Journal of Tropical Oceanography >
Competitive pressure inquiry and resource allocation of coral fishes based on LSH method
Received date: 2019-05-10
Request revised date: 2019-07-04
Online published: 2020-03-10
Supported by
National Natural Science Foundation of China(41671431)
Copyright
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.
ZHAO Danfeng , HUANG Zhou , XU Qiang , HUANG Dongmei . Competitive pressure inquiry and resource allocation of coral fishes based on LSH method[J]. Journal of Tropical Oceanography, 2020 , 39(2) : 118 -126 . DOI: 10.11978/2019046
表1 算法1——竞争值估计Tab. 1 Algorithm 1——Estimation of competition value |
输入: 一个竞争图G=(V, E) |
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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’ |
注: G和G’表示图, V和V’表示点集合, E和E’表示边集合, P表示压力值标签集合。Cv(v)表示点v的竞争值。BSet表示桶集合, 其中的任意一个元素b表示一个桶。L(v)表示点v的属性标签, L’(u)表示点u的大小。P(v)表示点v的压力值 |
表2 算法2——资源分配算法Tab. 2 Algorithm 2——The allocation of resources |
输入: 一个图G=(V, E, P), 依赖标准t, 压力标准p |
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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’ |
注: G和G’表示图, V和V’表示点集合, E和E’表示边集合, P表示压力值集合。ConceptV表示当前循环中构建一个珊瑚礁所考虑的点的集合, FarV表示当前循环中构建一个珊瑚礁不考虑的点集合(位置过远)。EARA表示经过计算后可以构建珊瑚礁的地理区域, 是经纬线围成的一个长方形地理区域。Ø表示空集合 |
表3 实验数据说明Tab. 3 Description of experiment data |
年份 | 点数/个 | 边数/条 | 密度 |
---|---|---|---|
2002 | 9855 | 738751 | 74.96 |
2003 | 10210 | 704663 | 69.02 |
2004 | 12773 | 827467 | 64.78 |
2005 | 7998 | 742866 | 92.88 |
2010 | 5918 | 333725 | 56.39 |
2011 | 8213 | 755249 | 91.96 |
2012 | 6042 | 556196 | 92.05 |
注:密度=边数/点数。这个变量反映了一个graph中平均每个结点的边的数目, 即这个graph的密集程度 |
图9 资源分配后鱼类种群竞争值查询结果(a)和资源分配后鱼类种群概况(b)图a中颜色从浅黄色到深红色, 颜色越深的点, 竞争值越高, 反之竞争值越低。图b中黄色标记为天然珊瑚礁; 红色标记为新添加的人工礁 Fig. 9 Query result graph of fish population competition value after resource allocation (a) and overview of fish populations after resource allocation (b) |
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