Marine ranch construction site selection evaluation method based on multi-target gray situation decision model

Abstract

The invention provides a marine ranch construction site selection evaluation method based on a multi-target gray situation decision-making model, and the method comprises the steps: carrying out the preliminary analysis of a site selection problem through a multi-target gray situation decision-making method, clearly evaluating target elements, and building a basic model; the comprehensiveness and accuracy of the data are ensured; analyzing the specific influence and importance of each target element on the marine ranching site selection; the comprehensive effects of different site selection schemes are quantified through effect measurement calculation; further calculating a comprehensive effect measure, and evaluating the overall advantages and disadvantages of each site selection scheme; and finally, carrying out site selection evaluation by applying a multi-target gray situation decision model and combining a comprehensive effect measurement result to obtain a comprehensive evaluation result. According to the method, marine ranch construction site selection evaluation is established on the basis of a scientific method and theory, the influence of human subjective judgment is completely eliminated through a systematized and modeled evaluation process, and the evaluation objectivity is ensured under the condition that information is incomplete or the information is an interval number.

Description

Technical Field

[0001] The invention belongs to the technical field of marine ranching, and in particular relates to a marine ranching construction site selection evaluation method based on a multi-objective grey situation decision model. Technical Background

[0002] The construction of marine ranches is not only an effective means to protect and restore the marine ecological environment, but also can effectively enhance marine biodiversity and promote the self-repair ability of damaged ecosystems. Through scientific and reasonable planning and management, marine ranches can continuously provide high-quality seafood 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 ranches will also help reduce the pressure of dependence on wild fishery resources, promote the transformation of traditional fisheries to modern leisure and experience-based fisheries, and achieve sustainable utilization of fishery resources.

[0003] In the process of marine ranch construction, site selection is a key factor in the success or failure of the project. However, traditional marine ranch site selection and evaluation methods rely too much on the experience and intuitive judgment of experts, which are easily affected by personal preferences and cognitive limitations, resulting in a lack of objectivity in site selection results. In addition, when processing complex information, only limited factors may be considered, making it difficult to fully reflect the multi-dimensional characteristics of the marine environment. These shortcomings have, to a certain extent, restricted the optimization and development of marine ranch construction. Summary of the invention

[0004] The marine ranch site selection evaluation method based on the multi-objective grey situation decision model of the present invention transforms complex marine environmental data into intuitive decision-making basis through sophisticated mathematical models and algorithms. This method comprehensively considers multiple objective factors such as seawater quality, sediment environment, chlorophyll a, plankton, and benthic animals. There are often complex relationships and influences between these factors. Therefore, it is necessary to quantify the conflicts between the factors to evaluate their importance in order to better deal with these uncertainties and complexities, so that the site selection work no longer relies on subjective experience judgment, but turns to a more scientific and systematic analysis.

[0005] The purpose of the present invention is to provide a marine ranch site selection evaluation method based on a multi-objective grey situation decision model to get rid of the subjectivity existing in traditional site selection, solve the problems of insufficient information utilization, lack of scientificity, and difficulty in comprehensively considering multiple objectives. By constructing this decision model, a comprehensive and objective evaluation and optimization of the various factors involved in the marine ranch site selection process can be achieved, so as to select the best construction location.

[0006] In order to achieve the above object, the technical solution of the present invention is as follows:

[0007] A method for evaluating the site selection of marine ranches based on a multi-objective grey situation decision model includes the following steps:

[0008] Step S1. Using a multi-objective grey situation decision method to solve the problem of marine ranch construction site selection, determine the target elements of site selection evaluation and conduct initial modeling;

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

[0010] Step S3. Perform decision target analysis based on the properties of the target elements;

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

[0012] Step S5. Calculate the effect measurement of the target element;

[0013] Step S6. Calculation of comprehensive effect measurement;

[0014] Step S7. Use the model to evaluate the site selection and obtain a comprehensive evaluation result;

[0015] In S1, a multi-objective grey situation decision is used to initially model the construction event. There may be m solutions for the site selection of the marine ranch, which are recorded as: a 1 、a 2 , ..., 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 selecting a site for marine ranch construction; each plan may have n elements, denoted as: b 1 , b 2 , ..., b n ;

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

[0017] In S3, the extracted site selection factors are analyzed for decision objectives, and the factors are divided into maximum, moderate, and minimum according to the importance of the properties of each evaluation factor on the site selection, which are recorded as m, a, and n respectively;

[0018] In S4, since multiple factors need to be objectively weighted, based on the differences between the factors in different schemes, and the different degrees of correlation between the indicators involved in the evaluation, it is necessary to calculate the standard deviation of each indicator to measure its volatility, so as to determine which factors show greater differences between different schemes, and comprehensively consider the information carrying capacity and proportion of each factor:

[0019] For the maximum elements, perform positive processing on them:

[0020]

[0021] For the minimum elements, perform the inverse processing:

[0022]

[0023] For moderate elements, moderate them:

[0024]

[0025] The standard deviation of the factor is then represented by σ, and the specific calculation is:

[0026]

[0027] Among them, σ n is the standard deviation to be calculated, μ j is the element value of the j-th solution, μ j The arithmetic mean of j Total number of

[0028] The comprehensive information contained in the elements is given by C n The specific calculation is as follows:

[0029]

[0030] where k n The correlation coefficient between each factor and other factors is calculated by Pearson correlation coefficient method;

[0031] Therefore, the weights of the factors are:

[0032]

[0033] In S5, the effect measurement is divided into upper limit effect measurement, center effect measurement, and lower limit effect measurement, and the mathematical calculation model is as follows:

[0034] (1) Upper limit effect measurement:

[0035]

[0036] 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;

[0037] (2) Center effect measurement:

[0038] r ij =min{μ ij , μ 0} / max{μ ij , μ 0}

[0039] where μ 0 is the reference point of the sample;

[0040] (3) Lower limit effect measurement:

[0041]

[0042] μ min For the situation ij The minimum value of the actual effect of the target element in all the schemes; in S4, during the site selection decision process, the measurement type is selected according to the polarity of the element, and the effect measurement calculation method of the decision target of the site selection of the marine ranch construction is as follows:

[0043] (1) Maximum polarity, online effect measurement (UEM)

[0044]

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

[0046]

[0047] (3) Minimum polarity, offline effect measure (LEM)

[0048]

[0049] In S6, the comprehensive effect measure is calculated based on the m target effect measures r of the n solutions. ij , and its corresponding decision element is The corresponding decision matrix M is:

[0050]

[0051] The comprehensive effect measure is:

[0052]

[0053] Therefore, the preliminary comprehensive effect measurement space of each marine ranch construction decision-making plan is:

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

[0055] In S7, the decision-making process is to select the most suitable situation from the various solutions of the site preliminary selection. The decision-making process of selecting the most suitable solution through events is called "row decision-making", and the process of selecting the most suitable decision-making solution through decision-making is called "column decision-making", and 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 the site selection for the marine ranch construction is:

[0061]

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

[0063] The present invention provides a method for evaluating the site selection of marine ranch construction based on a multi-objective grey situation decision model. The most suitable scheme is analyzed, each actual sample value of the scheme is compared with the target extreme value, and the number of samples of each scheme is tested. If the decision scheme is reasonable, the test result 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] The medium value is slightly smaller);

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

[0070] (1) The present invention provides a new perspective and tool for the site selection of marine ranches with its scientificity and efficiency. Through this method, we can comprehensively consider the diversity and complexity of the marine environment and transform the site selection process into a quantitative decision-making problem, thereby avoiding the subjectivity, insufficient information utilization, lack of scientificity, and difficulty in comprehensively considering multiple objectives that may occur in traditional site selection.

[0071] (2) It not only improves economic benefits, but also contributes to ecological protection and sustainable development. In addition, the adaptability and flexibility of the assessment method enables it to cope with the dynamic changes of the marine environment, providing strong support for the site selection of marine ranches. This method not only provides scientific guidance for the construction of marine ranches, but also injects new vitality into the sustainable development of the marine industry, demonstrating the huge potential of the integration of science and technology and industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 It is a flow chart of a method for evaluating the site selection of a marine ranch based on a multi-objective grey situation decision model of the present invention; DETAILED DESCRIPTION

[0073] The technical solution and effects of the present invention will be clearly and completely explained below in combination with examples and drawings.

[0074] like Figure 1 The flowchart of the method for evaluating the site selection of marine ranches based on the multi-objective grey situation decision model according to the present invention is shown below. Figure 1 To illustrate the marine ranch site selection evaluation method based on the multi-objective grey situation decision model implemented according to the present invention

[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 this embodiment respectively.

[0076] The embodiment of the present invention provides a method for evaluating the site selection of an ocean ranch based on a multi-objective grey situation decision model, which specifically includes the following steps:

[0077] The multi-objective grey situation decision-making model is used to initially model the site selection event of the marine ranch construction. In this case, there are three options for the site selection of the marine ranch: a 1 、a 2 、a 3 At this time, the three options adopted for the site selection event of the ocean ranch are situation s 11 、s 12 、s 13 , each solution may have m elements, denoted as: b 1 , b 2 , ..., bm ;

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

[0079] The factors and polarities of site selection for marine ranching are as follows:

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

[0081] Element b2: dissolved oxygen (mg / L). Marine organisms require a moderate level of dissolved oxygen, moderate, recorded as a;

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

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

[0084] Element b5: Inorganic nitrogen (μg / L), excessive presence will lead to eutrophication of water bodies, extremely small, denoted as n;

[0085] Element b6: Phosphate (μg / L), excessive presence will lead to eutrophication of water bodies, very small, denoted as n;

[0086] Element b7: Copper in water (μg / L), toxic to marine life and should be strictly limited, extremely small, denoted as n;

[0087] Element b8: Lead in water (μg / L), which is toxic to marine life and should be strictly restricted, is extremely small, and is recorded as n;

[0088] Element b9: Cadmium in water (μg / L), which is toxic to marine life and should be strictly limited, is extremely small, and is recorded as n;

[0089] Element b10: Mercury in water (μg / L), toxic to marine life and should be strictly limited, extremely small, denoted as n;

[0090] Element b11: Arsenic in water (μg / L), which is toxic to marine life and should be strictly limited, is extremely small, and is recorded as n;

[0091] Element b12: Zinc in water (μg / L), toxic to marine life and should be strictly limited, extremely small, denoted as n;

[0092] Element b13: Chromium in water (μg / L), which is toxic to marine life and should be strictly limited, is extremely small, and is recorded as n;

[0093] Element b14: Sulfide (10 -6 ), which has a negative impact on the marine ecosystem, is minimal, denoted by n;

[0094] Element b15: organic carbon (10 -6 ), reflecting the fertility and ecological function of sediments, maximum, denoted as m;

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

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

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

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

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

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

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

[0102] Element b23: Chlorophyll a content (μg / L), an important pigment for phytoplankton photosynthesis, maximum value, recorded as m;

[0103] Element b24: phytoplankton abundance, which is crucial for energy transfer in marine ecosystems and the stability of food webs, is maximal and is denoted by m;

[0104] Element b25: zooplankton abundance, which is crucial for energy transfer in marine ecosystems and the stability of food webs, is maximal and is denoted by m;

[0105] Element b26: benthic animal biomass (g / m 2 ), which is crucial for energy transfer in marine ecosystems and the stability of food webs, maximality, denoted by m;

[0106] Since multiple factors need to be objectively weighted, based on the differences between different schemes, and the different degrees of correlation between the indicators involved in the evaluation, it is necessary to calculate the standard deviation of each indicator to measure its volatility, so as to determine which factors show greater differences between different schemes, and comprehensively consider the information carrying capacity and proportion of each factor:

[0107] For the maximum elements, perform positive processing on them:

[0108]

[0109] For the minimum elements, perform the inverse processing:

[0110]

[0111] For moderate elements, moderate them:

[0112]

[0113] The standard deviation of the factor is then represented by σ, and the specific calculation is:

[0114]

[0115] Among them, σ n is the standard deviation to be calculated, μ j is the element value of the j-th solution, μ j The arithmetic mean of j Total number of

[0116] The comprehensive information contained in the elements is given by C n The specific calculation is as follows:

[0117]

[0118] where k n The correlation coefficient of each factor with other factors.

[0119] Therefore, the weights of the factors are:

[0120]

[0121] Finally, the weights output according to the m factor values ​​of Case 1, Case 2, and Case 3 are 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 survey of the three options in Laizhou Bay, the statistical results of each option are as follows:

[0123]

[0124]

[0125] Calculate the effect of different elements of each plan:

[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 measurement

[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 value, moderate polarity

[0140]

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

[0142]

[0143] (3) Minimum

[0144] b3 Suspended solids (mg / L), minimum polarity, lower limit effect measurement

[0145]

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

[0147]

[0148]

[0149] b5 Inorganic nitrogen (μg / L), minimum polarity, lower limit effect measurement

[0150]

[0151] b6 Phosphate (μg / L), minimum polarity, lower limit effect measure

[0152]

[0153] b7 Copper (μg / L), minimum polarity, lower limit effect measure

[0154]

[0155] b8 Lead (μg / L), minimum polarity, lower limit effect measure

[0156]

[0157] b9 Cadmium (μg / L), minimum polarity, lower limit effect measure

[0158]

[0159] b10 Mercury (μg / L), minimum polarity, lower limit effect measure

[0160]

[0161] b11 Arsenic (μg / L), minimum polarity, lower limit effect measure

[0162]

[0163] b12 Zinc (μg / L), minimum polarity, lower limit effect measure

[0164]

[0165] b13 Chromium (μg / L), minimum polarity, lower limit effect measure

[0166]

[0167]

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

[0169]

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

[0171]

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

[0173]

[0174] b18 Cadmium (10 -6 ), minimum polarity, lower limit effect measure

[0175]

[0176] b19 Mercury (10 -6 ), minimum polarity, lower limit effect measure

[0177]

[0178] b20 Arsenic (10 -6 ), minimum polarity, lower limit effect measure

[0179]

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

[0181]

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

[0183]

[0184] Based on the above calculations, the effect measurement decision matrix is ​​established:

[0185]

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

[0187]

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

[0189]

[0190] The comprehensive measurement space for marine ranch site selection events is:

[0191]

[0192] Make decisions about it:

[0193]

[0194] Description ＊ =1, (Select the western waters of Furong Island, plan 1) is a satisfactory situation, and a1 is a satisfactory plan.

[0195] Analyze the most suitable solution:

[0196] The characteristics of scheme 1 are:

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

[0198] (2) Dissolved oxygen was 8.73 mg / L, meeting the moderate target;

[0199] (3) The suspended solids content was 9.09 mg / L, which was in line with the minimum value target;

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

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

[0202] (6) The phosphate content was 6.71 μg / L, which was in line with the minimum value target;

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

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

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

[0206] (10) The mercury content in the water quality is 0.035 μg / L, which basically meets the minimum value target;

[0207] (11) The arsenic content in the water quality is 2.1 μg / L, which is in line with the minimum value target;

[0208] (12) The zinc content in the water quality is 55.23 μg / L, which is in line with the minimum value target;

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

[0210] (14) The sulfide content in the sediment is 19.1×10 -6 , minimum, fully meets the minimum value target;

[0211] (15) The organic carbon content in the sediment is 0.32×10 -6 , basically meets the maximum value target;

[0212] (16) The copper content in the sediment is 4.95×10 -6 , which is more in line with the minimum value target;

[0213] (17) The lead content in the sediment is 11.4×10 -6 , which is more in line with the minimum value target;

[0214] (18) The cadmium content in the sediment is 0.05×10 -6 , which is more in line with the minimum value target;

[0215] (19) The mercury content in the sediment is 0.009×10 -6 , which fully meets the minimum value target;

[0216] (20) The arsenic content in the sediment is 6.46×10 -6 , which is more in line with the minimum value target;

[0217] (21) The zinc content in the sediment is 24.6×10 -6 , which is more in line with the minimum value target;

[0218] (22) The chromium content in the sediment is 27.6×10 -6 , which is more in line with the minimum value target;

[0219] (23) The chlorophyll a content was 1.2 μg / L, the maximum, which fully met the maximum value target;

[0220] (24) The phytoplankton abundance is 0.41, the maximum, which fully meets the maximum value target;

[0221] (25) The zooplankton abundance is 1.8, the maximum, which fully meets the maximum value target;

[0222] (26) The biomass of benthic animals is 19.54 g / m 2 , maximum, fully meets the maximum value target;

[0223] Therefore, the final site selection for the marine ranch in Laizhou Bay was Plan 1, the western waters of Furong Island;

[0224] The site selection event was verified. In this case, the calculation results of Scheme 3. Sanshan Island West Sea Area showed the minimum value in the comprehensive measurement space, which is consistent with the actual situation, that is, the area has not yet been developed and constructed for marine ranching; in this case, the calculation results of Scheme 1. Furong Island West Sea Area showed the largest comprehensive measurement space, while the results of Scheme 2. Laizhou Bay Taiping Bay Sea Area ranked second. These two areas have built the "Shandong Province Furong Island West Sea Area National Marine Ranch Demonstration Zone" and "Shandong Province Laizhou City Taiping Bay Sea Area Mingbo National Marine Ranch Demonstration Zone".

[0225] Further analysis of the economic data of my country's marine ranches shows that the average annual gross output value of the national marine ranch demonstration area in the western waters of Furong Island, Shandong Province is as high as 800 million yuan, while the average annual gross output value of the Mingbo national marine ranch demonstration area in Taiping Bay, Laizhou City, Shandong Province is 39 million yuan, and the former is significantly higher than the latter. This difference in economic output not only reflects the site selection advantage of the western waters of Furong Island, but also confirms the effectiveness and feasibility of the multi-objective gray situation decision-making model proposed in this paper in the site selection of marine ranches.

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 multi-objective grey situation decision-making method to solve the problem of marine ranch construction site selection and determine the target elements of site selection evaluation for initial modeling; S2. Collect and obtain target element data within the site selection area based on the determined target elements; S3. Conduct decision-making target analysis based on the nature of target elements; S4. Calculate the weight according to the volatility of each target element and the correlation between the target elements; S5. Calculate the effect measurement of the target elements; S6. Calculation of comprehensive effect measure; S7. Use the model to conduct site selection evaluation and obtain comprehensive evaluation results.

2. The method for site selection and evaluation of marine ranch construction based on a multi-objective grey situation decision model according to claim 1 is characterized in that: 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 recorded 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 selecting a site for marine ranch construction; each plan may have n elements, denoted as: b1,b2,...,b n .

3. The method for site selection and evaluation of marine ranch construction based on a multi-objective grey situation decision model according to claim 1 is characterized in that: In the S1, the target elements include seawater quality, sediment environment, chlorophyll a content, plankton, and benthic animals; the seawater quality includes 13 elements: pH, dissolved oxygen, suspended matter, COD, inorganic nitrogen, phosphate, copper, lead, cadmium, mercury, arsenic, zinc, and chromium; the sediment environment includes 9 elements: sulfide, organic carbon, copper, lead, cadmium, mercury, arsenic, zinc, and chromium; and the plankton includes phytoplankton abundance and zooplankton abundance.

4. The method for site selection and evaluation 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 analyzed for decision targets, 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 denoted as m, a and n respectively.

5. The method for site selection and evaluation of marine ranch construction based on a multi-objective grey situation decision model according to claim 4 is characterized in that: 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 represented by 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 j 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 Pearson correlation coefficient method; The weight of the target element is:

6. The method for site selection and evaluation of marine ranch construction based on a multi-objective grey situation decision model according to claim 1 is characterized in that: In S5, the effect measurement is divided into upper limit effect measurement, center effect measurement, and lower limit effect measurement, and 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 actual effect of the target element among all the solutions.

7. The method for site selection and evaluation of marine ranch construction based on a multi-objective grey situation decision model according to claim 1 is characterized in that: In S5, during the decision-making process of site selection, the measurement type is selected according to the polarity of the target element, and the effect measurement calculation method of the decision-making target of the site selection of the marine ranch construction is as follows: (1) Maximum polarity (2) Moderate polarity (3) Minimum polarity 8. The method for site selection and evaluation of marine ranch construction based on a multi-objective grey situation decision model according to claim 1 is characterized in that: In S6, the comprehensive effect measure is calculated as m target effect measures r according to 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 (∑) )。 9. The method for site selection and evaluation 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 solutions. The decision-making process of selecting the most suitable solution through events is called "row decision-making", and the process of selecting the most suitable decision-making solution through decision-making 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:

10. The method for site selection and evaluation 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 the site selection for the marine ranch construction is: The most suitable solution is solution j; Analyze the most suitable solution, compare the actual sample values ​​of the solution with the target extreme value, and test the sample numbers of the 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) It is 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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