Mass industrial equipment structured organization method based on network computing control cooperation

Abstract

The invention provides a massive industrial equipment structured organization method based on network computing control cooperation, and relates to the technical field of industrial equipment networking. The method comprises the following steps: firstly, calculating a time-varying spatial position of industrial equipment according to an order demand without considering network and calculation delay; performing hierarchical structure division on the industrial equipment according to the spatial position and the control requirement of the industrial equipment; then industrial data transmission and calculation delay are substituted into the order production process, and the spatial position of the industrial equipment changing along with time is recalculated; if the organization structure of the current industrial equipment can support the network access of the industrial equipment at the new position, the current structure division is the organization structure of the final industrial equipment; otherwise, judging whether structure adjustment is carried out under the same control frequency of the industrial equipment, if not, adjusting the structure, and then recalculating the spatial position of the industrial equipment along with the time change; and if yes, reducing the control frequency, and then carrying out hierarchical structure division on the industrial equipment again.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial equipment networking, and in particular to a structured organization method for massive industrial equipment based on network computing and control collaboration. Background Art

[0002] With the development of Industry 4.0 and smart manufacturing, massive industrial equipment must be fully connected through networking. Currently, a variety of technical solutions and frameworks have been proposed to solve the organization and management problems of massive industrial equipment. In particular, driven by technologies such as the Industrial Internet of Things (IIoT), edge computing, and cloud computing, the collaborative work between devices has made some progress. However, the existing methods have the following shortcomings:

[0003] Network resources and computing resources are used to meet the needs of production control. Existing methods deal with production control through collaborative networks and computing. However, they ignore the adjustable space of production control, resulting in existing methods not fully utilizing the networking capabilities of industrial information systems.

[0004] Existing methods have poor ability to cope with the dynamic needs of industrial control. Although relevant studies have considered dynamic networking in industrial environments, the consideration of dynamics is too random, the most typical example is the node organization and management of the Internet of Vehicles. However, the mobility of nodes in industrial environments is not as large-scale and random as the Internet of Vehicles. Equipment and products in industry have a known range of movement. For example, automated guided vehicles only transport parts within a specified area, and products must also go through fixed manufacturing, warehousing and other processes. Therefore, there are regularities in the dynamics of industrial network nodes. Existing methods are too pessimistic about dynamics, consume a lot of resources to deal with dynamics, and limit the number of devices supported. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a structured organization method for massive industrial equipment based on network computing and control collaboration in response to the deficiencies of the above-mentioned prior art, so as to solve the problem that massive equipment is difficult to effectively organize and manage in large-scale industrial scenarios.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is: a structured organization method for massive industrial equipment based on network computing and control collaboration, coordinating network, computing, and control resources, planning the organizational structure of massive industrial equipment, so that limited resources can support the full interconnection of massive industrial equipment, including the following steps:

[0007] Step 1: Ignoring network and computing delays, calculate the spatial position of industrial equipment over time based on order requirements;

[0008] Step 2: Divide the industrial equipment into hierarchical structures according to their spatial location and control requirements;

[0009] Step 3: Analyze the delay of industrial data transmission and calculation, bring the delay into the order production process, and recalculate the spatial position of industrial equipment over time;

[0010] Step 4: If the organizational structure of the current industrial equipment can support the access of the industrial equipment at the new location, the current structural division is the final organizational structure of the industrial equipment; otherwise, determine whether the structural adjustment has been made under the control frequency of the same industrial equipment. If not, adjust the structure and then re-execute step 3; if yes, reduce the control frequency and then re-execute step 2.

[0011] The step 1 does not consider the network and computing delays, and calculates the spatial position of the industrial equipment over time according to the order requirements, including the following steps:

[0012] Step 1.1: Assume that industrial equipment includes immovable machines and movable logistics vehicles; the product corresponding to the i-th order needs n i The production can be completed through the process, each process corresponds to a production machine, the jth process p ij The corresponding production machine is m ij The execution time on the production machine is represented by t ij Indicates that the production machine cannot be moved, and the product is moved from one production machine to another by an intelligent logistics vehicle;

[0013] Step 1.2: Determine the priority of the orders based on the sum of the execution time of all orders using each production machine and the sum of the execution time of all processes of each order;

[0014] For any production machine m a , calculate the total execution time of all orders using the production machine, recorded as T a ;

[0015] Traverse all T a , find T a The largest production machine a ; Use production machines m a All orders are added to the order set R a middle;

[0016] For the order set R a For each order in , calculate the sum of the execution time of all processes of the order, denoted as TO i ;

[0017] According to TO i The size is R a The order setting priority in TO i The larger it is, the higher the order priority;

[0018] Remove machine m a , find the machine m' with the largest total execution time among the remaining machines a , and calculate the order set R' corresponding to the machine a , and then calculate the sum of the execution time of all processes of each order in the order set TO' i , according to TO' i Assign priorities to the orders; repeat the process for the remaining machines until all orders have been assigned priorities;

[0019] Step 1.3: All orders start to be executed at the same time, and each order uses each machine in turn according to the process sequence; when multiple orders start to use the same machine at the same time, the order with the highest priority will use the machine first, and other orders will wait;

[0020] Step 1.4: Based on the order execution process, we know the order in which the order passes through each machine in turn, and the moving path between adjacent machines is calculated using the Dijkstra algorithm; the moving speed of the logistics vehicle is known, and the location of the cth logistics vehicle at time t is calculated. c,t .

[0021] The step 2 divides the industrial equipment into hierarchical structures according to the spatial location and control requirements of the industrial equipment, including the following steps:

[0022] Step 2.1: The time when all orders are executed is taken as the starting point t = 0. The position of each logistics vehicle at this time is loc c,0 , the production machine is located at a fixed position m ;

[0023] Step 2.2: Use the k-means method to cluster all industrial devices based on location; at the same time, use the wireless network coverage distance D as a constraint, and the distance between each type of industrial equipment and the center location of the class cannot be greater than D;

[0024] Step 2.3: Connect all industrial devices in each class to an edge node, which is placed at the center of the class; then connect all edge nodes to the cloud through a wired network to form an initial hierarchical structure S0;

[0025] Step 2.4: Considering the mobility of the equipment, verify at different times t=1, 2, ... T whether the initial hierarchical structure S0 can still connect to the industrial equipment under the premise of satisfying the distance D constraint; if at a certain time t, the hierarchical structure S0 cannot connect to all the equipment, then re-execute steps 2.2 and 2.3 to calculate the hierarchical structure S t ; Follow this step until all devices have hierarchical structures accessible within T time, and multiple hierarchical structures are calculated; at this point, the hierarchical structure of the device is formed;

[0026] The step 3 analyzes the transmission and calculation delays, brings the delays into the order production process, and recalculates the spatial position of the industrial equipment over time, specifically including:

[0027] Based on the hierarchical structure division of industrial equipment, the production process of all orders is simulated, and this process brings in the network bandwidth and the computing power of the edge nodes. According to the data transmission volume and the computing amount, the transmission volume is divided by the network bandwidth, and the computing amount is divided by the computing power to obtain the transmission delay and computing delay. The transmission delay and computing delay are brought into the order production process to obtain the spatial position of the updated industrial equipment that changes over time.

[0028] In step 4, if the current hierarchical structure can support the network access of industrial equipment at the new location, the current structural division is the final organizational structure of the industrial equipment, specifically including: under the premise of satisfying the distance D constraint, if the industrial equipment can still connect to the edge node when the location is not updated at the updated location, then the current structural division is the final organizational structure of the industrial equipment.

[0029] The structural adjustment comprises the following steps:

[0030] Find the edge node within the distance D of the new location of the device, and select the edge node with the smallest computing load to connect;

[0031] If there is no edge node within the distance D, the K-means method is used again to cluster the devices in all adjacent classes of the new device location; each new class is connected to an edge node.

[0032] The control frequency reduction is specifically as follows: In order to make the network bandwidth and the computing power of the edge node meet the needs of production control, under the premise of stable control, the control frequency is reduced to max{f,fc-max{1,(fc-f) 2 / f}}, where fc represents the current control frequency of the industrial equipment and f represents the minimum frequency under stable control.

[0033] The beneficial effects of adopting the above technical solution are: the present invention provides a structured organization method for massive industrial equipment based on network computing and control collaboration, which not only coordinates network computing resources, but also adjusts control requirements according to network computing resources, and can support the structured organization of massive equipment with limited resources to achieve stable production of industrial systems; the present invention proposes a two-layer control closed loop of structural adjustment and control frequency adjustment, which balances the control granularity and the complexity of the method and is highly practical. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A flowchart of a method for structured organization of massive industrial equipment based on network computing and control collaboration is provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The specific implementation of the present invention is further described in detail below 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.

[0036] This embodiment takes the production line of a factory as an example, and adopts the structured organization method of massive industrial equipment based on network computing and control collaboration of the present invention to realize the structured organization of the industrial equipment of the factory.

[0037] In this embodiment, a structured organization method for massive industrial equipment based on network computing and control collaboration is provided. Figure 1 As shown, the following steps are included:

[0038] Step 1: Ignoring the delay of the network and computing (referred to as network computing), the spatial position of industrial equipment over time is calculated based on order requirements;

[0039] Step 1.1: Assume that industrial equipment includes immovable machines and movable logistics vehicles; the product corresponding to the i-th order needs n i The production can be completed through the process, each process corresponds to a production machine, the jth process p ij The corresponding production machine is m ij The execution time on the production machine is represented by t ij Indicates that the production machine cannot be moved, and the product is moved from one production machine to another by an intelligent logistics vehicle;

[0040] In this embodiment, there are two orders waiting for production; the product corresponding to the first order requires two production processes, the first process takes 2 minutes to execute on machine 1, and the second process takes 1 minute to execute on machine 2; the product corresponding to the second order requires three production processes, the first process takes 4 minutes to execute on machine 1, the second process takes 2 minutes to execute on machine 2, and the third process takes 1 minute to execute on machine 3. The production machine cannot be moved, and the product is transported from one production machine to another by the intelligent logistics vehicle;

[0041] Step 1.2: Determine the priority of the orders based on the sum of the execution time of all orders using each production machine and the sum of the execution time of all processes of each order;

[0042] For any production machine m a , calculate the total execution time of all orders using the production machine, recorded as T a ;

[0043] Traverse all T a , find T a The largest production machine a ; Use production machines ma All orders are added to the order set R a middle;

[0044] For the order set R a For each order in , calculate the sum of the execution time of all processes of the order, denoted as TO i ;

[0045] According to TO i The size is R a The order setting priority in TO i The larger it is, the higher the order priority;

[0046] Remove machine m a , find the machine m' with the largest total execution time among the remaining machines a , and calculate the order set R' corresponding to the machine a , and then calculate the sum of the execution time of all processes of each order in the order set TO' i , according to TO' i Assign priorities to the orders; repeat the process for the remaining machines until all orders have been assigned priorities;

[0047] In this embodiment, it can be calculated that the total execution time of the two orders on the three machines is: the total execution time of machine 1 is 6 minutes, the total execution time of machine 2 is 3 minutes, and the total execution time of machine 3 is 1 minute;

[0048] It can be seen that machine 1 has the longest execution time among all machines, so orders 1 and 2 using machine 1 are added to order set R1;

[0049] For order 1 and order 2 in R1, calculate the sum of the execution time of all processes of each order, that is, TO1 = 3, TO2 = 7;

[0050] Since TO2 is greater than TO1, order 2 has a higher priority than order 1;

[0051] Step 1.3: All orders start to be executed at the same time, and each order uses each machine in turn according to the process sequence; when multiple orders start to use the same machine at the same time, the order with the highest priority will use the machine first, and other orders will wait;

[0052] In this embodiment, order 1 and order 2 start to be executed at the same time. The first process of both needs to be completed on machine 1. Order 2 with a higher priority uses machine 1 first. During this process, order 1 waits. According to this rule, order 2 is executed on machine 1 between time 1 and 4, on machine 2 between time 5 and 6, and on machine 3 at time 7. Order 1 is executed on machine 1 between time 5 and 6, and on machine 2 at time 7.

[0053] Step 1.4: Based on the order execution process, we know the order in which the order passes through each machine in turn, and the moving path between adjacent machines is calculated using the Dijkstra algorithm; the moving speed of the logistics vehicle is known, and the location of the cth logistics vehicle at time t is calculated. c,t .

[0054] According to the order execution process, we know that order 1 needs to be transported from machine 1 to machine 2 by the logistics vehicle, and the transportation process of order 2 is machine 1, machine 2, and machine 3. It is known that the position coordinates of machine 1, machine 2, and machine 3 are (2, 2), (2, 4), and (4, 2), respectively. The moving path between adjacent machines is calculated using the Dijkstra algorithm; when the logistics vehicle moves at a speed of 1 m / s, the position of the logistics vehicle at each moment can be calculated.

[0055] Step 2: Divide the industrial equipment into hierarchical structures according to their spatial location and control requirements;

[0056] Step 2.1: The time when all orders are executed is taken as the starting point t = 0. The position of each logistics vehicle at this time is loc c,0 , the production machine is located at a fixed position m ;

[0057] In this embodiment, the time when all orders begin to be executed is taken as the starting point t=0, the positions of the two logistics vehicles are (0, 0), and the positions of the three production machines are fixed as (2, 2), (2, 4) and (4, 2);

[0058] Step 2.2: Use the k-means method to cluster all industrial devices based on location; at the same time, use the wireless network coverage distance D as a constraint, and the distance between each type of industrial equipment and the center location of the class cannot be greater than D;

[0059] In this embodiment, the k-means method is used to cluster all industrial equipment based on location. At the same time, the wireless network coverage distance 3 is used as a constraint, and the distance between the industrial equipment in each category and the center position of the category cannot be greater than 3. Three categories are obtained. The first category includes machine 1 and two logistics vehicles, the second category includes machine 2, and the third category includes machine 3.

[0060] Step 2.3: Connect all industrial devices in each class to an edge node, which is placed at the center of the class; then connect all edge nodes to the cloud through a wired network to form an initial hierarchical structure S0;

[0061] Step 2.4: Considering the mobility of the equipment, verify at different times t=1, 2, ... T whether the initial hierarchical structure S0 can still connect to the industrial equipment under the premise of satisfying the distance D constraint; if at a certain time t, the hierarchical structure S0 cannot connect to all the equipment, then re-execute steps 2.2 and 2.3 to calculate the hierarchical structure S t ; Follow this step until all devices have hierarchical structures accessible within T time, and multiple hierarchical structures are calculated; at this point, the hierarchical structure of the device is formed;

[0062] In this embodiment, considering the mobility of the device, at different times t=1, 2, ..., 15, it is verified whether the initial hierarchical structure S0 can still connect to the industrial device under the premise of satisfying the distance 3 constraint; the verification result is yes, and S0 is the hierarchical structure of the device;

[0063] Step 3: Analyze the delay of industrial data transmission and calculation, bring the delay into the order production process, and recalculate the spatial position of industrial equipment over time;

[0064] In this embodiment, based on the hierarchical structure division of industrial equipment, the production process of all orders is simulated, and this process brings in the network bandwidth and the computing power of the edge nodes; according to the industrial data transmission volume and the computing amount, the transmission volume is divided by the network bandwidth, and the computing amount is divided by the computing power to obtain a transmission delay of 1 second and a computing delay of 10 seconds; the transmission delay and computing delay are brought into the order production process to obtain the updated and accurate spatial position of the industrial equipment changing over time.

[0065] Step 4: If the organizational structure of the current industrial equipment can support the access of the industrial equipment at the new location, the current structural division is the final organizational structure of the industrial equipment; otherwise, determine whether the structural adjustment has been made under the control frequency of the same industrial equipment. If not, adjust the structure and then re-execute step 3; if yes, reduce the control frequency and then re-execute step 2.

[0066] In this embodiment, under the premise of satisfying the distance 3 constraint, the industrial equipment at the updated position can still be connected to the edge node when the position is not updated, so the current structural division is the final organizational structure of the industrial equipment.

[0067] In this embodiment, the method for adjusting the structure is: finding an edge node within a distance of 3 from the new location of the device, and selecting the edge node with the smallest computational load for connection;

[0068] If there is no edge node within a distance of 3, the K-means method is used again to cluster the devices in all adjacent classes at the new location of the device; each new class is connected to an edge node.

[0069] In this embodiment, in order to make the network bandwidth and the computing power of the edge node meet the needs of production control, the minimum frequency for stable control is 80Hz. The current control frequency is 100Hz, so the control frequency is reduced to max{80,100-max{1,(100-80) 2 / 80}}=95Hz.

[0070] 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 or all of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope defined by the claims of the present invention.

Claims

1. A structured organization method for massive industrial equipment based on network computing and control collaboration, coordinating network, computing, and control resources, planning the organizational structure of massive industrial equipment, and enabling limited resources to support the full interconnection of massive industrial equipment, characterized by: The following steps are involved: Step 1: Ignoring network and computing delays, calculate the spatial position of industrial equipment over time based on order requirements; Step 2: Divide the industrial equipment into hierarchical structures according to their spatial location and control requirements; Step 3: Analyze the delay of industrial data transmission and calculation, bring the delay into the order production process, and recalculate the spatial position of industrial equipment over time; Step 4: If the organizational structure of the current industrial equipment can support the network access of the industrial equipment at the new location, the current structural division is the final organizational structure of the industrial equipment; Otherwise, determine whether the structure adjustment is performed under the control frequency of the same industrial equipment. If not, adjust the structure and then re-execute step 3; if yes, reduce the control frequency and then re-execute step 2.

2. According to claim 1, a structured organization method for massive industrial equipment based on network computing and control collaboration is characterized by: The step 1 does not consider the network and computing delays, and calculates the spatial position of the industrial equipment over time according to the order requirements, including the following steps: Step 1.1: Assume that industrial equipment includes immovable machines and movable logistics vehicles; the product corresponding to the i-th order needs n i The production can be completed after the jth process. Each process corresponds to a production machine. ij The corresponding production machine is m ij The execution time on the production machine is represented by t ij Indicates that the production machine cannot be moved, and the product is moved from one production machine to another by an intelligent logistics vehicle; Step 1.2: Determine the priority of the orders based on the sum of the execution time of all orders using each production machine and the sum of the execution time of all processes of each order; Step 1.3: All orders start to be executed at the same time, and each order uses each machine in turn according to the process sequence; when multiple orders start to use the same machine at the same time, the order with the highest priority will use the machine first, and other orders will wait; Step 1.4: Based on the order execution process, we know the order in which the order passes through each machine in turn, and the moving path between adjacent machines is calculated using the Dijkstra algorithm; the moving speed of the logistics vehicle is known, and the location of the cth logistics vehicle at time t is calculated. c,t .

3. According to claim 2, a method for structured organization of massive industrial equipment based on network computing and control collaboration is characterized by: The specific method of step 1.2 is: For any production machine m a , calculate the total execution time of all orders using the production machine, recorded as T a ; Traverse all T a , find T a The largest production machine a ; Use production machines m a All orders are added to the order set R a middle; For the order set R a For each order in , calculate the sum of the execution time of all processes of the order, denoted as TO i ; According to TO i The size is R a The order setting priority in TO i The larger it is, the higher the order priority; Remove machine m a , find the machine m' with the largest total execution time among the remaining machines a , and calculate the order set R' corresponding to the machine a , and then calculate the sum of the execution time of all processes of each order in the order set TO' i , according to TO' i Assigning priorities to orders; This process is repeated for the remaining machines until all orders are assigned priorities.

4. A method for structured organization of massive industrial equipment based on network computing and control collaboration according to claim 3, characterized in that: The step 2 divides the industrial equipment into hierarchical structures according to the spatial location and control requirements of the industrial equipment, including the following steps: Step 2.1: The time when all orders are executed is taken as the starting point t = 0. The position of each logistics vehicle at this time is loc c,0 , the production machine is located at a fixed position m ; Step 2.2: Use the k-means method to cluster all industrial devices based on location; at the same time, use the wireless network coverage distance D as a constraint, and the distance between each type of industrial equipment and the center location of the class cannot be greater than D; Step 2.3: Connect all industrial devices in each class to an edge node, which is placed at the center of the class; then connect all edge nodes to the cloud through a wired network to form an initial hierarchical structure S0; Step 2.4: Considering the mobility of the equipment, verify at different times t=1, 2, ... T whether the initial hierarchical structure S0 can still connect to the industrial equipment under the premise of satisfying the distance D constraint; if at a certain time t, the hierarchical structure S0 cannot connect to all the equipment, then re-execute steps 2.2 and 2.3 to calculate the hierarchical structure S t ; Follow this step until all devices have a hierarchical structure that can be accessed within T time, and multiple hierarchical structures are calculated; at this point, the hierarchical structure of the device is formed.

5. A method for structured organization of massive industrial equipment based on network computing and control collaboration according to claim 4, characterized in that: The step 3 analyzes the transmission and calculation delays, brings the delays into the order production process, and recalculates the spatial position of the industrial equipment over time, specifically including: Based on the hierarchical structure division of industrial equipment, the production process of all orders is simulated, and this process brings in the network bandwidth and the computing power of the edge nodes. According to the data transmission volume and the computing amount, the transmission volume is divided by the network bandwidth, and the computing amount is divided by the computing power to obtain the transmission delay and computing delay. The transmission delay and computing delay are brought into the order production process to obtain the spatial position of the updated industrial equipment that changes over time.

6. A method for structured organization of massive industrial equipment based on network computing and control collaboration according to claim 5, characterized in that: In step 4, if the current hierarchical structure can support the network access of industrial equipment at the new location, the current structural division is the final organizational structure of the industrial equipment, specifically including: under the premise of satisfying the distance D constraint, if the industrial equipment can still connect to the edge node when the location is not updated at the updated location, then the current structural division is the final organizational structure of the industrial equipment.

7. A method for structured organization of massive industrial equipment based on network computing and control collaboration according to claim 6, characterized in that: The structural adjustment described in step 4 includes the following steps: Find the edge node within the distance D of the new location of the device, and select the edge node with the smallest computing load to connect; If there is no edge node within the distance D, the K-means method is used again to cluster the devices in all adjacent classes of the new device location; each new class is connected to an edge node.

8. The method for structured organization of massive industrial equipment based on network computing and control collaboration according to claim 7 is characterized by: The control frequency reduction described in step 4 is as follows: In order to make the network bandwidth and the computing power of the edge node meet the needs of production control, under the premise of stable control, the control frequency is reduced to max{f,fc-max{1,(fc-f) 2 / f}}, where fc represents the current control frequency of the industrial equipment and f represents the minimum frequency under stable control.

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