An industrial equipment automation control system based on edge computing

Abstract

The present invention discloses an industrial equipment automation control system based on edge computing, which relates to the field of equipment control technology and improves the accuracy of production equipment operation supervision. The present invention establishes a production process structure tree of corresponding types of products according to process records, and at the same time establishes a corresponding production line status supervision network according to the historical status data of the production equipment corresponding to each product, maps the production process structure tree in the production line status supervision network, and then obtains a number of production path pre-groups of each type of product, retrieves the production path pre-group according to the name of the product to be produced, and inputs each production path pre-group into the production line status supervision network for production simulation, outputs the best production path group according to the production simulation result, and then supervises the production process of related products according to the best production path group, and re-plans the best production path group when production abnormalities occur until the production is completed.

Description

Technical Field

[0001] The present invention relates to the field of equipment control technology, and in particular to an industrial equipment automation control system based on edge computing. Background Art

[0002] An automated production line is a self-flowing production and processing equipment, which refers to the route consisting of a series of production line activities starting from the entry of raw materials into the production site, through processing, transportation, assembly, inspection, etc. Since there may be dozens of automated equipment on the production line, and there may be multiple production lines in a production workshop, it makes it difficult to accurately detect whether there are production abnormalities in the production equipment on the production line, and it is particularly difficult to adjust the production steps.

[0003] Therefore, how to achieve simultaneous supervision of multiple production equipment while accurately locating and judging whether there are production anomalies in the production equipment is a difficulty in the existing technology. For this purpose, an industrial equipment automation control system based on edge computing is provided. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide an industrial equipment automation control system based on edge computing.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] An industrial equipment automation control system based on edge computing, comprising a cloud computing platform, wherein the cloud computing platform is communicatively connected with a production status data acquisition module, an automation control network module and an automation instruction module;

[0007] The production status data collection module is used to collect real-time status data of different types of products generated by each production equipment and several copies of historical status data;

[0008] The automation control network module pre-stores the production process steps of several kinds of products, and then establishes a production process structure tree of the corresponding kinds of products according to the process records, and at the same time establishes a corresponding production line status supervision network according to the historical status data of the production equipment corresponding to each product, maps the production process structure tree to the production line status supervision network, and then obtains several production path pre-groups of each kind of product;

[0009] The automated instruction module is used to retrieve the production path pre-group according to the name of the product to be produced, and input each production path pre-group into the production line status supervision network for production simulation, output the best production path group according to the production simulation result, and then supervise the production process of the relevant products according to the best production path group, and re-plan the best production path group when production abnormalities occur until the production is completed.

[0010] Furthermore, the production equipment is connected to form several production lines through a conveyor belt, and a production stack is provided between each production line;

[0011] The production stack consists of a rotary valve, a data processing unit and several sensors;

[0012] According to the production line where each production equipment is located, the production status data acquisition module sets a number for each production equipment and each production stack.

[0013] Furthermore, the collection process of the real-time status data and the historical status data includes:

[0014] The production status data acquisition module sets multiple sensors for each production equipment and production stack, and sets the same data upload delay for each sensor. When the production equipment or production stack where each sensor is located is processing products, each sensor collects various status data of the product and the production equipment or production stack, and uploads all the collected status data to the production status data acquisition module when the data upload delay ends. The status data is divided into video status data and chart status data.

[0015] Through the above-mentioned process of collecting status data, the production status data collection module collects real-time status data and several copies of historical status data of each type of product during processing, and marks each real-time status data and historical status data with the corresponding product type name, collection time, and production equipment or production stack number.

[0016] Further, the production process steps include product components, a total production raw material list, processing steps for each product component, and an assembly sequence for the product components;

[0017] The processing steps of the product components include the types of raw materials required for production, the corresponding quantities, the processing steps and the time required for each processing step.

[0018] Furthermore, the process of establishing the production process structure tree includes:

[0019] According to the number of processing steps of each component of each product in the production process steps of each type of product, corresponding leaf nodes are established, and production data nodes and component finished product nodes are set at the beginning and end of the production process structure subtree respectively, where the production data nodes of different production process structure subtrees can contain the information of component finished product nodes in other production process structure subtrees;

[0020] The means of production nodes, leaf nodes and component finished product nodes are connected in sequence to obtain the production process structure subtree of the corresponding product components. It should be noted that the leaf nodes in the production process structure subtree are arranged in chronological order, and there are two or more leaf nodes arranged in parallel. Then, the various production process structure subtrees are spliced ​​according to the assembly order of the product components to obtain the production process structure tree of the corresponding type of product.

[0021] Furthermore, the process of establishing the production line status supervision network includes:

[0022] According to the spatial distribution and quantity of production equipment on each production stack and production line, corresponding spatial nodes are established and connected in sequence, and the same number of data local areas are divided in each spatial node according to the number of product types;

[0023] A historical processing dynamic image model is established according to the historical video state data, the historical processing dynamic image model is divided into a number of model areas of the same size, and the pixel value of each surface of the model area is obtained;

[0024] Establish a three-dimensional coordinate system, map the historical processing dynamic image model corresponding to the same type of video status data of the same type of product at different acquisition times onto the same three-dimensional coordinate system, perform normal distribution on the pixel values ​​of each surface of the model area at the same position, and select the pixel value in the center area of ​​the normal distribution as the standard pixel value interval;

[0025] Then, a standard processing dynamic image model is established according to the standard pixel value interval of each surface;

[0026] A two-dimensional coordinate system is established, and several time nodes are set on the two-dimensional coordinate system. The historical chart status data of the same type of products at different collection times are normally distributed, and then the data part in the central area of ​​the normal distribution is selected as the standard value range interval of the corresponding chart status data at each time node;

[0027] Connect the standard value range intervals of each time node in sequence to obtain the standard state value range interval of the corresponding type of historical chart state data;

[0028] And input each standard processing dynamic image model and standard state value range interval into the corresponding data area, and mark each standard processing dynamic image model and standard state value range interval with the corresponding product name and processing step, so as to obtain the production line status supervision network.

[0029] Furthermore, the process of obtaining the production path pre-group includes:

[0030] According to the processing steps corresponding to each leaf node in the production process structure tree, the leaf nodes are matched with the spatial nodes of the corresponding processing steps in the production line status supervision network, so that the spatial nodes corresponding to each production line are matched one-to-one with the leaf nodes in the production process structure subtree;

[0031] The leaf nodes in the production process structure subtree match the spatial nodes corresponding to multiple groups of production lines, and then the spatial nodes corresponding to each group of production lines are recorded as component production spatial node groups;

[0032] When each production process structure subtree is matched with the corresponding spatial nodes of all group production lines, the component finished product node in the production process structure subtree is matched with the spatial node of the corresponding production stack in the production line status supervision network, and the matched spatial node is recorded as the transfer spatial node;

[0033] Using the transfer space node as the connection point, connect the component production space node groups of each type of product and map them into the production line state supervision network. If the connection result is continuous in the production line state supervision network, record the group of connection results and record it as the production path pre-group, and mark each space node in the production path pre-group with the time taken for the corresponding processing step;

[0034] If the connection results are not in a continuous state within the production line status supervision network, the group of connection results will be discarded.

[0035] Furthermore, the process of selecting the optimal production path group includes:

[0036] The automation instruction module calls the corresponding production process structure tree and production path pre-group according to the product name, takes the optimal production path group currently being implemented by the production line status supervision network as a constant factor, and maps each production path pre-group to the production line status supervision network in turn for production simulation. If the production equipment time node required by the production path pre-group conflicts with the production equipment time node in any of the optimal production path groups being implemented, the corresponding production path pre-group is ignored;

[0037] If the production equipment time node required by the production path pre-group does not conflict with the production equipment time node in any optimal production path group being implemented, the corresponding production path pre-group is marked as the optimal production path group;

[0038] The optimal production path group is mapped into the production line state supervision network, and then the spatial nodes of the production stack that the optimal production path group will pass through are marked in the production line state supervision network. At the same time, the production process structure tree associated with each spatial node of the corresponding production stack is retrieved according to the production process structure tree of the product name to be produced, and the various data in the corresponding spatial node of the associated production process structure subtree are sent to the data processing unit of the corresponding production stack. At the same time, according to the sequence of the associated production process structure subtrees in the production process structure tree, the rotary valve switch instruction is sent to the data processing unit of the corresponding production stack, so that the production stack transfers the product components to the next production line according to the assembly sequence of the product components.

[0039] Furthermore, the supervision process of the product production process includes:

[0040] During the production process, every time a production device completes processing of a product, the sensors on the production device collect various real-time status data along the production direction and send it to the corresponding production stack;

[0041] Then, the data processing unit in the production stack generates a corresponding real-time processing dynamic image model according to each real-time status data, and retrieves the corresponding standard status value range interval and the standard processing dynamic image model according to the production equipment number carried by the real-time status data, overlaps and maps the real-time processing dynamic image model with the standard processing dynamic image model, and inputs the chart status data in the real-time status data into the corresponding standard status value range interval;

[0042] If there is a discrepancy between the real-time processing dynamic image model and the standard processing dynamic image model, or if the chart status data in the real-time status data is not within the corresponding standard status value range, it is determined that the corresponding production equipment has a production abnormality, and then the data processing unit sends the number of the production equipment with the production abnormality and the product name to the automation instruction module;

[0043] The automation instruction module obtains the processing steps corresponding to the production equipment with production abnormalities according to the production equipment number, and then replans the best production path group so that the corresponding products can perform the corresponding processing steps;

[0044] If there is no discrepancy between the real-time processing dynamic image model and the standard processing dynamic image model, or if the chart status data in the real-time status data is within the corresponding standard status value range, no operation is performed;

[0045] When the product enters the production stack, the data processing unit in the production stack controls the rotary valve according to the rotary valve switch instruction, so that the product enters the next production line until the product production is completed.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] The present invention establishes a production process structure tree for corresponding types of products according to process records, and at the same time establishes a corresponding production line status supervision network according to the historical status data of the production equipment corresponding to each product, maps the production process structure tree in the production line status supervision network, and then obtains a plurality of production path pre-groups for each type of product, retrieves the production path pre-group according to the name of the product to be produced, and inputs each production path pre-group into the production line status supervision network for production simulation, outputs the best production path group according to the production simulation result, and then supervises the production process of related products according to the best production path group, and re-plans the best production path group when a production abnormality occurs until the production is completed, thereby realizing precise positioning and judging whether there is a production abnormality in the production equipment while simultaneously supervising a variety of production equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention.

[0049] Figure 1 It is a schematic diagram of the present invention. DETAILED DESCRIPTION

[0050] To make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0051] like Figure 1 As shown, an industrial equipment automation control system based on edge computing includes a cloud computing platform, and the cloud computing platform is communicatively connected with a production status data acquisition module, an automation control network module, and an automation instruction module;

[0052] The production status data acquisition module is used to collect real-time status data of different types of products generated by various production equipment and several copies of historical status data, and send all historical status data to the automation control network module;

[0053] The automation control network module pre-stores the production process steps of several kinds of products, and then establishes a production process structure tree of the corresponding kinds of products according to the process records, and at the same time establishes a corresponding production line status supervision network according to the historical status data of the production equipment corresponding to each product, maps the production process structure tree to the production line status supervision network, and then obtains several production path pre-groups of each kind of product;

[0054] The automated instruction module is used to retrieve the production path pre-group according to the name of the product to be produced, and input each production path pre-group into the production line status supervision network for production simulation, output the best production path group according to the production simulation result, and then supervise the production process of the relevant products according to the best production path group, and re-plan the best production path group when production abnormalities occur until the production is completed.

[0055] Further, the working principle of the present invention is described below by way of examples:

[0056] The production equipment is connected by a conveyor belt to form a plurality of production lines, and a production stack is arranged between each production line;

[0057] The production stack consists of a rotary valve, a data processing unit and several sensors;

[0058] It should be noted that the production stack can be connected to two or more production lines at the same time, and the processing functions of production stacks at different locations may be different;

[0059] According to the production line where each production equipment is located, the production status data acquisition module sets a number s for each production equipment 1，1 、s 1，2 ,……,s n，m , and set the number w for each production stack 1，2 、w 2，3，5 ,……,w i，j，n , where s n，m represents the mth production equipment on the nth production line, w i，j，n It represents the production stack at the intersection of the i, j, and n production lines, where n, m, i, and j are natural numbers greater than 0, and i and j are less than n;

[0060] The production status data acquisition module sets multiple sensors for each production equipment and production stack, and sets the same data upload delay for each sensor. When the production equipment or production stack where each sensor is located is processing a product, each sensor collects various status data of the product and the production equipment or production stack, and uploads all the collected status data to the production status data acquisition module when the data upload delay ends;

[0061] The types of sensors include optical cameras, infrared cameras, laser sensors, etc. The types of sensors carried by production equipment on different production lines are not exactly the same;

[0062] The state data is divided into video state data and chart state data, wherein the video state data includes optical video data, infrared video data, etc., and the chart state data includes temperature change curve, pressure change curve, etc.;

[0063] Through the above process of collecting status data, the production status data collection module collects the real-time status data of each type of product during processing and several copies of historical status data, and marks each real-time status data and historical status data with the corresponding product type name, collection time, and production equipment or production stack number;

[0064] The production status data acquisition module sends all historical status data to the automation control network module.

[0065] Furthermore, the automation control network module pre-stores production process steps of several products, wherein the production process steps include product components, a total production raw material list, processing steps of each product component, and an assembly sequence of the product components;

[0066] The processing steps of the product components include the types of raw materials required for production, the corresponding quantities, the processing steps and the duration of each processing step, such as dyeing, packaging, welding, etc.;

[0067] According to the number of processing steps of each component of each product in the production process steps of each type of product, corresponding leaf nodes are established, and production data nodes and component finished product nodes are set at the beginning and end of the production process structure subtree respectively, where the production data nodes of different production process structure subtrees can contain the information of component finished product nodes in other production process structure subtrees;

[0068] The production means nodes, leaf nodes and component finished product nodes are sequentially connected to obtain the production process structure subtree of the corresponding product component. It should be noted that the leaf nodes in the production process structure subtree are arranged in chronological order, and there are two or more leaf nodes arranged in parallel;

[0069] Then, each production process structure subtree is spliced ​​according to the assembly order of the product components to obtain the production process structure tree of the corresponding type of product.

[0070] Furthermore, according to the spatial distribution and quantity of production equipment on each production stack and production line, corresponding spatial nodes are established and connected in sequence, and the same number of data local areas are divided in each spatial node according to the number of product types;

[0071] Each spatial node is marked with the number of the corresponding production equipment or production stack, and the historical status data with the same number is mapped to the same spatial node, and the historical status data with the same product category name is stored in the same data local area;

[0072] For various types of historical status data of any product, the automation control network module establishes a corresponding historical processing dynamic image model according to the historical video status data in the historical status data, divides the historical processing dynamic image model into a number of model areas of the same size, and obtains the pixel value of each surface of the model area;

[0073] Establish a three-dimensional coordinate system, map the historical processing dynamic image model corresponding to the same type of video status data of the same type of product at different acquisition times onto the same three-dimensional coordinate system, perform normal distribution on the pixel values ​​of each surface of the model area at the same position, and select the pixel value in the center area of ​​the normal distribution as the standard pixel value interval;

[0074] According to the standard pixel value interval of each surface, a corresponding standard model area is established, and the corresponding standard pixel value interval is marked on each surface of the standard model area, and each standard pixel value interval is sequentially spliced ​​to obtain a standard processing dynamic image model;

[0075] Through the above process of obtaining the standard processing dynamic image model, the standard processing dynamic image model of each type of product in different processing equipment is obtained;

[0076] At the same time, a two-dimensional coordinate system is established, and several time nodes are set on the two-dimensional coordinate system. The historical chart status data of the same type of products at different collection times are normally distributed, and then the data part of the central area of ​​the normal distribution is selected as the standard value range interval of the corresponding chart status data at each time node;

[0077] Connect the standard value range intervals of each time node in sequence to obtain the standard state value range interval of the corresponding type of historical chart state data;

[0078] Bind the standard state value range interval and the standard processing dynamic image model corresponding to the same processing step and product type, input each standard processing dynamic image model and the standard state value range interval into the corresponding data area, and annotate each standard processing dynamic image model and the standard state value range interval with the corresponding product name and processing step, thereby obtaining a production line state supervision network;

[0079] According to the processing steps corresponding to each leaf node in the production process structure tree, the leaf nodes are matched with the spatial nodes of the corresponding processing steps in the production line status supervision network, so that the spatial nodes corresponding to each production line are matched one-to-one with the leaf nodes in the production process structure subtree;

[0080] The leaf nodes in the production process structure subtree match the spatial nodes corresponding to multiple groups of production lines, and then the spatial nodes corresponding to each group of production lines are recorded as component production spatial node groups;

[0081] When each production process structure subtree is matched with the corresponding spatial nodes of all group production lines, the component finished product node in the production process structure subtree is matched with the spatial node of the corresponding production stack in the production line status supervision network, and the matched spatial node is recorded as the transfer spatial node;

[0082] Using the transfer space node as the connection point, connect the component production space node groups of each type of product and map them into the production line state supervision network. If the connection result is continuous in the production line state supervision network, record the group of connection results and record it as the production path pre-group, and mark each space node in the production path pre-group with the time taken for the corresponding processing step;

[0083] If the connection result is not in a continuous state in the production line status supervision network, the group of connection results is discarded;

[0084] Repeat the above established production path pre-grouping to obtain several production path pre-groups for each type of product.

[0085] Further, the automation control network module sends the production line status supervision network and the production process structure tree of each type of product and all production paths to the automation instruction module;

[0086] The staff will send the name of the product to be produced to the automation instruction module, and then the automation instruction module will call the corresponding production process structure tree and production path pre-group according to the product name, and select the best production path group through production path simulation;

[0087] The selection process of the best production path group includes: taking the best production path group currently being implemented by the production line state supervision network as a constant factor, mapping each production path pre-group to the production line state supervision network in turn for production simulation; if the production equipment time node required by the production path pre-group conflicts with the production equipment time node in any of the best production path groups being implemented, then ignoring the corresponding production path pre-group;

[0088] If the production equipment time node required by the production path pre-group does not conflict with the production equipment time node in any optimal production path group being implemented, the corresponding production path pre-group is marked as the optimal production path group;

[0089] If there are multiple optimal production path groups at the same time, one optimal production path group is randomly selected and retained, and the other optimal production path groups are automatically ignored;

[0090] The optimal production path group is mapped into the production line state supervision network, and then the spatial nodes of the production stack that the optimal production path group will pass through are marked in the production line state supervision network. At the same time, the production process structure subtree associated with each spatial node of the corresponding production stack is retrieved according to the production process structure tree of the product name to be produced, and each data in the corresponding spatial node of the associated production process structure subtree is sent to the data processing unit of the corresponding production stack. At the same time, according to the sequence of the associated production process structure subtree in the production process structure tree, a rotary valve switch instruction is sent to the data processing unit of the corresponding production stack, so that the production stack transfers the product components to the next production line according to the assembly sequence of the product components;

[0091] It should be noted that the rotary valve switch instruction includes the valve rotation direction, rotation angle and rotation time node.

[0092] Furthermore, when the automation instruction module sends the switch instruction to the rotary valve according to the data processing unit of each production stack of the optimal production path group, the staff will place the production raw materials on the production line and start product production;

[0093] During the production process, every time a production device completes processing of a product, the sensors on the production device collect various real-time status data along the production direction and send it to the corresponding production stack;

[0094] Then, the data processing unit in the production stack generates a corresponding real-time processing dynamic image model according to each real-time status data, and retrieves the corresponding standard status value range interval and the standard processing dynamic image model according to the production equipment number carried by the real-time status data, overlaps and maps the real-time processing dynamic image model with the standard processing dynamic image model, and inputs the chart status data in the real-time status data into the corresponding standard status value range interval;

[0095] If there is a discrepancy between the real-time processing dynamic image model and the standard processing dynamic image model, or if the chart status data in the real-time status data is not within the corresponding standard status value range, it is determined that the corresponding production equipment has a production abnormality, and then the data processing unit sends the number of the production equipment with the production abnormality and the product name to the automation instruction module;

[0096] The automation instruction module obtains the processing steps corresponding to the production equipment with production abnormalities according to the production equipment number, and then replans the best production path group so that the corresponding products can perform the corresponding processing steps;

[0097] If there is no discrepancy between the real-time processing dynamic image model and the standard processing dynamic image model, or if the chart status data in the real-time status data is within the corresponding standard status value range, no operation is performed;

[0098] When the product enters the production stack, the data processing unit in the production stack controls the rotary valve according to the rotary valve switch instruction, so that the product enters the next production line until the product production is completed.

[0099] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An industrial equipment automation control system based on edge computing, including a cloud computing platform, characterized in that: The cloud computing platform is communicatively connected with a production status data acquisition module, an automation control network module and an automation instruction module; The production status data collection module is used to collect real-time status data of different types of products generated by various production equipment and several copies of historical status data; The production equipment is connected by a conveyor belt to form a plurality of production lines, and a production stack is arranged between each production line; The production stack is composed of a rotary valve, a data processing unit and several sensors. Each production equipment and each production stack is numbered according to the production line where each production equipment is located; The automation control network module pre-stores the production process steps of several kinds of products, and then establishes a production process structure tree of the corresponding kinds of products according to the process records, and at the same time establishes a corresponding production line status supervision network according to the historical status data of the production equipment corresponding to each product, maps the production process structure tree to the production line status supervision network, and then obtains several production path pre-groups of each kind of product; The process of establishing the production line status supervision network includes: According to the spatial distribution and quantity of production equipment on each production stack and production line, corresponding spatial nodes are established and connected in sequence, and a historical processing dynamic image model is established according to the historical video status data. The historical processing dynamic image model is divided into several model areas of the same size, and the pixel value of each surface of the model area is obtained; Establish a three-dimensional coordinate system, map the historical processing dynamic image model corresponding to the same type of video status data of the same type of product at different acquisition times onto the same three-dimensional coordinate system, perform normal distribution on the pixel values ​​of each surface of the model area at the same position, and select the pixel value in the center area of ​​the normal distribution as the standard pixel value interval; Then, a standard processing dynamic image model is established according to the standard pixel value interval of each surface; Normally distribute the historical chart status data of the same type of products at different collection times, and then select the data part of the center area of ​​the normal distribution as the standard value range interval of the corresponding chart status data at each time node, and connect the standard value range interval of each time node in sequence to obtain the standard state value range interval of the historical chart status data of the corresponding type; Each standard processing dynamic image model and standard state value range interval is input into the corresponding data area, and each standard processing dynamic image model and standard state value range interval is marked with the corresponding product name and processing step, thereby obtaining a production line state supervision network; The process of obtaining the production path pre-group includes: According to the processing steps corresponding to each leaf node, the leaf node is matched with the spatial node of the same processing step in the production line state supervision network, and the spatial nodes corresponding to each group of production lines are recorded as component production spatial node groups according to the matching results; Match the component finished product node in the production process structure subtree with the spatial node of the corresponding production stack in the production line status supervision network, and record the matched spatial node as the transfer spatial node; Taking the transfer space node as the connection point, the component production space node groups of each type of product are connected and mapped in the production line state supervision network. If it is judged that the connection results of each component production space node group are continuous in the production line state supervision network, the connection results of the component production space node groups of the corresponding type are recorded as the production path pre-group, and the corresponding time used is marked for each space node in the production path pre-group, otherwise the group of connection results is eliminated; The automated instruction module is used to retrieve the production path pre-group according to the name of the product to be produced, and input each production path pre-group into the production line status supervision network for production simulation, output the best production path group according to the production simulation result, and then supervise the production process of the relevant products according to the best production path group, and re-plan the best production path group when production abnormalities occur until the production is completed.

2. According to the edge computing-based industrial equipment automation control system of claim 1, it is characterized in that: The collection process of the real-time status data and the historical status data includes: A variety of sensors and data upload delays are set for each production equipment and production stack. Each sensor collects various status data of the product and the production equipment or production stack, and when the data upload delay ends, all the collected status data are uploaded to the production status data acquisition module. The status data is divided into video status data and chart status data; Then, the production status data collection module obtains the real-time status data and several copies of historical status data of each type of product during processing, and marks each real-time status data and historical status data with the corresponding product type name, collection time, and production equipment or production stack number.

3. According to claim 2, an industrial equipment automation control system based on edge computing is characterized in that: The production process steps include product components, a total production raw material list, processing steps for each product component, and the order in which the product components are assembled; The processing steps of the product components include the types of raw materials required for production, the corresponding quantities, the processing steps and the time required for each processing step.

4. According to claim 3, an industrial equipment automation control system based on edge computing is characterized in that: The process of establishing the production process structure tree includes: Establish leaf nodes according to the number of processing steps of each product component in the production process steps, and set production data nodes and component finished product nodes at the beginning and end of the production process structure subtree; Connect the means of production nodes, leaf nodes and component finished product nodes in sequence to obtain the production process structure subtree of the corresponding product components, and then splice each production process structure subtree according to the assembly order of the product components to obtain the production process structure tree of the corresponding type of product.

5. The industrial equipment automation control system based on edge computing according to claim 4 is characterized in that: The process of selecting the optimal production path group includes: According to the product name, the corresponding production process structure tree and production path pre-group are retrieved. The optimal production path group currently being implemented by the production line status supervision network is taken as a constant factor. Each production path pre-group is sequentially mapped to the production line status supervision network for production simulation. If the production equipment time node required by the production path pre-group conflicts with the production equipment time node in any of the optimal production path groups being implemented, the corresponding production path pre-group is ignored. If there is no conflict, the corresponding production path pre-group is marked as the best production path group; Mark the spatial nodes of the production stack that the optimal production path group will pass through in the production line status supervision network, and at the same time, call the production process structure subtree associated with the spatial node of the corresponding production stack according to the production process structure tree of the product name to be produced, and generate the rotary valve switch instruction according to the sequence of the associated production process structure subtrees in the production process structure tree.

6. The industrial equipment automation control system based on edge computing according to claim 5 is characterized in that: The supervision process of the product production process includes: Whenever a production device completes processing of a product, the sensors on the production device collect various real-time status data along the production direction and send them to the production stack; The data processing unit in the production stack generates a real-time processing dynamic image model according to various real-time status data, and retrieves the corresponding standard status value range interval and the standard processing dynamic image model according to the production equipment number carried by the real-time status data, overlaps and maps the real-time processing dynamic image model with the standard processing dynamic image model, and inputs the chart status data in the real-time status data into the corresponding standard status value range interval; If there is a discrepancy between the real-time processing dynamic image model and the standard processing dynamic image model, or if the chart status data in the real-time status data is not within the corresponding standard status value range, it is determined that the corresponding production equipment has a production abnormality, and then the data processing unit sends the number of the production equipment with the production abnormality and the product name to the automation instruction module; The automation instruction module re-plans the best production path group according to the production equipment number, so that the corresponding product can perform the corresponding processing steps, otherwise no operation will be performed; When the product enters the production stack, the data processing unit in the production stack controls the rotary valve according to the rotary valve switch instruction, so that the product enters the next production line until the product production is completed.

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