A method, system and device for scheduling offline maintenance tasks of IoT system equipment

Abstract

The present invention provides a method, system and device for scheduling offline maintenance tasks of IoT system equipment. In view of the problem of scheduling offline maintenance tasks of IoT system equipment, an improved symbiotic biological search algorithm with local random micro-mutation is proposed to solve it. Improved mutually beneficial symbiotic operations and improved partial beneficial symbiotic operations are proposed to improve the global search ability of the algorithm, and a local random micro-mutation strategy is proposed to enhance the local search ability of the algorithm. The present invention can effectively solve the problem of scheduling offline maintenance tasks of IoT system equipment, and has the characteristics of high efficiency, strong convergence ability, high precision, etc.

Description

Technical Field

[0001] The present application relates to the technical field of Internet of Things, and in particular to a method, system and device for scheduling offline maintenance tasks for Internet of Things system equipment. Background Art

[0002] The stable and reliable operation of IoT system equipment is the key to the normal operation of the entire IoT system. IoT system equipment includes IoT system communication equipment, data acquisition devices, data processing equipment, data storage equipment, controllers, and actuators. In order to ensure the stable and reliable operation of IoT system equipment, they must be reasonably maintained offline. The offline maintenance tasks of IoT system equipment include inspection, testing, repair, maintenance, and upgrading of IoT system equipment. Timely, fast, and high-quality offline maintenance task scheduling is an important issue that needs to be solved urgently. Therefore, it is of great practical significance to develop advanced and practical offline maintenance task scheduling methods for IoT system equipment. Summary of the invention

[0003] The purpose of this application is to solve at least one of the above-mentioned technical defects, especially the technical defects of low efficiency and low precision in the offline maintenance task scheduling method of IoT system equipment in the prior art.

[0004] The present application provides a method for scheduling offline maintenance tasks for IoT system equipment, the method comprising the following steps:

[0005] S1. Establish a mathematical model for offline maintenance task scheduling of IoT system equipment:

[0006]

[0007] Constraints:

[0008]

[0009]

[0010]

[0011]

[0012] x ijk (i,j=0,1,...,n,k=1,2...,K)=0,1

[0013] y ik (i=1,2,...,n,k=1,2,...,K)=0,1

[0014] Where F is the total working time of all maintenance teams, including travel time and offline maintenance time; n (a positive integer, unit: piece) is the number of devices, the devices are numbered 1, 2, ..., n in sequence, and the maintenance service center is numbered 0; K (a positive integer, unit: piece) is the number of maintenance teams owned by the maintenance service center; t ij (i, j = 0, 1, .., n, i≠j, positive real number, unit: hour) is the travel time from device i to device j, i, j = 0 represents the maintenance service center; x ijk (i, j = 0, 1, ..., n, k = 1, 2 ..., K) are 0, 1 variables, x ijk =1 indicates that maintenance team k goes from device i to device j, that is, maintenance team k maintains device j after maintaining device i; ijk = 0 means that there is no maintenance team k from device i to device j; t i (positive real number, i=0, 1, ..., n, unit: hour) is the offline maintenance time of device i, y ik (i=1,2,...,n,k=1,2,...,K) are 0 and 1 variables, y ik =1 means that equipment i is maintained by maintenance team k, y ik =0 means that there is no situation where equipment i is maintained by maintenance team k; T k (positive real number, unit: hour) represents the maximum working time of maintenance team k (k=1, 2..., K);

[0015] S2. Define the fitness function Fitness(S) of individual S as:

[0016]

[0017] S3. Set the control parameters of the algorithm: set the population size PopuSize (positive integer) and the maximum number of iterations of the algorithm IterMax (positive integer);

[0018] S4, initialization;

[0019] S4-1, control parameter initialization, current iteration number IterCnt=0;

[0020] S4-2. Population initialization: S i (i=1,2,...,PopuSize) is initialized, and the individual coding adopts real number coding. The i-th (i=1,2,...,PopuSize) individual is S i :

[0021] S i =(s i1 ,s i2 ,...,sij ,..., s i,n+K-1 )

[0022] where s ij is the j-th gene of the i-th individual, j = 1, 2,..., n + K - 1, and PopuSize is the population size;

[0023] S5. Individual decoding and fitness value calculation: Decode each individual and calculate the fitness value of each individual;

[0024] S6. Determine the global optimal individual: Select the individual with the largest fitness value in the current population as the global optimal individual S * ;

[0025] S7. Perform the improved mutualism operation;

[0026] S8. Perform the improved commensalism operation;

[0027] S9. Perform local random micro-mutation;

[0028] S10. Individual decoding and fitness value calculation: Decode each individual and calculate the fitness value of each individual;

[0029] S11. Update the optimal solution: If the fitness value of the optimal individual in the current population is greater than that of S * , then S * is replaced by the optimal individual in the current population;

[0030] S12. IterCnt = IterCnt + 1; If IterCnt < IterMax, then go to S7, otherwise go to S13;

[0031] S13. The algorithm ends, output the optimal solution, and the optimal solution is the solution corresponding to the global optimal individual S * corresponding to.

[0032] Optionally, the step S7 of performing the improved mutualism operation further includes:

[0033] For each individual S i (i = 1, 2,..., PopuSize), calculate:

[0034]

[0035]

[0036] where S j with subscript j (j = 1, 2,..., PopuSize, j ≠ i) is randomly generated, and S inew is obtained from S iThe new individual generated, S jnew By S j The generated new individual; rand(x,y) is a random number uniformly distributed on [x,y], round() is a rounding function, and ||X|| represents the modulus of vector X;

[0037] If Fitness(S inew )>Fitness(S i ), then S inew Instead of S i ; If Fitness(S jnew )>Fitness(S j ), then S inew Instead of S j .

[0038] Optionally, the step S8 performs an improved partial benefit symbiosis operation, further comprising:

[0039] For each individual S i (i=1,2,...,PopuSize), calculate:

[0040]

[0041] Among them, S inew is a new individual, S j The subscript j (j=1,2,...,PopuSize,j≠i) is randomly generated;

[0042] If Fitness(S inew )>Fitness(S i ), then S inew Instead of S i .

[0043] Optionally, the step S9 performs local random micro-variation, further comprising:

[0044] For each individual S i (i=1,2,...,PopuSize), define its local adjoint individual S locali :

[0045] S locali =(s locali,1 ,s locali,2 ,...,s locali,n+K-1 )

[0046] Among them, s locali,j Calculate as follows:

[0047]

[0048] Among them, s ij For S i The j-th gene of , j = 1, 2, ..., n + K-1;

[0049] If rand(0,1)>0.95, use S locali Instead of S i .

[0050] The present application also provides an Internet of Things system equipment offline maintenance task scheduling system, and the system is implemented using the Internet of Things system equipment offline maintenance task scheduling method as described in any of the above embodiments.

[0051] The present application also provides an IoT system equipment offline maintenance task scheduling device, comprising: a processor, a memory for storing instructions executable by the processor;

[0052] The processor is configured to execute the method for scheduling offline maintenance tasks for Internet of Things system equipment described in any one of the above embodiments.

[0053] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0054] The present application provides a method, system and device for scheduling offline maintenance tasks for IoT system equipment. In view of the shortcomings of low efficiency and low precision in the existing offline maintenance task scheduling methods for IoT system equipment, the present invention proposes an improved symbiotic biological search algorithm with local random micro-mutations for solution. Improved mutually beneficial symbiotic operations and improved partial beneficial symbiotic operations are proposed to improve the global search capability of the algorithm, and a local random micro-mutation strategy is proposed to enhance the local search capability of the algorithm. The present invention can effectively solve the problem of scheduling offline maintenance tasks for IoT system equipment, and has the characteristics of high efficiency, strong convergence ability and high precision. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0056] Figure 1 A flowchart of a method for scheduling offline maintenance tasks for IoT system equipment provided in an embodiment of the present application;

[0057] Figure 2 A topological structure diagram of the Internet of Things system equipment and the maintenance service center on the edge-weighted 8×8 grid diagram provided in an embodiment of the present application. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0059] The offline maintenance tasks of IoT system equipment include the inspection, testing, repair, maintenance, and upgrading of IoT system equipment. Timely, fast, and high-quality offline maintenance task scheduling is an important issue that needs to be solved. Therefore, the development of advanced and practical offline maintenance task scheduling methods for IoT system equipment has important practical significance. Based on this, this application proposes the following technical solutions, see below for details:

[0060] In one embodiment, Figure 1 As shown, Figure 1 The present invention provides a flowchart of a method for scheduling offline maintenance tasks for IoT system devices in an embodiment of the present invention; the technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments. The present invention embodiment is a problem of scheduling offline maintenance tasks for IoT system devices on an 8×8 grid graph with a system topology structure diagram with edge weights, such as Figure 2 As shown, Figure 2 A topological structure diagram of the Internet of Things system equipment and the maintenance service center on the edge-weighted 8×8 grid diagram provided in an embodiment of the present application; Figure 2 In the graph, the vertices are numbered 1, 2, ..., 64, and the weight of an edge represents the travel time between the two vertices corresponding to the edge. Based on the weighted graph, the travel time between devices and between devices and the maintenance service center is calculated.

[0061] The information of the maintenance service center and the equipment in the embodiment of the present application is shown in Table 1, including the vertex corresponding to the maintenance service center, the vertices corresponding to the 23 equipments that need to be maintained and their offline maintenance time.

[0062]

[0063] Table 1 shows the relevant information of the maintenance service center and the equipment to be maintained.

[0064] The present application provides a method for scheduling offline maintenance tasks for IoT system equipment, which may include the following steps:

[0065] S1. Establish a mathematical model for offline maintenance task scheduling of IoT system equipment:

[0066]

[0067] Constraints:

[0068]

[0069]

[0070]

[0071]

[0072] x ijk (i,j=0,1,...,n,k=1,2...,K)=0,1(6)

[0073] y ik (i=1,2,...,n,k=1,2,...,K)=0,1(7)

[0074] Where F is the total working time of all maintenance teams, including travel time and offline maintenance time; n = 23 (positive integer, unit: piece) is the number of devices, the devices are numbered 1, 2, ..., n in sequence, and the maintenance service center is numbered 0; K = 5 (positive integer, unit: piece) is the number of maintenance teams owned by the maintenance service center; t ij (i, j = 0, 1, .., n, i≠j, positive real number, unit: hour) is the travel time from device i to device j, i, j = 0 represents the maintenance service center; x ijk (i, j = 0, 1, ..., n, k = 1, 2 ..., K) are 0, 1 variables, x ijk =1 indicates that maintenance team k goes from device i to device j, that is, maintenance team k maintains device j after maintaining device i; ijk = 0 means that there is no maintenance team k from device i to device j; t i (positive real number, i=0, 1, ..., n, unit: hour) is the offline maintenance time of device i, y ik (i=1,2,...,n,k=1,2,...,K) are 0 and 1 variables, y ik =1 means that equipment i is maintained by maintenance team k, y ik =0 means that there is no situation where equipment i is maintained by maintenance team k; T k (positive real number, unit: hour) represents the maximum working time of maintenance team k (k = 1, 2..., K) where T1 = 8, T2 = 8, T3 = 8, T4 = 7, T5 = 7.5. Formula (1) is the objective function, Formulas (2) to (4) indicate that a piece of equipment is maintained by exactly one team and each maintenance team must return to the maintenance service center, Formula (5) represents the total working time constraint of each maintenance team, and Formulas (6) to (7) are variable value constraints.

[0075] S2. Define the fitness function Fitness(S) of individual S as:

[0076]

[0077] S3. Set the control parameters of the algorithm: set the population size PopuSize = 50 (positive integer) and the maximum number of iterations of the algorithm IterMax (= 1000 positive integer).

[0078] S4. Initialization.

[0079] S4-1. Control parameters are initialized, and the current number of iterations IterCnt=0.

[0080] S4-2. Population initialization: S i (i=1,2,...,PopuSize) is initialized, and the individual coding adopts real number coding. The i-th (i=1,2,...,PopuSize) individual is S i :

[0081] S i =(s i1 ,s i2 ,...,s ij ,...,s i,n+K-1 ) (9)

[0082] Among them, s ij is the jth gene of the ith individual, j=1,2,...,n+K-1, and PopuSize is the population size.

[0083] S5. Individual decoding and fitness value calculation: Decode each individual and calculate the fitness value of each individual.

[0084] S6. Determine the global optimal individual: Select the individual with the largest fitness value in the current population as the global optimal individual S *。 .

[0085] S7. Perform improved mutualism operation; for each individual S i (i=1,2,...,PopuSize), calculate:

[0086]

[0087]

[0088] Among them, S j The subscript j (j = 1, 2, ..., PopuSize, j ≠ i) is randomly generated, S inew By S i The new individual generated, Sjnew By S j The new individual generated; rand(x,y) is a random number uniformly distributed on [x,y], round() is the rounding function, and ||X|| represents the modulus of vector X.

[0089] If Fitness(S inew )>Fitness(S i ), then S inew Instead of S i ; If Fitness(S jnew )>Fitness(S j ), then S inew Instead of S j .

[0090] S8, perform improved partial benefit symbiosis operation; for each individual S i (i=1,2,...,PopuSize), calculate:

[0091]

[0092] Among them, S inew is a new individual, S j The subscript j (j=1,2,...,PopuSize,j≠i) is randomly generated;

[0093] If Fitness(S inew )>Fitness(S i ), then S inew Instead of S i .

[0094] S9, perform local random micro-mutation; for each individual S i (i=1,2,...,PopuSize), define its local adjoint individual S locali :

[0095] S locali =(s locali,1 ,s locali,2 ,...,s locali,n+K-1 ) (13)

[0096] Among them, s locali,j Calculate according to formula (14):

[0097]

[0098] Among them, s ij For S i The j-th gene of , j=1,2,...,n+K-1.

[0099] If rand(0,1) > 0.95, then use S locali to replace S i .

[0100] S10. Individual decoding and fitness value calculation: Decode each individual and calculate the fitness value of each individual.

[0101] S11. Update the optimal solution: If the fitness value of the optimal individual in the current population is greater than that of S * , then S * is replaced by the optimal individual in the current population.

[0102] S12. IterCnt = IterCnt + 1; If IterCnt < IterMax, then go to S7, otherwise go to S13.

[0103] S13. The algorithm ends, output the optimal solution, and the optimal solution is the solution corresponding to the global optimal individual S * .

[0104] The optimal solution of the above embodiment is as Figure 2 shown. In the optimal solution, 3 maintenance teams are used, namely Maintenance Team 1 to 3. Among them, the equipment maintained by Maintenance Team 1 and the maintenance order are: 20, 19, 18, 17, 9, 10, 11, 12 (the numbers here are the vertex numbers of the edge-weighted 8×8 grid graph in the embodiment, and the following explanations are similar), and the actual working hours of Maintenance Team 1 are 8 hours; the equipment maintained by Maintenance Team 2 and the maintenance order are: 22, 23, 24, 32, 40, 39, 38, 30, and the actual working hours of Maintenance Team 2 are 8 hours; the equipment maintained by Maintenance Team 3 and the maintenance order are: 43, 51, 59, 60, 61, 52, 44, and the actual working hours of Maintenance Team 3 are 7.9 hours; Min F = 23.9 hours.

[0105] In one embodiment, the present application also provides an offline maintenance task scheduling system for Internet of Things system devices, and the system is implemented by using the offline maintenance task scheduling method for Internet of Things system devices described in any one of the above embodiments.

[0106] In one embodiment, the present application also provides an offline maintenance task scheduling device for Internet of Things system devices, including: a processor and a memory for storing processor-executable instructions.

[0107] Wherein, the processor is configured to execute the offline maintenance task scheduling method for Internet of Things system devices described in any one of the above embodiments.

[0108] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0109] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can refer to each other.

[0110] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for scheduling offline maintenance tasks for IoT system equipment, characterized in that: The method includes the following steps: S1. Establish a mathematical model for the offline maintenance task scheduling of Internet of Things system devices: Constraint conditions: x ijk (i,j=0,1,...,n,k=1,2...,K)=0,1 y ik (i=1,2,...,n,k=1,2,...,K)=0,1 Where F is the total working time of all maintenance teams, including travel time and offline maintenance time; n is the number of devices, the devices are numbered 1, 2, ..., n, and the maintenance service center is numbered 0; K is the number of maintenance teams owned by the maintenance service center; t ij is the travel time from device i to device j, i, j = 0 represents the maintenance service center; x ijk is a variable of 0 or 1, x ijk =1 indicates that maintenance team k goes from device i to device j, that is, maintenance team k maintains device j after maintaining device i; ijk = 0 means that there is no situation where maintenance team k moves from device i to device j; t i is the offline maintenance time of device i, y ik is a variable of 0 or 1, y ik =1 means that equipment i is maintained by maintenance team k, y ik =0 means that there is no situation where equipment i is maintained by maintenance team k; T k represents the maximum working time of maintenance team k; S2. Define the fitness function Fitness(S) of individual S as: S3. Set the control parameters of the algorithm: Set the population size PopuSize and the maximum number of algorithm iterations IterMax; S4. Initialize; S4-1. Initialize the control parameters, and the current iteration count IterCnt = 0; S4-2. Population initialization: S i Initialize, individual coding uses real number coding, and the i-th individual is S i : S i =(s i1 ,s i2 ,...,s ij ,...,s i,n+K-1 ) Among them, s ij is the jth gene of the ith individual, j = 1, 2, ..., n + K-1, PopuSize is the population size; S5. Decode the individual and calculate the fitness value: Decode each individual and calculate the fitness value of each individual; S6. Determine the global optimal individual: Select the individual with the largest fitness value in the current population as the global optimal individual S * ; S7. Perform an improved mutualism operation; S8. Perform an improved commensalism operation; S9. Perform local random micro-mutation; S10. Decode the individual and calculate the fitness value: Decode each individual and calculate the fitness value of each individual; S11, Update the optimal solution: If the fitness value of the optimal individual in the current population is greater than S * The fitness value of S * Replace with the best individual in the current population; S12. IterCnt = IterCnt + 1; If IterCnt < IterMax, then go to S7, otherwise go to S13; S13, the algorithm ends and the optimal solution is output. The optimal solution is the global optimal individual S * The corresponding solution; The improved mutualism operation performed in step S7 further includes: For each individual S i ,calculate: Among them, S j The subscript j is randomly generated, S inew By S i The new individual generated, S jnew By S j The generated new individual; rand(x,y) is a random number uniformly distributed on [x,y], round() is a rounding function, and ||X|| represents the modulus of vector X; If Fitness(S inew )>Fitness(S i ), then S inew Instead of S i ; If Fitness(S jnew )>Fitness(S j ), then S inew Instead of S j ; The improved commensalism operation performed in step S8 further includes: For each individual S i ,calculate: Among them, S inew is a new individual, S j The subscript j is randomly generated; If Fitness(S inew )>Fitness(S i ), then S inew Instead of S i ; The local random micro-mutation performed in step S9 further includes: For each individual S i , define its local adjoint individual S locali : S locali =(s locali,1 ,s locali,2 ,...,s locali,n+K-1 ) Among them, s locali,j Calculate as follows: Among them, s ij For S i The j-th gene of , j = 1, 2, ..., n + K-1; If rand(0,1)>0.95, use S locali Instead of S i .

2. An offline maintenance task scheduling system for IoT system equipment, characterized in that: The system is implemented by using the method for offline maintenance task scheduling of Internet of Things system devices described in claim 1 above.

3. A device for scheduling offline maintenance tasks for IoT system equipment, characterized in that: It includes: A processor and a memory for storing processor-executable instructions; Wherein, the processor is configured to execute the method for offline maintenance task scheduling of Internet of Things system devices described in claim 1 above.

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