

[0118] Above Figure 3 And Figure 4 The reinforcement device based on the reinforcement optimization model in the present application is described in detail from the perspective of modular functional entities. Next, the reinforcement device based on the reinforcement optimization model in the present application is described in detail from the perspective of hardware processing.

[0119] See Figure 5 As shown, the reinforcement device based on the reinforcement optimization model includes a processor 500 and a memory 501. The memory 501 stores machine-executable instructions that can be executed by the processor 500. The processor 500 executes the machine-executable instructions to implement the above-mentioned reinforcement method based on the reinforcement optimization model.

[0120] Furthermore, Figure 5 The reinforcement device based on the reinforcement optimization model shown also includes a bus 502 and a communication interface 503. The processor 500, the communication interface 503, and the memory 501 are connected through the bus 502.

[0121] Among them, the memory 501 may include high-speed random access memory (RAM), and may also include non-volatile memory, for example, at least one disk memory. The communication connection between this system network element and at least one other network element is realized through at least one communication interface 503 (which can be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. can be used. The bus 502 can be an ISA bus, a PCI bus, an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 only a bidirectional arrow is used in Figure 5 , but it does not mean that there is only one bus or one type of bus.

[0122] The processor 500 may be an integrated circuit chip with signal processing capabilities. In the implementation process, the steps of the above method can be completed by the integrated logic circuit in the hardware of the processor 500 or instructions in software form. The above-mentioned processor 500 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present disclosure. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present disclosure can be directly embodied as being executed and completed by a hardware decoding processor, or executed and completed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the art such as random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, register, etc. This storage medium is located in the memory 501, and the processor 500 reads the information in the memory 501 and combines its hardware to complete the method steps of the foregoing embodiments.

[0123] The present application also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are run on a computer, the computer is caused to execute the steps of the steel bar arrangement method based on the steel bar arrangement optimization model.

[0124] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0125] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0126] The above embodiments are only used to illustrate the technical solution of the present application and are not intended to limit it; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A reinforcement method based on a reinforcement optimization model, characterized in that The steel bar reinforcement method based on the reinforcement optimization model includes: Obtaining each reinforcement constraint parameter of the target business scenario and the set of candidate steel bars; Dynamically configuring the preset reinforcement optimization model based on each of the reinforcement constraint parameters and the set of candidate steel bars to obtain a reinforcement optimization model corresponding to each constraint matrix set; Performing integer programming through the reinforcement optimization model corresponding to each constraint matrix set to obtain a list of alternative reinforcement plans; Determining the target reinforcement plan from the list of alternative reinforcement plans according to each screening condition preset for the target business scenario.

2. The reinforcement method based on the reinforcement optimization model according to claim 1, characterized in that The dynamically configuring the preset reinforcement optimization model based on each of the reinforcement constraint parameters and the set of candidate steel bars to obtain a reinforcement optimization model corresponding to each constraint matrix set includes: Setting each scenario constraint condition of the reinforcement optimization model according to each of the reinforcement constraint parameters; Generating each steel bar candidate matrix according to a preset steel bar diameter difference threshold and the set of candidate steel bars; Arranging and combining each scenario constraint condition and each steel bar candidate matrix to initialize the reinforcement optimization model to obtain a reinforcement optimization model corresponding to each constraint matrix set.

3. The reinforcement method based on the reinforcement optimization model according to claim 2, characterized in that The reinforcement constraint parameters include a reinforcement coefficient interval, a target reinforcement value, and the cross-sectional area of the component to be reinforced, and the scenario constraint condition is a reinforcement value constraint condition; The setting each scenario constraint condition of the reinforcement optimization model according to each of the reinforcement constraint parameters includes: Generating a magnification coefficient sequence according to the target ratio between the reinforcement coefficient interval, the target reinforcement value, and the cross-sectional area of the component to be reinforced; Setting the reinforcement value constraint condition of the reinforcement optimization model according to the target reinforcement value and the magnification coefficient sequence.

4. The reinforcement method based on the reinforcement optimization model according to claim 3, characterized in that The generating a magnification coefficient sequence according to the target ratio between the reinforcement coefficient interval, the target reinforcement value, and the cross-sectional area of the component to be reinforced includes: Determining the target reinforcement coefficient range in a preset number of reinforcement coefficient range grades according to the target ratio; Generating a magnification coefficient sequence according to the target reinforcement coefficient range between the reinforcement coefficient intervals.

5. The reinforcement method based on the reinforcement optimization model according to any one of claims 2-4, characterized in that, The scenario constraint condition further includes: a steel bar quantity constraint condition for at least one target steel bar.

6. The reinforcement method based on the reinforcement optimization model according to claim 1, wherein, The constraint matrix set includes each scenario constraint condition and each steel bar candidate matrix, and the reinforcement optimization model is a linear programming model; The performing integer programming through the reinforcement optimization model corresponding to each constraint matrix set to obtain a list of alternative reinforcement plans includes: Traversing each steel bar candidate matrix in each constraint matrix set to determine all initial reinforcement plans that satisfy each scenario constraint condition; Selecting the initial reinforcement plan corresponding to the minimum value of the objective function as the candidate reinforcement plan corresponding to each constraint matrix set to obtain a list of alternative reinforcement plans.

7. The reinforcement method based on the reinforcement optimization model according to claim 1, wherein Wherein, The screening conditions are: Screening candidate reinforcement plans with the number of steel bars within a preset number range; and / or, Screening candidate reinforcement plans with the number of steel bar specifications less than a preset specification threshold; and / or, Screening candidate reinforcement plans with the smallest reinforcement magnification coefficient.

8. A reinforcement device based on a reinforcement optimization model, characterized in that, The reinforcement device based on the reinforcement optimization model includes: An obtaining module, configured to obtain each reinforcement constraint parameter of the target business scenario and the set of candidate steel bars; A dynamic configuration module, configured to dynamically configure a preset reinforcement optimization model based on each of the reinforcement constraint parameters and the set of candidate steel bars, so as to obtain a reinforcement optimization model corresponding to each set of constraint matrices; A traversal module, configured to perform integer programming through the reinforcement optimization model corresponding to each set of constraint matrices to obtain a list of alternative reinforcement schemes; A screening module, configured to determine a target reinforcement scheme from the list of alternative reinforcement schemes according to each screening condition preset for the target business scenario.

9. A reinforcement device based on a reinforcement optimization model, characterized in that, The reinforcement device based on the reinforcement optimization model includes: a memory and at least one processor, and instructions are stored in the memory; The at least one processor calls the instructions in the memory, so that the reinforcement device based on the reinforcement optimization model executes the reinforcement method based on the reinforcement optimization model according to any one of claims 1-7.

10. A computer-readable storage medium, on which instructions are stored, characterized in that, When the instructions are read and run, they execute the reinforcement method based on the reinforcement optimization model according to any one of claims 1-7.

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