Modeling method and device for space-time correlation characteristics of industrial control task

Abstract

The invention relates to the technical field of data processing, in particular to a modeling method and device for space-time correlation characteristics of an industrial control task. The method comprises the following steps: acquiring a space-time correlation topological graph of a control task in an industrial control process; extracting control task nodes and time sequence dependence among the control task nodes according to the space-time correlation topological graph; and according to the node types of the control tasks, respectively establishing a space-time constraint relationship between the control tasks, and establishing a mapping association relationship between the control tasks and calculation-communication resources, thereby completing modeling for space-time association characteristics of the industrial control tasks. According to the method, an industrial control task space-time correlation model is constructed for an actual industrial production control scene, and the space-time task correlation model is mainly used for representing time sequence dependence and space dependence between industrial control tasks; according to the method, the mapping incidence relation between the control task and the communication-computing resources is established, and a task model basis is provided for deep fusion 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] The industrial production control process is generally completed by the collaboration of multiple control tasks. The temporal and spatial characteristics of these control tasks are generally reflected in timing dependency and spatial dependency. 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] In current research, industrial control services are separated from industrial networks, and communication-computing and industrial control production have not been deeply integrated. This is mainly reflected in the fact that the temporal and spatial characteristics between industrial control tasks and the correlation between processes have not been directly mapped to network resources and computing resources. At present, only the maximum allowable delay of the task is used as the delay boundary, and the dynamic allocation of communication and computing resources is optimized under this constraint, which makes the coordination ability between heterogeneous resources and industrial control production insufficient, making it difficult to provide high reliability for industrial control production.

[0004] Existing computational task modeling methods generally do not consider the relationship between tasks or only consider the temporal dependency of tasks. However, industrial control tasks generally have strong temporal and spatial correlations. Any interruption of control tasks and unexpected execution delays will cause serious production accidents, thereby affecting factory production efficiency. Therefore, considering the temporal and spatial characteristics of industrial control tasks and establishing the relationship between industrial control tasks and computing-communication resources is necessary to ensure safe and efficient industrial production.

[0005] For existing industrial task modeling methods, on the one hand, they model computationally intensive tasks or delay-sensitive tasks, generally setting larger computational constraints and smaller task execution delays, without considering the temporal correlation between tasks. Traditional computational task modeling methods generally give specific information about the task, such as the amount of task data, the maximum task completion delay, the device index that generates the task, and the task computational complexity; on the other hand, some scholars also analyze and model the temporal correlation between tasks, but do not consider the spatial dependency of tasks at the same time. Therefore, how to combine the real industrial control production scenario, fully consider the temporal dependency and spatial correlation of industrial control tasks, so that the industrial control system can reasonably allocate communication-computing resources and efficiently complete the control task calculation under the constraints of temporal and spatial correlation is challenging and of research significance. Summary of the invention

[0006] In order to solve the technical problem that in the prior art, only the maximum allowable delay of the task is used as the delay boundary, and the dynamic allocation of communication and computing resources is optimized under this constraint, resulting in insufficient coordination between heterogeneous resources and industrial control production, the embodiment of the present invention provides a modeling method and device for 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, obtain the spatiotemporal correlation topology of the control tasks in the industrial control process;

[0009] S2, extracting control task nodes and the timing dependencies between control task nodes according to the spatiotemporal correlation topology graph;

[0010] S3. Establish the 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 the 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 map 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 timing correlation dependency topology graph, three types of control task nodes in the timing correlation dependency topology graph are extracted: 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 task nodes, including:

[0018] Complex tasks or simple tasks;

[0019] When a task node is a simple task, the task node is an independent task and there is no spatial dependency relationship between the task nodes;

[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 coordination relationship between the subtasks.

[0021] Optionally, in S3, time-space constraint relationships between control tasks are established respectively 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 a simple task, the task information can be expressed as:

[0024]

[0025] Among them, a i Represents the amount of data, in bits; 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; 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; at this time, the task completion delay 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 by the system to the task;

[0027] Preset p i The threshold value is p i When it is greater than or equal to the threshold, task S i Transmitted to the edge server for calculation; when p i When it is less than the threshold, task S 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 is 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; when p i,j When it is less than the threshold, subtask S 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, time-space constraint relationships between control tasks are established respectively 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 a simple task, the 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. For M i Represents a list of predecessor task nodes, 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 The 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, then the delay constraint that the final completion delay needs to satisfy is:

[0055] Optionally, in S3, time-space constraint relationships between control tasks are established respectively 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 a simple task, the 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. 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 termination 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 by {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] Calculate 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 The 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, then the delay constraint that the final completion delay needs to satisfy is:

[0070] On the other hand, a modeling device for time-space correlation characteristics of industrial control tasks is provided, and the device is applied to a modeling method for time-space correlation characteristics of industrial control tasks, and the device includes:

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

[0072] A data extraction module, used for extracting 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, so as to complete the modeling of the spatiotemporal association characteristics of industrial control tasks.

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

[0075] On the other hand, a computer-readable storage medium is provided, 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 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 foundation 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 temporal and spatial correlation features of industrial control tasks provided by an embodiment of the present invention;

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

[0081] Figure 3 A diagram of a physical platform for intelligent sorting of materials using a double overhead crane based on cloud-based PLC provided in an embodiment of the present invention;

[0082] Figure 4 A timing dependency topology 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 temporal and spatial correlation characteristics of industrial control tasks provided by an embodiment of the present invention;

[0084] Figure 6 A schematic diagram of the structure 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 "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be 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 more clear, 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, which can be implemented by a modeling device for the spatiotemporal correlation characteristics of industrial control tasks, and 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 by the present invention may include the following steps:

[0090] S1, obtain the spatiotemporal correlation topology of the control tasks in the industrial control process;

[0091] In a feasible implementation, in S1, obtaining a spatiotemporal correlation topology diagram of control tasks in an 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 map 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 4 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 timing dependencies between control task nodes according to 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 timing correlation dependency topology graph, three types of control task nodes in the timing correlation dependency topology graph are extracted: 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 manner, controlling the task node includes:

[0100] Complex tasks or simple tasks;

[0101] When a task node is a simple task, the task node is an independent task and there is no spatial dependency relationship between the task nodes;

[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 coordination relationship between the subtasks.

[0103] In a feasible implementation, the directed acyclic graph G includes three control task nodes: source task node, intermediate task node, and termination 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 there is a spatial coordination relationship between the subtasks, as shown in the 4 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, 1 remote computing server (whose computing power is stronger than that of local computing devices). The present invention establishes the spatiotemporal constraint relationship between control tasks according to the control task node type, and establishes the association relationship between control tasks and computing-communication resources.

[0105] S3. Establish the 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 the spatiotemporal correlation characteristics of industrial control tasks.

[0106] In a feasible implementation, in S3, the spatiotemporal constraint relationships between the control tasks are respectively established according to the control task node types, and a mapping association relationship between the control tasks and the 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 a simple task, the task information can be expressed as:

[0109]

[0110] Among them, a i Represents the amount of data, in bits; 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; 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; at this time, the task completion delay 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 computing delay of the task are respectively related to the communication resources and computing 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, 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 it is less than the threshold, task S i Calculated by the equipment near the industrial site, its transmission delay T i,tran = 0, and calculate the delay T i,cmp The computing resources of the local computing device 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. 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 computation.

[0120] According to the following formula (4) and formula (5), the waiting delay T for m subtasks is calculated: 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 it is less than the threshold, subtask S i,j Calculated by the equipment near the industrial site, its transmission delay T i,j,tran = 0, and calculate the delay T i,k,cmp The computing resources of the local computing device 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] Among them, Δ represents the subtask S i,j The tolerance of the processing delay of the source task node S is the deviation from the execution delay 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. i The delay constraint that needs to be met is: T i,complet ≤τ i .

[0130] In a feasible implementation, in S3, the spatiotemporal constraint relationships between the control tasks are respectively established according to the control task node types, and a mapping association relationship between the control tasks and the 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 a simple task, the 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. For M i Represents a list of predecessor task nodes, 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: It is the intermediate task node M i The set of all predecessor 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 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 calculation. 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 The completion delay T i,j,complet The delay constraints that need to be met are:

[0144]

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

[0146] In a feasible implementation, in S3, the spatiotemporal constraint relationships between the control tasks are respectively established according to the control task node types, and a mapping association relationship between the control tasks and the 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 a simple task, the 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. 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 predecessor 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 by {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 computation.

[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 The 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, then the delay constraint that the final completion delay needs to satisfy is: It is the intermediate task node D i The set of all predecessor task nodes, such as {1,2,…,i-1}.

[0165] In a feasible implementation mode, 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 of 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 the 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 according to its own task load. The timing-dependent topological structure of the task node is shown 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 according to 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 positions and the three-axis slide positions predicted by the previous tasks M5 and M4, the three servos are driven 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 cooperate 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 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 1 is a block diagram of a modeling device 300 for time-space correlation characteristics of industrial control tasks according to an exemplary embodiment. The device 300 is used for a modeling method for time-space correlation characteristics of industrial control tasks. Figure 5 The device includes a topology acquisition module 310, a data extraction module 320 and a modeling module 330. Among them:

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

[0170] A data extraction module 320, for extracting 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 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, so as to complete the modeling of the spatiotemporal association characteristics of industrial control tasks.

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

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

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

[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 task nodes, including:

[0177] Complex tasks or simple tasks;

[0178] When a task node is a simple task, the task node is an independent task and there is no spatial dependency relationship between the task nodes;

[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 coordination 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 a simple task, the task information can be expressed as:

[0182]

[0183] Among them, a i Represents the amount of data, in bits; 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; 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; at this time, the task completion delay 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 by the system to the task;

[0185] Preset p i The threshold value is p i When it is greater than or equal to the threshold, task S i Transmitted to the edge server for calculation; when p i When it is less than the threshold, task S 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 is 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; when p i,j When it is less than the threshold, subtask S 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 a simple task, the 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. For M i Represents a list of predecessor task nodes, 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 The 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, then the delay constraint that the final completion delay needs to satisfy 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 a simple task, the 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. 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 termination 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 by {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] Calculate 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 The 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, then the delay constraint that the final completion delay needs to satisfy is:

[0226] 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 relationship between control tasks and communication-computing resources, providing a task model foundation for realizing the deep integration of industrial control-communication-computing.

[0227] Figure 6 is a schematic diagram of a structure of a modeling device for the spatiotemporal correlation characteristics of industrial control tasks provided by an embodiment of the present invention, such as Figure 6 As shown, the modeling device for the spatiotemporal correlation characteristics of industrial control tasks may include the above Figure 5 The modeling device for the temporal and spatial correlation characteristics of industrial control tasks shown in the figure. Optionally, the modeling device 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 also 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] Combine the following Figure 6 The components of the modeling device 410 for the spatiotemporal correlation characteristics of industrial control tasks are specifically introduced:

[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 processor or a general 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 the embodiments of the present invention, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (field programmable gate arrays, FPGAs).

[0232] Optionally, the first processor 2001 may perform various functions of the modeling device 410 for the spatiotemporal correlation characteristics of the industrial control task 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 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). The processor here 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 to be executed by the first processor 2001. The specific implementation method can refer to the above method embodiment, which will not be repeated here.

[0236] Optionally, the memory 2002 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices 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 compressed optical 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 the 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 may exist independently, and may 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.

[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 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 may 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 identification 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 the processor may also be 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 by software, hardware (such as circuits), firmware or any other combination. When implemented by 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 programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable sensors. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (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 one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0245] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[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. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

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

[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 in the form of 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 part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention.

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

Claims

1. A modeling method for the spatiotemporal correlation characteristics of industrial control tasks, characterized in that: The method comprises: S1, obtain the spatiotemporal correlation topology of the 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 the 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 the spatiotemporal correlation characteristics of industrial control tasks.

2. The modeling method for the spatiotemporal correlation characteristics of industrial control tasks according to claim 1 is characterized in that: In S1, obtaining a spatiotemporal correlation topology diagram of 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 map of different numbers of control subtasks in the industrial control process.

3. The modeling method for the spatiotemporal correlation characteristics of industrial control tasks according to claim 2 is characterized in that: 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 types of 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; According to the spatial dependency topology graph of different numbers of control subtasks, spatial dependency relationships of different numbers of control subtasks in the spatial dependency topology graph are extracted.

4. The modeling method for the spatiotemporal correlation characteristics of industrial control tasks according to claim 3 is characterized in that: The control task node includes: Complex tasks or simple tasks; When a 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 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 coordination relationship between the subtasks.

5. The modeling method for the spatiotemporal correlation characteristics of industrial control tasks according to claim 4 is characterized in that: In S3, the time-space constraint relationship between the control tasks is established according to the control task node type, and the mapping association relationship between the control tasks and the computing-communication resources is established, including: Get the source task node S i , determine the type of the source task node: When the source task node S i For a simple task, the task information can be expressed as: Among them, a i Represents the amount of data, in bits; 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; 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; at this time, the task completion delay 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 ; 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 by the system to the task; Preset p i The threshold value is p i When it is greater than or equal to the threshold, task S i Transmitted to the edge server for calculation; when p i When it is less than the threshold, task S i Calculated by equipment close to the industrial site; Compute source task node S i Total completion delay: T i,complet =T i,wait +T i,tran +T i,cmp The delay constraint that the source task node needs to meet is: T i,complet ≤τ i ; 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: Based on the subtask information, calculate the waiting delay T for m subtasks i,wait =0, transmission delay and calculation delay; Preset p i,j The threshold value is 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; when p i,j When it is less than the threshold, subtask S i,j Calculated by equipment close to the industrial site; Compute subtask S i,j Total completion delay: T i,j,complet =T i,j,wait +T i,j,tran +T i,j,cmp Then for the source task node S i The total completion delay is: The spatial dependency constraints that need to be satisfied for each subtask are:

6. The modeling method for the spatiotemporal correlation characteristics of industrial control tasks according to claim 5 is characterized in that: In S3, the time-space constraint relationship between the control tasks is established according to the control task node type, and the mapping association relationship between the control tasks and the computing-communication resources is established, including: Get the intermediate task node M i , determine the type of the intermediate task node: When the intermediate task node M i For a simple task, the task information can be expressed as: 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. For M i Represents a list of predecessor task nodes, For M i Represents the 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; 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 ; For the intermediate task node M i , the delay constraints that need to be met are: 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: The waiting delay of the intermediate subtask node is the maximum completion delay of the predecessor task node; 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 ; For subtask node M i,j The completion delay T i,j,complet The delay constraints that need to be met are: Calculate the intermediate task node M i The final completion delay, then the delay constraint that the final completion delay needs to satisfy is:

7. The modeling method for the spatiotemporal correlation characteristics of industrial control tasks according to claim 6 is characterized in that: In S3, the time-space constraint relationship between the control tasks is established according to the control task node type, and the mapping association relationship between the control tasks and the computing-communication resources is established, including: Get the terminated task node D i , determine the type of the terminated task node: When the task node D is terminated i For a simple task, the task information can be expressed as: 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. 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 termination task node is the maximum completion delay of the predecessor task node; 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 ; For subtask node D i The delay constraints that need to be met are: 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 by {D i,1 ,…,D i,m }; then for subtask D i,j The information is represented as: The waiting delay of the terminated task node is the maximum completion delay of the predecessor task node; Calculate 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 ; For subtask node D i,j The completion delay T i,j,complet The delay constraints that need to be met are: Calculate the termination task node D i The final completion delay, then the delay constraint that the final completion delay needs to satisfy is:

8. A modeling device for time-space correlation characteristics of industrial control tasks, the modeling device for time-space correlation characteristics of industrial control tasks is used to implement the modeling method for time-space correlation characteristics of industrial control tasks as described in any one of claims 1-7, characterized in that: The device comprises: A topology acquisition module is used to acquire the spatiotemporal correlation topology of the control tasks in the industrial control process; A data extraction module, used for extracting 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, so as to complete the modeling of the spatiotemporal association characteristics of industrial control tasks.

9. A modeling device for the spatiotemporal correlation characteristics of industrial control tasks, characterized in that: 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 method according to any one of claims 1 to 7 is implemented.

10. 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 any of the above-mentioned modeling methods for spatiotemporal correlation characteristics of industrial control tasks.

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