A modeling method and device for temporal and spatial correlation characteristics of industrial control tasks

Abstract

The present invention relates to the field of data processing technology, and in particular to a modeling method and device for the spatiotemporal correlation characteristics of industrial control tasks. The method comprises: obtaining a spatiotemporal correlation topology diagram of control tasks in an industrial control process; extracting control task nodes and the temporal dependencies between control task nodes according to the spatiotemporal correlation topology diagram; establishing spatiotemporal constraint relationships between control tasks according to the types of control task nodes, and establishing a mapping association relationship between control tasks and computing-communication resources, thereby completing the modeling of the spatiotemporal correlation characteristics of industrial control tasks. The present invention constructs a spatiotemporal correlation model for industrial control tasks based on actual industrial production control scenarios. The spatiotemporal task association model is mainly used to characterize the temporal dependencies and spatial dependencies between industrial control tasks; the present invention establishes a mapping association relationship between control tasks and communication-computing resources, providing a task model foundation for achieving deep integration of industrial control-communication-computing.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a modeling method and device for temporal and spatial correlation characteristics of industrial control tasks. Background Art

[0002] Industrial production control processes are generally completed by the collaborative work of multiple control tasks. The spatiotemporal characteristics of these control tasks are generally reflected in temporal dependence and spatial dependence. The temporal characteristics mainly include real-time, periodicity, delay tolerance, and timing, while the spatial characteristics mainly include distribution and synchronization. That is, the control tasks involve the coordination of multiple physical locations and require multiple industrial equipment to work together and be executed synchronously in time.

[0003] Current research separates industrial control services from industrial networks, failing to deeply integrate communications and computing with industrial control production. This is primarily due to the lack of a direct mapping between the spatiotemporal characteristics of industrial control tasks and the inter-process dependencies between them, and network and computing resources. Currently, the dynamic allocation of communication and computing resources is optimized within this constraint, limiting the ability of heterogeneous resources to collaborate with industrial control production and hindering the provision of high-reliability assurance for industrial control production.

[0004] Existing computational task modeling methods generally ignore the relationships between tasks or only consider their temporal dependencies. However, industrial control tasks generally have strong spatiotemporal dependencies. Any interruption of a control task and unexpected execution delays can cause serious production accidents, further impacting factory production efficiency. Therefore, considering the spatiotemporal characteristics of industrial control tasks and establishing a relationship between industrial control tasks and computing and communication resources is essential to ensure safe and efficient industrial production.

[0005] Existing industrial task modeling methods, on the one hand, focus on modeling computationally intensive or latency-sensitive tasks, typically imposing large computational constraints and small execution delays, without considering the temporal dependencies between tasks. Traditional computational task modeling approaches typically provide specific task information, such as the task data volume, maximum task completion delay, the device index generating the task, and the task computational complexity. On the other hand, some researchers analyze and model the temporal dependencies between tasks, but do not simultaneously consider the spatial dependencies of tasks. Therefore, how to fully consider the temporal dependencies and spatial dependencies of industrial control tasks in real-world industrial control production scenarios, so that industrial control systems can rationally allocate communication and computing resources and efficiently complete control task computations within the constraints of temporal and spatial dependencies, is a challenging and research-oriented issue. Summary of the Invention

[0006] To address the technical problem in the prior art whereby only the maximum allowable delay of a task is used as the delay boundary, and the dynamic allocation of both communication and computing resources is optimized under this constraint, resulting in insufficient coordination between heterogeneous resources and industrial control production, the present invention provides a method and apparatus for modeling the spatiotemporal correlation characteristics of industrial control tasks. The technical solution is as follows:

[0007] On the one hand, a modeling method for spatiotemporal correlation characteristics of industrial control tasks is provided, characterized in that the method includes:

[0008] S1. Obtaining the spatiotemporal correlation topology of control tasks in the industrial control process;

[0009] S2. Extracting control task nodes and the temporal dependencies between control task nodes based on the spatiotemporal correlation topology graph;

[0010] S3. Establish spatiotemporal constraint relationships between control tasks according to the control task node types, and establish a mapping relationship between control tasks and computing-communication resources to complete the modeling of spatiotemporal correlation characteristics of industrial control tasks.

[0011] Optionally, in S1, obtaining a spatiotemporal correlation topology diagram of control tasks in the industrial control process includes:

[0012] Obtain the temporal correlation dependency topology of control tasks in the industrial control process;

[0013] Obtain the spatial dependency topology of different numbers of control subtasks in the industrial control process.

[0014] Optionally, in S2, extracting the control task nodes and the timing dependencies between the control task nodes according to the spatiotemporal correlation topology graph includes:

[0015] According to the time sequence correlation dependency topology graph, three types of control task nodes are extracted from the time sequence correlation dependency topology graph: source task node, intermediate task node and termination task node;

[0016] According to the spatial dependency topology graph with different numbers of control subtasks, the spatial dependency relationship of different numbers of control subtasks in the spatial dependency topology graph is extracted.

[0017] Optionally, control the task node, including:

[0018] Complex tasks or simple tasks;

[0019] When a task node is a simple task, it is an independent task and has no spatial dependency relationship.

[0020] When a task node is a complex task, the task node contains multiple subtasks, the subtasks are located in different physical locations, and there is a spatial collaborative relationship between the subtasks.

[0021] Optionally, in S3, spatiotemporal constraint relationships between control tasks are established according to the control task node types, and a mapping association relationship between control tasks and computing-communication resources is established, including:

[0022] Get the source task node S i , determine the type of source task node:

[0023] When the source task node S i For simple tasks, task information can be expressed as:

[0024]

[0025] Among them, a i Represents the amount of data, in bits; τ i represents the maximum allowable execution delay of the task; c i Represents the number of CPU cycles required to calculate 1 bit of data; Representative for S i Represents a list of post-task nodes; p i Representative task S i The probability of being assigned to the edge server for calculation; the completion delay of the task at this time includes: task waiting delay T i,wait and task processing delay T i,process , where the task processing delay includes the task transmission delay T i,tran and task computation delay T i,cmp ;

[0026] At this time, the source node task has no predecessor task node; the waiting delay of the source node task is T i,wait =0, the transmission delay and computation delay of the task are respectively related to the communication resources and computation resources allocated to the task by the system;

[0027] Preset p i The threshold value, when p i When it is greater than or equal to the threshold, the task S i Transmit to edge server for calculation; when p i When the value of task S is less than the threshold, i Calculated by equipment close to the industrial site;

[0028] Compute source task node S i Total completion delay:

[0029] T i,complet =T i,wait +T i,tran +Ti,cmp

[0030] The delay constraint that the source task node needs to meet is: T i,complet ≤τ i ;

[0031] When the source task node S i When it is a complex task, the task contains m subtasks, and its subtask set is represented as {S i,1 ,…,S i,m}; then for subtask S i,j The information is represented as:

[0032]

[0033] Based on the subtask information, calculate the waiting delay T for m subtasks i,wait =0, transmission delay and calculation delay;

[0034] Preset p i,j The threshold value, when p i,j When it is greater than or equal to the threshold, the subtask S i,j Transmit to edge server for calculation; when p i,j When the value of subtask S is less than the threshold, i,j Calculated by equipment close to the industrial site;

[0035] Compute subtask S i,j Total completion delay:

[0036] T i,j,complet =T i,j,wait +T i,j,tran +T i,j,cmp

[0037] Then for the source task node S i The total completion delay is:

[0038]

[0039] The spatial dependency constraints that need to be satisfied for each subtask are:

[0040]

[0041] Optionally, in S3, spatiotemporal constraint relationships between control tasks are established according to the control task node types, and a mapping association relationship between control tasks and computing-communication resources is established, including:

[0042] Get the intermediate task node M i , determine the type of intermediate task node:

[0043] When the intermediate task node M iFor simple tasks, task information can be expressed as:

[0044]

[0045] Among them, a i represents the amount of data, τ i represents the maximum allowed execution delay of the task, c i Represents the number of CPU cycles required to calculate 1 bit of data. Representative for M i Represents a list of predecessor task nodes, Representative for M i Represents a list of post-task nodes, p i Representative task M i The probability of being assigned to the edge server for calculation; the completion delay of the task includes: task waiting delay T i,wait and task processing delay T i,process , where the task processing delay includes the task transmission delay T i,tran and task computation delay T i,cmp The waiting delay of the intermediate task node is the maximum completion delay of the predecessor task node;

[0046] Calculate the transmission delay T of the intermediate task node i,tran , calculate the delay T i,cmp and the total completion delay T i,complet ;

[0047] For the intermediate task node M i , the delay constraints that need to be met are:

[0048] When the intermediate task node M i When it is a complex task, the task contains m subtasks, and its subtask set is represented as {M i,1 ,…,M i,m}; then for subtask M i,j The information is represented as:

[0049]

[0050] The waiting delay of the intermediate subtask node is the maximum completion delay of the predecessor task node;

[0051] Calculate the intermediate subtask node M i,j The transmission delay T i,j,tran , calculate the delay T i,j,cmp and the total completion delay T i,j,complet ;

[0052] For subtask node M i,j Completion delay T i,j,completThe delay constraints that need to be met are:

[0053]

[0054] Calculate the intermediate task node M i The final completion delay of , then the delay constraint that the final completion delay needs to meet is:

[0055] Optionally, in S3, spatiotemporal constraint relationships between control tasks are established according to the control task node types, and a mapping association relationship between control tasks and computing-communication resources is established, including:

[0056] Get the terminated task node D i , determine the type of the terminated task node:

[0057] When the task node D is terminated i For simple tasks, task information can be expressed as:

[0058]

[0059] Among them, a i represents the amount of data, τ i represents the maximum allowed execution delay of the task, c i Represents the number of CPU cycles required to calculate 1 bit of data. Representative for D i Represents the list of predecessor task nodes, p i Representative Task D i The probability of being assigned to the edge server for calculation; the completion delay of the task includes: task waiting delay T i,wait and task processing delay T i,process , where the task processing delay includes the task transmission delay T i,tran and task computation delay T i,cmp The waiting delay of the terminated task node is the maximum completion delay of the predecessor task node;

[0060] Calculate the termination task node D i The transmission delay T i,tran , calculate the delay T i,cmp and the total completion delay T i,complet ;

[0061] For subtask node D i The delay constraints that need to be met are:

[0062]

[0063] When the task node D is terminated i,j When it is a complex task, the task contains m subtasks, and its subtask set is represented as {Di,1 ,…,D i,m}; then for subtask D i,j The information is represented as:

[0064]

[0065] The waiting delay of the terminated task node is the maximum completion delay of the predecessor task node;

[0066] Compute the termination subtask node D i The transmission delay T i,j,tran , calculate the delay T i,j,cmp and the total completion delay T i,j,complet ;

[0067] For subtask node D i,j Completion delay T i,j,complet The delay constraints that need to be met are:

[0068]

[0069] Calculate the termination task node D i The final completion delay of , then the delay constraint that the final completion delay needs to meet is:

[0070] On the other hand, a modeling device for spatiotemporal correlation characteristics of industrial control tasks is provided. The device is applied to a modeling method for spatiotemporal correlation characteristics of industrial control tasks. The device includes:

[0071] A topology acquisition module is used to obtain the spatiotemporal correlation topology of control tasks in the industrial control process;

[0072] A data extraction module, configured to extract control task nodes and timing dependencies between control task nodes according to the spatiotemporal correlation topology graph;

[0073] The modeling module is used to establish the spatiotemporal constraint relationships between control tasks according to the control task node types, and to establish a mapping association relationship between control tasks and computing-communication resources, thereby completing the modeling of the spatiotemporal correlation characteristics of industrial control tasks.

[0074] On the other hand, a modeling device for the spatiotemporal correlation characteristics of industrial control tasks is provided, and the modeling device for the spatiotemporal correlation characteristics of industrial control tasks includes: a processor; a memory, on which computer-readable instructions are stored, and when the computer-readable instructions are executed by the processor, any one of the above-mentioned modeling methods for the spatiotemporal correlation characteristics of industrial control tasks is implemented.

[0075] On the other hand, a computer-readable storage medium is provided, wherein the storage medium stores at least one instruction, and the at least one instruction is loaded and executed by a processor to implement any of the above-mentioned modeling methods for spatiotemporal correlation characteristics of industrial control tasks.

[0076] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0077] In an embodiment of the present invention, a spatiotemporal correlation model of industrial control tasks is constructed for actual industrial production control scenarios. The spatiotemporal task correlation model is mainly used to characterize the temporal dependency and spatial dependency between industrial control tasks. Secondly, this patent establishes a mapping relationship between control tasks and communication-computing resources, providing a task model basis for realizing the deep integration of industrial control, communication and computing. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0079] Figure 1 A flow chart of a modeling method for spatiotemporal correlation characteristics of industrial control tasks provided by an embodiment of the present invention;

[0080] Figure 2 A spatiotemporal topological diagram of industrial control tasks provided by an embodiment of the present invention;

[0081] Figure 3 A diagram of the cloud-based PLC-based dual overhead crane material intelligent sorting platform provided by an embodiment of the present invention;

[0082] Figure 4 A time-dependent topological diagram of the dual overhead crane material intelligent sorting task provided in an embodiment of the present invention;

[0083] Figure 5 A block diagram of a modeling device for spatiotemporal correlation characteristics of industrial control tasks provided by an embodiment of the present invention;

[0084] Figure 6 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0085] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0086] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0087] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0088] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0089] The embodiment of the present invention provides a modeling method for the spatiotemporal correlation characteristics of industrial control tasks. The method can be implemented by a modeling device for the spatiotemporal correlation characteristics of industrial control tasks. The modeling device for the spatiotemporal correlation characteristics of industrial control tasks can be a terminal or a server. Figure 1 The flow chart of the modeling method for the spatiotemporal correlation characteristics of industrial control tasks is shown in Figure 1 As shown, the modeling method for the spatiotemporal correlation characteristics of industrial control tasks proposed in the present invention may include the following steps:

[0090] S1. Obtaining the spatiotemporal correlation topology of control tasks in the industrial control process;

[0091] In a feasible implementation, in S1, obtaining a spatiotemporal correlation topology diagram of control tasks in the industrial control process includes:

[0092] Obtain the temporal correlation dependency topology of control tasks in the industrial control process;

[0093] Obtain the spatial dependency topology of different numbers of control subtasks in the industrial control process.

[0094] In a feasible implementation, Figure 2 As shown in Figure 1, it is a time-space correlation topology diagram of an industrial control process. Figure (a) is a directed acyclic graph G =<N,E> , which represents the control task nodes in the industrial control process and the timing dependencies between the control task nodes; as shown in Figure (b), there are four undirected connected graphs and A spatial dependency topology diagram representing the number of different control subtasks in an industrial control process.

[0095] S2. Extracting control task nodes and the temporal dependencies between control task nodes based on the spatiotemporal correlation topology graph;

[0096] In a feasible implementation, in S2, extracting the control task nodes and the timing dependencies between the control task nodes according to the spatiotemporal correlation topology graph includes:

[0097] According to the time sequence correlation dependency topology graph, three types of control task nodes are extracted from the time sequence correlation dependency topology graph: source task node, intermediate task node and termination task node;

[0098] According to the spatial dependency topology graph with different numbers of control subtasks, the spatial dependency relationship of different numbers of control subtasks in the spatial dependency topology graph is extracted.

[0099] In a feasible implementation, controlling the task node includes:

[0100] Complex tasks or simple tasks;

[0101] When a task node is a simple task, it is an independent task and has no spatial dependency relationship.

[0102] When a task node is a complex task, the task node contains multiple subtasks, the subtasks are located in different physical locations, and there is a spatial collaborative relationship between the subtasks.

[0103] In a feasible implementation, the directed acyclic graph G includes three types of control task nodes: source task node, intermediate task node, and terminal task node. The weight of edge e is τ i Represents the maximum allowed execution delay constraint of the task node at the starting point of the directed edge. Each task node belongs to a task type: complex task or simple task. When the task node is a simple task, the task node is an independent task and there is no spatial dependency relationship between the task nodes. When the task node is a complex task, the task node contains multiple subtasks, which are located in different physical locations and have spatial coordination relationships between the subtasks, as shown in the four undirected connected graphs in Figure (b). and They represent the spatial dependencies of different numbers of subtasks. For a complex task, all subtasks share the maximum allowable execution delay constraint of the same task node. Therefore, the edge weight of the undirected graph is the maximum allowable execution delay of the task node.

[0104] Assume that an industrial control process P contains N task nodes, and the task node set is S = {S1,…,M n ,…,D N}, 1 5G base station, and 1 remote computing server (whose computing power is stronger than that of local computing devices). The present invention establishes spatiotemporal constraints between control tasks based on the types of control task nodes, and establishes an association between control tasks and computing-communication resources.

[0105] S3. Establish spatiotemporal constraint relationships between control tasks according to the control task node types, and establish a mapping relationship between control tasks and computing-communication resources to complete the modeling of spatiotemporal correlation characteristics of industrial control tasks.

[0106] In a feasible implementation, in S3, spatiotemporal constraints are established between control tasks according to the control task node types, and a mapping relationship between control tasks and computing-communication resources is established, including:

[0107] Get the source task node S i , determine the type of source task node:

[0108] When the source task node S i For simple tasks, task information can be expressed as:

[0109]

[0110] Among them, a i Represents the amount of data, in bits; τ i represents the maximum allowable execution delay of the task; c i Represents the number of CPU cycles required to calculate 1 bit of data; Representative for S i Represents a list of post-task nodes; p i Representative task S i The probability of being assigned to the edge server for calculation; the completion delay of the task at this time includes: task waiting delay T i,wait and task processing delay T i,process , where the task processing delay includes the task transmission delay T i,tran and task computation delay T i,cmp ;

[0111] The source node task has no predecessor task node; the waiting delay of the source node task is T i,wait = 0, the transmission delay and computational delay of the task are respectively related to the communication resources and computational resources allocated to the task by the system, and are calculated according to the following formulas (1) and (2):

[0112]

[0113] Preset p i The threshold is 0.5, when p i When it is greater than or equal to the threshold, the task S i Transmitted to the edge server for calculation, its transmission delay is T i,tran The amount of data transmitted i , channel status snr i, and the bandwidth resources w allocated by the 5G base station i Related, and calculate the delay T i,cmp The computing resources f allocated to the task by the edge server i,E Related; when p i When the value of task S is less than the threshold, i Calculated by the equipment close to the industrial site, its transmission delay T i,tran =0, and calculate the delay T i,cmp Computational resources of local computing devices i Related;

[0114] Calculate the source task node S according to the following formula (3): i Total completion delay:

[0115] T i,complet =T i,wait +T i,tran +T i,cmp (3)

[0116] The delay constraint that the source task node needs to meet is: T i,complet ≤τ i .

[0117] When the source task node S i When it is a complex task, the task contains m subtasks, and its subtask set is represented as {S i,1 ,…,S i,m}; then for subtask S i,j The information is represented as:

[0118]

[0119] Among them, a i,j represents the amount of data (unit: bit), τ i represents the maximum allowed execution delay of the task, c i,j Represents the number of CPU cycles required to calculate 1 bit of data. Representative for S i,j Represents a list of post-task nodes, Representative and subtask S i,j A list of subtasks with spatial dependencies, p i,j Representative task S i,j The probability of being assigned to the edge server for calculation.

[0120] Calculate the waiting delay T for m subtasks according to the following formulas (4) and (5): i,wait =0, transmission delay and calculation delay:

[0121]

[0122] When p i,j When it is greater than or equal to the threshold, the subtask S i,j Transmitted to the edge server for calculation, its transmission delay is T i,j,tran The amount of data transmitted i,j , channel status snr i,j , and the bandwidth resources w allocated by the 5G base station i,j Related, and calculate the delay T i,j,cmp The computing resources f allocated to the task by the edge server i,j,E Related; when p i,j When the value of subtask S is less than the threshold, i,j Calculated by the equipment close to the industrial site, its transmission delay T i,j,tran =0, and calculate the delay T i,k,cmp Computational resources of local computing devices i,j Related;

[0123] Compute subtask S i,j Total completion delay:

[0124] T i,j,complet =T i,j,wait +T i,j,tran +T i,j,cmp (6)

[0125] Then for the source task node S i The total completion delay is:

[0126]

[0127] in, The spatial dependency constraints that need to be satisfied for each subtask are:

[0128]

[0129] Where Δ represents the subtask S i,j The tolerance of the processing delay of the task and the execution delay deviation of other subtasks in the list with spatial dependencies. Since the subtasks with spatial dependencies have time synchronization, the constraint formula (8) needs to be satisfied to ensure the spatial dependencies between complex subtasks. Similarly, the source task node S i The delay constraint that needs to be met is: T i,complet ≤τ i .

[0130] In a feasible implementation, in S3, spatiotemporal constraints are established between control tasks according to the control task node types, and a mapping relationship between control tasks and computing-communication resources is established, including:

[0131] Get the intermediate task node Mi , determine the type of intermediate task node:

[0132] When the intermediate task node M i For simple tasks, task information can be expressed as:

[0133]

[0134] Among them, a i represents the amount of data, τ i represents the maximum allowed execution delay of the task, c i Represents the number of CPU cycles required to calculate 1 bit of data. Representative for M i Represents a list of predecessor task nodes, Representative for M i Represents a list of post-task nodes, p i Representative task M i The probability of being assigned to the edge server for calculation; the completion delay of the task includes: task waiting delay T i,wait and task processing delay T i,process , where the task processing delay includes the task transmission delay T i,tran and task computation delay T i,cmp Since there is a timing dependency between task nodes, the waiting delay of the intermediate task node is the maximum completion delay of the predecessor task node:

[0135]

[0136] According to the calculation method of formula (1), formula (2) and formula (3), the transmission delay T of the intermediate task node is calculated i,tran , calculate the delay T i,cmp and the total completion delay T i,complet ;

[0137] For the intermediate task node M i , the delay constraints that need to be met are: is the intermediate task node M i The set of all preceding task nodes, such as {1,2,…,i-1}.

[0138] When the intermediate task node M i When it is a complex task, the task contains m subtasks, and its subtask set is represented as {M i,1 ,…,M i,m}; then for subtask M i,j The information is represented as:

[0139]

[0140] Among them, a i,j represents the amount of data (unit: bit), τ i Represents subtask M i,j Maximum allowed execution delay, c i,j Represents the number of CPU cycles required to calculate 1 bit of data. Represents M i,j A list of predecessor task nodes, Represents M i,j A list of post-task nodes, Representative and subtask M i,j A list of subtasks with spatial dependencies, p i,j Representative task M i,j The probability of being assigned to the edge server for computation. The waiting delay of the intermediate subtask node is the maximum completion delay of the predecessor task node:

[0141]

[0142] According to the calculation method of formula (4), formula (5) and formula (6), calculate the intermediate subtask node M i,j The transmission delay T i,j,tran , calculate the delay T i,j,cmp and the total completion delay T i,j,complet ;

[0143] For subtask node M i,j Completion delay T i,j,complet The delay constraints that need to be met are:

[0144]

[0145] Calculate the intermediate task node M according to the calculation method of formula (7) i The final completion delay of , then the delay constraint that the final completion delay needs to meet is: is the intermediate task node M i The set of all preceding task nodes, such as {1,2,…,i-1}.

[0146] In a feasible implementation, in S3, spatiotemporal constraints are established between control tasks according to the control task node types, and a mapping relationship between control tasks and computing-communication resources is established, including:

[0147] Get the terminated task node D i , determine the type of the terminated task node:

[0148] When the task node D is terminated i For simple tasks, task information can be expressed as:

[0149]

[0150] Among them, a i represents the amount of data, τ i represents the maximum allowed execution delay of the task, c i Represents the number of CPU cycles required to calculate 1 bit of data. Representative for D i Represents the list of predecessor task nodes, p i Representative Task D i The probability of being assigned to the edge server for calculation; the completion delay of the task includes: task waiting delay T i,wait and task processing delay T i,process , where the task processing delay includes the task transmission delay T i,tran and task computation delay T i,cmp Since there is a timing dependency between task nodes, the waiting delay of the terminated task node is the maximum completion delay of the predecessor task node:

[0151]

[0152] Similarly, according to the calculation method of formula (1), formula (2) and formula (3), the termination task node D is calculated. i The transmission delay T i,tran , calculate the delay T i,cmp and the total completion delay T i,complet ;

[0153] For the termination task node D i The delay constraints that need to be met are:

[0154]

[0155] u is the termination task node D i The set of all preceding task nodes, such as {1,2,…,i-1}.

[0156] When the task node D is terminated i,j When it is a complex task, the task contains m subtasks, and its subtask set is represented as {D i,1 ,…,D i,m}; then for subtask D i,j The information is represented as:

[0157]

[0158] Among them, a i,j represents the amount of data (unit: bit), τ i Represents subtask D i,j Maximum allowed execution delay, ci,j Represents the number of CPU cycles required to calculate 1 bit of data. Representative D i,j A list of predecessor task nodes, Representative and subtask D i,j A list of subtasks with spatial dependencies, p i,j Representative Task D i,j The probability of being assigned to the edge server for calculation.

[0159] The waiting delay of the terminated task node is the maximum completion delay of the predecessor task node:

[0160]

[0161] Similarly, according to the calculation method of formula (4), formula (5) and formula (6), the termination subtask node D is calculated. i The transmission delay T i,j,tran , calculate the delay T i,j,cmp and the total completion delay T i,j,complet ;

[0162] For subtask node D i,j Completion delay T i,j,complet The delay constraints that need to be met are:

[0163]

[0164] Calculate the termination task node D according to formula (7) i The final completion delay of , then the delay constraint that the final completion delay needs to meet is: It is the intermediate task node D i The set of all preceding task nodes, such as {1,2,…,i-1}.

[0165] In a feasible implementation, the present invention builds a small cloud-based PLC-based dual overhead crane material intelligent sorting physical platform according to the above design scheme, such as Figure 3As shown. The main control process completed by the physical platform for intelligent sorting of materials by dual overhead cranes based on cloud PLC is: controlling the dual overhead cranes to realize intelligent sorting according to the color of chess pieces on the uniformly moving conveyor belt. This patent uses the designed spatiotemporal correlation model of industrial control tasks to theoretically model the control process of the overhead crane. The tasks included in the control process of the overhead crane material sorting are: image acquisition task S1, position acquisition task S2, image recognition task M3, three-axis slide position prediction task M4, chess piece position prediction task M5, and robotic arm sorting task D6. Among them, the image acquisition task and the position acquisition task can be regarded as source task nodes, the image recognition task, the three-axis slide position prediction task and the chess piece position prediction task can be regarded as intermediate task nodes, and the robotic arm sorting task is the termination task node. Each task node decides whether to send the computing task to the edge server for computing through 5G wireless communication, or directly calculate it by the local industrial computer, based on its own task load. The timing-dependent topology of the task node is as follows. Figure 4 shown.

[0166] The specific process of the double overhead crane material sorting control process is as follows: the image acquisition task S1 is collected in real time by the camera. The image acquisition task processes the real-time video stream into picture frames. The picture frames are used as the input of the image recognition task M3, and the position of the chess piece on the conveyor belt is identified by the YOLO detection algorithm; the position acquisition task S2 mainly needs to collect the physical position of the three-axis slide (X axis, Y axis, Z axis), and input the physical position of the three-axis slide collected by S2 to the position prediction task M4. Task M4 calculates the physical position of the three-axis slide according to the image recognition position, conveyor belt speed, and the physical position of the three-axis slide (X axis, Y axis, Z axis); Task M5 predicts the chess piece movement position based on the chess piece position identified by task M3 and the conveyor belt movement speed. The industrial computer executes task D6, which includes three subtasks {D 6,1 ,D 6,2 ,D 6,3}, according to the chess piece position and the three-axis slide position predicted by the previous tasks M5 and M4, drive the three servos to control the X axis, Y axis, and Z axis to sort the chess pieces. Subtask D 6,1 Drive servo 1 to control the X axis, subtask D 6,2 Drive servo 2 to control the Y axis, subtask D 6,3 The driving servo 3 controls the Z axis. These three servos have a spatial dependency and work together to complete the chess piece sorting action.

[0167] In the embodiment of the present invention, this patent constructs a spatiotemporal correlation model of industrial control tasks for actual industrial production control scenarios. The spatiotemporal task correlation model is mainly used to characterize the temporal dependency and spatial dependency between industrial control tasks. Secondly, this patent establishes a mapping correlation relationship between control tasks and communication-computing resources, filling the gap in the theoretical modeling of spatiotemporal correlation of industrial control tasks. Under the spatiotemporal constraints of control tasks, the correlation relationship between control tasks and communication-computing resources is established, which is committed to breaking the current situation of network-industry separation in factories and providing a task model foundation for realizing the deep integration of industrial control-communication-computing.

[0168] Figure 5 This is a block diagram of a modeling device 300 for temporal and spatial correlation features of industrial control tasks according to an exemplary embodiment. The device 300 is used for a modeling method for temporal and spatial correlation features of industrial control tasks. Figure 5 The device includes a topology acquisition module 310, a data extraction module 320, and a modeling module 330.

[0169] A topology map acquisition module 310 is used to acquire a spatiotemporal correlation topology map of control tasks in an industrial control process;

[0170] A data extraction module 320 is configured to extract control task nodes and timing dependencies between control task nodes according to the spatiotemporal correlation topology graph;

[0171] The modeling module 330 is used to establish the spatiotemporal constraint relationship between control tasks according to the control task node type, and to establish a mapping association relationship between control tasks and computing-communication resources, thereby completing the modeling of the spatiotemporal association characteristics of industrial control tasks.

[0172] Optionally, the topology map acquisition module 310 is used to obtain a time sequence correlation dependency topology map of control tasks in the industrial control process;

[0173] Obtain the spatial dependency topology of different numbers of control subtasks in the industrial control process.

[0174] Optionally, the topology map acquisition module 310 is configured to extract three types of control task nodes in the timing correlation dependency topology map: a source task node, an intermediate task node, and a termination task node;

[0175] According to the spatial dependency topology graph with different numbers of control subtasks, the spatial dependency relationship of different numbers of control subtasks in the spatial dependency topology graph is extracted.

[0176] Optionally, control the task node, including:

[0177] Complex tasks or simple tasks;

[0178] When a task node is a simple task, it is an independent task and has no spatial dependency relationship.

[0179] When a task node is a complex task, the task node contains multiple subtasks, the subtasks are located in different physical locations, and there is a spatial collaborative relationship between the subtasks.

[0180] Optionally, the modeling module 330 is used to obtain the source task node S i , determine the type of source task node:

[0181] When the source task node S i For simple tasks, task information can be expressed as:

[0182]

[0183] Among them, a i Represents the amount of data, in bits; τ i represents the maximum allowable execution delay of the task; c i Represents the number of CPU cycles required to calculate 1 bit of data; Representative for S i Represents a list of post-task nodes; p i Representative task S i The probability of being assigned to the edge server for calculation; the completion delay of the task at this time includes: task waiting delay T i,wait and task processing delay T i,process , where the task processing delay includes the task transmission delay T i,tran and task computation delay T i,cmp ;

[0184] At this time, the source node task has no predecessor task node; the waiting delay of the source node task is T i,wait =0, the transmission delay and computation delay of the task are respectively related to the communication resources and computation resources allocated to the task by the system;

[0185] Preset p i The threshold value, when p i When it is greater than or equal to the threshold, the task S i Transmit to edge server for calculation; when p i When the value of task S is less than the threshold, i Calculated by equipment close to the industrial site;

[0186] Compute source task node S i Total completion delay:

[0187] T i,complet =T i,wait +T i,tran +T i,cmp

[0188] The delay constraint that the source task node needs to meet is: T i,complet ≤τ i ;

[0189] When the source task node S i When it is a complex task, the task contains m subtasks, and its subtask set is represented as {S i,1 ,…,S i,m}; then for subtask S i,j The information is represented as:

[0190]

[0191] Based on the subtask information, calculate the waiting delay T for m subtasks i,wait =0, transmission delay and calculation delay;

[0192] Preset p i,j The threshold value, when p i,j When it is greater than or equal to the threshold, the subtask S i,j Transmit to edge server for calculation; when p i,j When the value of subtask S is less than the threshold, i,j Calculated by equipment close to the industrial site;

[0193] Compute subtask S i,j Total completion delay:

[0194] T i,j,complet =T i,j,wait +T i,j,tran +T i,j,cmp

[0195] Then for the source task node S i The total completion delay is:

[0196]

[0197] The spatial dependency constraints that need to be satisfied for each subtask are:

[0198]

[0199] Optionally, the modeling module 330 is used to obtain the intermediate task node M i , determine the type of intermediate task node:

[0200] When the intermediate task node M i For simple tasks, task information can be expressed as:

[0201]

[0202] Among them, ai represents the amount of data, τ i represents the maximum allowed execution delay of the task, c i Represents the number of CPU cycles required to calculate 1 bit of data. Representative for M i Represents a list of predecessor task nodes, Representative for M i Represents a list of post-task nodes, p i Representative task M i The probability of being assigned to the edge server for calculation; the completion delay of the task includes: task waiting delay T i,wait and task processing delay T i,process , where the task processing delay includes the task transmission delay T i,tran and task computation delay T i,cmp The waiting delay of the intermediate task node is the maximum completion delay of the predecessor task node;

[0203] Calculate the transmission delay T of the intermediate task node i,tran , calculate the delay T i,cmp and the total completion delay T i,complet ;

[0204] For the intermediate task node M i , the delay constraints that need to be met are:

[0205] When the intermediate task node M i When it is a complex task, the task contains m subtasks, and its subtask set is represented as {M i,1 ,…,M i,m}; then for subtask M i,j The information is represented as:

[0206]

[0207] The waiting delay of the intermediate subtask node is the maximum completion delay of the predecessor task node;

[0208] Calculate the intermediate subtask node M i,j The transmission delay T i,j,tran , calculate the delay T i,j,cmp and the total completion delay T i,j,complet ;

[0209] For subtask node M i,j Completion delay T i,j,complet The delay constraints that need to be met are:

[0210]

[0211] Calculate the intermediate task node Mi The final completion delay of , then the delay constraint that the final completion delay needs to meet is:

[0212] Optionally, the modeling module 330 is used to obtain the termination task node D i , determine the type of the terminated task node:

[0213] When the task node D is terminated i For simple tasks, task information can be expressed as:

[0214]

[0215] Among them, a i represents the amount of data, τ i represents the maximum allowed execution delay of the task, c i Represents the number of CPU cycles required to calculate 1 bit of data. Representative for D i Represents the list of predecessor task nodes, p i Representative Task D i The probability of being assigned to the edge server for calculation; the completion delay of the task includes: task waiting delay T i,wait and task processing delay T i,process , where the task processing delay includes the task transmission delay T i,tran and task computation delay T i,cmp The waiting delay of the terminated task node is the maximum completion delay of the predecessor task node;

[0216] Calculate the termination task node D i The transmission delay T i,tran , calculate the delay T i,cmp and the total completion delay T i,complet ;

[0217] For subtask node D i The delay constraints that need to be met are:

[0218]

[0219] When the task node D is terminated i,j When it is a complex task, the task contains m subtasks, and its subtask set is represented as {D i,1 ,…,D i,m}; then for subtask D i,j The information is represented as:

[0220]

[0221] The waiting delay of the terminated task node is the maximum completion delay of the predecessor task node;

[0222] Compute the termination subtask node D i The transmission delay T i,j,tran , calculate the delay T i,j,cmp and the total completion delay T i,j,complet ;

[0223] For subtask node D i,j Completion delay T i,j,complet The delay constraints that need to be met are:

[0224]

[0225] Calculate the termination task node D i The final completion delay of , then the delay constraint that the final completion delay needs to meet is:

[0226] In an embodiment of the present invention, this patent constructs a spatiotemporal correlation model of industrial control tasks for actual industrial production control scenarios. The spatiotemporal task correlation model is mainly used to characterize the temporal dependency and spatial dependency between industrial control tasks. Secondly, this patent establishes a mapping relationship between control tasks and communication-computing resources, providing a task model foundation for realizing the deep integration of industrial control, communication and computing.

[0227] Figure 6 FIG. 1 is a schematic diagram of a structure of a modeling device for temporal and spatial correlation characteristics of industrial control tasks provided by an embodiment of the present invention. Figure 6 As shown, the modeling equipment for the spatiotemporal correlation characteristics of industrial control tasks may include the above Figure 5 The modeling apparatus for the temporal and spatial correlation characteristics of industrial control tasks shown in FIG. Optionally, the modeling apparatus 410 for the temporal and spatial correlation characteristics of industrial control tasks may include a first processor 2001 .

[0228] Optionally, the modeling device 410 for the spatiotemporal correlation characteristics of industrial control tasks may further include a memory 2002 and a transceiver 2003 .

[0229] The first processor 2001, the memory 2002 and the transceiver 2003 may be connected via a communication bus.

[0230] The following combination Figure 6 The components of the modeling device 410 for the spatiotemporal correlation characteristics of industrial control tasks are described in detail:

[0231] The first processor 2001 is the control center of the modeling device 410 for the spatiotemporal correlation characteristics of industrial control tasks, and can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 is one or more central processing units (CPUs), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement an embodiment of the present invention, such as one or more microprocessors (digital signal processors, DSPs) or one or more field programmable gate arrays (FPGAs).

[0232] Optionally, the first processor 2001 may execute various functions of the modeling device 410 for the spatiotemporal correlation characteristics of industrial control tasks by running or executing a software program stored in the memory 2002 and calling data stored in the memory 2002 .

[0233] In a specific implementation, as an embodiment, the first processor 2001 may include one or more CPUs, such as Figure 6 CPU0 and CPU1 are shown in FIG.

[0234] In a specific implementation, as an embodiment, the modeling device 410 for the spatiotemporal correlation characteristics of industrial control tasks may also include multiple processors, such as Figure 6 1 and 2. The first processor 2001 and the second processor 2004 are shown in FIG. Each of these processors can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). A processor herein can refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0235] The memory 2002 is used to store the software program for executing the solution of the present invention, and is controlled by the first processor 2001 for execution. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0236] Alternatively, the memory 2002 may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 2002 may be integrated with the first processor 2001 or exist independently and access the memory 2002 through the interface circuit ( Figure 6 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0237] The transceiver 2003 is used to communicate with a network device or a terminal device.

[0238] Optionally, the transceiver 2003 may include a receiver and a transmitter ( Figure 6 (not shown separately in the figure). The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0239] Optionally, the transceiver 2003 may be integrated with the first processor 2001 or may exist independently and be connected to the first processor 2001 through the interface circuit ( Figure 6 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0240] It should be noted that Figure 6 The structure of the modeling device 410 for the spatiotemporal correlation characteristics of industrial control tasks shown in the figure does not constitute a limitation on the router. The actual knowledge structure recognition device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0241] In addition, the technical effects of the modeling device 410 for the spatiotemporal correlation characteristics of industrial control tasks can refer to the technical effects of the modeling method for the spatiotemporal correlation characteristics of industrial control tasks described in the above method embodiment, and will not be repeated here.

[0242] It should be understood that the first processor 2001 in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0243] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0244] The above embodiments can be implemented in whole or in part through software, hardware (such as circuits), firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable sensor. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired method (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, or tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0245] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0246] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0247] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0248] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0249] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0250] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention.

[0251] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A modeling method for the spatiotemporal correlation characteristics of industrial control tasks, characterized by: The method comprises: S1. Obtaining the spatiotemporal correlation topology of control tasks in the industrial control process; S2. Extracting control task nodes and timing dependencies between control task nodes according to the spatiotemporal correlation topology graph; S3. Establish spatiotemporal constraints between control tasks based on their node types, and establish mapping relationships between control tasks and computing and communication resources to complete the modeling of spatiotemporal correlation characteristics of industrial control tasks. Wherein, in said S1, obtaining the spatiotemporal correlation topology of the control tasks in the industrial control process includes: Obtain the temporal correlation dependency topology of control tasks in the industrial control process; Obtain the spatial dependency topology of different numbers of control subtasks in industrial control processes; In S2, extracting the control task nodes and the timing dependencies between the control task nodes according to the spatiotemporal correlation topology graph includes: According to the timing association dependency topology graph, three control task nodes in the timing association dependency topology graph are extracted: a source task node, an intermediate task node, and a termination task node; Extracting spatial dependency relationships between different numbers of control subtasks in the spatial dependency topology graph according to the spatial dependency topology graph of different numbers of control subtasks; In S3, the spatiotemporal constraint relationships between control tasks are established according to the control task node types, and a mapping association relationship between control tasks and computing-communication resources is established, including: Get the source task node , determine the type of the source task node: When the source task node For simple tasks, task information can be expressed as: ， in, Represents the amount of data, in bits; Represents the maximum allowed execution delay of the task; Represents the number of CPU cycles required to calculate 1 bit of data; Representative for Represents a list of post-task nodes; Representative tasks The probability of being assigned to the edge server for calculation; the completion delay of the task at this time includes: task waiting delay and task processing latency , where task processing delay includes task transmission delay and task computation latency ; At this time, the source node task has no predecessor task node; the waiting delay of the source node task , then the transmission delay and computation delay of the task are respectively related to the communication resources and computation resources allocated by the system to the task; Preset The threshold value, when When the value is greater than or equal to the threshold, the task Transmit to edge server for calculation; when When the value is less than the threshold, the task Calculated by equipment close to the industrial site; Compute source task node Total completion delay: ； The delay constraints that need to be met by the source task node are: ; When the source task node For complex tasks, the task includes subtasks, and its subtask set is represented as ; then for the subtask The information is represented as: ； Based on the subtask information, calculate The waiting delay of each subtask , transmission delay and computation delay; Preset The threshold value, when When the threshold is greater than or equal to the subtask Transmit to edge server for calculation; when When the value is less than the threshold, the subtask Calculated by equipment close to the industrial site; Computation subtasks Total completion delay: ； For the source task node The total completion delay is: ； The spatial dependency constraints that need to be satisfied for each subtask are: 。 2. The modeling method for spatiotemporal correlation characteristics of industrial control tasks according to claim 1 is characterized in that: The control task node includes: Complex tasks or simple tasks; When a task node is a simple task, it is an independent task and has no spatial dependency relationship. When a task node is a complex task, the task node contains multiple subtasks, the subtasks are located in different physical locations, and there is a spatial collaborative relationship between the subtasks.

3. The modeling method for spatiotemporal correlation characteristics of industrial control tasks according to claim 1 is characterized in that: In S3, the spatiotemporal constraint relationships between control tasks are established according to the control task node types, and a mapping association relationship between control tasks and computing-communication resources is established, including: Get intermediate task nodes , determine the type of the intermediate task node: When the intermediate task node For simple tasks, task information can be expressed as: ， in, Represents the amount of data, Represents the maximum allowed execution delay of the task, Represents the number of CPU cycles required to calculate 1 bit of data. Representative for Represents a list of predecessor task nodes, Representative for Represents a list of post-task nodes, Representative tasks The probability of being assigned to the edge server for calculation; the task completion delay includes: task waiting delay and task processing latency , where task processing delay includes task transmission delay and task computation latency The waiting delay of the intermediate task node is the maximum completion delay of the predecessor task node; Calculate the transmission delay of intermediate task nodes , calculate the delay and total completion delay ; For intermediate task nodes , the delay constraints that need to be met are: ; When the intermediate task node For complex tasks, the task includes subtasks, and its subtask set is represented as ; then for the subtask The information is represented as: ； The waiting delay of the intermediate subtask node is the maximum completion delay of the predecessor task node; Calculate intermediate subtask nodes Transmission delay , calculate the delay and total completion delay ; For subtask nodes Completion delay The delay constraints that need to be met are: ； Compute intermediate task nodes The final completion delay of , then the delay constraint that the final completion delay needs to meet is: .

4. The modeling method for spatiotemporal correlation characteristics of industrial control tasks according to claim 3 is characterized in that: In S3, the spatiotemporal constraint relationships between control tasks are established according to the control task node types, and a mapping association relationship between control tasks and computing-communication resources is established, including: Get the terminated task node , determine the type of the terminated task node: When terminating a task node For simple tasks, task information can be expressed as: ； in, Represents the amount of data, Represents the maximum allowed execution delay of the task, Represents the number of CPU cycles required to calculate 1 bit of data. Representative for Represents a list of predecessor task nodes, Representative tasks The probability of being assigned to the edge server for calculation; the task completion delay includes: task waiting delay and task processing latency , where task processing delay includes task transmission delay and task computation latency The waiting delay of the terminated task node is the maximum completion delay of the predecessor task node; Compute the termination task node Transmission delay , calculate the delay and total completion delay ; For subtask nodes The delay constraints that need to be met are: ； When terminating a task node For complex tasks, the task includes subtasks, and its subtask set is represented as ; then for the subtask The information is represented as: ； The waiting delay of the terminated task node is the maximum completion delay of the predecessor task node; Compute the termination subtask node Transmission delay , calculate the delay and total completion delay ; For subtask nodes Completion delay The delay constraints that need to be met are: ； Compute the termination task node The final completion delay of , then the delay constraint that the final completion delay needs to meet is: .

5. A modeling device for spatiotemporal correlation characteristics of industrial control tasks, wherein the modeling device for spatiotemporal correlation characteristics of industrial control tasks is used to implement the modeling method for spatiotemporal correlation characteristics of industrial control tasks according to any one of claims 1 to 4, characterized in that: The device comprises: A topology acquisition module is used to obtain the spatiotemporal correlation topology of control tasks in the industrial control process; A data extraction module, configured to extract control task nodes and timing dependencies between control task nodes according to the spatiotemporal correlation topology graph; The modeling module is used to establish the spatiotemporal constraint relationships between control tasks according to the control task node types, and to establish a mapping association relationship between control tasks and computing-communication resources, thereby completing the modeling of the spatiotemporal correlation characteristics of industrial control tasks.

6. A modeling device for the spatiotemporal correlation characteristics of industrial control tasks, characterized by: The modeling device for the spatiotemporal correlation characteristics of industrial control tasks includes: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the modeling method for spatiotemporal correlation characteristics of industrial control tasks according to any one of claims 1 to 4 is implemented.

7. A computer-readable storage medium, wherein at least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement the modeling method for spatiotemporal correlation characteristics of industrial control tasks according to any one of claims 1 to 4.

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