Improved reinforced immune algorithm-based agricultural internet-of-things resource allocation method

An immune algorithm and resource allocation technology, applied in logistics, computing, genetic models, etc., can solve the problems of single allocation object, poor learning ability, poor adaptability, etc.

Active Publication Date: 2018-02-23
DONGHUA UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide an improvement-based and enhanced system with strong learning ability, good adaptability and comprehensive deployment of multiple resources for the defects of the prior art, such as poor learning ability, poor adaptability, and single allocation object. Agricultural Internet of Things resource allocation method based on immune algorithm

Method used

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  • Improved reinforced immune algorithm-based agricultural internet-of-things resource allocation method
  • Improved reinforced immune algorithm-based agricultural internet-of-things resource allocation method
  • Improved reinforced immune algorithm-based agricultural internet-of-things resource allocation method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0136] In this example, the total number of resource types N=1, the number of resource demand points H=10, the number of resource supply points T=2, the relevant information of each resource supply point and the information of the vehicles owned are shown in Table 1 and the relevant information of each resource demand point is as follows As shown in Table 2, all points are distributed in 100×100m 2 in the area.

[0137] Table 1

[0138] supply point

Coordinate X

Coordinate Y

N1 resource supply

vehicle

Vehicle capacity

T 1

50

30

8

C 1 、C 2

4.5

T 2

50

70

7

C 3 、C 4

4.5

[0139] Table 2

[0140]

[0141]

[0142] Utilize the improved enhanced immune algorithm of the present invention, the operation result of present embodiment 1 is as shown in table 3:

[0143] table 3

[0144] fitness value

590.9272

frequency

10

total

10

average fitness value ...

Embodiment 2

[0149] In this example, the total number of resource types N=2, the number of resource demand points H=10, the number of resource supply points T=3, the relevant information of each resource supply point and the information of the vehicles owned are shown in Table 5 and the relevant information table of each resource demand point 6, all points are distributed in 100×100m 2 in the area.

[0150] table 5

[0151]

[0152]

[0153] Table 6

[0154] demand point

Coordinate X

Coordinate Y

N1 resource supply

N2 resource supply

H 1

21.6848

39.8254

1.32

0.69

H 2

48.9923

4.081

1.13

1.31

H 3

34.9283

33.753

2.1

0.52

H 4

43.5551

70.0772

1.42

2.26

H 5

65.9102

18.5486

0.75

0.92

H 6

7.2851

8.6185

1.28

0.55

H 7

1.0981

67.1339

1.66

1.3

H 8

98.2158

19.9237

1.2

0.78

H 9

25.7646

55.9766

0.54

1.69...

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PUM

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Abstract

The invention relates to an improved reinforced immune algorithm-based agricultural internet-of-things resource allocation method. Related information of resource demand points and supply points in agriculture is obtained by adopting an internet-of-things technology, and the supply points perform efficient resource allocation on the demand points through allocation vehicles by utilizing an improved reinforced immune algorithm; and the improved reinforced immune algorithm is an algorithm obtained after an immune algorithm is improved by utilizing a reinforcement learning thought, the improvement refers to the initialization of a Q table according to the distances between the resource supply points and demand points where the vehicles are located, the Q table is updated according to an antibody with a minimum fitness value currently in each iteration, mutation of the antibody is guided by using the current Q table in a mutation stage, and meanwhile, when the Q table is continuously updated in the same position, the Q table is adjusted to escape from local optimum. The method has the characteristics of strong learning capability, high self-adaptivity, numerous allocation objects and high response speed.

Description

technical field [0001] The invention belongs to the field of intelligent agriculture, and relates to an intelligent optimization allocation method, in particular to an agricultural Internet of Things resource allocation method based on an improved enhanced immune algorithm. Background technique [0002] In recent years, under the joint promotion of the government, scientific research institutions and agricultural production enterprises, my country's agricultural and rural economy has maintained a good momentum of development. Some areas have actively explored the application of agricultural Internet of Things technology and achieved initial results. Transforming traditional agriculture with modern information technology is an important way to realize agricultural digital design, intelligent control, precise operation and scientific management. As a new force of modern information technology, Internet of Things technology is an important entry point to promote the integration...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q50/02G06Q10/08G06N3/12
CPCG06N3/126G06Q10/083G06Q50/02
Inventor 郝矿荣蒋益敏丁永生
Owner DONGHUA UNIV
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