Edge computing task scheduling method and device, medium and equipment

Abstract

The invention provides an edge computing task scheduling method and device, a medium and equipment. The method comprises the steps of obtaining task information corresponding to an edge computing task generated by each industrial field device in an industrial edge computing architecture; obtaining computing power information of each edge computing server in the industrial edge computing architecture; determining a task scheduling moment; establishing a multi-objective optimization model; the multi-objective optimization model comprises optimization objectives and constraint conditions, and the optimization objectives comprise the minimum task average energy consumption, the minimum weighted average task response time delay and the minimum load balance degree of the server cluster; according to the constraint condition, solving an optimal scheduling strategy meeting the optimization target; and when the task scheduling moment arrives, scheduling the edge computing task according to the optimal scheduling strategy. According to the method, the average task response time delay and the average energy consumption can be effectively reduced, and the load balancing degree of the edge computing server cluster can also be optimized.

Description

Technical Field

[0001] The present invention relates to the field of task scheduling technology, and in particular to an edge computing task scheduling method, device, medium, and equipment. Background Art

[0002] The calculations involved in current industrial scenarios are becoming more and more complex, so edge computing servers are set up on the industrial field equipment side. However, since there are multiple industrial field devices on the industrial field equipment side and multiple edge computing servers, how to schedule the tasks generated by each industrial field device to each edge computing server is a problem that needs to be solved. Summary of the invention

[0003] In response to at least one of the above technical problems, an embodiment of the present invention provides an edge computing task scheduling method, device, medium, and equipment.

[0004] According to the first aspect, the edge computing task scheduling method provided by the embodiment of the present invention includes:

[0005] Obtain task information corresponding to edge computing tasks generated by each industrial field device in the industrial edge computing architecture;

[0006] Obtain computing power information of each edge computing server in the industrial edge computing architecture;

[0007] Determine the task scheduling time;

[0008] Establishing a multi-objective optimization model; wherein the multi-objective optimization model includes optimization objectives and constraints, and the optimization objectives include minimizing the average energy consumption of tasks, minimizing the weighted average task response delay, and minimizing the load balancing degree of the server cluster;

[0009] According to the constraint conditions, solving the optimal scheduling strategy that meets the optimization goal;

[0010] When the task scheduling time arrives, the edge computing task is scheduled according to the optimal scheduling strategy.

[0011] In one embodiment, the task information corresponding to each edge computing task includes the data volume, computing volume, expected completion time of the task, power uploaded from the industrial field equipment to the edge computing server, computing load factor, memory capacity requirements, hard disk capacity requirements, required computing resource types and the demand for each computing resource.

[0012] In one embodiment, the computing power information of each edge computing server includes: the processing speed of the edge computing server, the communication bandwidth, the additional power when processing tasks, the memory capacity, the hard disk capacity, the types of computing resources, and the amount of each computing resource provided per unit time.

[0013] In one embodiment, the objective function includes:

[0014]

[0015] f3=min S Ω

[0016] Wherein, f1 is the average energy consumption of the minimized task; f2 is the minimized weighted average task response delay; f3 is the load balancing degree of the minimized server cluster; N is the number of edge computing tasks; M is the number of edge computing servers; i is the number of edge computing tasks; j is the number of edge computing servers; E ij is the uplink transmission energy consumption of the i-th edge computing task offloaded from the industrial field device to the j-th edge computing server; x ij is a decision variable. If the i-th edge computing task is assigned to the j-th edge computing server, then x ij is 1, otherwise x ij is 0; d i is the expected completion time of the i-th edge computing task; w is the task scheduling time; T ij S is the uplink transmission time of offloading the i-th edge computing task to the j-th edge computing server; Ω is the load balancing degree of the edge computing server cluster.

[0017] In one embodiment, the uplink transmission energy consumption E of the i-th edge computing task offloaded from the industrial field device to the j-th edge computing server is ij The calculation formula is:

[0018]

[0019] The uplink transmission time T of the i-th edge computing task offloaded from the industrial field device to the j-th edge computing server ij The calculation formula is:

[0020]

[0021] Among them, D i is the data volume of the i-th edge computing task; B i is the computational effort of the ith edge computing task; C jis the processing speed of the jth edge computing server; R ij P is the data transmission rate of the i-th edge computing task uploaded from the industrial field device to the j-th edge computing server; i is the power of the i-th edge computing task; p j is the execution power of the jth edge computing server;

[0022] in, σ 2 is the Gaussian noise power inside the channel, Los = d -α , d is the physical distance between the industrial field device of the i-th edge computing task and the j-th edge computing server, and α is 2.

[0023] In one embodiment, the load balancing degree S of the edge computing server cluster is Ω The calculation formula includes:

[0024]

[0025] Among them, Ω j is the total computing load of the jth edge computing server; is the average computing load of all edge computing servers; Φ i is the computing load of the i-th edge computing task; ε i is the computing load factor of the i-th edge computing task; B i is the computational amount of the i-th edge computing task.

[0026] In one embodiment, the constraints include:

[0027]

[0028]

[0029] Among them, K ij is the network bandwidth allocated between the i-th edge computing task and the j-th edge computing server; W j is the communication bandwidth of the jth edge computing server; M fi is the memory capacity requirement of the i-th edge computing task; M Fj is the memory capacity of the jth edge computing server; S fi is the hard disk capacity requirement of the i-th edge computing task; S Fj is the hard disk capacity of the jth edge computing server; i is the computing resource demand vector corresponding to the i-th edge computing task, which includes various computing resources required by the i-th edge computing task and the demand for each computing resource; Lj is the computing resource capability vector corresponding to the j-th edge computing server, and the computing resource capability vector includes various computing resources possessed by the j-th edge computing server and the provision amount of each computing resource per unit time.

[0030] According to the second aspect, the edge computing task scheduling device provided by the embodiment of the present invention includes:

[0031] The first acquisition module is used to obtain task information corresponding to the edge computing tasks generated by each industrial field device in the industrial edge computing architecture;

[0032] A second acquisition module is used to obtain computing capacity information of each edge computing server in the industrial edge computing architecture;

[0033] A time determination module is used to determine the task scheduling time;

[0034] A model building module, used to build a multi-objective optimization model; wherein the multi-objective optimization model includes optimization objectives and constraints, and the optimization objectives include minimizing the average energy consumption of tasks, minimizing the weighted average task response delay, and minimizing the load balancing degree of the server cluster;

[0035] A strategy solving module, used to solve the optimal scheduling strategy that meets the optimization goal according to the constraint conditions;

[0036] The strategy execution module is used to schedule the edge computing task according to the optimal scheduling strategy when the task scheduling time arrives.

[0037] According to a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, enables the computer to execute the method provided in the first aspect.

[0038] According to a fourth aspect, an embodiment of the present invention provides a computing device, including a memory and a processor, wherein the memory stores executable code, and when the processor executes the executable code, the method provided in the first aspect is implemented.

[0039] The edge computing task scheduling method, device, medium, and equipment provided by the embodiment of the present invention obtain the task information corresponding to each edge computing task and the computing power information of each edge computing server, and establish a multi-objective optimization model based on this information. The multi-objective optimization model includes optimization objectives and constraints. The optimization objectives include minimizing the average energy consumption of the task, minimizing the weighted average task response delay, and minimizing the load balancing degree of the server cluster; then according to the constraints, the optimal scheduling strategy that meets the optimization objectives is solved. It can be seen that the optimal scheduling strategy obtained by the embodiment of the present invention can effectively reduce the response delay and average energy consumption of the edge computing task, and can also optimize the load balancing degree of the edge computing server cluster. Moreover, the embodiment of the present invention takes into account the demand for computing resource information of the edge computing task and the computing resource information that can be provided by the edge computing server, and is suitable for the industrial edge computing architecture in the industrial field. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a flowchart of an edge computing task scheduling method in one embodiment of the present invention;

[0041] Figure 2 This is a structural block diagram of an edge computing task scheduling device in one embodiment of the present invention. DETAILED DESCRIPTION

[0042] In a first aspect, an embodiment of the present invention provides an edge computing task scheduling method, see Figure 1 The method comprises the following steps S110 to S150:

[0043] S110, obtaining task information corresponding to edge computing tasks generated by each industrial field device in the industrial edge computing architecture;

[0044] Among them, the task information corresponding to each edge computing task may include the data volume, computing volume, expected completion time of the task, power uploaded from industrial field equipment to the edge computing server, computing load factor, memory capacity requirements, hard disk capacity requirements, required computing resource types and the demand for each computing resource.

[0045] For example, for the i-th edge computing task, its task information includes the data volume D i The amount of calculation is B i , the expected completion time is d i The power of uploading the edge computing task from the industrial field equipment to the edge computing server is P i , calculate the load factor as ε i , Required memory capacity M fi , Required hard disk capacity S fi, the required computing resource information. The required computing resource information O i It can be expressed as: i =[o i1 ,o i2 ,…,o iu ] T , u is the number of types of computing resources, o iu It represents the demand of the i-th edge computing task on the u-th type of computing resources. When the i-th edge computing task does not need a type of computing resources, the demand of this type of computing resources is 0, and T is the inverted sign.

[0046] S120, obtaining computing capacity information of each edge computing server in the industrial edge computing architecture;

[0047] Among them, the computing power information of each edge computing server may include: the processing speed of the edge computing server, the communication bandwidth, the additional power when processing tasks, the memory capacity, the hard disk capacity, the types of computing resources, and the amount of each computing resource provided per unit time.

[0048] For example, for the jth edge computing server, the computing capacity information includes the processing speed C j , the communication bandwidth is W j , the additional power when processing the task is p j 、The memory capacity is M Fj , with a hard disk capacity of S Fj , computing resource information. Wherein, the computing resource information can be expressed as: L j =[l j1 ,l j2 ,…,l ju ] T ; u is the number of types of computing resources, l ju It indicates the amount of computing resources that the j-th edge computing server can provide per unit time on the u-th type of computing resources. When the j-th edge computing server does not have a type of computing resources, the amount of computing resources provided by this type of computing resources is 0.

[0049] It is understandable that each edge computing server can process multiple edge computing tasks at the same time, and the task processing speed of the same edge computing server is the same; the communication between industrial field devices and edge computing servers is based on orthogonal frequency division multiple access technology, and the edge computing server can communicate with multiple industrial field devices at the same time.

[0050] For example, the number of collected edge computing tasks is 100, the number of edge computing servers is 5, the number of types of computing resources such as GPU and NPU required by the task is 5, the number of types of computing resources such as GPU and NPU possessed by the edge computing server is 5, the data transmission power of industrial field equipment is 0.5-2.5W, the additional power of edge computing server for calculation is 50-250W, the internal noise power of the channel is -174dBm, and the distance between all industrial field equipment and edge computing servers is 100m. The value range of the task information of edge computing tasks and the computing capacity information of edge computing servers is shown in Table 1 below.

[0051] Table 1 Value ranges of edge computing tasks and edge computing server related information

[0052] information Parameter setting range Data volume of edge computing tasks 0.1-0.5MB The computational load of edge computing tasks 50-250MI Memory capacity required for edge computing tasks 5-10MB Hard disk capacity required for edge computing tasks 256-512MB Expected completion time of edge computing tasks 8:00:01-8:00:05 The processing speed of the server 1000-3500MIPS Network bandwidth of edge computing servers 8MHz Memory capacity of edge computing servers 8, 16, 32GB Hard disk capacity of edge computing servers 256,512,1024GB Resource modules of edge computing servers 20,000-40,000 FLOPS

[0053] S130, determining the task scheduling time;

[0054] The task scheduling time can be represented as w. For example, it is set to 8:00:00.

[0055] S140, establishing a multi-objective optimization model; wherein the multi-objective optimization model includes optimization objectives and constraints, and the optimization objectives include minimizing the average energy consumption of tasks, minimizing the weighted average task response delay, and minimizing the load balancing degree of the server cluster;

[0056] Wherein, the objective function may include:

[0057]

[0058] f3=min S Ω

[0059] Wherein, f1 is the average energy consumption of the minimized task; f2 is the minimized weighted average task response delay; f3 is the load balancing degree of the minimized server cluster; N is the number of edge computing tasks; M is the number of edge computing servers; i is the number of edge computing tasks; j is the number of edge computing servers; E ij is the uplink transmission energy consumption of the i-th edge computing task offloaded from the industrial field device to the j-th edge computing server; x ij is a decision variable. If the i-th edge computing task is assigned to the j-th edge computing server, then x ij is 1, otherwise x ij is 0; d i is the expected completion time of the i-th edge computing task; w is the task scheduling time; T ijS is the uplink transmission time of offloading the i-th edge computing task to the j-th edge computing server; Ω is the load balancing degree of the edge computing server cluster.

[0060] It can be understood that the overall optimization objective function is f=f1+f2+f3.

[0061] Furthermore, the uplink transmission energy consumption E of the i-th edge computing task offloaded from the industrial field device to the j-th edge computing server is ij The calculation formula can be:

[0062]

[0063] The uplink transmission time T of the i-th edge computing task offloaded from the industrial field device to the j-th edge computing server ij The calculation formula can be:

[0064]

[0065] Among them, D i is the data volume of the i-th edge computing task; B i is the computational effort of the ith edge computing task; C j is the processing speed of the jth edge computing server; R ij P is the data transmission rate of the i-th edge computing task uploaded from the industrial field device to the j-th edge computing server; i is the power of the i-th edge computing task; p j is the execution power of the jth edge computing server;

[0066] in, σ 2 is the Gaussian noise power inside the channel, Los = d -α , d is the physical distance between the industrial field device of the i-th edge computing task and the j-th edge computing server, and α is 2.

[0067] Among them, Los is a distance-based function constructed based on the wireless interference model of the cellular wireless environment.

[0068] Furthermore, the load balancing degree S of the edge computing server cluster Ω The calculation formula may include:

[0069]

[0070] Among them, Ω j is the total computing load of the jth edge computing server; is the average computing load of all edge computing servers; Φ i is the computing load of the i-th edge computing task; ε i is the computing load factor of the i-th edge computing task; B i is the computational amount of the i-th edge computing task.

[0071] It should be noted that S Ω ≥0, so S Ω The larger the value, the more unbalanced the task distribution in the edge computing server cluster. Ideally, the computing load value of each edge computing server is When , the load balancing degree of the edge computing server cluster reaches the optimal level.

[0072] In one embodiment, the constraints may include:

[0073]

[0074]

[0075] Among them, K ij is the network bandwidth allocated between the i-th edge computing task and the j-th edge computing server; W j is the communication bandwidth of the jth edge computing server; M fi is the memory capacity requirement of the i-th edge computing task; M Fj is the memory capacity of the jth edge computing server; S fi is the hard disk capacity requirement of the i-th edge computing task; S Fj is the hard disk capacity of the jth edge computing server; i is the computing resource demand vector corresponding to the i-th edge computing task, which includes various computing resources required by the i-th edge computing task and the demand for each computing resource; L j is the computing resource capability vector corresponding to the j-th edge computing server, and the computing resource capability vector includes various computing resources possessed by the j-th edge computing server and the provision amount of each computing resource per unit time.

[0076] It can be seen that constraint (1) indicates that any edge computing task can only be assigned to one edge computing server. Constraint (2) indicates that the sum of the bandwidths assigned to the same edge computing server is equal to the network bandwidth of the edge computing server. Constraint (3) indicates that the sum of the memory requirements assigned to the same edge computing server cannot exceed the total memory of the edge computing server. Constraint (4) indicates that the sum of the hard disk requirements assigned to the same edge computing server cannot exceed the total hard disk capacity of the edge computing server. Constraint (5) indicates that the sum of the resource requirements assigned to the same edge computing server cannot exceed the total computing module resources of the edge computing server. Constraints (6) and (7) represent the value range of the decision variables.

[0077] S150, solving the optimal scheduling strategy that meets the optimization goal according to the constraint conditions;

[0078] For example, the optimal scheduling strategy may be solved by a solver.

[0079] The scheduling strategy refers to the matching relationship between edge computing tasks and edge computing servers, that is, how each edge computing task is allocated to each edge computing server.

[0080] S160. When the task scheduling time arrives, schedule the edge computing task according to the optimal scheduling strategy.

[0081] It can be seen that the embodiment of the present invention specifically describes the relevant information of edge computing tasks and edge computing servers, establishes a multi-objective optimization model with the goal of minimizing the average energy consumption of the task system, minimizing the weighted average task response delay, and minimizing the load balancing degree of the server cluster, and solves the multi-objective optimization model to obtain the optimal scheduling strategy, which can effectively reduce the response delay and average energy consumption of edge computing tasks, and can also optimize the load balancing degree of edge computing server clusters. Moreover, the embodiment of the present invention takes into account the demand for computing resource information of edge computing tasks and the computing resource information that can be provided by edge computing servers, and is suitable for industrial edge computing architectures in the industrial field.

[0082] In a second aspect, an embodiment of the present invention provides an edge computing task scheduling device, see 2, the device 100 includes:

[0083] A first acquisition module 110 is used to acquire task information corresponding to edge computing tasks generated by each industrial field device in the industrial edge computing architecture;

[0084] A second acquisition module 120 is used to obtain computing capacity information of each edge computing server in the industrial edge computing architecture;

[0085] A time determination module 130, used to determine the task scheduling time;

[0086] A model building module 140 is used to build a multi-objective optimization model; wherein the multi-objective optimization model includes optimization objectives and constraints, and the optimization objectives include minimizing the average energy consumption of tasks, minimizing the weighted average task response delay, and minimizing the load balancing degree of the server cluster;

[0087] A strategy solving module 150, used to solve the optimal scheduling strategy that meets the optimization goal according to the constraint conditions;

[0088] The strategy execution module 160 is used to schedule the edge computing task according to the optimal scheduling strategy when the task scheduling time arrives.

[0089] In one embodiment, the task information corresponding to each edge computing task includes the data volume, computing volume, expected completion time of the task, power uploaded from the industrial field equipment to the edge computing server, computing load factor, memory capacity requirements, hard disk capacity requirements, required computing resource types and the demand for each computing resource.

[0090] In one embodiment, the computing power information of each edge computing server includes: the processing speed of the edge computing server, the communication bandwidth, the additional power when processing tasks, the memory capacity, the hard disk capacity, the types of computing resources, and the amount of each computing resource provided per unit time.

[0091] In one embodiment, the objective function includes:

[0092]

[0093] f3=min S Ω

[0094] Wherein, f1 is the average energy consumption of the minimized task; f2 is the minimized weighted average task response delay; f3 is the load balancing degree of the minimized server cluster; N is the number of edge computing tasks; M is the number of edge computing servers; i is the number of edge computing tasks; j is the number of edge computing servers; E ij is the uplink transmission energy consumption of the i-th edge computing task offloaded from the industrial field device to the j-th edge computing server; x ij is a decision variable. If the i-th edge computing task is assigned to the j-th edge computing server, then x ij is 1, otherwise x ij is 0; d i is the expected completion time of the i-th edge computing task; w is the task scheduling time; Tij S is the uplink transmission time of offloading the i-th edge computing task to the j-th edge computing server; Ω is the load balancing degree of the edge computing server cluster.

[0095] In one embodiment, the uplink transmission energy consumption E of the i-th edge computing task offloaded from the industrial field device to the j-th edge computing server is ij The calculation formula is:

[0096]

[0097] The uplink transmission time T of the i-th edge computing task offloaded from the industrial field device to the j-th edge computing server ij The calculation formula is:

[0098]

[0099] Among them, D i is the data volume of the i-th edge computing task; B i is the computational effort of the ith edge computing task; C j is the processing speed of the jth edge computing server; R ij P is the data transmission rate of the i-th edge computing task uploaded from the industrial field device to the j-th edge computing server; i is the power of the i-th edge computing task; p j is the execution power of the jth edge computing server;

[0100] in, σ 2 is the Gaussian noise power inside the channel, Los = d -α , d is the physical distance between the industrial field device of the i-th edge computing task and the j-th edge computing server, and α is 2.

[0101] In one embodiment, the load balancing degree S of the edge computing server cluster is Ω The calculation formula includes:

[0102]

[0103] Among them, Ω j is the total computing load of the jth edge computing server; is the average computing load of all edge computing servers; Φ i is the computing load of the i-th edge computing task; ε i is the computing load factor of the i-th edge computing task; B i is the computational amount of the i-th edge computing task.

[0104] In one embodiment, the constraints include:

[0105]

[0106] Among them, K ij is the network bandwidth allocated between the i-th edge computing task and the j-th edge computing server; W j is the communication bandwidth of the jth edge computing server; M fi is the memory capacity requirement of the i-th edge computing task; M Fj is the memory capacity of the jth edge computing server; S fi is the hard disk capacity requirement of the i-th edge computing task; S Fj is the hard disk capacity of the jth edge computing server; i is the computing resource demand vector corresponding to the i-th edge computing task, which includes various computing resources required by the i-th edge computing task and the demand for each computing resource; L j is the computing resource capability vector corresponding to the j-th edge computing server, and the computing resource capability vector includes various computing resources possessed by the j-th edge computing server and the provision amount of each computing resource per unit time.

[0107] It is understandable that the explanation, specific implementation, beneficial effects, examples, etc. of the relevant contents in the device provided in the embodiment of the present invention can be found in the corresponding parts of the method provided in the first aspect, and will not be repeated here.

[0108] In a third aspect, an embodiment of the present invention provides a computer-readable medium, on which computer instructions are stored. When the computer instructions are executed by a processor, the processor executes the method provided in the first aspect.

[0109] Specifically, a system or device equipped with a storage medium can be provided, on which software program code that implements the functions of any of the above-mentioned embodiments is stored, and a computer (or CPU or MPU) of the system or device can be enabled to read and execute the program code stored in the storage medium.

[0110] In this case, the program code itself read from the storage medium can realize the function of any one of the above-mentioned embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.

[0111] The storage medium embodiments for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a non-volatile memory card, and a ROM. Alternatively, the program code can be downloaded from a server computer by a communication network.

[0112] In addition, it should be clear that the functions of any of the above embodiments can be implemented not only by executing the program code read by the computer, but also by enabling an operating system operating on the computer to complete part or all of the actual operations based on instructions from the program code.

[0113] In addition, it can be understood that the program code read from the storage medium is written to a memory provided in an expansion board inserted into the computer or to a memory provided in an expansion module connected to the computer, and then based on the instructions of the program code, a CPU installed on the expansion board or expansion module is enabled to perform part or all of the actual operations, thereby realizing the functions of any of the above-mentioned embodiments.

[0114] It is understandable that the explanation, specific implementation methods, beneficial effects, examples, etc. of the relevant contents in the computer-readable medium provided in the embodiment of the present invention can be found in the corresponding parts of the method provided in the first aspect, and will not be repeated here.

[0115] In a fourth aspect, an embodiment of the present specification provides a computing device, including a memory and a processor, wherein the memory stores executable code, and when the processor executes the executable code, it implements the method in any embodiment of the specification.

[0116] It is understandable that the explanation, specific implementation, beneficial effects, examples, etc. of the relevant contents in the computing device provided in the embodiment of the present invention can be found in the corresponding parts of the method provided in the first aspect, and will not be repeated here.

[0117] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0118] Those skilled in the art should be aware that in one or more of the above examples, the functions described in the present invention can be implemented by hardware, software, widgets, or any combination thereof. When implemented by software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium.

[0119] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solution of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for scheduling edge computing tasks, characterized in that: include: Obtain task information corresponding to edge computing tasks generated by each industrial field device in the industrial edge computing architecture; Obtain computing power information of each edge computing server in the industrial edge computing architecture; Determine the task scheduling time; Establishing a multi-objective optimization model; wherein the multi-objective optimization model includes optimization objectives and constraints, and the optimization objectives include minimizing the average energy consumption of tasks, minimizing the weighted average task response delay, and minimizing the load balancing degree of the server cluster; According to the constraint conditions, solving the optimal scheduling strategy that meets the optimization goal; When the task scheduling time arrives, the edge computing task is scheduled according to the optimal scheduling strategy.

2. The method according to claim 1, characterized in that The task information corresponding to each edge computing task includes the data volume, computing volume, expected completion time of the task, power uploaded from industrial field equipment to the edge computing server, computing load factor, memory capacity requirements, hard disk capacity requirements, required computing resource types and the demand for each computing resource.

3. The method according to claim 1, characterized in that The computing power information of each edge computing server includes: the processing speed of the edge computing server, communication bandwidth, additional power when processing tasks, memory capacity, hard disk capacity, types of computing resources, and the amount of each computing resource provided per unit time.

4. The method according to claim 1, characterized in that: The objective function includes: f3=min S Ω Wherein, f1 is the average energy consumption of the minimized task; f2 is the minimized weighted average task response delay; f3 is the load balancing degree of the minimized server cluster; N is the number of edge computing tasks; M is the number of edge computing servers; i is the number of edge computing tasks; j is the number of edge computing servers; E ij is the uplink transmission energy consumption of the i-th edge computing task offloaded from the industrial field device to the j-th edge computing server; x ij is a decision variable. If the i-th edge computing task is assigned to the j-th edge computing server, then x ij is 1, otherwise x ij is 0; d i is the expected completion time of the i-th edge computing task; w is the task scheduling time; T ij S is the uplink transmission time of offloading the i-th edge computing task to the j-th edge computing server; Ω is the load balancing degree of the edge computing server cluster.

5. The method according to claim 4, characterized in that The uplink transmission energy consumption E of the i-th edge computing task offloaded from the industrial field device to the j-th edge computing server ij The calculation formula is: The uplink transmission time T of the i-th edge computing task offloaded from the industrial field device to the j-th edge computing server ij The calculation formula is: Among them, D i is the data volume of the i-th edge computing task; B i is the computational effort of the ith edge computing task; C j is the processing speed of the jth edge computing server; R ij P is the data transmission rate of the i-th edge computing task uploaded from the industrial field device to the j-th edge computing server; i is the power of the i-th edge computing task; p j is the execution power of the jth edge computing server; in, σ 2 is the Gaussian noise power inside the channel, Los = d -α , d is the physical distance between the industrial field device of the i-th edge computing task and the j-th edge computing server, and α is 2.

6. The method according to claim 4, characterized in that The load balancing degree S of the edge computing server cluster Ω The calculation formula includes: Among them, Ω j is the total computing load of the jth edge computing server; is the average computing load of all edge computing servers; Φ i is the computing load of the i-th edge computing task; ε i is the computing load factor of the i-th edge computing task; B i is the computational amount of the i-th edge computing task.

7. The method according to claim 4, characterized in that The constraints include: Among them, K ij is the network bandwidth allocated between the i-th edge computing task and the j-th edge computing server; W j is the communication bandwidth of the jth edge computing server; M fi is the memory capacity requirement of the i-th edge computing task; M Fj is the memory capacity of the jth edge computing server; S fi is the hard disk capacity requirement of the i-th edge computing task; S Fj is the hard disk capacity of the jth edge computing server; i is the computing resource demand vector corresponding to the i-th edge computing task, which includes various computing resources required by the i-th edge computing task and the demand for each computing resource; L j is the computing resource capability vector corresponding to the j-th edge computing server, and the computing resource capability vector includes various computing resources possessed by the j-th edge computing server and the provision amount of each computing resource per unit time.

8. An edge computing task scheduling device, characterized in that: include: The first acquisition module is used to obtain task information corresponding to the edge computing tasks generated by each industrial field device in the industrial edge computing architecture; A second acquisition module is used to obtain computing capacity information of each edge computing server in the industrial edge computing architecture; A time determination module is used to determine the task scheduling time; A model building module, used to build a multi-objective optimization model; wherein the multi-objective optimization model includes optimization objectives and constraints, and the optimization objectives include minimizing the average energy consumption of tasks, minimizing the weighted average task response delay, and minimizing the load balancing degree of the server cluster; A strategy solving module, used to solve the optimal scheduling strategy that meets the optimization goal according to the constraint conditions; The strategy execution module is used to schedule the edge computing task according to the optimal scheduling strategy when the task scheduling time arrives.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed in a computer, the computer is caused to execute the method according to any one of claims 1 to 7.

10. A computing device, characterized in that: The method comprises a memory and a processor, wherein the memory stores executable codes, and when the processor executes the executable codes, the method according to any one of claims 1 to 7 is implemented.

Images