Factory automation control method and system based on edge computing and cloud computing collaboration

Abstract

The present invention provides a factory automation control method and system based on the collaboration of edge computing and cloud computing, the method comprising: determining a reference node set in an edge computing environment, evaluating and predicting the reference node set through a prediction device in a cloud computing environment, and obtaining a prediction weight of the factory automation control node; performing sharing permission verification on a data request in the edge computing environment according to the prediction weight and a preset sharing permission verification algorithm; when the sharing permission verification passes, sharing factory automation control data across network areas in the cloud computing environment; performing access permission verification on a service access request in the edge computing environment according to the shared control data and a preset access permission verification algorithm; when the access permission verification passes and service fusion is required, performing service collaboration in the cloud computing environment according to a preset service collaboration algorithm, which can effectively monitor factory equipment, reduce costs and increase efficiency, and improve the security of factory automation control.

Description

Technical Field

[0001] The present invention relates to the field of industrial control technology, and provides a factory automation control method and system based on the collaboration of edge computing and cloud computing. Background Art

[0002] Smart factories, as advanced forms of digital factories, optimize information management and service processes by integrating IoT technology and advanced monitoring systems, thereby enhancing the controllability of the production process, reducing manual intervention on the production line, and achieving more reasonable production scheduling. Taking the rail transit industry as an example, although traditional factories have made significant progress in the process of transformation to intelligence, the safety hazards caused by inadequate monitoring of factory equipment are still an issue that cannot be ignored. Therefore, creating a factory environment that is both efficient and energy-saving, green and environmentally friendly, comfortable and humane is of vital importance to improving the efficiency of the entire process from production to delivery of rail trains and reducing costs. Summary of the invention

[0003] The present invention provides a factory automation control method and system based on the collaboration of edge computing and cloud computing, which is used to solve the defects of the prior art in that factory equipment is not properly monitored and the potential safety hazards are caused. The present invention can effectively monitor factory equipment, reduce costs and increase efficiency, and improve the safety of factory automation control through the collaboration of edge computing and cloud computing.

[0004] The present invention provides a factory automation control method based on the collaboration of edge computing and cloud computing, comprising: determining a reference node set in an edge computing environment, evaluating and predicting the reference node set through a prediction device in a cloud computing environment, and obtaining a prediction weight of the factory automation control node; the reference node set includes a number of automation control nodes to be predicted and evaluated; according to the prediction weight and a preset sharing authority verification algorithm, performing sharing authority verification on a data request in the edge computing environment; when the sharing authority verification passes, sharing the factory automation control data across network areas in the cloud computing environment to obtain shared control data; according to the shared control data and a preset access authority verification algorithm, performing access authority verification on a service access request in the edge computing environment; when the access authority verification passes and service fusion is required, performing service collaboration in the cloud computing environment according to a preset service collaboration algorithm to control the automation control node.

[0005] According to a factory automation control method based on the collaboration of edge computing and cloud computing provided by the present invention, the determination of a reference node set in an edge computing environment includes: determining a monitoring position function based on information of an evaluated node and an automation control effect of the evaluated node; the evaluated node is an automation control node that has been evaluated and predicted by the prediction device; determining the monitoring position accuracy of the prediction device based on the monitoring position function; and determining the reference node set in the edge computing environment based on the monitoring position accuracy, the monitoring position function and the information of the evaluated node.

[0006] According to a factory automation control method based on edge computing and cloud computing collaboration provided by the present invention, the expression of the prediction weight is: , in, is the prediction weight, is the prediction weight function, For predictive equipment u For reference items i assessment, i For reference, For predictive equipment u Reference Node Set The average value of the evaluation given by all factory automation control nodes in b is the correction amount.

[0007] According to a factory automation control method based on the collaboration of edge computing and cloud computing provided by the present invention, when the sharing authority verification is passed, before the factory automation control data is shared across network areas in the cloud computing environment, it also includes: determining the label of the automation control data of the factory automation control node; determining the network area information where the automation control data is located according to the label, so as to share across network areas according to the network area information.

[0008] According to a factory automation control method based on the collaboration of edge computing and cloud computing provided by the present invention, the access permission verification is performed on the service access request according to the shared control data and a preset access permission verification algorithm, including: determining the services provided by each network area; based on the shared control data and the services provided by each of the network areas, taking the services required by each of the automation control nodes as access permission information; and performing access permission verification on the service access request according to the access permission information.

[0009] According to a factory automation control method based on edge computing and cloud computing collaboration provided by the present invention, service collaboration is performed in a cloud computing environment according to a preset service collaboration algorithm, including: inputting automation control data of an automation control node into a preset dispute model to obtain a control information classification result output by the dispute model; and in the cloud computing environment, performing service collaboration according to the control information classification result.

[0010] The present invention also provides a factory automation control system based on the collaboration of edge computing and cloud computing, including: an evaluation and prediction module, used to determine a reference node set in an edge computing environment, and evaluate and predict the reference node set through a prediction device in a cloud computing environment to obtain a prediction weight of the factory automation control node; the reference node set includes a number of automation control nodes to be predicted and evaluated; a sharing authority verification module, used to perform sharing authority verification on data requests in the edge computing environment according to the prediction weight and a preset sharing authority verification algorithm; when the sharing authority verification passes, the factory automation control data is shared across network areas in the cloud computing environment to obtain shared control data; an access authority verification module, used to perform access authority verification on service access requests in the edge computing environment according to the shared control data and a preset access authority verification algorithm; when the access authority verification passes and service fusion is required, service collaboration is performed in the cloud computing environment according to a preset service collaboration algorithm to control the automation control node.

[0011] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the above-described factory automation control methods based on the collaboration of edge computing and cloud computing.

[0012] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the factory automation control method based on the collaboration of edge computing and cloud computing as described in any one of the above is implemented.

[0013] The present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned factory automation control methods based on the collaboration of edge computing and cloud computing.

[0014] The present invention provides a factory automation control method and system based on the collaboration of edge computing and cloud computing. The method includes: determining a reference node set in an edge computing environment, evaluating and predicting the reference node set through a prediction device in a cloud computing environment, and obtaining a prediction weight of the factory automation control node; performing sharing permission verification on a data request in the edge computing environment according to the prediction weight and a preset sharing permission verification algorithm; when the sharing permission verification passes, sharing factory automation control data across network areas in the cloud computing environment; performing access permission verification on a service access request in the edge computing environment according to the shared control data and a preset access permission verification algorithm; when the access permission verification passes and service fusion is required, performing service collaboration in the cloud computing environment according to a preset service collaboration algorithm, which can effectively monitor factory equipment, reduce costs and increase efficiency, and improve the security of factory automation control. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 It is a flow chart of the factory automation control method based on the collaboration of edge computing and cloud computing provided by the present invention.

[0017] Figure 2 It is a structural schematic diagram of a factory automation control system based on the collaboration of edge computing and cloud computing provided by the present invention.

[0018] Figure 3 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0019] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0020] With the development of IoT technology, enterprise-level solutions need to combine edge computing and cloud computing to reduce latency, improve scalability and enhance information access. Edge computing is close to the data source, reduces the data transmission distance, reduces latency, and is suitable for real-time processing and control, while cloud computing excels at large-scale data storage, analysis and global system monitoring.

[0021] Edge computing refers to the use of an open platform that integrates network, computing, storage, and application core capabilities to provide the nearest service on the side close to the source of objects or smart factory automation control data. Its application is initiated on the edge side, resulting in faster network service response, meeting the industry's basic needs in real-time business, application intelligence, security and privacy protection, and at the same time effectively alleviating the pressure of smart factory automation control data processing on the cloud computing side. However, the accuracy of edge computing is lower than that of cloud computing technology that centrally processes smart factory automation control data.

[0022] Cloud computing is a distributed system in smart factory automation control data processing and smart factory automation control data storage. A distributed system in smart factory automation control data processing and smart factory automation control data storage. Cloud computing is a demand-based network access model that shares a set of configured resources, including networks, servers, storage locations, applications, and services, while minimizing latency, management requirements, and interactions with service providers. Cloud computing enables distributed and parallel computing, making it a common choice for large smart factory automation control data processing that cannot be processed by a single machine and limited memory. When using cloud computing for large smart factory automation control data processing, factory automation control nodes always want to complete the work in a short time and at the lowest cost. On the other hand, the goal of cloud service providers is to maximize resource efficiency and profits.

[0023] Please refer to Figure 1 , Figure 1 A flow chart of a factory automation control method based on the collaboration of edge computing and cloud computing provided by the present invention.

[0024] The present invention provides a factory automation control method based on the collaboration of edge computing and cloud computing, comprising: 101: Determine a reference node set in an edge computing environment, evaluate and predict the reference node set through a prediction device in a cloud computing environment, and obtain a prediction weight of a factory automation control node; the reference node set includes a number of automation control nodes to be predicted and evaluated.

[0025] As a preferred embodiment, a reference node set is determined in an edge computing environment, including: determining a monitoring position function based on information of an evaluated node and an automation control effect of the evaluated node; the evaluated node is an automation control node that has been evaluated and predicted by a predicted device; determining a monitoring position accuracy of the predicted device based on the monitoring position function; and determining a reference node set in the edge computing environment based on the monitoring position accuracy, the monitoring position function and information of the evaluated node.

[0026] In this embodiment, the existing evaluation intelligent factory automation control data is used to calculate the factory automation control effect of each factory automation control node and the evaluation factory automation control node number information based on the monitoring position function of the factory automation control effect, and store it according to the factory automation control node item. The factory automation control node item storage can be to process the factory automation control effect of all factory automation control nodes and the evaluation factory automation control node number and other related information at one time, and then store it in the form of an intelligent factory automation control database or other intelligent factory automation control data files for future evaluation and prediction; the storage of intelligent factory automation control data takes one factory automation control node as one record.

[0027] The factory automation control effect is the average of the evaluations of all predicted devices obtained by a factory automation control node. The average reflects the expectation of the factory automation control node evaluation obtained by the factory automation control node in the entire market and is used to distinguish the position of each factory automation control node in the market. , monitoring position function Control effects with factory automation To calculate: , in, To monitor the position function, The factory automation control effect of the evaluated node. The information of the evaluated node includes: For factory automation control nodes The set of all forecasting devices that have been evaluated, For predictive equipment u For factory automation control nodes assessment, For collection The number of smart factory automation control data.

[0028] When the factory automation control effect of the factory automation control nodes and the related information of the number of evaluated factory automation control nodes that have been calculated and stored need to be recalculated due to changes in the evaluated smart factory automation control data, only the factory automation control node items whose evaluated smart factory automation control data have changed are processed, while the factory automation control node items whose evaluated smart factory automation control data have not changed do not need to be processed again, nor is there any need to process the relationship between the various factory automation control node items after the evaluated smart factory automation control data has changed.

[0029] In the evaluation prediction, the monitoring position accuracy of the predicted device is determined using the monitoring position function based on the predicted device and the number of evaluated factory automation control nodes of the related factory automation control nodes evaluated by the predicted device. , the expression is: , in, For predictive equipment The average evaluation saturation of all evaluated factory automation control nodes is expressed as: , in, For predictive equipment All the project sets evaluated, The actual number of factory automation control nodes evaluated for each relevant project, The total number of factory automation control nodes owned by the system.

[0030] The evaluation saturation of the prediction device is the ratio of the number of actual evaluation items conducted by the prediction device to the total number of evaluations that these corresponding prediction devices should receive. If the evaluation saturation of all items evaluated by the prediction device is low, the value of the monitoring position accuracy should be larger, otherwise it should be smaller; if the existing number of evaluations of the prediction items is small, the value of the monitoring position accuracy should be larger, otherwise it should be smaller. , used to predict factory automation control nodes The reference node set for evaluation prediction The expression is: , in, is the reference node set, i For reference, For the evaluation of the reference item, For predictive equipment u A collection of evaluated factory automation control nodes.

[0031] As a preferred embodiment, the expression of the prediction weight is: , in, is the prediction weight, is the prediction weight function, For predictive equipment u For reference items i assessment, i For reference, For predictive equipment u Reference Node Set The average value of the evaluation given by all factory automation control nodes inb is the correction amount.

[0032] In this embodiment, the prediction weight of the predicted factory automation control node is calculated by using the evaluation of the reference factory automation control node by the prediction device and the factory automation control effect values ​​of the predicted factory automation control node and the reference factory automation control node.

[0033] , In the case where the factory automation control effect is a function of the monitoring position, the deviation correction value is estimated by the average difference between the factory automation control effect of the predicted project and the factory automation control effect of the reference project, and a correction value is used to correct the deviation of the average value. b It is expressed as: , in, Correction values ​​for biases in predicting factory automation control nodes The factory automation control effect value, It is the factory automation control effect value of the reference factory automation control node.

[0034] Of course, the evaluation prediction results can be further revised by using the number of commonly evaluated factory automation control nodes between projects as weights.

[0035] 102: Based on the predicted weight and the preset sharing authority verification algorithm, the data request is verified for sharing authority in the edge computing environment; when the sharing authority verification is passed, the factory automation control data is shared across network areas in the cloud computing environment to obtain shared control data.

[0036] As a preferred embodiment, when the sharing permission verification is passed, before sharing the factory automation control data across network areas in the cloud computing environment, it also includes: determining the label of the automation control data of the factory automation control node; determining the network area information where the automation control data is located based on the label, so as to share the data across network areas based on the network area information.

[0037] In order to realize cross-network area collaboration of smart factory automation control data, in this embodiment, according to the label of the automation control data of the factory automation control node, the sub-network area queries the label management center for the network area information where the smart factory automation control data is located; the label management center parses the label and verifies the sharing authority of the data request. If the sharing authority verification is passed, the network area information where the smart factory automation control data is located is returned to the sub-network area; otherwise, it is not processed. The sub-network area directly requests the smart factory automation control data from the target network area to complete the sharing of the smart factory automation control data.

[0038] 103: Based on the shared control data and the preset access permission verification algorithm, the access permission of the service access request is verified in the edge computing environment; when the access permission verification passes and service integration is required, service collaboration is performed in the cloud computing environment according to the preset service collaboration algorithm to control the automation control node.

[0039] As a preferred embodiment, access rights are verified for service access requests based on shared control data and a preset access rights verification algorithm, including: determining the services provided by each network area; based on the shared control data and the services provided by each network area, taking the services required by each automation control node as access rights information; and verifying access rights for service access requests based on the access rights information.

[0040] As a preferred embodiment, service collaboration is performed in a cloud computing environment according to a preset service collaboration algorithm, including: inputting automation control data of an automation control node into a preset dispute model to obtain a control information classification result output by the dispute model; and performing service collaboration in a cloud computing environment according to the control information classification result.

[0041] In order to achieve service collaboration, in this embodiment, each network area that normally provides services registers the services provided by this network area with the central network area for external calls; based on the shared control data and the services provided by each network area, each node to be controlled subscribes to the services it needs as access permission information, and these access permission information are recorded on the blockchain of the target network area for access permission verification during service access (service access request); when service integration is required, the response services are retrieved from each network area and coordinated through the service collaboration algorithm.

[0042] Build The norm collaborative positive optimization term automatically selects collaborative or discrete expression according to the relationship between training edge computing and cloud computing samples, and The norm collaborative positive optimization term is trained to obtain the optimal collaborative compilation result; The norm collaborative positive optimization term designs the contention weight according to the structural information between the test edge computing and cloud computing samples and the training edge computing and cloud computing samples, and selects the discrete or dense collaborative compilation method.

[0043] The expression of the contention constraint is: , in, y is the vector expression of the image newly captured by the system, The matrix of edge computing and cloud computing samples used for training the model. are model parameters,c is the total number of people included in the entire training edge computing and cloud computing samples, To correspond to i The dispute weights of edge computing and cloud computing samples, Remove the first i A matrix of edge computing and cloud computing samples used. To correspond to The co-compilation coefficient.

[0044] The expression of dispute weight is: , in, For use in The maximum value, To test edge computing and cloud computing samples and i Euclidean distance between edge computing and cloud computing samples, is the sample weight.

[0045] Establish an optimal dispute model and solve , the expression is: , in, M is the model parameter, y is the vector expression of the image newly captured by the system, X The matrix of edge computing and cloud computing samples used for training the model. Remove the first i A matrix of edge computing and cloud computing samples used. To remove the i A diagonal matrix of the class, To correspond to i The dispute weights of edge computing and cloud computing samples, c is the total number of people included in the entire training edge computing and cloud computing samples, is the co-compilation coefficient.

[0046] In the process of service collaboration according to the preset service collaboration algorithm in the cloud computing environment, the data first input into the preset dispute model includes: the collected image of each employee is converted into a vector expression, all the collected smart factory automation control data is spliced ​​into a matrix as a training edge computing and cloud computing sample set, and then the face image captured by the system is converted into a vector as a test edge computing and cloud computing sample, and the coefficients in the model.

[0047] Construct the unit matrix according to the number of training edge computing and cloud computing samples, and convert the iThe columns corresponding to edge computing and cloud computing samples are removed, and we get ; Calculate the newly captured image y and train the first i The matrix of categories X i spatial distance, obtain the dispute weight The value of .

[0048] The output of the default dispute model is: r When the distance between the tested edge computing and cloud computing samples and the smart factory automation control data in the training library is the smallest, the possibility that the edge computing and cloud computing samples belong to this category is the greatest. The factory automation control information is classified into this category and the control information classification result is obtained. In the cloud computing environment, service collaboration is performed according to the control information classification result to complete the entire control verification.

[0049] From the above embodiments, it can be seen that the present invention proposes a new type of classification dispute and collaborative representation method (dispute), which Norm regularization uses the properties of training edge computing and cloud computing samples to create a contention environment, which enables the correct classification to make a greater contribution to collaborative compilation. In addition, the contention weights recommended in this algorithm improve the contention relationship between all classifications and are beneficial for the classifier to find the correct classification. The proposed method of the present invention has been applied to many public classified intelligent factory automation control data sets, such as target detection and scene classification image intelligent factory automation control data sets. Based on The norm regularized algorithm achieves relatively discrete co-compilation with less computation.

[0050] The conflict constraint model of the present invention can utilize appropriate edge computing and cloud computing samples and reduce the contribution of other classifications, so that the classification collaborative compilation has the same tendency.

[0051] In order to improve the contention capability of correct classification, the present invention defines contention weights referring to the geographic structure of training smart factory automation control data. This contention weight makes a greater contribution to the co-compilation of test edge computing and cloud computing samples using closely classified test edge computing and cloud computing samples.

[0052] The present invention uses contention constraints and weights to propose a contention model. This method can control the discrete degree of consistent collaborative compilation by controlling data according to appropriate smart factory automation and creating a contention environment to make the correct classification better perform testing edge computing and cloud computing samples.

[0053] use Norm regularization improves the model, making it more stable, more robust, and more practical.

[0054] The present invention solves the problem of inadequate monitoring of factory equipment, which can easily cause potential accidents. In addition, the present invention uses edge computing and cloud computing to coordinate the automation control of smart factories, which reduces the amount of data information calculation, the calculation cost, and the equipment investment, making intelligent control widely applicable in actual production.

[0055] The factory automation control system based on the collaboration of edge computing and cloud computing provided by the present invention is described below. The factory automation control system based on the collaboration of edge computing and cloud computing described below and the factory automation control method based on the collaboration of edge computing and cloud computing described above can be referenced to each other.

[0056] Please refer to Figure 2 , Figure 2 This is a schematic diagram of the structure of a factory automation control system based on the collaboration of edge computing and cloud computing provided by the present invention.

[0057] The present invention also provides a factory automation control system based on the collaboration of edge computing and cloud computing, including: an evaluation and prediction module 201, which is used to determine a reference node set in an edge computing environment, and evaluate and predict the reference node set through a prediction device in a cloud computing environment to obtain a prediction weight of the factory automation control node; the reference node set includes a number of automation control nodes to be predicted and evaluated; a sharing authority verification module 202, which is used to perform sharing authority verification on data requests in an edge computing environment according to the prediction weights and a preset sharing authority verification algorithm; when the sharing authority verification is passed, the factory automation control data is shared across network areas in the cloud computing environment to obtain shared control data; an access authority verification module 203, which is used to perform access authority verification on service access requests in an edge computing environment according to the shared control data and a preset access authority verification algorithm; when the access authority verification is passed and service fusion is required, service collaboration is performed in the cloud computing environment according to a preset service collaboration algorithm to control the automation control node.

[0058] Figure 3 An example of a structural diagram of an electronic device is shown in FIG. Figure 3As shown, the electronic device may include: a processor (processor) 301, a communication interface (Communications Interface) 302, a memory (memory) 303 and a communication bus 304, wherein the processor 301, the communication interface 302, and the memory 303 communicate with each other through the communication bus 304. The processor 301 can call the logic instructions in the memory 303 to execute a factory automation control method based on the collaboration of edge computing and cloud computing, the method including: determining a reference node set in an edge computing environment, evaluating and predicting the reference node set through a prediction device in a cloud computing environment, and obtaining a prediction weight of the factory automation control node; the reference node set includes a number of automation control nodes to be predicted and evaluated; according to the prediction weight and a preset sharing permission verification algorithm, a data request is verified for sharing permission in the edge computing environment; if the sharing permission verification passes, the factory automation control data is shared across network areas in the cloud computing environment to obtain shared control data; according to the shared control data and a preset access permission verification algorithm, a service access request is verified in the edge computing environment; if the access permission verification passes and service fusion is required, service collaboration is performed in the cloud computing environment according to a preset service collaboration algorithm to control the automation control node.

[0059] In addition, the logic instructions in the above-mentioned memory 303 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the relevant technology or the 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 a number of 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 methods of various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0060] An embodiment of the present invention discloses a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the factory automation control method based on edge computing and cloud computing collaboration provided by the above-mentioned method embodiments, the method including: determining a reference node set in an edge computing environment, and evaluating and predicting the reference node set through a prediction device in a cloud computing environment to obtain a prediction weight of the factory automation control node; the reference node set includes a number of automation control nodes to be predicted and evaluated; according to the prediction weight and a preset sharing authority verification algorithm, a data request is verified for sharing authority in the edge computing environment; if the sharing authority verification passes, the factory automation control data is shared across network areas in the cloud computing environment to obtain shared control data; according to the shared control data and a preset access authority verification algorithm, a service access request is verified for access authority in the edge computing environment; if the access authority verification passes and service fusion is required, service collaboration is performed in the cloud computing environment according to a preset service collaboration algorithm to control the automation control node.

[0061] On the other hand, an embodiment of the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the factory automation control method based on edge computing and cloud computing collaboration provided in the above-mentioned embodiments, the method comprising: determining a reference node set in an edge computing environment, and evaluating and predicting the reference node set through a prediction device in a cloud computing environment to obtain a prediction weight of the factory automation control node; the reference node set includes a number of automation control nodes to be predicted and evaluated; based on the prediction weight and a preset sharing permission verification algorithm, performing sharing permission verification on data requests in the edge computing environment; if the sharing permission verification passes, sharing factory automation control data across network areas in the cloud computing environment to obtain shared control data; based on the shared control data and a preset access permission verification algorithm, performing access permission verification on service access requests in the edge computing environment; if the access permission verification passes and service fusion is required, performing service collaboration in the cloud computing environment according to a preset service collaboration algorithm to control the automation control node.

[0062] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0063] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the relevant technology can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiment.

[0064] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A factory automation control method based on the collaboration of edge computing and cloud computing, characterized in that: include: Determine a reference node set in an edge computing environment, and evaluate and predict the reference node set through a prediction device in a cloud computing environment to obtain a prediction weight of a factory automation control node; the reference node set includes a plurality of automation control nodes to be predicted and evaluated; According to the prediction weight and a preset sharing authority verification algorithm, performing sharing authority verification on the data request in the edge computing environment; In the case where the sharing authority verification is passed, the factory automation control data is shared across network areas in the cloud computing environment to obtain shared control data; According to the shared control data and a preset access permission verification algorithm, verifying the access permission of the service access request in the edge computing environment; In the case where the access authority verification is passed and service integration is required, service collaboration is performed in the cloud computing environment according to a preset service collaboration algorithm to control the automation control node.

2. The factory automation control method based on edge computing and cloud computing collaboration according to claim 1 is characterized in that: Determining a reference node set in an edge computing environment includes: Determine the monitoring position function according to the information of the evaluated node and the automation control effect of the evaluated node; the evaluated node is the automation control node that has been evaluated and predicted by the prediction device; determining the monitoring position accuracy of the prediction device according to the monitoring position function; The reference node set is determined in the edge computing environment according to the monitoring location accuracy, the monitoring location function and the information of the evaluated nodes.

3. The factory automation control method based on edge computing and cloud computing collaboration according to claim 1 is characterized in that: The expression of the prediction weight is: ， in, is the prediction weight, is the prediction weight function, For predictive equipment u For reference items i assessment, i For reference, For predictive equipment u Reference Node Set The average value of the evaluation given by all factory automation control nodes in b is the correction amount.

4. The factory automation control method based on edge computing and cloud computing collaboration according to claim 1 is characterized in that: In the case where the sharing authority verification is passed, before the cross-network area sharing of the factory automation control data in the cloud computing environment is performed, the method further includes: Determine labels for automation control data of factory automation control nodes; The network area information where the automation control data is located is determined according to the tag, so as to share the data across network areas according to the network area information.

5. The factory automation control method based on edge computing and cloud computing collaboration according to claim 1 is characterized in that: The performing access rights verification on the service access request according to the shared control data and a preset access rights verification algorithm includes: Determine the services provided by each network area; According to the shared control data and the services provided by each of the network areas, the services required by each of the automation control nodes are used as access permission information; The access permission verification is performed on the service access request according to the access permission information.

6. The factory automation control method based on the collaboration of edge computing and cloud computing according to any one of claims 1 to 5, characterized in that: The performing service collaboration according to a preset service collaboration algorithm in a cloud computing environment includes: Inputting the automation control data of the automation control node into a preset dispute model to obtain a control information classification result output by the dispute model; In the cloud computing environment, service collaboration is performed according to the control information classification result.

7. A factory automation control system based on the collaboration of edge computing and cloud computing, characterized in that: include: An evaluation and prediction module is used to determine a reference node set in an edge computing environment, and to evaluate and predict the reference node set through a prediction device in a cloud computing environment to obtain a prediction weight of a factory automation control node; the reference node set includes a number of automation control nodes to be predicted and evaluated; A sharing authority verification module, configured to perform sharing authority verification on a data request in the edge computing environment according to the prediction weight and a preset sharing authority verification algorithm; and, if the sharing authority verification is passed, perform cross-network area sharing of factory automation control data in the cloud computing environment to obtain shared control data; an access rights verification module, configured to verify access rights for a service access request in the edge computing environment according to the shared control data and a preset access rights verification algorithm; In the case where the access authority verification is passed and service integration is required, service collaboration is performed in the cloud computing environment according to a preset service collaboration algorithm to control the automation control node.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the program, the factory automation control method based on the collaboration of edge computing and cloud computing as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the factory automation control method based on the collaboration of edge computing and cloud computing as described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the factory automation control method based on the collaboration of edge computing and cloud computing as described in any one of claims 1 to 6 is implemented.

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