A method for site selection evaluation of marine ranching construction based on multi-objective grey situation decision model

Abstract

The application provides a marine ranching construction site selection evaluation method based on a multi-target grey situation decision model, uses a multi-target grey situation decision method to preliminarily analyze a site selection problem, clearly defines evaluation target elements and establishes a basic model, ensures the comprehensiveness and accuracy of data, analyzes the specific influence and importance of each target element on the marine ranching site selection, quantifies the comprehensive effect of different site selection schemes through effect measure calculation, further calculates the comprehensive effect measure, evaluates the overall advantages and disadvantages of each site selection scheme, finally applies the multi-target grey situation decision model, combines the comprehensive effect measure result to perform site selection evaluation, and obtains a comprehensive evaluation result. The marine ranching construction site selection evaluation is established on the basis of scientific methods and theories, the influence of human subjective judgment is completely eliminated through a systematic and modeled evaluation process, and the objectivity of the evaluation is ensured under the condition that information is incomplete or interval numbers.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of marine ranching, and particularly relates to a marine ranching construction site selection evaluation method based on a multi-target grey situation decision model. TECHNICAL BACKGROUND

[0002] The construction of marine ranching is not only an effective means to protect and restore the marine ecological environment, but also can effectively improve the marine biodiversity and promote the self-repairing ability of the damaged ecological system. Through scientific and reasonable planning and management, the marine ranching can continuously provide high-quality marine products to meet the growing market demand, thereby improving the living standards of fishermen and promoting the economic development of coastal areas. At the same time, the construction of marine ranching can also help to reduce the dependence on wild fishery resources, promote the transformation of traditional fishery to modern leisure and experience fishery, and realize the sustainable utilization of fishery resources.

[0003] In the process of marine ranching construction, site selection is a key factor for the success of the project. The traditional site selection evaluation method of marine ranching relies too much on the experience and intuitive judgment of experts, which is easily affected by personal preferences and cognitive limitations, resulting in a lack of objectivity in the site selection results. Moreover, when dealing with complex information, only limited factors can be considered, making it difficult to fully reflect the multi-dimensional characteristics of the marine environment. These shortcomings to some extent restrict the optimization and development of marine ranching construction. SUMMARY

[0004] The marine ranching site selection evaluation method based on the multi-target grey situation decision model of the present application converts complex marine environmental data into intuitive decision-making basis through fine mathematical models and algorithms. This method comprehensively considers seawater quality, sediment environment, chlorophyll a, plankton, and benthic animals, and these multiple target elements often have complex mutual relationships and influences. Therefore, it is necessary to quantify the conflict between elements to evaluate their importance in order to better handle these uncertainties and complexities, so that the site selection work is no longer dependent on subjective experience and judgment, but turns to more scientific and systematic analysis.

[0005] The purpose of the present application is to provide a marine ranching site selection evaluation method based on a multi-target grey situation decision model to overcome the subjectivity in traditional site selection, solve the problem of insufficient information utilization, lack of scientificity, and difficulty in comprehensively considering multiple targets. By constructing this decision model, a comprehensive and objective evaluation and optimization of various factors involved in the site selection process of marine ranching is realized, so as to select the best construction location.

[0006] In order to achieve the above purpose, the technical scheme of the present application is as follows:

[0007] A marine ranching site selection evaluation method based on a multi-target grey situation decision model, comprising the following steps:

[0008] Step S1. The multi-objective gray situation decision method is used for the site selection problem of the marine ranching construction, and the target elements of the site selection evaluation are initially modeled;

[0009] Step S2. According to the determined target elements, the target element data in the site selection area is collected and obtained;

[0010] Step S3. The decision target analysis is performed according to the properties of the target elements;

[0011] Step S4. The weight calculation is performed according to the volatility of each target element and the correlation between the target elements;

[0012] Step S5. The effect measure calculation is performed on the target elements;

[0013] Step S6. The comprehensive effect measure calculation is performed;

[0014] Step S7. The site selection evaluation is performed by using the model, and the comprehensive evaluation result is obtained;

[0015] In the S1, the multi-objective gray situation decision is used for the initial modeling of the construction event, and there may be m schemes for the marine ranching site selection, denoted as a1, a2,..., a m , and the m schemes taken for the marine ranching site selection event are the situation s ij (i = 1, j = 1, 2,..., m), i is the marine ranching construction site selection event; each scheme may have n elements, denoted as b1, b2,..., b n ;

[0016] In the S1, the multi-objective gray situation decision is used for the site selection evaluation of the marine ranching construction, and the target elements are selected, including seawater quality, sediment environment, chlorophyll a, plankton, and benthic animals;

[0017] In the S3, the decision target analysis is performed on the extracted site selection elements, and the elements are divided into maximum, moderate, and minimum according to the importance of the influence of each evaluation element property on the site selection, denoted as m, a, and n, respectively;

[0018] In the S4, since multiple elements need to be objectively weighted, the standard deviation of each index is calculated to measure its volatility, so as to determine which elements show greater differences between different schemes, and the information carrying capacity and proportion of each element are comprehensively considered:

[0019] For the maximum element, positive processing is performed:

[0020]

[0021] For the minimum element, reverse processing is performed:

[0022]

[0023] For the moderate element, moderate processing is performed:

[0024]

[0025] Then the standard deviation of the element is represented by σ, which is calculated as:

[0026]

[0027] where σ n is the standard deviation to be calculated, μ j is the element value of the jth scheme, is the arithmetic mean of μ j , and N is the total number of μ j .

[0028] The amount of comprehensive information contained in the element is represented by C n , which is calculated as follows:

[0029]

[0030] where k n is the correlation coefficient of each element with other elements, which is obtained by the Pearson correlation coefficient method;

[0031] Therefore, the weighting weight of the element is:

[0032]

[0033] In the S5, the effectiveness measure is divided into upper limit effectiveness measure, central effectiveness measure, and lower limit effectiveness measure, and the mathematical calculation model is as follows:

[0034] (1) Upper limit effectiveness measure:

[0035]

[0036] where μ ij is the actual effectiveness of the target element in the scheme; μ max is the maximum value of the actual effectiveness of the target element in all schemes of the situation s ij .

[0037] (2) Central effectiveness measure:

[0038] r ij = min{μ ij , μ0} / max{μ ij , μ0}

[0039] where μ0 is the reference point of the sample;

[0040] (3) Lower limit effect measure:

[0041]

[0042] μ min is the minimum value of the actual effect of the target element in all schemes of situation s ij ; in S4, according to the polarity of the element, the type of the measure is selected in the site decision process, and the effect measure of the decision target of the marine ranching site is calculated as follows:

[0043] (1) Maximum value polarity, upper limit effect measure (UEM)

[0044]

[0045] (2) Moderate value polarity, central effect measure (MEM)

[0046]

[0047] (3) Minimum value polarity, lower limit effect measure (LEM)

[0048]

[0049] In S6, the comprehensive effect measure is calculated according to the m target effect measures r ij of the n schemes, and the corresponding decision element is The corresponding decision matrix M is:

[0050]

[0051] The comprehensive effect measure is:

[0052]

[0053] Therefore, the comprehensive effect measure space preliminarily obtained by each marine ranching decision scheme is:

[0054] (r i1 (∑) , r i2 (∑) ,..., r ij (∑) );

[0055] In S7, the decision-making process is to select the most suitable situation from the various options for the initial site selection. The decision-making process of selecting the most suitable option through events is called "row decision-making", and the process of selecting the most suitable decision-making option through decisions is called "column decision-making". Its mathematical model is as follows:

[0056] (1) Row decision: In the decision row of the comprehensive matrix M, select the decision element with the largest effect measure.

[0057]

[0058] (2) Column decision: In the decision column of the comprehensive matrix M, select the decision element with the largest effect measure.

[0059]

[0060] In S7, the optimal situation for the decision on site selection for marine ranching construction is:

[0061]

[0062] That is, the most suitable solution is solution j;

[0063] This paper presents a method for evaluating the site selection of marine ranches based on a multi-objective grey situation decision model. The method analyzes the most suitable solution, compares the actual sample values ​​of each solution with the target extreme value, and verifies the number of samples in each solution. If the solution is reasonable, the test results should be as follows:

[0064] (1) Completely conform to the maximum value, minimum value, and moderate value (maximum, medium, or minimum);

[0065] (2) Relatively consistent with the maximum value, minimum value, moderate value (much larger than the medium value, close to the medium or larger than the medium value)

[0066] The medium value is much smaller);

[0067] (3) Basically meet the maximum value, moderate value or minimum value (slightly larger than the medium value, close to the medium value or smaller than the medium value)

[0068] medium value is slightly smaller);

[0069] The beneficial effects of the present invention are:

[0070] (1) This invention, with its scientific and efficient approach, provides a new perspective and tool for marine ranch site selection. This approach allows us to comprehensively consider the diversity and complexity of the marine environment and transform the site selection process into a quantitative decision-making problem, thus avoiding the subjectivity, insufficient information utilization, lack of scientificity, and difficulty in comprehensively considering multiple objectives that may arise in traditional site selection.

[0071] (2) Not only improve the economic benefit, also help ecological protection and sustainable development. In addition, the adaptability and flexibility of the evaluation method make it can cope with the dynamic changes of marine environment, provide strong support for the site selection of marine ranching. This method not only provides scientific guidance for the construction of marine ranching, but also injects new vitality into the sustainable development of marine industry, and shows the great potential of the integration of science and technology and industry. BRIEF DESCRIPTION OF DRAWINGS

[0072] Figure 1 is a flow chart of a marine ranching site selection evaluation method based on a multi-objective gray situation decision model of the present application; DETAILED DESCRIPTION

[0073] The technical solutions and effects of the present application will be clearly and completely described below in combination with examples and drawings.

[0074] As Figure 1 shown is a flow chart of a marine ranching site selection evaluation method based on a multi-objective gray situation decision model according to the present application, which will be described below in combination with Figure 1 to explain the marine ranching site selection evaluation method based on a multi-objective gray situation decision model according to the present application

[0075] The following description takes Laizhou Bay as an example, and selects the western waters of Furong Island, the Taiping Bay waters in the Laizhou Bay area, and the western waters of Sanshan Island as scheme 1, scheme 2, and scheme 3 of the present embodiment, respectively.

[0076] The embodiment of the present application provides a marine ranching site selection evaluation method based on a multi-objective gray situation decision model, which specifically includes the following steps:

[0077] A multi-objective gray situation decision model is used to initially model the marine ranching construction site selection event. There are three schemes for the marine ranching site selection in the present case, denoted as: a1, a2, a3. At this time, the three schemes taken for the marine ranching site selection event are the situations s 11 , s 12 , s 13 , each of which may have m elements, denoted as: b1, b2,..., bm. m ;

[0078] The target elements are selected, the seawater quality, sediment environment, chlorophyll a, plankton and benthic organisms are selected as the main target elements, wherein the seawater quality includes the following 13 elements: pH, dissolved oxygen, suspended matter, COD (chemical oxygen demand), inorganic nitrogen, phosphate, copper, lead, cadmium, mercury, arsenic, zinc and chromium, and is marked as b1-b13; the sediment environment includes 9 elements: sulfide, organic carbon, copper, lead, cadmium, mercury, arsenic, zinc and chromium, and is marked as b14-b22; the element chlorophyll a content is marked as b23; the plankton includes the following elements: phytoplankton abundance and zooplankton abundance, and is marked as b24 and b25; and the benthic animal biomass is marked as b26;

[0079] The elements and polarity of the site selection decision of the marine ranching construction are as follows:

[0080] Element b1: the pH value of seawater, which is usually suitable in a certain range, moderate, and marked as a;

[0081] Element b2: dissolved oxygen (mg / L), marine organisms need moderate dissolved oxygen level, moderate, and marked as a;

[0082] Element b3: suspended matter (mg / L), which will affect the transparency and photosynthesis of water quality, and is extremely small, marked as n;

[0083] Element b4: COD (mg / L), higher COD indicates that the water quality is polluted, and is extremely small, marked as n;

[0084] Element b5: inorganic nitrogen (μg / L), excessive existence will lead to water eutrophication, and is extremely small, marked as n;

[0085] Element b6: phosphate (μg / L), excessive existence will lead to water eutrophication, and is extremely small, marked as n;

[0086] Element b7: copper (μg / L) in water quality, which should be strictly limited due to toxicity to marine organisms, and is extremely small, marked as n;

[0087] Element b8: lead (μg / L) in water quality, which should be strictly limited due to toxicity to marine organisms, and is extremely small, marked as n;

[0088] Element b9: cadmium (μg / L) in water quality, which should be strictly limited due to toxicity to marine organisms, and is extremely small, marked as n;

[0089] Element b10: mercury (μg / L) in water quality, which should be strictly limited due to toxicity to marine organisms, and is extremely small, marked as n;

[0090] Element b11: arsenic (μg / L) in water quality, which should be strictly limited due to toxicity to marine organisms, and is extremely small, marked as n;

[0091] Element b12: Zinc in water quality (pg / L), which should be strictly limited for the toxicity to marine organisms, very small, denoted by n;

[0092] Element b13: Chromium in water quality (pg / L), which should be strictly limited for the toxicity to marine organisms, very small, denoted by n;

[0093] Element b14: Sulfide (10 -6 ), which has a negative impact on marine ecosystems, very small, denoted by n;

[0094] Element b15: Organic carbon (10 -6 ), which reflects the fertility and ecological function of sediments, very large, denoted by m;

[0095] Element b16: Copper in sediments (10 -6 ), the content of heavy metals in sediments should be limited, very small, denoted by n;

[0096] Element b17: Lead in sediments (10 -6 ), the content of heavy metals in sediments should be limited, very small, denoted by n;

[0097] Element b18: Cadmium in sediments (10 -6 ), the content of heavy metals in sediments should be limited, very small, denoted by n;

[0098] Element b19: Mercury in sediments (10 -6 ), the content of heavy metals in sediments should be limited, very small, denoted by n;

[0099] Element b20: Arsenic in sediments (10 -6 ), the content of heavy metals in sediments should be limited, very small, denoted by n;

[0100] Element b21: Zinc in sediments (10 -6 ), the content of heavy metals in sediments should be limited, very small, denoted by n;

[0101] Element b22: Chromium in sediments (10 -6 ), the content of heavy metals in sediments should be limited, very small, denoted by n;

[0102] Element b23: Chlorophyll a content (pg / L), an important pigment for phytoplankton photosynthesis, very large, denoted by m;

[0103] Element b24: Phytoplankton abundance, which is crucial for energy transfer and food web stability in marine ecosystems, very large, denoted by m;

[0104] Element b25: Plankton abundance, which is crucial for energy transmission and stability of food web in marine ecosystem, and is denoted as m;

[0105] Element b26: Benthic animal biomass (g / m 2 ), which is crucial for energy transmission and stability of food web in marine ecosystem, and is denoted as m;

[0106] Since multiple elements need to be objectively weighted, and there are different degrees of correlation between the indicators involved in the evaluation, the standard deviation of each indicator needs to be calculated to measure its volatility, so as to determine which elements show greater differences between different schemes, and to consider the information carrying capacity and proportion of each element:

[0107] For the maximality element, it is processed in the positive direction:

[0108]

[0109] For the minimality element, it is processed in the reverse direction:

[0110]

[0111] For the intermediate element, it is processed in the moderate direction:

[0112]

[0113] Then the standard deviation of the element is denoted as σ, which is calculated as follows:

[0114]

[0115] Where σ n is the standard deviation to be calculated, μ j is the value of the jth scheme element, is the arithmetic mean of μ j , and N is the total number of μ j .

[0116] The comprehensive information contained in the element is denoted as C n , which is calculated as follows:

[0117]

[0118] Where k n is the correlation coefficient between each element and other elements.

[0119] Therefore, the weighting weight of the element is:

[0120]

[0121] Finally, the weight output according to the m element values of Case 1, Case 2, and Case 3 is ω = (0.32%, 1.23%, 3.75%, 5.28%, 4.37%, 3.95%, 3.29%, 4.71%, 4.30%, 4.53%, 4.85%, 4.21%, 3.79%, 4.26%, 4.65%, 3.94%, 4.67%, 3.79%, 3.50%, 4.23%, 3.77%, 3.77%, 3.52%, 3.65%, 3.59%, 4.08%)

[0122] According to the target area investigation of the three schemes in Laizhou Bay, the statistical results of each scheme are as follows:

[0123]

[0124]

[0125] The effect measure calculation is performed on different elements of each scheme:

[0126] (1) Maximum value

[0127] b15: Organic carbon (10 -6 ), maximum polarity, upper limit effect measure

[0128]

[0129]

[0130] b23 Chlorophyll a content (μg / L), maximum polarity, upper limit effect measure

[0131]

[0132] b24 Phytoplankton abundance, maximum polarity, upper limit effect measure

[0133]

[0134] b25 Zooplankton abundance, maximum polarity, upper limit effect measure

[0135]

[0136] b26 Benthic animal biomass (g / m 2 ), maximum polarity, upper limit effect measure

[0137]

[0138] (2) Moderate value

[0139] b1 PH, moderate value, polarity

[0140]

[0141] b2 dissolved oxygen (mg / L), moderate value, polarity

[0142]

[0143] (3) minimum value

[0144] b3 suspended solids (mg / L), minimum value, polarity, lower limit effect measure

[0145]

[0146] b4 COD (mg / L), minimum value, polarity, lower limit effect measure

[0147]

[0148]

[0149] b5 inorganic nitrogen (ug / L), minimum value, polarity, lower limit effect measure

[0150]

[0151] b6 phosphate (ug / L), minimum value, polarity, lower limit effect measure

[0152]

[0153] b7 copper (ug / L), minimum value, polarity, lower limit effect measure

[0154]

[0155] b8 lead (ug / L), minimum value, polarity, lower limit effect measure

[0156]

[0157] b9 cadmium (ug / L), minimum value, polarity, lower limit effect measure

[0158]

[0159] b10 mercury (ug / L), minimum value, polarity, lower limit effect measure

[0160]

[0161] b11 arsenic (ug / L), minimum value, polarity, lower limit effect measure

[0162]

[0163] b12 zinc (pg / L), minima polarity, lower limit effect measure

[0164]

[0165] b13 chromium (pg / L), minima polarity, lower limit effect measure

[0166]

[0167]

[0168] b14 sulfide (10 -6 ), minima polarity, lower limit effect measure

[0169]

[0170] b16 copper (10 -6 ), minima polarity, lower limit effect measure

[0171]

[0172] b17 lead (10 -6 ), minima polarity, lower limit effect measure

[0173]

[0174] b18 cadmium (10 -6 ), minima polarity, lower limit effect measure

[0175]

[0176] b19 mercury (10 -6 ), minima polarity, lower limit effect measure

[0177]

[0178] b20 arsenic (10 -6 ), minima polarity, lower limit effect measure

[0179]

[0180] b21 zinc (10 -6 ), minima polarity, lower limit effect measure

[0181]

[0182] b22 chromium (10 -6 ), minima polarity, lower limit effect measure

[0183]

[0184] Based on the above calculation, the effect measure decision matrix is established:

[0185]

[0186] According to the decision matrix, the comprehensive effect measure is calculated:

[0187]

[0188] Therefore, the calculation results are as follows:

[0189]

[0190] The comprehensive measure space of the marine ranching site selection event is:

[0191]

[0192] Make a decision:

[0193]

[0194] Explanation j ＊ = 1, (selecting the western sea area of Furong Island, scheme 1) is a satisfactory situation, and a1 is a satisfactory scheme.

[0195] Analysis of the selected most suitable scheme:

[0196] The characteristics of scheme 1 are:

[0197] (1) The pH value is 8.03, which meets the moderate value target;

[0198] (2) The dissolved oxygen is 8.73 mg / L, which meets the moderate value target;

[0199] (3) The suspended solids content is 9.09 mg / L, which is relatively consistent with the minimum value target;

[0200] (4) The COD content is 1.08 mg / L, which is relatively consistent with the minimum value target;

[0201] (5) The inorganic nitrogen content is 70.1 μg / L, which is relatively consistent with the minimum value target;

[0202] (6) The phosphate content is 6.71 μg / L, which is relatively consistent with the minimum value target;

[0203] (7) The copper content in the water quality is 1.48 μg / L, which is the minimum and completely meets the minimum value target;

[0204] (8) The lead content in the water quality is 1 μg / L, which is relatively consistent with the minimum value target;

[0205] (9) The cadmium content in water was 0.11 μg / L, which was relatively consistent with the minimum target value;

[0206] (10) The mercury content in water was 0.035 μg / L, which was basically consistent with the minimum target value;

[0207] (11) The arsenic content in water was 2.1 μg / L, which was relatively consistent with the minimum target value;

[0208] (12) The zinc content in water was 55.23 μg / L, which was relatively consistent with the minimum target value;

[0209] (13) The chromium content in water was 0.9 μg / L, which was relatively consistent with the minimum target value;

[0210] (14) The sulfide content in sediment was 19.1 x 10 -6 , which was completely consistent with the minimum target value;

[0211] (15) The organic carbon content in sediment was 0.32 x 10 -6 , which was basically consistent with the maximum target value;

[0212] (16) The copper content in sediment was 4.95 x 10 -6 , which was relatively consistent with the minimum target value;

[0213] (17) The lead content in sediment was 11.4 x 10 -6 , which was relatively consistent with the minimum target value;

[0214] (18) The cadmium content in sediment was 0.05 x 10 -6 , which was relatively consistent with the minimum target value;

[0215] (19) The mercury content in sediment was 0.009 x 10 -6 , which was completely consistent with the minimum target value;

[0216] (20) The arsenic content in sediment was 6.46 x 10 -6 , which was relatively consistent with the minimum target value;

[0217] (21) The zinc content in sediment was 24.6 x 10 -6 , which was relatively consistent with the minimum target value;

[0218] (22) The chromium content in sediment was 27.6 x 10 -6 , which was relatively consistent with the minimum target value;

[0219] (23) The content of chlorophyll a was 1.2 μg / L, which was the maximum, and was completely consistent with the maximum target value;

[0220] (24) Phytoplankton abundance is 0.41, the maximum, fully consistent with the maximum target;

[0221] (25) Zooplankton abundance is 1.8, the maximum, fully consistent with the maximum target;

[0222] (26) Benthic animal biomass is 19.54 g / m 2 , the maximum, fully consistent with the maximum target;

[0223] Therefore, the final selection scheme 1, the west sea area of Furong Island, is selected for the Laizhou Bay regional marine ranching site;

[0224] The verification of the site selection event shows that, in this case, scheme 3, the west sea area of Sanshan Island, is the minimum in the calculation result of the comprehensive measure space, which is consistent with the actual situation, i.e., the area has not been developed and constructed as a marine ranching area. In this case, the calculation result of scheme 1, the west sea area of Furong Island, shows that the comprehensive measure space is the maximum, and the result of scheme 2, the Taiping Bay sea area of Laizhou Bay, ranks the second. These two areas have been constructed as the "State-level Marine Ranching Demonstration Area of the West Sea Area of Furong Island, Shandong Province" and the "State-level Marine Ranching Demonstration Area of the Taiping Bay Sea Area of Laizhou City, Shandong Province", respectively.

[0225] Further analysis of the economic data of the marine ranching in China shows that the average annual total output value of the State-level Marine Ranching Demonstration Area of the West Sea Area of Furong Island, Shandong Province, is as high as 800 million yuan, and the average annual total output value of the State-level Marine Ranching Demonstration Area of the Taiping Bay Sea Area of Laizhou City, Shandong Province, is 39 million yuan, which is significantly higher than the latter. The difference in economic output not only reflects the site selection advantage of the west sea area of Furong Island, but also verifies the effectiveness and feasibility of the multi-target grey situation decision-making model proposed in the present application in the site selection of the marine ranching.

Claims

1. A method for evaluating the site selection of marine ranch construction based on a multi-objective grey situation decision model, characterized in that: The following steps are involved: S1. Use a multi-objective grey situation decision-making method to determine the target factors for site selection and conduct initial modeling for the marine ranch construction site selection problem; S2. Based on the identified target elements, collect and obtain target element data within the site selection area; S3. Conduct decision-making target analysis based on the nature of target elements; S4. Calculate weights based on the volatility of each target factor and the correlation between target factors; S5. Calculate the effect measurement of the target elements; S6. Calculation of comprehensive effect measurement; S7. Use the model to conduct site selection assessment and obtain comprehensive evaluation results; In S4, the standard deviation of each target element is calculated to measure its volatility, so as to determine which target elements show greater differences between different schemes, and comprehensively consider the information carrying capacity and proportion of each element: For the maximum elements, perform positive processing on them: For the minimum elements, perform the inverse processing: For moderate elements, moderate them: The standard deviation of the target element is then expressed as σ, which is calculated as follows: Among them, σ n is the standard deviation to be calculated, μ j is the actual effect value of the target element of the j-th solution, μ j The arithmetic mean of N is μ j the total number of The comprehensive information contained in the target element is represented by C n The specific calculation is as follows: where k n The correlation coefficient between each factor and other factors is calculated by the Pearson correlation coefficient method; The weight of the target element is: In S1, a multi-objective grey situation decision is used to initially model the construction event. The m possible solutions for the site selection of the marine ranch are denoted as: a1, a2, ..., a m At this time, the m options adopted for the site selection event of the ocean ranch are situation s ij (i=1,j=1、2、…、m), i is the event of site selection for marine ranch construction; each plan may have n elements, recorded as: b1,b2,…,b n ; In S1, the target factors include seawater quality, sediment environment, chlorophyll a content, plankton, and benthic animals; seawater quality includes 13 factors: pH, dissolved oxygen, suspended solids, COD, inorganic nitrogen, phosphate, copper, lead, cadmium, mercury, arsenic, zinc, and chromium; sediment environment includes 9 factors: sulfide, organic carbon, copper, lead, cadmium, mercury, arsenic, zinc, and chromium; plankton includes phytoplankton abundance and zooplankton abundance; In S5, the effect measurement is divided into upper limit effect measurement, center effect measurement, and lower limit effect measurement. The mathematical calculation model is as follows: (1) Upper limit effect measurement: where μ ij is the actual effect of the target elements in the plan; μ max For the situation ij The maximum value of the actual effect of the target element in all plans; (2) Center effect measurement: r ij =min{μ ij ,μ0} / max{μ ij ,μ0} Where μ0 is the reference point of the sample; (3) Lower limit effect measurement: where μ min For the situation ij The minimum value of the actual effect of the target element in all the plans; In the decision-making process of site selection, the measurement type is selected according to the polarity of the target element. The calculation method of the effect measurement of each decision-making target of marine ranch construction site selection is as follows: (1) Maximum polarity maxmm 1j =max{μ 11 ,mm 12 ,…,m 1j }; (2) Moderate polarity (3) Minimum polarity minm 1j =min{μ 11 ,m 12 ,…,m 1j }; In S6, the comprehensive effect measure is calculated as the m target effect measures r based on the n solutions. ij , and its corresponding decision element is The corresponding decision matrix M is: The comprehensive effect measure is: Therefore, the preliminary comprehensive effect measurement space of each marine ranch construction decision-making plan is: (r i1 (∑) ，r i2 (∑) ，…，r ij (∑) )。 2. The method for evaluating the site selection of marine ranch construction based on a multi-objective grey situation decision model according to claim 1 is characterized in that: In S3, the extracted target elements are subjected to decision-making target analysis, and the target elements are divided into maximum, moderate, and minimum according to the importance of the properties of each target element on the site selection, which are respectively denoted as m, a, and n.

3. The method for evaluating the site selection of marine ranch construction based on a multi-objective grey situation decision model according to claim 1 is characterized in that: In S7, the decision-making process is to select the most suitable situation from various options. The decision-making process of selecting the most suitable option through events is called "row decision-making", and the process of selecting the most suitable decision-making option through decisions is called "column decision-making". Its mathematical model is as follows: (1) Row decision: In the decision row of the comprehensive matrix M, select the decision element with the largest effect measure: (2) Column decision: In the decision column of the comprehensive matrix M, select the decision element with the largest effect measure:

4. The method for evaluating the site selection of marine ranch construction based on a multi-objective grey situation decision model according to claim 1 is characterized in that: In S7, the optimal situation for the decision on site selection for marine ranching construction is: The most suitable solution is solution j; Analyze the most suitable solution selected, compare the actual sample values ​​of the solution with the target extreme value, and test the sample numbers of each solution. If the decision solution is reasonable, the test results should be as follows: (1) Completely conform to the maximum value, minimum value, and moderate value; (2) relatively consistent with the maximum value, minimum value, and moderate value; (3) Basically conform to the maximum value, moderate value or minimum value.

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