基于LSH方法的珊瑚礁鱼类竞争压力查询和资源分配方法

doi:10.11978/2019046

Abstract

Abstract:

Coral fish protection strategy based on ocean big data query technology is an important subject in marine science research. Among them, the competition of fish populations is of great significance to fish protection. In order to study the competition of fish populations, it is necessary to model the relationship between fish populations and coral reefs. As a simple and effective big data model, a graph model is a practical model to express this relationship. Therefore, we propose a competition graph model of coral fish populations and fish-dependent coral reef resources. Then, a big data graph query method based on Local Sensitive Hashing (LSH) is proposed to analyze the competitive pressure of fish populations and obtain real-time competitive pressure of fish populations. Third, according to the results of LSH query, the fish population that needs priority protection is analyzed. Finally, a resource allocation strategy based on constructing artificial reefs was designed for these fish populations to improve the overall competition of coral fishes in the region.

Key words: coral fishes, competition graph, Local Sensitive Hashing, resource allocation, overall competition

CLC Number:

P735.541

Danfeng ZHAO, Zhou HUANG, Qiang XU, Dongmei HUANG. Competitive pressure inquiry and resource allocation of coral fishes based on LSH method[J].Journal of Tropical Oceanography, 2020, 39(2): 118-126.

Figures/Tables 15

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References 17

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Competitive pressure inquiry and resource allocation of coral fishes based on LSH method

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输入: 一个竞争图G=(V, E)
输出: 一个查询结果图G’=(V’, E’, P)
1. 构建图G’=(V’, E’, P)
2. V’ <-V, E’ <-E
3. 对图G构建邻接矩阵M
4. 对邻接矩阵M做最小哈希, 得到最小哈希矩阵M’
5. 对最小哈希矩阵M’做二次哈希映射, 得到行条矩阵M’’
6. 对行条矩阵M’’做桶归纳, 得到桶集合BSet
7. FOR each v∈V
8. IF L(v)= “鱼类种群”
9. Cv(v) <-0
10. 初始化空集合C(v) <-Ø
11. FOR each桶b∈BSet
12. IF 点v∈b, 则
13. C(v) <-C(v)∪{b}
14. FOR each u∈C(v)
15. Cv(v) <-Cv(v)+L’(u)×J.Sim(v, u) ×CR(v, u)
16. P(v) <-Cv(v)
17. ELSE
18. P(v) <-0
19. RETURN 图G’

输入: 一个图G=(V, E, P), 依赖标准t, 压力标准p
输出: 一个图G’=(V’, E’, P)
1. 构建集合ConceptV和FarV
2. DO
3. 将图G中所有P>p的鱼类种群点归纳为一个集合VP
4. 初始化ConceptV<-VP, FarV= Ø
5. DO
6. FOR each v∈ConceptV
7. 做以v为中心, 以t为半径的圆的内接正方形
8. 将这些正方形叠加得到区域EARA
9. IF EARA为空
10. 对ConceptV中点的经纬度取均值
11. 找出ConceptV中偏离均值点最远的离群点k
12. FarV<-FarV∪{k}
13. ConceptV<-ConceptV-{k}
14. ELSE
15. 在EARA的中间位置构建人工礁点r
16. V<-V∪{r}
17. ConceptV<-FarV
18. 对图G根据t重新规整E
19. WHILE ConceptV≠Ø
20. 对图G重新做算法1得到G’
21. G<-G’
22. 重新获取ConceptV, 将FarV置空
23. WHILE ConceptV≠Ø
24. RETURN 图G’