Steel structure intelligent production scheduling method

Abstract

The invention discloses an intelligent production scheduling method for a steel structure, and the method comprises the steps: S1, obtaining a plurality of production orders, and carrying out the grouping of the plurality of production orders according to the priority similarity; s2, performing various combinations on the production orders of each group, calculating production time consumption, production energy consumption and production material consumption of each combination, and selecting an optimal combination according to a calculation result; and S3, performing production on the production order of each group according to the optimal combination. According to the invention, production order completion can be ensured, energy and production material consumption is reduced, and production cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field related to steel structure production, and more specifically, to an intelligent production scheduling method for steel structures. Background Art

[0002] There are many types and sizes of steel structures. The existing technology usually relies on manual scheduling or simple computer-aided scheduling, which obviously cannot significantly improve production efficiency and causes waste of energy and production materials and increased costs. Therefore, it is necessary to design a technical solution that can overcome the above defects. Summary of the invention

[0003] One object of the present invention is to provide an intelligent production scheduling method for steel structures, which can ensure the completion of production orders, reduce energy and production material consumption, and reduce production costs.

[0004] In order to achieve these purposes and other advantages of the present invention, according to one aspect of the present invention, the present invention provides an intelligent scheduling method for steel structures, including: S1: obtaining multiple production orders, and grouping the multiple production orders according to priority similarity; S2: making multiple combinations of the production orders in each group, calculating the production time, production energy consumption, and production material consumption of each combination, and selecting the optimal combination according to the calculation results; S3: executing production for the production orders in each group according to the optimal combination.

[0005] Furthermore, in S1, priority values ​​of the multiple production orders are calculated respectively according to the production time limit, current time and priority coefficient of each production order, and the priorities of the multiple production orders are determined according to the priority values.

[0006] Furthermore, in S1, the production orders whose priority values ​​are within a predetermined variation range are grouped into one group.

[0007] Furthermore, in S2, all parts required for the production orders of each group are counted, multiple cutting sequence combinations are performed on all parts, and then the cutting process, forming process and welding assembly process are simulated in sequence, and the production time, production energy consumption and production material consumption of the simulation process are counted.

[0008] Furthermore, the production energy consumption includes at least cutting energy consumption, forming energy consumption, equipment idling energy consumption, transportation energy consumption and welding and assembly energy consumption.

[0009] Furthermore, based on historical production data, a cutting model, a forming model and a welding assembly model are pre-established; the production material parameters and the parameters of each part are input into the cutting model to obtain the cutting energy consumption; the parameters of each part are input into the forming model to obtain the forming energy consumption; the steel structure parameters corresponding to each production order are input into the forming model to obtain the welding assembly energy consumption.

[0010] Furthermore, based on the historical yield rate data, a correction model is pre-established; the parameters of the steel structure corresponding to each production order are input into the correction model to obtain a production energy consumption correction coefficient, and the production energy consumption is corrected using the production energy consumption correction coefficient. Furthermore, in S2, a comprehensive score is calculated using the production time, production energy consumption, and production material consumption of each combination, and the optimal combination is selected based on the comprehensive score.

[0011] The present invention has at least the following beneficial effects: The present invention groups multiple production orders according to priority similarity, combines the production orders of each group, calculates the production time, production energy consumption, and production material consumption of each combination, selects the optimal combination, and executes production according to the optimal combination; the present invention optimizes the configuration of production orders, reduces the energy, production material waste and cost increase caused by unreasonable production scheduling.

[0012] Other advantages, objectives and features of the present invention will be embodied in part through the following description, and in part will be understood by those skilled in the art through study and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is a flow chart of an embodiment of the present application. DETAILED DESCRIPTION

[0014] The present invention is further described in detail below in conjunction with the accompanying drawings so that those skilled in the art can implement the invention with reference to the description.

[0015] It should be understood that the terms such as "having", "including" and "comprising" used in the embodiments of the present application do not exclude the existence or addition of one or more other elements or their combinations. All directional indications (such as up, down, left, right, front, back...) in the embodiments of the present application are only used to explain the relative position relationship, movement, etc. between the components in a certain specific posture. If the specific posture changes, the directional indication will also change accordingly. When an element is referred to as "fixed on" or "set on" another element, it can be directly on the other element or there may be a centering element at the same time. When an element is referred to as "connecting" another element, it can be directly connected to another element or it can be indirectly connected to another element through a centering element. The description of "first", "second", etc. in the embodiments of the present application is only for descriptive purposes, and cannot be understood as indicating or implying its relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" can explicitly or implicitly include at least one of the features.

[0016] It should be noted that the technical solutions between the various embodiments of the present application can be combined with each other, but it must be based on the fact that ordinary technicians in the field can implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0017] like Figure 1 As shown, the embodiment of the present application provides a steel structure intelligent production scheduling method, including: S1: Acquire multiple production orders, and group the multiple production orders according to priority similarity; Exemplarily, the steel structure includes H-shaped steel, channel steel, and U-shaped and curved steel structures; For example, the production order includes information such as the type and size of the steel structure; Exemplarily, the priority may be determined according to the production deadline of the production order, or according to the priority mark attached to the production order, and the production orders with the same or slightly different priorities are grouped together; S2: Combining the production orders of each group into multiple combinations, calculating the production time, production energy consumption, and production material consumption of each combination, and selecting the optimal combination according to the calculation results; The production of steel structures generally includes production equipment such as cutting equipment, forming equipment, and welding and assembly equipment. Since the parts used in each steel structure are of different shapes, sizes, and production time, blind production may result in the inability to maximize the use of production materials and ineffective waiting of various production equipment, which in turn leads to waste of production materials, energy, and time. This step uses the production order combination strategy to solve this problem; Exemplarily, multiple production orders are queued under multiple production equipment, and multiple combinations are made for the production equipment selected for each production order and the queueing order of the production orders at each production equipment, and the production time, production energy consumption, and production material consumption of each combination in the production process are simulated; Exemplarily, the production time is the time from the start of processing of the first production order to the completion of the last production order, and the production energy consumption and production material consumption are the energy consumption and production material consumption within the production time respectively; Exemplarily, the production time consumption, production energy consumption, and production material consumption are evaluated using pre-established evaluation rules, and the optimal combination is selected based on the evaluation results. The evaluation rules can be established based on the cost of energy and materials, and can be determined according to actual conditions. For example, a combination with the least production time consumption, production energy consumption, or production material consumption can be selected; S3: Execute production for each group of production orders according to the optimal combination; This step gives production instructions based on the optimal combination obtained in S2, and uses raw materials to perform cutting, forming, welding and assembly processes to complete the production of steel structures; It can be seen that this embodiment groups production orders with similar priorities and processes them simultaneously to avoid failure to complete production orders on time, and optimizes the configuration of production orders in each group to obtain a production order combination with better production time, production energy consumption, and production material consumption, thereby reducing energy, production material waste and cost increase caused by unreasonable production scheduling.

[0018] In another embodiment, in S1, priority values ​​of the multiple production orders are calculated respectively according to the production time limit, current time and priority coefficient of each production order, and the priorities of the multiple production orders are determined according to the priority values; Exemplarily, the priority value p=(Tt)*λ, where T, t and λ represent the production time limit, the current time and the priority coefficient respectively, and the priority coefficient is added by the user according to the actual operation and sales situation.

[0019] In another embodiment, in S1, the production orders having priority values ​​within a predetermined variation range are grouped into one group; In this embodiment, the predetermined variation range may be determined according to the actual number of production orders. Exemplarily, the production orders are grouped with ±10% as the predetermined variation range.

[0020] In another embodiment, in S2, all parts required for the production order of each group are counted, multiple cutting sequence combinations are performed on all parts, and then the cutting process, forming process and welding assembly process are simulated in sequence, and the production time consumption, production energy consumption and production material consumption of the simulation process are counted; In this embodiment, all production orders of each group are divided into parts, and all parts L of all production orders are counted. The L parts are first queued at M cutting devices, and multiple queueing orders are determined. For example, a common H-beam can be divided into three parts: two wing plates and one web plate. Exemplarily, with the minimum amount of surplus production materials used as the objective function, and the parameters of the production materials, the parameters of the parts, and the number of cutting devices as constraints, the ant colony algorithm is used to calculate various queuing orders at M cutting devices, simulate cutting at the M cutting devices, simulate programming at the N forming devices, and simulate welding and assembly at the P welding and assembly devices, and historical statistical data are used to determine the production time, production energy consumption, and production material consumption in the simulated process of cutting, forming, welding and assembly.

[0021] In another embodiment, the production energy consumption includes at least cutting energy consumption, forming energy consumption, equipment idling energy consumption, transportation energy consumption and welding and assembly energy consumption; This embodiment provides a preferred production energy consumption content, which includes not only the energy consumption of cutting, forming, welding and assembly processes, but also the energy consumption of idling and transportation. The idling energy consumption is the energy consumed by the cutting equipment, forming equipment, and welding equipment in the processing gap, and the transportation energy consumption is the energy consumed by the transportation equipment when the intermediate product is transported between the cutting equipment, forming equipment, and welding and assembly equipment. Exemplarily, the idling energy consumption and the transfer energy consumption can be determined based on historical data to determine the unit energy consumption, and then determined based on the idling time and the transfer time.

[0022] Exemplarily, based on historical production data, a cutting model, a forming model and a welding assembly model are pre-established; production material parameters and parameters of each part are input into the cutting model to obtain the cutting energy consumption; each part parameter is input into the forming model to obtain the forming energy consumption; the steel structure parameters corresponding to each production order are input into the forming model to obtain the welding assembly energy consumption; Exemplarily, historical production data includes parameters of production materials, parameters of each part, parts included in steel structure products, parameters of steel structure products and corresponding energy consumption. For any part, the parameters of the corresponding production materials and part parameters are composed into a matrix and input into a convolutional neural network. The cutting energy consumption is used as the output to train a cutting model; the parameters of each part are composed into a matrix and input into a convolutional neural network. The forming energy consumption is used as the output to train a forming model; the parameters of the steel structure products are composed into a matrix and input into a convolutional neural network. The welding assembly energy consumption is used as the output to train a welding assembly model; the cutting model, the forming model and the welding assembly model are used to calculate the cutting energy consumption, the forming energy consumption and the welding assembly energy consumption respectively.

[0023] In another embodiment, a correction model is pre-established based on historical yield rate data; the parameters of the steel structure corresponding to each production order are input into the correction model to obtain a production energy consumption correction coefficient, and the production energy consumption is corrected using the production energy consumption correction coefficient. This embodiment takes into account the yield rate of steel structure products, and the yield rate will affect the production energy consumption, so it is necessary to correct the production energy consumption obtained in the previous embodiment; Exemplarily, historical yield data is used to estimate the number of reprocessed steel structure products, and the additional energy consumption of reprocessed steel structure products is calculated. Combined with the production energy consumption, correction coefficient data is obtained, and a correction model is established using the parameters of the steel structure products and the correction coefficient data. A matrix is ​​formed according to the parameters of the steel structure products and then input into a convolutional neural network. The correction coefficient data is output to obtain a correction model. The correction model is used to predict the production energy consumption correction coefficient corresponding to each production order, and the production energy consumption is corrected to make the production energy consumption more accurate.

[0024] In another embodiment, in S2, a comprehensive score is calculated using the production time, production energy consumption, and production material consumption of each combination, and the optimal combination is selected according to the comprehensive score; Exemplarily, the comprehensive score is a weighted average of production time, production energy consumption, and production material consumption, and the respective weights can be determined based on the cost of energy and materials.

[0025] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and the implementation modes, and they can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the illustrations shown and described herein.

Claims

1. Intelligent production scheduling method for steel structure, characterized in that: include: S1: Acquire multiple production orders, and group the multiple production orders according to priority similarity; S2: Combining the production orders of each group into multiple combinations, calculating the production time, production energy consumption, and production material consumption of each combination, and selecting the optimal combination according to the calculation results; S3: Execute production for each group of production orders according to the optimal combination.

2. The intelligent production scheduling method for steel structures according to claim 1, characterized in that: In S1, priority values ​​of the multiple production orders are calculated respectively according to the production time limit, current time and priority coefficient of each production order, and the priorities of the multiple production orders are determined according to the priority values.

3. The intelligent production scheduling method for steel structures according to claim 2, characterized in that: In S1, the production orders whose priority values ​​are within a predetermined variation range are grouped into one group.

4. The intelligent production scheduling method for steel structures according to claim 1, characterized in that: In S2, all parts required for the production orders of each group are counted, multiple cutting sequence combinations are performed on all parts, and then the cutting process, forming process and welding assembly process are simulated in sequence, and the production time, production energy consumption and production material consumption of the simulation process are counted.

5. The intelligent production scheduling method for steel structures according to claim 4, characterized in that: The production energy consumption includes at least cutting energy consumption, forming energy consumption, equipment idling energy consumption, transportation energy consumption and welding and assembly energy consumption.

6. The intelligent production scheduling method for steel structures according to claim 4, characterized in that: According to historical production data, pre-establish cutting model, forming model and welding assembly model; Inputting production material parameters and each part parameter into the cutting model to obtain the cutting energy consumption; Inputting each part parameter into the forming model to obtain the forming energy consumption; The steel structure parameters corresponding to each production order are input into the forming model to obtain the welding assembly energy consumption.

7. The intelligent production scheduling method for steel structures according to claim 4, characterized in that: Based on historical yield data, a correction model is established in advance; The parameters of the steel structure corresponding to each production order are input into the correction model to obtain a production energy consumption correction coefficient, and the production energy consumption is corrected using the production energy consumption correction coefficient.

8. The intelligent production scheduling method for steel structures according to claim 1, characterized in that: In S2, a comprehensive score is calculated using the production time, production energy consumption, and production material consumption of each combination, and the optimal combination is selected based on the comprehensive score.

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