CN117172464A - Method, system and device for scheduling offline maintenance tasks of Internet of things system equipment - Google Patents
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Abstract
The application provides a method, a system and a device for scheduling offline maintenance tasks of equipment of an Internet of things system. Aiming at the problem of scheduling maintenance tasks under the equipment line of the Internet of things system, an improved symbiotic searching algorithm solution with local random micro variation is provided. Improved mutualistic symbiotic operation and improved partialistic operation are respectively proposed to improve the global searching capability of the algorithm, and a local random micro-variation strategy is proposed to improve the local searching capability of the algorithm. The method can effectively solve the problem of scheduling the offline maintenance tasks of the equipment of the Internet of things system, and has the characteristics of high efficiency, strong convergence capacity, high precision and the like.
Description
Technical Field
The application relates to the technical field of the Internet of things, in particular to a method, a system and a device for scheduling offline maintenance tasks of equipment of an Internet of things system.
Background
The stable and reliable operation of the equipment of the Internet of things system is the key of the normal operation of the whole Internet of things system. The internet of things system equipment comprises communication equipment, a data acquisition device, data processing equipment, data storage equipment, a controller and an actuator of the internet of things system. In order to ensure that the devices of the internet of things system run stably and reliably, the devices must be maintained reasonably offline. The offline maintenance tasks of the equipment of the Internet of things system comprise inspection, detection, maintenance, upgrading and reconstruction of the equipment of the Internet of things system, and timely, rapid and high-quality offline maintenance task scheduling is an important problem to be solved urgently. Therefore, the development of the advanced and practical offline maintenance task scheduling method for the Internet of things system equipment has important practical significance.
Disclosure of Invention
The application aims to at least solve one of the technical defects, and particularly the technical defects of low efficiency and low precision of the offline maintenance task scheduling method of the Internet of things system equipment in the prior art.
The application provides a scheduling method of offline maintenance tasks of equipment of an Internet of things system, which comprises the following steps:
s1, establishing a mathematical model of offline maintenance task scheduling of equipment of an Internet of things system:
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
wherein F is the total working time of all maintenance groups, including the time-in-route and the off-line maintenance time; n (positive integer, unit: number) is the number of devices, and the devices are based on the number of devicesThe sub-numbers are 1,2, …, n, and the maintenance service center number is 0; k (positive integer, unit: number) is the maintenance group number owned by the maintenance service center; t is t ij (i, j=0, 1,., n, i+.j, positive real number, unit: hours) is the time-of-flight from device i to device j, i, j=0 representing the maintenance service center; x is x ijk (i, j=0, 1,) n, k=1, 2,) K is a 0,1 variable, x ijk When=1, it means that maintenance team k maintains device j from device i to device j, i.e., maintenance team k maintains device j after maintaining device i; x is x ijk When=0, it indicates that there is no maintenance group k from device i to device j; t is t i (positive real number, i=0, 1,..n, units: hours) is the offline maintenance time of device i, y ik (i=1, 2,) n, k=1, 2,) K is a 0,1 variable, y ik When=1, it means that device i is maintained by maintenance group k, y ik When=0, it means that there is no case where the device i is maintained by the maintenance group k; t (T) k (positive real number, unit: hours) represents the longest working time of maintenance team K (k=1, 2.., K);
s2, defining Fitness function Fitness (S) of the individual S as follows:
s3, setting control parameters of an algorithm: setting a population size popufsize (positive integer) and an algorithm maximum iteration number IterMax (positive integer);
s4, initializing;
s4-1, initializing control parameters, wherein the current iteration times IterCnt=0;
s4-2, initializing a population: for S i (i=1, 2., popuSize) is initialized, the individual codes are coded with real numbers, and the ith (i=1, 2., popuSize) individual is S i ：
S i ＝(s i1 ,s i2 ,...,s ij ,...,s i,n+K-1 )
Wherein s is ij J=1, 2 for the j gene of the i individual, n+k-1, popusize is population size;
s5, individual decoding is carried out, and a fitness value is calculated: decoding each individual and calculating the fitness value of each individual;
s6, determining a global optimal individual: selecting an individual with the largest fitness value in the current population as a global optimal individual S * ；
S7, executing improved mutilation operation;
s8, executing improved partial profit symbiotic operation;
s9, performing local random micro variation;
s10, individual decoding is carried out, and a fitness value is calculated: decoding each individual and calculating the fitness value of each individual;
s11, updating an optimal solution: if the fitness value of the current population optimal individual is greater than S * Is adapted to the value of S * Replacing with the current population optimal individuals;
s12, itercnt=itercnt+1; if IterCnt is less than IterMax, turning to S7, otherwise turning to S13;
s13, outputting an optimal solution after the algorithm is finished, wherein the optimal solution is a global optimal individual S * The corresponding solution.
Optionally, the step S7 performs a modified mutually-friendly symbiotic operation, and further includes:
for each individual S i (i=1, 2,) popuwsize), calculation:
wherein S is j Subscript j (j=1, 2,..popusize, j+.i) is randomly generated, S inew Is composed of S i New individuals produced, S jnew Is composed of S j New individuals generated; rand (x, y) is [ x, y]The random numbers evenly distributed over, round () is a rounding function, the term X represents the modulus of vector X;
if Fitness (S) inew )＞Fitness(S i ) Will S inew Instead of S i The method comprises the steps of carrying out a first treatment on the surface of the If Fitness (S) jnew )＞Fitness(S j ) Will S inew Instead of S j 。
Optionally, the step S8 performs a modified partial commensal operation, further including:
for each individual S i (i=1, 2,) popuwsize), calculation:
wherein S is inew For new individuals, S j The subscript j (j=1, 2,.., popuSize, j+.i) is randomly generated;
if Fitness (S) inew )＞Fitness(S i ) Will S inew Instead of S i 。
Optionally, the step S9 performs local random micro mutation, and further includes:
for each individual S i (i=1, 2,., popuSize), defined as its locally concomitant individual S locali ：
S locali ＝(s locali,1 ,s locali,2 ,...,s locali,n+K-1 )
Wherein s is locali,j Calculated as follows:
wherein s is ij Is S i J=1, 2,..n+k-1;
if rand (0, 1)>0.95, then use S locali Instead of S i 。
The application also provides an offline maintenance task scheduling system of the equipment of the Internet of things system, which is realized by adopting the offline maintenance task scheduling method of the equipment of the Internet of things system according to any one of the embodiments.
The application also provides an offline maintenance task scheduling device of the Internet of things system equipment, which comprises the following steps: a processor, a memory for storing processor-executable instructions;
the processor is configured to execute the offline maintenance task scheduling method of the internet of things system device according to any one of the above embodiments.
From the above technical solutions, the embodiment of the present application has the following advantages:
the application provides a method, a system and a device for scheduling offline maintenance tasks of equipment of an Internet of things system, and provides an improved symbiotic biological search algorithm solution with local random micro-variation, aiming at the defects of low efficiency, low precision and the like of the existing method for scheduling the offline maintenance tasks of the equipment of the Internet of things system. Improved mutualistic symbiotic operation and improved partialistic operation are respectively proposed to improve the global searching capability of the algorithm, and a local random micro-variation strategy is proposed to improve the local searching capability of the algorithm. The method can effectively solve the problem of scheduling the offline maintenance tasks of the equipment of the Internet of things system, and has the characteristics of high efficiency, strong convergence capacity, high precision and the like.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the application, and that other drawings can be obtained from these drawings without inventive faculty for a person skilled in the art.
Fig. 1 is a schematic flow chart of a method for scheduling offline maintenance tasks of an internet of things system device according to an embodiment of the present application;
fig. 2 is a topology structure diagram of an internet of things system device and a maintenance service center on an edge weighted 8×8 mesh provided by an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The offline maintenance tasks of the equipment of the Internet of things system comprise inspection, detection, maintenance, upgrading and reconstruction of the equipment of the Internet of things system, and timely, rapid and high-quality offline maintenance task scheduling is an important problem to be solved urgently. Therefore, the development of the advanced and practical offline maintenance task scheduling method for the Internet of things system equipment has important practical significance. Based on the above, the application provides the following technical scheme, and the specific scheme is as follows:
in one embodiment, as shown in fig. 1, fig. 1 is a flow chart of an offline maintenance task scheduling method for an internet of things system device according to an embodiment of the present application; the technical scheme of the application is further described below with reference to the accompanying drawings and examples. The embodiment of the application discloses a system topology structure diagram, which is a topology structure diagram of an offline maintenance task scheduling problem of an internet of things system device on an edge weighted 8×8 grid diagram, as shown in fig. 2, and fig. 2 is a topology structure diagram of the internet of things system device on the edge weighted 8×8 grid diagram and a maintenance service center provided by the embodiment of the application; in fig. 2, vertex numbers 1, 2..64, the weight of an edge represents the time-in-transit between two vertices corresponding to the edge. Based on the weighting map, time-in-transit between the devices and the maintenance service center are calculated.
The information of the maintenance service center and the equipment in the embodiment of the application is shown in table 1, and comprises vertexes corresponding to the maintenance service center, vertexes corresponding to 23 pieces of equipment to be maintained and offline maintenance time.
Table 1 shows the information about the maintenance service center and the equipment to be maintained
The application provides a scheduling method for offline maintenance tasks of equipment of an Internet of things system, which comprises the following steps:
s1, establishing a mathematical model of offline maintenance task scheduling of equipment of an Internet of things system:
constraint conditions:
x ijk (i，j＝0,1,...,n，k＝1,2...,K)＝0，1(6)
y ik (i＝1,2,...,n，k＝1,2,...,K)＝0，1(7)
wherein F is the total working time of all maintenance groups, including the time-in-route and the off-line maintenance time; n=23 (positive integer, unit: number) 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: number) is the maintenance subgroup number owned by the maintenance service center; t is t ij (i, j=0, 1,., n, i+.j, positive real number, unit: hours) is the time-of-flight from device i to device j, i, j=0 representing the maintenance service center; x is x ijk (i, j=0, 1,) n, k=1, 2,) K is a 0,1 variable, x ijk When=1, it means that maintenance team k maintains device j from device i to device j, i.e., maintenance team k maintains device j after maintaining device i;x ijk when=0, it indicates that there is no maintenance group k from device i to device j; t is t i (positive real number, i=0, 1,..n, units: hours) is the offline maintenance time of device i, y ik (i=1, 2,) n, k=1, 2,) K is a 0,1 variable, y ik When=1, it means that device i is maintained by maintenance group k, y ik When=0, it means that there is no case where the device i is maintained by the maintenance group k; t (T) k (positive real number, unit: hours) represents the longest working time of maintenance team K (k=1, 2.., K) where T 1 ＝8，T 2 ＝8，T 3 ＝8，T 4 ＝7，T 5 =7.5. Equation (1) is an objective function, equations (2) to (4) represent that a piece of equipment is exactly maintained by one group and each maintenance group must return to the maintenance service center, equation (5) represents the total operating time constraint of each maintenance group, and equations (6) to (7) are variable value constraints.
S2, defining Fitness function Fitness (S) of the individual S as follows:
s3, setting control parameters of an algorithm: the population size popusize=50 (positive integer), the algorithm maximum iteration number IterMax (=1000 positive integers) is set.
S4, initializing.
S4-1, initializing a control parameter, wherein the current iteration number IterCnt=0.
S4-2, initializing a population: for S i (i=1, 2., popuSize) is initialized, the individual codes are coded with real numbers, and the ith (i=1, 2., popuSize) individual is S i ：
S i ＝(s i1 ,s i2 ,...,s ij ,...,s i,n+K-1 ) (9)
Wherein s is ij J=1, 2 for the j-th gene of the i-th individual, n+k-1, popusize is population size.
S5, individual decoding is carried out, and a fitness value is calculated: decoding each individual and calculating the fitness value of each individual.
S6, determining a global optimal individual: selecting an individual with the largest fitness value in the current population as a global optimal individual S *。 。
S7, executing improved mutilation operation; for each individual S i (i=1, 2,) popuwsize), calculation:
wherein S is j Subscript j (j=1, 2,..popusize, j+.i) is randomly generated, S inew Is composed of S i New individuals produced, S jnew Is composed of S j New individuals generated; rand (x, y) is [ x, y]The random numbers evenly distributed over, round () is a rounding function, the term X represents the modulus of vector X.
If Fitness (S) inew )＞Fitness(S i ) Will S inew Instead of S i The method comprises the steps of carrying out a first treatment on the surface of the If Fitness (S) jnew )＞Fitness(S j ) Will S inew Instead of S j 。
S8, executing improved partial profit symbiotic operation; for each individual S i (i=1, 2,) popuwsize), calculation:
wherein S is inew For new individuals, S j The subscript j (j=1, 2,.., popuSize, j+.i) is randomly generated;
if Fitness (S) inew )＞Fitness(S i ) Will S inew Instead of S i 。
S9, performing local random micro variation; for each individual S i (i=1, 2,., popuSize), defined as its locally concomitant individual S locali ：
S locali ＝(s locali,1 ,s locali,2 ,...,s locali,n+K-1 ) (13)
Wherein s is locali,j Calculated according to formula (14):
wherein s is ij Is S i J=1, 2,..n+k-1.
If rand (0, 1)>0.95, then use S locali Instead of S i 。
S10, individual decoding is carried out, and a fitness value is calculated: decoding each individual and calculating the fitness value of each individual.
S11, updating an optimal solution: if the fitness value of the current population optimal individual is greater than S * Is adapted to the value of S * And replacing with the current population optimal individual.
S12, itercnt=itercnt+1; if IterCnt < IterMax, go to S7, otherwise go to S13.
S13, outputting an optimal solution after the algorithm is finished, wherein the optimal solution is a global optimal individual S * The corresponding solution.
The optimal solution for the above embodiment is shown in fig. 2. In the optimal solution, 3 maintenance teams are used, namely maintenance teams 1-3. The equipment maintained by the maintenance group 1 and the maintenance sequence are as follows: 20. 19, 18, 17, 9, 10, 11, 12 (where the numbers are the vertex numbers of the example side weighting 8 x 8 grid graph, as similarly explained below), the actual working time of maintenance team 1 is 8 hours; the equipment maintained by the maintenance team 2 and the maintenance sequence are: 22. 23, 24, 32, 40, 39, 38, 30, the actual working time of the maintenance team 2 is 8 hours; the equipment maintained by the maintenance team 3 and the maintenance sequence are: 43. 51, 59, 60, 61, 52, 44, the actual working time of the maintenance team 3 is 7.9 hours; min f=23.9 hours.
In an embodiment, the application further provides an offline maintenance task scheduling system of the internet of things system, and the system is realized by adopting the offline maintenance task scheduling method of the internet of things system equipment according to any one of the above embodiments.
In an embodiment, the application further provides an offline maintenance task scheduling device of the internet of things system, which comprises: and a processor, memory for storing processor-executable instructions.
The processor is configured to execute the offline maintenance task scheduling method of the internet of things system device according to any one of the above embodiments.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the present specification, each embodiment is described in a progressive manner, and each embodiment focuses on the difference from other embodiments, and may be combined according to needs, and the same similar parts may be referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (6)
1. An offline maintenance task scheduling method for equipment of an internet of things system is characterized by comprising the following steps of:
s1, establishing a mathematical model of offline maintenance task scheduling of equipment of an Internet of things system:
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
wherein F is the total working time of all maintenance groups, including the time-in-route and the off-line maintenance time; n (positive integer, unit: number) is the number of devices, the devices are numbered 1,2, …, n in sequence, and the number of the maintenance service center is 0; k (positive integer, unit: number) is the maintenance group number owned by the maintenance service center; t is t ij (i, j=0, 1,..n, i+.j, positive real number, unit: hours) is the way from device i to device jTime-in-transit, i, j=0, represents a maintenance service center; x is x ijk (i, j=0, 1,) n, k=1, 2,) K is a 0,1 variable, x ijk When=1, it means that maintenance team k maintains device j from device i to device j, i.e., maintenance team k maintains device j after maintaining device i; x is x ijk When=0, it indicates that there is no maintenance group k from device i to device j; t is t i (positive real number, i=0, 1,..n, units: hours) is the offline maintenance time of device i, y ik (i=1, 2,) n, k=1, 2,) K is a 0,1 variable, y ik When=1, it means that device i is maintained by maintenance group k, y ik When=0, it means that there is no case where the device i is maintained by the maintenance group k; t (T) k (positive real number, unit: hours) represents the longest working time of maintenance team K (k=1, 2.., K);
s2, defining Fitness function Fitness (S) of the individual S as follows:
s3, setting control parameters of an algorithm: setting a population size popufsize (positive integer) and an algorithm maximum iteration number IterMax (positive integer);
s4, initializing;
s4-1, initializing control parameters, wherein the current iteration times IterCnt=0;
s4-2, initializing a population: for S i (i=1, 2., popuSize) is initialized, the individual codes are coded with real numbers, and the ith (i=1, 2., popuSize) individual is S i ：
S i ＝(s i1 ,s i2 ,...,s ij ,...,s i,n+K-1 )
Wherein s is ij J=1, 2 for the j gene of the i individual, n+k-1, popusize is population size;
s5, individual decoding is carried out, and a fitness value is calculated: decoding each individual and calculating the fitness value of each individual;
s6, determining a global optimal individual: selecting the individual with the largest fitness value in the current population as the whole populationLocal optimum individual S * ；
S7, executing improved mutilation operation;
s8, executing improved partial profit symbiotic operation;
s9, performing local random micro variation;
s10, individual decoding is carried out, and a fitness value is calculated: decoding each individual and calculating the fitness value of each individual;
s11, updating an optimal solution: if the fitness value of the current population optimal individual is greater than S * Is adapted to the value of S * Replacing with the current population optimal individuals;
s12, itercnt=itercnt+1; if IterCnt is less than IterMax, turning to S7, otherwise turning to S13;
s13, outputting an optimal solution after the algorithm is finished, wherein the optimal solution is a global optimal individual S * The corresponding solution.
2. The method for scheduling offline maintenance tasks of an internet of things system device according to claim 1, wherein the step S7 performs an improved mutualistic symbiotic operation, and further comprises:
for each individual S i (i=1, 2,) popuwsize), calculation:
wherein S is j Subscript j (j=1, 2,..popusize, j+.i) is randomly generated, S inew Is composed of S i New individuals produced, S jnew Is composed of S j New individuals generated; rand (x, y) is [ x, y]The random numbers evenly distributed over, round () is a rounding function, the term X represents the modulus of vector X;
if Fitness (S) inew )＞Fitness(S i ) Will S inew Instead of S i The method comprises the steps of carrying out a first treatment on the surface of the If Fitness (S) jnew )＞Fitness(S j ) Will S inew Instead of S j 。
3. The method for scheduling offline maintenance tasks of an internet of things system device according to claim 1, wherein the step S8 performs an improved disfavor symbiotic operation, and further comprises:
for each individual S i (i=1, 2,) popuwsize), calculation:
wherein S is inew For new individuals, S j The subscript j (j=1, 2,.., popuSize, j+.i) is randomly generated;
if Fitness (S) inew )＞Fitness(S i ) Will S inew Instead of S i 。
4. The method for scheduling offline maintenance tasks of an internet of things system according to claim 1, wherein the step S9 performs local random micro-mutation, and further comprises:
for each individual S i (i=1, 2,., popuSize), defined as its locally concomitant individual S locali ：
S locali ＝(s locali,1 ,s locali,2 ,...,s locali,n+K-1 )
Wherein s is locali,j Calculated as follows:
wherein s is ij Is S i J=1, 2,..n+k-1;
if rand (0, 1)>0.95, then use S locali Instead of S i 。
5. An offline maintenance task scheduling system for an internet of things system device, which is characterized in that the system is realized by adopting the offline maintenance task scheduling method for the internet of things system device according to any one of claims 1-4.
6. An offline maintenance task scheduling device for an internet of things system device, comprising: a processor, a memory for storing processor-executable instructions;
wherein the processor is configured to perform the method for scheduling offline maintenance tasks of an internet of things system device according to any of the preceding claims 1-4.
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| CN112561225A (en) * | 2020-09-30 | 2021-03-26 | 北京工业大学 | Flexible job shop scheduling method based on benchmarking coevolution algorithm |
| CN113723803A (en) * | 2021-08-30 | 2021-11-30 | 东北大学秦皇岛分校 | Parallel machine system processing optimization method combining maintenance strategy and task scheduling |
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