Metal plate production scheduling method and system based on artificial intelligence

Abstract

The invention discloses a metal plate production scheduling method and system based on artificial intelligence, and belongs to the technical field of intelligent production scheduling. Real-time production order information and real-time production line information are updated by analyzing the number of scrapped metal plates and a production line with mechanical fault equipment; analyzing a predicted delivery condition according to the updated information, sending a regeneration instruction to a plan generation module according to the predicted delivery condition, and generating a plurality of production scheduling plans capable of enabling all orders not to be delivered overdue after the plan generation module receives the regeneration instruction, and finally, a plan selection module calculates the priority coefficient of each production scheduling plan according to the predicted production time and predicted total energy consumption of the number of production lines so as to select the optimal production scheduling plan, so that the overall production efficiency is improved and the production energy consumption is reduced on the basis of completing the delivery of each order in the delivery period of each order.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent production scheduling, and in particular to a metal plate production scheduling method and system based on artificial intelligence. Background Art

[0002] Sheet metal refers to plate-like materials made of various metal materials. These sheets are widely used in many fields such as construction, manufacturing, aerospace, and automotive industries. Therefore, the production demand for sheet metal is very large, which makes it very important for production workshops to plan production plans and complete production orders on time.

[0003] However, during the production process, some of the produced panels may not be of quality sufficient for delivery and therefore need to be re-produced. There may also be problems with a device in a production line causing the production line to stop operating. These situations greatly affect production efficiency and production may not be completed on time according to the original production plan. Therefore, scheduling is required according to the situation. Therefore, how to quickly and intelligently generate a production scheduling plan based on the situation has become an urgent problem to be solved. Summary of the invention

[0004] In order to solve the technical problems existing in the prior art, the present invention provides a metal sheet production scheduling system based on artificial intelligence, comprising: A data acquisition module is used to obtain real-time production order information of metal sheets and real-time production line information of production workshops, wherein the real-time production order information includes the real-time number of sheets to be produced and the delivery deadline of each order, and the real-time production line information includes the production line number, production efficiency, energy consumption information and real-time status corresponding to each production line, wherein the real-time status includes normal status and maintenance status; A sheet quality assessment module, used to identify defect information of produced metal sheets and assess the quality type of the corresponding metal sheets according to the defect information, wherein the quality types include qualified, repairable and scrapped; The fault analysis module is used to locate the faulty equipment in the corresponding production line according to the defect information of the metal sheet and analyze the fault type of the faulty equipment, including parameter fault and mechanical fault; An information updating module, used for updating the real-time production order information and the real-time production line information; The delivery analysis module is used to analyze and predict the delivery situation based on the real-time production order information and the real-time production line information, and send a regeneration instruction to the plan generation module based on the predicted delivery situation; A plan generation module is used to generate, after receiving a regeneration instruction, a plurality of production scheduling plans that ensure that all orders are delivered on time based on real-time production order information and real-time production line information through a trained scheduling algorithm model, wherein the production scheduling plan records corresponding scheduling information, including the number of production lines started S, the estimated production time T, and the estimated total energy consumption V, and the production lines started in the production scheduling plan are all in normal status; The plan selection module is used to calculate the priority coefficient P of each production scheduling plan based on the scheduling information, and finally select the production scheduling plan with the largest priority coefficient as the new production scheduling plan.

[0005] Furthermore, the plate quality assessment module includes: An image acquisition module, used for acquiring an image of a metal sheet; An image preprocessing module, used for performing an image preprocessing operation on the metal plate image to obtain a preprocessed image; A defect recognition module, used for obtaining defect information of the corresponding metal sheet according to the pre-processed image recognition; The quality classification module is used to evaluate the quality type of the corresponding metal sheet according to the defect information and a preset classification strategy.

[0006] Furthermore, the defect recognition module specifically obtains defect information of the corresponding metal sheet through the trained first deep learning model recognition, and the first deep learning model includes: A ResNet50 backbone network is used to extract feature information of the preprocessed image and obtain first feature maps of multiple different levels; A feature pyramid network is used to fuse the first feature maps at different levels to generate a plurality of second feature maps at different scales; A region proposal network, used to generate defect feature candidate regions in the second feature map; The RoI Align pooling layer is used to extract the region of interest from the defect feature candidate region and then perform an alignment operation using the RoI Align technology. Finally, a third feature map is obtained through a pooling operation. The fully connected layer is used to perform a classification regression operation on the third feature map and output defect information.

[0007] Furthermore, the information update module updates the real-time production order information and real-time production line information, specifically: adding the number of metal sheets with a quality type of scrap to the real-time number of sheets to be produced, and updating the real-time status of the production line where the equipment with mechanical failure is located to a maintenance status.

[0008] Further, the delivery situation is predicted based on the real-time production order information and the real-time production line information, and a regeneration instruction is sent to the plan generation module based on the predicted delivery situation, specifically: According to the real-time production order information and real-time production line information, analyze whether the current production scheduling plan can complete all orders within the latest delivery deadline in the order. If not, send a regeneration instruction; If the real-time status of the production line started in the current production scheduling plan changes to maintenance status, then based on the real-time production order information and real-time production line information, analyze whether all orders can be completed within the latest delivery deadline in the order by using the remaining production lines in the current production scheduling plan whose real-time status is normal. If not, send a regeneration instruction.

[0009] Furthermore, the specific calculation method of the priority coefficient P is: P=1 / (K1×S+K2×T+K3×N), K1, K2 and K3 represent the first preset weight, the second preset weight and the third preset weight respectively.

[0010] Furthermore, the fault analysis module locates the faulty equipment in the corresponding production line, specifically by locating the faulty equipment in the corresponding production line according to the defect information of the metal sheet through a trained second deep learning model.

[0011] Furthermore, the fault analysis module analyzes the fault type of the faulty equipment, specifically by analyzing the similarity of defect location areas of the same defect type based on the defect information of multiple consecutive metal plates through a trained third deep learning model. If the location areas are similar, the fault type of the faulty equipment corresponding to the defect type is a parameter fault; otherwise, the fault type of the corresponding faulty equipment is a mechanical fault.

[0012] The present invention also provides a metal sheet production scheduling method based on artificial intelligence, comprising the following steps: Acquire real-time production order information of metal sheets and real-time production line information of production workshops, wherein the real-time production order information includes the real-time number of sheets to be produced and the delivery deadline of each order, and the real-time production line information includes the production line number, production efficiency, energy consumption information and real-time status corresponding to each production line, wherein the real-time status includes normal status and maintenance status; Identify defect information of produced metal sheets and evaluate the quality type of the corresponding metal sheets according to the defect information, wherein the quality type includes qualified, repairable and scrapped; According to the defect information of the metal sheet, locate the faulty equipment in the corresponding production line and analyze the fault type of the faulty equipment, which includes parameter fault and mechanical fault; The real-time production order information and the real-time production line information are updated, specifically: the number of metal sheets with a quality type of scrap is added to the real-time number of sheets to be produced, and the real-time status of the production line where the equipment with mechanical failure is located is updated to a maintenance status; Analyze and predict delivery status based on real-time production order information and real-time production line information, and send regeneration instructions based on the predicted delivery status; After receiving the regeneration instruction, multiple production scheduling plans are generated through the trained scheduling algorithm model according to the real-time production order information and the real-time production line information so that all orders are delivered on time. The production scheduling plan records the corresponding scheduling information, including the number of production lines started S, the estimated production time T and the estimated total energy consumption V. The production lines started in the production scheduling plan are all in normal status; The priority coefficient P of each production scheduling plan is calculated according to the scheduling information, and finally the production scheduling plan with the largest priority coefficient is selected as the new production scheduling plan.

[0013] Further, the delivery situation is predicted based on the real-time production order information and the real-time production line information, and a regeneration instruction is sent to the plan generation module based on the predicted delivery situation, specifically: According to the real-time production order information and real-time production line information, analyze whether the current production scheduling plan can complete all orders within the latest delivery deadline in the order. If not, send a regeneration instruction; If the real-time status of the production line started in the current production scheduling plan changes to maintenance status, then based on the real-time production order information and real-time production line information, analyze whether all orders can be completed within the latest delivery deadline in the order by using the remaining production lines in the current production scheduling plan whose real-time status is normal. If not, send a regeneration instruction.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The present invention updates the real-time production order information and the real-time production line information by analyzing the number of scrapped metal sheets and the production lines with mechanical failure equipment, predicts the delivery situation based on the updated information analysis, and sends a regeneration instruction to the plan generation module based on the predicted delivery situation. After receiving the regeneration instruction, the plan generation module generates multiple production scheduling plans that can ensure that all orders are delivered on time. Finally, the plan selection module calculates the priority coefficient of each production scheduling plan based on the estimated production time and the estimated total energy consumption of the number of production lines to select the optimal production scheduling plan, so as to improve the overall production efficiency and reduce the production energy consumption on the basis of completing the delivery of each order within the delivery period of each order. In the first deep learning model of the present invention, the ResNet50 backbone network is used to replace the feature extraction network of the original Faster R-CNN learning model, and the residual block is introduced to solve the common gradient disappearance or explosion problems in deep network training, effectively learn deeper feature representations, and add a feature pyramid network to the basic architecture of the original Faster R-CNN learning model to achieve high-level semantics while retaining details, thereby helping the model to better detect smaller features, and by combining feature information at different levels, the model is more flexible and powerful in processing irregular defects, and more effectively detects various defect types on the surface of metal sheets, improving the overall detection accuracy, and using the RoI Align pooling layer to replace the region of interest pooling layer of the original Faster R-CNN learning model to avoid position deviation when the region of interest is divided on the second feature map, better retain information, improve detection accuracy, and thus improve the accuracy of quality classification evaluation of metal sheets, thereby ensuring the accuracy of equipment failure analysis, and then ensuring the accuracy of predicted delivery situation analysis, and ensuring that the generated production scheduling plan can ensure that all orders are delivered on time. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0017] Figure 1 It is a structural block diagram of a metal sheet production scheduling system based on artificial intelligence of the present invention; Figure 2 It is a structural block diagram of a sheet quality assessment module of a metal sheet production scheduling system based on artificial intelligence of the present invention; Figure 3 It is a flow chart of a metal sheet production scheduling method based on artificial intelligence of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0020] In addition, the descriptions of "first", "second", etc. in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0021] Embodiment 1 See also Figure 1 As shown, the present invention provides a metal sheet production scheduling system based on artificial intelligence, comprising: A data acquisition module, a plate quality assessment module, a fault analysis module, an information update module, a delivery analysis module, a plan generation module and a plan selection module, as well as a central processing unit that communicates with the above modules.

[0022] The data acquisition module is used to obtain the real-time production order information of metal plates and the real-time production line information of the production workshop. The real-time production order information includes the real-time number of plates to be produced and the delivery deadline of each order. The real-time production line information includes the production line number, production efficiency, energy consumption information and real-time status corresponding to each production line.

[0023] The data acquisition module obtains the real-time production order information of the metal sheet, which can be obtained by connecting with the ERP system. The ERP system, namely Enterprise Resource Planning (Enterprise Resource Planning), is used to generate and store the production order information.

[0024] The data acquisition module obtains the real-time production line information of the production workshop, which can be obtained by docking with the MES system. The MES system, namely the Manufacturing Execution System, is used to perform real-time monitoring and data collection on each production line in the production workshop, and can obtain production line information through data analysis. The energy consumption information of the production line reflects the total energy consumption generated by the corresponding production line on average per hour.

[0025] The sheet quality assessment module is used to identify defect information of the produced metal sheet and assess the quality type of the corresponding metal sheet according to the defect information, wherein the quality type includes qualified, repairable and scrapped.

[0026] See also Figure 2 As shown, the plate quality assessment module includes: an image acquisition module, an image preprocessing module, a defect recognition module and a quality classification module.

[0027] The image acquisition module is used to acquire the metal plate image.

[0028] The image acquisition module can specifically acquire images through an industrial camera, which has the characteristics of high resolution, high precision, high definition, good color reproduction and low noise.

[0029] In some embodiments, the industrial camera may optionally be an area array CCD camera.

[0030] The image preprocessing module is used to perform image preprocessing operations on the metal plate image to obtain a preprocessed image.

[0031] The image preprocessing operation includes noise removal processing, image enhancement processing, image normalization processing, etc.

[0032] During the process of image acquisition and transmission, images are easily disturbed by the surrounding environment and other factors, and noise is inevitable. In order to meet the visual requirements for image clarity and subsequent image processing, it is necessary to remove noise from the image. Usually, noise is statistically approximated as additive Gaussian white noise. The noise removal process can be performed by any existing filtering method such as Gaussian filtering, median filtering, mean filtering and bilateral filtering.

[0033] In some embodiments, the image is first edge detected before the noise removal process, and the image is divided into an edge area and a smooth area, and then the edge area and the smooth area are denoised using a non-local mean filtering denoising algorithm and a bilateral filtering denoising algorithm. Bilateral filtering has the characteristics of being simple to calculate and easy to implement. Since bilateral filtering is based on adjacent pixels, the effect of maintaining the edge structure is not very obvious. Non-local mean filtering is a filtering method based on the similarity between image blocks, which can well maintain the structural information of the image, but the computational complexity is relatively high. Therefore, this solution uses a non-local mean filtering denoising algorithm and a bilateral filtering denoising algorithm to perform denoising on the edge area and the smooth area respectively, and quickly achieves the purpose of clearly retaining the denoising of the edge area and completely removing the noise in the smooth area.

[0034] The defect recognition module is used to obtain defect information of the corresponding metal plate according to the pre-processed image recognition.

[0035] The defect information includes: defect type, defect location and defect degree.

[0036] In some embodiments, the defect recognition module obtains defect information of the corresponding metal sheet according to the metal sheet image recognition after image preprocessing operation, and selects to recognize and obtain it through the trained Faster R-CNN learning model. The existing Faster R-CNN model includes a feature extraction network, which uses a set of basic convolutional layers, activation layers and pooling layers to extract useful feature maps from the input image; a region proposal network, which is used to generate candidate regions that potentially contain detection targets; a region of interest pooling layer, which combines the input feature map and the candidate region, and then integrates this information to extract a specific feature map of interest; a fully connected network, which classifies the image through an activation function and uses border regression to obtain a precisely positioned detection frame; the specific recognition method and algorithm of the Faster R-CNN learning model are existing technologies and will not be repeated here.

[0037] In some embodiments, the defect recognition module obtains defect information of the corresponding metal sheet based on the image recognition of the metal sheet after image preprocessing, and selects to perform recognition and acquisition through a trained first deep learning model.

[0038] The first deep learning model is improved based on the existing Faster R-CNN model, and the first deep learning model includes: A ResNet50 backbone network is used to extract feature information of the preprocessed image and obtain first feature maps of multiple different levels; A feature pyramid network is used to fuse the first feature maps at different levels to generate a plurality of second feature maps at different scales; A region proposal network, used to generate defect feature candidate regions in the second feature map; The RoI Align pooling layer is used to extract the region of interest from the defect feature candidate region and then perform an alignment operation using the RoI Align technology. Finally, a third feature map is obtained through a pooling operation. The fully connected layer is used to perform a classification regression operation on the third feature map and output defect information.

[0039] In the first deep learning model, the ResNet50 backbone network is used to replace the feature extraction network of the original Faster R-CNN learning model. The feature extraction network of the original Faster R-CNN learning model basically uses convolutional neural networks (such as the VGG series). When the number of layers increases, it is easy to have gradient vanishing or exploding problems, which will lead to training difficulties, model convergence difficulties, and relatively weak feature representation capabilities, especially when dealing with complex scenes or multi-scale targets. ResNet50 is a deep residual network that introduces residual blocks to solve the common gradient vanishing or exploding problems in deep network training, and can effectively learn deeper feature representations.

[0040] In the first deep learning model, a feature pyramid network is added to the infrastructure of the original Faster R-CNN learning model. The feature pyramid network (FPN) is an image feature extraction method for object detection and semantic segmentation. The fusion process is an existing technology and will not be repeated here. After obtaining the features through ResNet50, the image will be compressed, resulting in a smaller feature candidate box generated subsequently, which to a certain extent reduces the model's feature discrimination function. The addition of the feature pyramid network fusion technology can achieve high-level semantics while retaining details, thereby helping the model to better detect smaller features, and by combining feature information at different levels, the model is more flexible and powerful in dealing with irregular defects, and more effectively detects various types of defects on the surface of metal sheets, thereby improving overall detection accuracy.

[0041] In the first deep learning model, the RoI Align pooling layer is used to replace the region of interest pooling layer of the original Faster R-CNN learning model. The region of interest pooling layer of the original Faster R-CNN learning model will produce two quantizations when quantizing the input feature map. These two quantization operations are prone to information loss, especially for smaller regions of interest. RoI Align uses a bilinear interpolation method to convert the two quantization processes into a continuous operation, avoiding position deviations when the region of interest is divided on the second feature map, better retaining information, and improving inspection accuracy.

[0042] The quality classification module is used to evaluate the quality type of the corresponding metal sheet according to the defect information and a preset classification strategy.

[0043] The preset classification strategy is specifically formulated by the user according to actual needs.

[0044] The metal sheet with qualified quality type is the sheet that can be delivered immediately, the metal sheet with repairable quality type is the sheet whose defects can be reduced to qualified or eliminated, and the metal sheet with scrap quality type is the sheet whose defects cannot be reduced to qualified or eliminated, that is, the sheet cannot be delivered.

[0045] The fault analysis module is used to locate the faulty equipment in the corresponding production line according to the defect information of the metal sheet, and analyze the fault type of the faulty equipment, which includes parameter fault and mechanical fault.

[0046] The fault analysis module locates the faulty equipment in the corresponding production line, specifically by locating the faulty equipment in the corresponding production line according to the defect information of the metal sheet through a trained second deep learning model.

[0047] Generally speaking, in a metal sheet production line, each major device corresponds to a different production process, and failures in different major devices lead to different major defect types in the metal sheet.

[0048] The fault analysis module analyzes the fault type of the faulty equipment, specifically by analyzing the similarity of the defect location areas of the same defect type based on the defect information of multiple consecutive metal plates through a trained third deep learning model. If the location areas are similar, the fault type of the faulty equipment corresponding to the defect type is a parameter fault; otherwise, the fault type of the corresponding faulty equipment is a mechanical fault.

[0049] Generally speaking, in case of parameter failure, the location areas where defects of the same defect type appear in continuous metal sheets are basically similar, while in case of mechanical failure, due to the instability of the equipment machinery, the location areas where defects of the same defect type appear in continuous metal sheets are also unstable and irregular.

[0050] Parameter failure means that there are inaccuracies in the settings or programs of the equipment parameters, and the parameters can be corrected again; while mechanical failure means that there are failures in the equipment components or other parts, so it is necessary to stop operation and notify staff for maintenance. During this process, the production of the production line needs to be stopped.

[0051] The information update module is used to update the real-time production order information and the real-time production line information, specifically: adding the number of metal plates with quality type as scrap to the real-time number of plates to be produced, and updating the real-time status of the production line where the equipment with mechanical failure is located to the maintenance status.

[0052] The delivery analysis module is used to analyze and predict the delivery situation based on the real-time production order information and the real-time production line information, and send a regeneration instruction to the plan generation module based on the predicted delivery situation, specifically: According to the real-time production order information and real-time production line information, analyze whether the current production scheduling plan can complete all orders within the latest delivery deadline in the order. If not, send a regeneration instruction; If the real-time status of the production line started in the current production scheduling plan changes to maintenance status, then based on the real-time production order information and real-time production line information, analyze whether all orders can be completed within the latest delivery deadline in the order by using the remaining production lines in the current production scheduling plan whose real-time status is normal. If not, send a regeneration instruction.

[0053] The plan generation module is used to generate multiple production scheduling plans that ensure that all orders are delivered on time based on real-time production order information and real-time production line information through a trained scheduling algorithm model after receiving a regeneration instruction. The production scheduling plan records the corresponding scheduling information, including the number of production lines started S, the estimated production time T and the estimated total energy consumption V. The production lines started in the production scheduling plan are all in normal status.

[0054] It should be noted that the production scheduling plan at the beginning of production is also generated and formulated by the plan generation module.

[0055] The plan selection module is used to calculate the priority coefficient P of each production scheduling plan according to the scheduling information, and finally select the production scheduling plan with the largest priority coefficient as the new production scheduling plan, P=1 / (K1×S+K2×T+K3×N), K1, K2 and K3 represent the first preset weight, the second preset weight and the third preset weight respectively.

[0056] If it is known that all orders can be delivered on time, the production scheduling plan needs to take into account energy consumption, the number of production lines used and working hours, and reduce equipment wear, K3>K1>K2, and K1+K2+K3=1.

[0057] Embodiment 2 See also Figure 3 As shown, the present invention also provides a metal sheet production scheduling method based on artificial intelligence, which is applied to the above-mentioned metal sheet production scheduling system based on artificial intelligence, and specifically includes the following steps: S1. Obtaining real-time production order information of metal sheets and real-time production line information of production workshops; S2, identifying defect information of the produced metal sheet and evaluating the quality type of the corresponding metal sheet according to the defect information; S3. According to the defect information of the metal sheet, locate the faulty equipment in the corresponding production line and analyze the fault type of the faulty equipment; S4, updating the real-time production order information and real-time production line information; S5. Analyze and predict the delivery situation based on the real-time production order information and the real-time production line information, and send a regeneration instruction based on the predicted delivery situation; S6. After receiving the regeneration instruction, multiple production scheduling plans are generated through a trained scheduling algorithm model according to the real-time production order information and the real-time production line information so that all orders are delivered on time, and the production scheduling plans record the corresponding scheduling information; S7. Calculate the priority coefficient P of each production scheduling plan based on the scheduling information, and finally select the production scheduling plan with the largest priority coefficient as the new production scheduling plan.

[0058] For step S1, the real-time production order information includes the real-time number of plates to be produced and the delivery deadline of each order, and the real-time production line information includes the production line number, production efficiency, energy consumption information and real-time status corresponding to each production line.

[0059] For step S2, the quality types include qualified, repairable and scrapped. The specific defect identification and quality type assessment steps are as follows: T1, obtain metal sheet image; T2, performing image preprocessing operation on the metal plate image to obtain a preprocessed image; T3. Obtaining defect information of the corresponding metal sheet according to the preprocessed image recognition, wherein the defect information includes: defect type, defect location and defect degree; T4. Evaluate the quality type of the corresponding metal sheet according to the defect information and a preset classification strategy.

[0060] For step T3, the defect information is obtained by identifying through a trained first deep learning model, wherein the first deep learning model is improved based on the existing Faster R-CNN model, and the first deep learning model includes: A ResNet50 backbone network is used to extract feature information of the preprocessed image and obtain first feature maps of multiple different levels; A feature pyramid network is used to fuse the first feature maps at different levels to generate a plurality of second feature maps at different scales; A region proposal network, used to generate defect feature candidate regions in the second feature map; The RoI Align pooling layer is used to extract the region of interest from the defect feature candidate region and then perform an alignment operation using the RoI Align technology. Finally, a third feature map is obtained through a pooling operation. The fully connected layer is used to perform a classification regression operation on the third feature map and output defect information.

[0061] Corresponding to step S3, the fault types include parameter fault and mechanical fault; Locating the faulty equipment in the corresponding production line, specifically locating the faulty equipment in the corresponding production line according to the defect information of the metal sheet by using the trained second deep learning model; Analyze the fault type of the faulty equipment. Specifically, use the trained third deep learning model to analyze the similarity of defect location areas of the same defect type based on the defect information of multiple consecutive metal plates. If the location areas are similar, the fault type of the faulty equipment corresponding to the defect type is a parameter fault. Otherwise, the fault type of the corresponding faulty equipment is a mechanical fault.

[0062] For step S4, specifically, the number of metal sheets with quality type of scrap is added to the real-time number of sheets to be produced, and the real-time status of the production line where the equipment with mechanical failure is located is updated to the maintenance status.

[0063] For step S5, the delivery status is predicted based on the real-time production order information and the real-time production line information, and a regeneration instruction is sent to the plan generation module based on the predicted delivery status, specifically: According to the real-time production order information and real-time production line information, analyze whether the current production scheduling plan can complete all orders within the latest delivery deadline in the order. If not, send a regeneration instruction; If the real-time status of the production line started in the current production scheduling plan changes to maintenance status, then based on the real-time production order information and real-time production line information, analyze whether all orders can be completed within the latest delivery deadline in the order by using the remaining production lines in the current production scheduling plan whose real-time status is normal. If not, send a regeneration instruction.

[0064] For step S6, the scheduling information includes the number of production lines started S, the estimated production time T and the estimated total energy consumption V, and the production lines started in the production scheduling plan are all in normal status.

[0065] For step S7, P=1 / (K1×S+K2×T+K3×N), K1, K2 and K3 represent the first preset weight, the second preset weight and the third preset weight, respectively.

[0066] If it is known that all orders can be delivered on time, the production scheduling plan needs to take into account energy consumption, the number of production lines used and working hours, and reduce equipment wear, K3>K1>K2, and K1+K2+K3=1.

[0067] Embodiment 3 The present invention also provides an electronic device, including: a processor, a sending device, an input device, an output device and a memory. The processor can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit, or one or more integrated circuits, and is used to execute relevant programs to implement the technical solution provided in the embodiment of the present application. The memory can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device or a random access memory (RAM), and is used to store computer program code. The computer program code includes computer instructions. When the processor executes the computer instructions, the electronic device executes a method as described in any of the above possible implementation methods.

[0068] Embodiment 4 The present invention also provides a computer-readable storage medium, in which a computer program is stored. The computer program includes program instructions. When the program instructions are executed by a processor of an electronic device, the processor executes a method as described in any possible implementation manner.

[0069] The beneficial effects of the present invention are: The present invention updates the real-time production order information and the real-time production line information by analyzing the number of scrapped metal sheets and the production lines with mechanical failure equipment, predicts the delivery situation based on the updated information analysis, and sends a regeneration instruction to the plan generation module based on the predicted delivery situation. After receiving the regeneration instruction, the plan generation module generates multiple production scheduling plans that can ensure that all orders are delivered on time. Finally, the plan selection module calculates the priority coefficient of each production scheduling plan based on the estimated production time and the estimated total energy consumption of the number of production lines to select the optimal production scheduling plan, so as to improve the overall production efficiency and reduce the production energy consumption on the basis of completing the delivery of each order within the delivery period of each order. In the first deep learning model of the present invention, the ResNet50 backbone network is used to replace the feature extraction network of the original Faster R-CNN learning model, and the residual block is introduced to solve the common gradient disappearance or explosion problems in deep network training, effectively learn deeper feature representations, and add a feature pyramid network to the basic architecture of the original Faster R-CNN learning model to achieve high-level semantics while retaining details, thereby helping the model to better detect smaller features, and by combining feature information at different levels, the model is more flexible and powerful in processing irregular defects, and more effectively detects various defect types on the surface of metal sheets, improving the overall detection accuracy, and using the RoI Align pooling layer to replace the region of interest pooling layer of the original Faster R-CNN learning model to avoid position deviation when the region of interest is divided on the second feature map, better retain information, improve detection accuracy, and thus improve the accuracy of quality classification evaluation of metal sheets, thereby ensuring the accuracy of equipment failure analysis, and then ensuring the accuracy of predicted delivery situation analysis, and ensuring that the generated production scheduling plan can ensure that all orders are delivered on time.

[0070] In the description of the specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0071] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. 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 this understanding, the technical solution of the present application is essentially 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, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method of each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (RandomAccess Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0072] The foregoing is merely a specific embodiment of the present invention, which enables those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A metal sheet production scheduling system based on artificial intelligence, characterized in that: include: A data acquisition module is used to obtain real-time production order information of metal sheets and real-time production line information of production workshops, wherein the real-time production order information includes the real-time number of sheets to be produced and the delivery deadline of each order, and the real-time production line information includes the production line number, production efficiency, energy consumption information and real-time status corresponding to each production line, wherein the real-time status includes normal status and maintenance status; A sheet quality assessment module, used to identify defect information of produced metal sheets and assess the quality type of the corresponding metal sheets according to the defect information, wherein the quality types include qualified, repairable and scrapped; The fault analysis module is used to locate the faulty equipment in the corresponding production line according to the defect information of the metal sheet and analyze the fault type of the faulty equipment, including parameter fault and mechanical fault; An information updating module, used for updating the real-time production order information and the real-time production line information; The delivery analysis module is used to analyze and predict the delivery situation based on the real-time production order information and the real-time production line information, and send a regeneration instruction to the plan generation module based on the predicted delivery situation; A plan generation module is used to generate, after receiving a regeneration instruction, a plurality of production scheduling plans that ensure that all orders are delivered on time based on real-time production order information and real-time production line information through a trained scheduling algorithm model, wherein the production scheduling plan records corresponding scheduling information, including the number of production lines started S, the estimated production time T, and the estimated total energy consumption V, and the production lines started in the production scheduling plan are all in normal status; The plan selection module is used to calculate the priority coefficient P of each production scheduling plan according to the scheduling information, and finally select the production scheduling plan with the largest priority coefficient as the new production scheduling plan.

2. The metal sheet production scheduling system based on artificial intelligence according to claim 1 is characterized in that: The plate quality assessment module comprises: An image acquisition module, used for acquiring an image of a metal sheet; An image preprocessing module, used for performing an image preprocessing operation on the metal plate image to obtain a preprocessed image; A defect recognition module, used for obtaining defect information of the corresponding metal sheet according to the pre-processed image recognition; The quality classification module is used to evaluate the quality type of the corresponding metal sheet according to the defect information and a preset classification strategy.

3. The metal sheet production scheduling system based on artificial intelligence according to claim 2 is characterized in that: The defect recognition module specifically obtains defect information of the corresponding metal sheet through the trained first deep learning model recognition, and the first deep learning model includes: A ResNet50 backbone network is used to extract feature information of the preprocessed image and obtain first feature maps of multiple different levels; A feature pyramid network is used to fuse the first feature maps at different levels to generate a plurality of second feature maps at different scales. A region proposal network, used to generate defect feature candidate regions in the second feature map; The RoI Align pooling layer is used to extract the region of interest from the defect feature candidate region and then perform an alignment operation using the RoI Align technology. Finally, a third feature map is obtained through a pooling operation. The fully connected layer is used to perform a classification regression operation on the third feature map and output defect information.

4. The metal sheet production scheduling system based on artificial intelligence according to claim 1 is characterized in that: The information update module updates the real-time production order information and real-time production line information, specifically: adding the number of metal sheets with quality type as scrap to the real-time number of sheets to be produced, and updating the real-time status of the production line where the equipment with mechanical failure is located to the maintenance status.

5. The metal sheet production scheduling system based on artificial intelligence according to claim 1 is characterized in that: The method of analyzing and predicting the delivery situation based on the real-time production order information and the real-time production line information, and sending a regeneration instruction to the plan generation module based on the predicted delivery situation, is specifically: According to the real-time production order information and real-time production line information, analyze whether the current production scheduling plan can complete all orders within the latest delivery deadline in the order. If not, send a regeneration instruction; If the real-time status of the production line started in the current production scheduling plan changes to maintenance status, then based on the real-time production order information and real-time production line information, analyze whether all orders can be completed within the latest delivery deadline in the order by using the remaining production lines in the current production scheduling plan whose real-time status is normal. If not, send a regeneration instruction.

6. The metal sheet production scheduling system based on artificial intelligence according to claim 1 is characterized in that: The specific calculation method of the priority coefficient P is: P=1 / (K1×S+K2×T+K3×N), where K1, K2 and K3 represent the first preset weight, the second preset weight and the third preset weight respectively.

7. The metal sheet production scheduling system based on artificial intelligence according to claim 1 is characterized in that: The fault analysis module locates the faulty equipment in the corresponding production line, specifically by locating the faulty equipment in the corresponding production line according to the defect information of the metal sheet through a trained second deep learning model.

8. The metal sheet production scheduling system based on artificial intelligence according to claim 1 is characterized in that: The fault analysis module analyzes the fault type of the faulty equipment, specifically by analyzing the similarity of defect location areas of the same defect type based on the defect information of multiple consecutive metal plates through a trained third deep learning model. If the location areas are similar, the fault type of the faulty equipment corresponding to the defect type is a parameter fault; otherwise, the fault type of the corresponding faulty equipment is a mechanical fault.

9. A metal sheet production scheduling method based on artificial intelligence, applied to the metal sheet production scheduling system based on artificial intelligence according to any one of claims 1 to 8, characterized in that: The following steps are involved: Acquire real-time production order information of metal sheets and real-time production line information of production workshops, wherein the real-time production order information includes the real-time number of sheets to be produced and the delivery deadline of each order, and the real-time production line information includes the production line number, production efficiency, energy consumption information and real-time status corresponding to each production line, wherein the real-time status includes normal status and maintenance status; Identify defect information of produced metal sheets and evaluate the quality type of the corresponding metal sheets according to the defect information, wherein the quality type includes qualified, repairable and scrapped; According to the defect information of the metal sheet, locate the faulty equipment in the corresponding production line and analyze the fault type of the faulty equipment, which includes parameter fault and mechanical fault; The real-time production order information and the real-time production line information are updated, specifically: the number of metal sheets with a quality type of scrap is added to the real-time number of sheets to be produced, and the real-time status of the production line where the equipment with mechanical failure is located is updated to a maintenance status; Analyze and predict delivery status based on real-time production order information and real-time production line information, and send regeneration instructions based on the predicted delivery status; After receiving the regeneration instruction, multiple production scheduling plans are generated through the trained scheduling algorithm model according to the real-time production order information and the real-time production line information so that all orders are delivered on time. The production scheduling plan records the corresponding scheduling information, including the number of production lines started S, the estimated production time T and the estimated total energy consumption V. The production lines started in the production scheduling plan are all in normal status; The priority coefficient P of each production scheduling plan is calculated according to the scheduling information, and finally the production scheduling plan with the largest priority coefficient is selected as the new production scheduling plan.

10. The metal sheet production scheduling method based on artificial intelligence according to claim 9, characterized in that: The method of analyzing and predicting the delivery situation based on the real-time production order information and the real-time production line information, and sending a regeneration instruction to the plan generation module based on the predicted delivery situation, is specifically: According to the real-time production order information and real-time production line information, analyze whether the current production scheduling plan can complete all orders within the latest delivery deadline in the order. If not, send a regeneration instruction; If the real-time status of the production line started in the current production scheduling plan changes to maintenance status, then based on the real-time production order information and real-time production line information, analyze whether all orders can be completed within the latest delivery deadline in the order by using the remaining production lines in the current production scheduling plan whose real-time status is normal. If not, send a regeneration instruction.

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