A control method and system for an IEC 61499 controller

Abstract

The application discloses a control method and system for an IEC61499 controller, and particularly relates to the technical field of controller optimization, and has the technical points that the method comprises the following steps: detecting production products on a production line by using a pre-constructed product detection model to obtain actual production data of the production products, and acquiring running state data of an IEC61499 controller on the production line; comparing the actual production data of the production products with standard production data of the production products to obtain error data of the production products, inputting the error data of the production products and the running state data into a pre-constructed controller optimization model to obtain a controller parameter optimization scheme; and optimizing and correcting controller parameters of the IEC61499 controller by using the controller parameter optimization scheme to obtain a final IEC61499 controller.

Description

Technical Field

[0001] The present invention relates to the technical field of controller optimization, and in particular to a control method and system for an IEC61499 controller. Background Art

[0002] IEC61499 is a standard for industrial automation control systems developed by the International Electrotechnical Commission (IEC). It provides a function-block-based, event-driven approach to describing and implementing control systems. Existing IEC61499 technology is largely static, requiring field engineers to model and design the system to implement control logic. This is complex and tedious, requiring experienced field engineers to be familiar with various function blocks, and rarely requires optimization or adjustments during system operation.

[0003] Existing IEC61499 controllers are widely used in industrial production, but they suffer from performance degradation and difficulty adapting to dynamic environments when dealing with complex real-time control requirements and system changes. At the same time, in existing technologies, when products do not meet product standards, on-site engineers are usually required to manually adjust the controllers. This is not only time-consuming and labor-intensive, but also difficult to quickly and effectively correct and optimize errors in real time, resulting in problems such as waste of raw materials or equipment wear.

[0004] Therefore, the present invention aims to provide a control method and system for an IEC61499 controller to solve the above-mentioned related problems. Summary of the Invention

[0005] The technical problem to be solved by the present invention is that the existing technology uses manual control when the produced products do not meet the product standards, which is not only time-consuming and labor-intensive, but also difficult to quickly and real-timely correct and optimize the errors, thereby causing related problems such as waste of raw materials or equipment wear. The purpose is to provide a control method and system for an IEC61499 controller, which obtains actual production data of products on the product production line through image recognition by a product detection model, compares the actual production data with the product standard production data to obtain product error data, and then inputs the product error data and operating status data into a controller optimization model to obtain a controller parameter optimization scheme, thereby achieving the ability to use real-time product errors to feedback and optimize the controller parameters of the controller, so as to achieve the effect of quickly and real-time correction and optimization of the errors when the products do not meet the product standards. This solves the problem that the existing technology uses manual control when the produced products do not meet the product standards, which is not only time-consuming and labor-intensive, but also difficult to quickly and real-timely correct and optimize the errors, thereby causing related problems such as waste of raw materials or equipment wear.

[0006] The present invention is achieved through the following technical solutions:

[0007] A control method for an IEC61499 controller, the method comprising:

[0008] Use the pre-built product inspection model to inspect the products on the production line to obtain the actual production data of the products and obtain the operating status data of the IEC61499 controller on the production line;

[0009] By comparing the actual production data of the product with the standard production data of the product, the product error data is obtained. The product error data and operating status data are input into the pre-built controller optimization model to obtain the controller parameter optimization solution;

[0010] The controller parameters of the IEC61499 controller are optimized and corrected using the controller parameter optimization scheme to obtain the final IEC61499 controller.

[0011] Furthermore, the construction process of the product detection model is as follows:

[0012] Acquire product image data, mark the product image data with image information, and obtain a marked image training dataset and an image verification dataset;

[0013] By inputting the training data set into the convolutional neural network, the convolutional neural network is trained to obtain an initial product detection model;

[0014] The image verification dataset is input into the initial product detection model, and the particle optimization algorithm is used to iteratively optimize the initial product detection model until the loss function converges to obtain the optimal product detection model.

[0015] Furthermore, the construction process of the controller optimization model is as follows:

[0016] Obtain historical product error data and historical operating status data of the IEC61499 controller corresponding to the historical product error data, and modify controller parameters of the IEC61499 controller by combining the historical product error data with the historical operating status data of the IEC61499 controller to obtain a controller parameter optimization solution;

[0017] The historical product error data, the operating status data of the IEC61499 controller, and the controller parameter optimization scheme are input into the convolutional neural network for training to obtain the controller optimization model.

[0018] Furthermore, the actual production data of the product includes the actual production size of the product, the actual production surface defects of the product and the actual production weight of the product; the operating status data includes the controller CPU parameters, the controller storage parameters and the controller memory parameters.

[0019] The present invention further provides a control system for an IEC61499 controller, which is used in any one of the control methods for an IEC61499 controller described above, and includes:

[0020] The data acquisition module is used to use the pre-built product detection model to detect the products produced on the production line to obtain the actual production data of the products, and to obtain the operating status data of the IEC61499 controller on the production line;

[0021] The optimization solution generation module is used to compare the actual production data of the product with the standard production data of the product to obtain product error data, input the product error data and operating status data into the pre-built controller optimization model to obtain the controller parameter optimization solution;

[0022] The optimization and correction module is used to optimize and correct the controller parameters of the IEC61499 controller using a controller parameter optimization solution to obtain a final IEC61499 controller.

[0023] Furthermore, the construction process of the product detection model is as follows:

[0024] Acquire product image data, mark the product image data with image information, and obtain a marked image training dataset and an image verification dataset;

[0025] By inputting the training data set into the convolutional neural network, the convolutional neural network is trained to obtain an initial product detection model;

[0026] The image verification dataset is input into the initial product detection model, and the particle optimization algorithm is used to iteratively optimize the initial product detection model until the loss function converges to obtain the optimal product detection model.

[0027] Furthermore, the construction process of the controller optimization model is as follows:

[0028] Obtain historical product error data and historical operating status data of the IEC61499 controller corresponding to the historical product error data, and modify controller parameters of the IEC61499 controller by combining the historical product error data with the historical operating status data of the IEC61499 controller to obtain a controller parameter optimization solution;

[0029] The historical product error data, the operating status data of the IEC61499 controller, and the controller parameter optimization scheme are input into the convolutional neural network for training to obtain the controller optimization model.

[0030] The present invention also provides a computer device, comprising a system memory and a processor, wherein the system memory stores a computer program, and the processor implements the steps of the method according to any one of claims 1 to 4 when executing the computer program.

[0031] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any one of the methods described above are implemented.

[0032] The present invention also provides a computer program product comprising instructions, which, when executed by a computer device cluster, enables the computer device cluster to perform any of the above methods.

[0033] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0034] In the present invention, the actual production data of products on the product production line is obtained through image recognition by a product detection model, the actual production data of the product is compared with the standard production data of the product to obtain product error data, and then the product error data and the operating status data are input into the controller optimization model to obtain a controller parameter optimization scheme, thereby realizing the ability to use real-time product errors to feed back the controller parameters of the optimized controller, so as to achieve the effect of rapid and real-time correction and optimization of errors when the products do not meet the product standards, solving the problem of the existing technology that manual control is not only time-consuming and labor-intensive, but also difficult to rapidly and real-time correct and optimize errors when the products produced do not meet the product standards, thereby causing related problems such as waste of raw materials or equipment wear. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the examples. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be considered as limiting the scope. A person of ordinary skill in the art can also derive other relevant drawings based on these drawings without inventive effort. In the drawings:

[0036] Figure 1 4 is a flow chart of a control method for an IEC61499 controller in this embodiment;

[0037] Figure 2 This is a schematic diagram of system module connections for a control system of an IEC61499 controller in this embodiment;

[0038] Figure 3 This is a structural diagram of a computer device in this embodiment. DETAILED DESCRIPTION

[0039] Exemplary embodiments of the present disclosure are described herein with reference to the accompanying drawings, which are presented for the purpose of illustration and description. It is to be understood that the embodiments described herein are merely exemplary and that various changes and modifications can be made to the embodiments described herein without departing from the scope of the present disclosure. As such, it is to be understood that the embodiments described herein are not to be read in a manner that requires that all features be addressed by any embodiment thereof. Rather, embodiments of the present disclosure can address a non- comprehensive set of examples with respect to the described usage and functionality.

[0040] In the present disclosure, the terms "first", "second", and the like are used to describe various elements only for the purpose of distinguishing one element from another, and are not intended to imply a relative importance or a specific spatial, chronological, or interacting sequence of the elements. In some examples, a first element and a second element can refer to the same instance at different times. In some cases, based on the context of description, they can also refer to different instances.

[0041] The terminology used in the description of the various examples described herein is for the purpose of describing particular examples only and is not intended to be limiting. Unless specifically defined herein, any use of terms, including technical and scientific terms, can be understood to be synonymous with the commonly understood equivalents unless the context clearly dictates otherwise. Furthermore, the use of the terms "a", "an", "the", etc. do not exclude a plurality, and "comprises", "comprising", "includes", "including" and "contains", "containing" do not exclude other elements. In addition, as used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0042] Embodiment 1

[0043] Referring to Figure 1 As shown in the embodiment, a control method for an IEC61499 controller is provided, the method comprising:

[0044] S1: detecting the production products on the production line by using a pre-constructed product detection model to obtain product actual production data, and obtaining running state data of the IEC61499 controller on the production line;

[0045] S2: comparing the product actual production data with product standard production data to obtain product error data, inputting the product error data and the running state data into a pre-constructed controller optimization model to obtain a controller parameter optimization scheme;

[0046] S3: optimizing and correcting the controller parameters of the IEC61499 controller by using the controller parameter optimization scheme to obtain a final IEC61499 controller.

[0047] It should be noted that the final IEC61499 controller obtained is used for production line product production, and this technical scheme is a conventional technical means, which will not be described in detail here.

[0048] Specifically, in the embodiment, the product detection model obtains product actual production data on a product production line through image recognition, compares the product actual production data with product standard production data to obtain product error data, and inputs the product error data and running state data into the controller optimization model to obtain a controller parameter optimization scheme, so that the controller parameter of the controller can be optimized through real-time product error feedback, the product error can be quickly and real-timely corrected and optimized when the product does not meet the product standard, and the problems of time and labor consumption and difficulty in quick and real-time correction and optimization of the error when the product does not meet the product standard in the prior art are solved, and the problems of raw material waste or equipment wear are solved.

[0049] In another embodiment, the construction process of the product detection model is specifically as follows:

[0050] S101: Obtain product image data, mark the product image data with image information, and obtain a marked image training data set and an image verification data set;

[0051] S102: Train the convolutional neural network by inputting the training data set into the convolutional neural network, and obtain an initial product detection model;

[0052] S103: Input the image verification data set into the initial product detection model, and iteratively optimize the initial product detection model by using a particle optimization algorithm until the loss function converges, and obtain an optimal product detection model.

[0053] In another embodiment, the construction process of the controller optimization model is specifically as follows:

[0054] S201: Obtain historical product error data and historical running state data of an IEC61499 controller corresponding to the historical product error data, and correct the controller parameter of the IEC61499 controller by combining the historical product error data and the historical running state data of the IEC61499 controller, and obtain a controller parameter optimization scheme;

[0055] S202: Train the convolutional neural network by inputting the historical product error data, the running state data of the IEC61499 controller, and the controller parameter optimization scheme into the convolutional neural network, and obtain a controller optimization model.

[0056] In another embodiment, the product actual production data includes product actual production size, product actual production surface defect, and product actual production weight; and the running state data includes controller CPU parameter, controller storage parameter, and controller memory parameter.

[0057] Embodiment 2

[0058]

[0058] See also Figure 2 As shown, this embodiment further provides a control system for an IEC61499 controller, which is used in any one of the control methods for an IEC61499 controller described above, and includes:

[0059] The data acquisition module 100 is used to detect the products on the production line using the pre-built product detection model to obtain the actual production data of the products and obtain the operating status data of the IEC61499 controller on the production line;

[0060] The optimization solution generation module 200 is used to compare the actual production data of the product with the standard production data of the product to obtain product error data, input the product error data and operating status data into the pre-built controller optimization model to obtain the controller parameter optimization solution;

[0061] The optimization and correction module 300 is used to optimize and correct the controller parameters of the IEC61499 controller using a controller parameter optimization solution to obtain a final IEC61499 controller.

[0062] Furthermore, the construction process of the product detection model is as follows:

[0063] Acquire product image data, mark the product image data with image information, and obtain a marked image training dataset and an image verification dataset;

[0064] By inputting the training data set into the convolutional neural network, the convolutional neural network is trained to obtain an initial product detection model;

[0065] The image verification dataset is input into the initial product detection model, and the particle optimization algorithm is used to iteratively optimize the initial product detection model until the loss function converges to obtain the optimal product detection model.

[0066] Furthermore, the construction process of the controller optimization model is as follows:

[0067] Obtain historical product error data and historical operating status data of the IEC61499 controller corresponding to the historical product error data, and modify controller parameters of the IEC61499 controller by combining the historical product error data with the historical operating status data of the IEC61499 controller to obtain a controller parameter optimization solution;

[0068] The historical product error data, the operating status data of the IEC61499 controller, and the controller parameter optimization scheme are input into the convolutional neural network for training to obtain the controller optimization model.

[0069] It should be noted that the modules in the system of embodiment 2 correspond to the steps in the method of embodiment 1, and the steps in the method of embodiment 1 have been described in detail in embodiment 1, and the content of the modules in the system will not be described in detail in this embodiment 2.

[0070] Embodiment 3

[0071] Referring to Figure 3 As shown in the figure, the embodiment further provides a computer device, including a system memory 1005 and a processor 1001, the system memory 1005 stores a computer program, and the processor 1001 implements the steps of the method of any one of the above embodiments when executing the computer program.

[0072] It should be noted that the processor 1001 is configured to execute the steps of the method embodiments described above according to the instructions in the program code. Alternatively, the processor 1001 implements the functions of each module / unit in each system / device embodiment described above when executing the computer program.

[0073] Specifically, in this embodiment, the computer program can be divided into one or more modules / units, one or more modules / units are stored in the system memory 1005 and executed by the processor 1001 to complete the present application. One or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which is used to describe the execution process of the computer program in the terminal device.

[0074] The terminal device can be a desktop computer, a notebook computer, a palm computer, and a cloud server, etc. The terminal device can include, but is not limited to, the processor 1001, the system memory 1005. Those skilled in the art can understand that it does not constitute a limitation on the terminal device, and can include more or fewer components than the illustration, or combine certain components, or different components, for example, the terminal device can also include an input / output device 1003, a network access device 1002, a bus 1006, etc.

[0075] The processor 1001 can be a central processing unit (CPU), and can also be other general-purpose processors 1001, digital signal processors 1001 (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor 1001 can be a microprocessor or the processor can also be any conventional processor.

[0076] The system memory 1005 can be an internal storage unit of the terminal device, such as a hard disk or memory of the terminal device. The system memory 1005 can also be the storage device 1004 of the terminal device, such as a plug-in hard disk, a SmartMedia Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the terminal device. Furthermore, the system memory 1005 can also include both the internal storage unit of the terminal device and the storage device 1004. The system memory 1005 is used to store computer programs and other programs and data required by the terminal device. The system memory 1005 can also be used to temporarily store data that has been output or is about to be output.

[0077] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0078] Example 4

[0079] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of any one of the above methods are implemented.

[0080] Among them, the computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, system or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk. Random Access Memory (RAM), Read-Only Memory (ROM), Erasable Programmable Read Only Memory (EPROM), a register, a hard disk, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above, or any other form of computer-readable storage medium known in the art.

[0081] An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and the storage medium can be located in an application-specific integrated circuit (ASIC). In an embodiment of the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device.

[0082] Example 5

[0083] This embodiment also provides a computer program product comprising instructions. When the instructions are executed by a computer device cluster, the computer device cluster executes the method described in Embodiment 1.

[0084] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A control method for an IEC61499 controller, characterized in that the method include: Use the pre-built product inspection model to inspect the products on the production line to obtain the actual production data of the products and obtain the operating status data of the IEC61499 controller on the production line; By comparing the actual production data of the product with the standard production data of the product, the product error data is obtained. The product error data and operating status data are input into the pre-built controller optimization model to obtain the controller parameter optimization solution; Optimize and modify the controller parameters of the IEC61499 controller using the controller parameter optimization scheme to obtain the final IEC61499 controller; The construction process of the controller optimization model is as follows: Obtain historical product error data and historical operating status data of the IEC61499 controller corresponding to the historical product error data, and modify controller parameters of the IEC61499 controller by combining the historical product error data with the historical operating status data of the IEC61499 controller to obtain a controller parameter optimization solution; The historical product error data, the operating status data of the IEC61499 controller, and the controller parameter optimization scheme are input into the convolutional neural network for training to obtain the controller optimization model.

2. A control method for an IEC61499 controller according to claim 1, characterized in that: The specific process of building a product detection model is as follows: Acquire product image data, mark the product image data with image information, and obtain a marked image training dataset and an image verification dataset; By inputting the training data set into the convolutional neural network, the convolutional neural network is trained to obtain an initial product detection model; The image verification dataset is input into the initial product detection model, and the particle optimization algorithm is used to iteratively optimize the initial product detection model until the loss function converges to obtain the optimal product detection model.

3. The control method for an IEC61499 controller according to claim 1, characterized in that: The actual production data of the product includes the actual production size of the product, the actual production surface defects of the product and the actual production weight of the product; the operating status data includes the controller CPU parameters, controller storage parameters and controller memory parameters.

4. A control system for an IEC61499 controller, characterized in that: The system is used in a control method for an IEC61499 controller according to any one of claims 1 to 3, and the system includes: The data acquisition module is used to use the pre-built product detection model to detect the products produced on the production line to obtain the actual production data of the products, and to obtain the operating status data of the IEC61499 controller on the production line; The optimization solution generation module is used to compare the actual production data of the product with the standard production data of the product to obtain product error data, input the product error data and operating status data into the pre-built controller optimization model to obtain the controller parameter optimization solution; An optimization and correction module is used to optimize and correct the controller parameters of the IEC61499 controller using a controller parameter optimization solution to obtain a final IEC61499 controller; The construction process of the controller optimization model is as follows: Obtain historical product error data and historical operating status data of the IEC61499 controller corresponding to the historical product error data, and modify controller parameters of the IEC61499 controller by combining the historical product error data with the historical operating status data of the IEC61499 controller to obtain a controller parameter optimization solution; The historical product error data, the operating status data of the IEC61499 controller, and the controller parameter optimization scheme are input into the convolutional neural network for training to obtain the controller optimization model.

5. A control system for an IEC61499 controller according to claim 4, characterized in that: The specific process of building a product detection model is as follows: Acquire product image data, mark the product image data with image information, and obtain a marked image training dataset and an image verification dataset; By inputting the training data set into the convolutional neural network, the convolutional neural network is trained to obtain an initial product detection model; The image verification dataset is input into the initial product detection model, and the particle optimization algorithm is used to iteratively optimize the initial product detection model until the loss function converges to obtain the optimal product detection model.

6. A computer device comprising a system memory and a processor, wherein the system memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 3 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

8. A computer program product comprising instructions, characterized in that When the instructions are executed by a computer device cluster, the computer device cluster is caused to perform the method according to any one of claims 1 to 3.

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