Marine ranch water quality risk assessment method

Abstract

The invention belongs to the technical field of risk assessment, and discloses a marine ranch water quality risk assessment method, and the method comprises the steps: determining the water quality grade of a marine ranch according to a plurality of water quality parameters of the marine ranch and a risk interval corresponding to each water quality parameter; when the water quality grade belongs to a standard risk grade, constructing a risk fuzzy matrix according to the distribution weight of each water quality parameter and the membership degree of each water quality parameter in the risk interval, and determining the comprehensive membership degree of each risk interval according to the risk fuzzy matrix; and according to the comprehensive membership degree and the measurement value of each risk interval, determining a comprehensive evaluation index of the marine ranch water quality so as to evaluate the marine ranch water quality risk. According to the method, a fuzzy comprehensive evaluation method is adopted, through reasonable weight distribution and construction of a fuzzy relation matrix, the independent influence of each water quality parameter and the comprehensive effect among multiple factors are fully considered, and therefore scientific and objective evaluation of the water quality risk is achieved.

Description

Technical Field

[0001] The invention discloses a method for assessing the risk of water quality in a marine ranch, and belongs to the technical field of risk assessment. Background Art

[0002] As an important part of the marine economy, marine ranches are not only a key area for the proliferation and conservation of fishery resources, but also play an irreplaceable role in promoting the sustainable development of fisheries, improving fishery production efficiency, and protecting the marine ecological environment. Through scientific and reasonable planning and management, marine ranches can optimize the allocation of fishery resources, improve the quality of fishery products, ensure the long-term utilization of fishery resources, and promote the diversified development of the marine economy.

[0003] However, with climate change and intensified human activities, marine ranches are facing unprecedented multiple risks. Among them, water pollution is particularly prominent, including eutrophication, heavy metal pollution, organic pollution, etc. These pollutants pose a serious threat to the growth, reproduction and survival of marine organisms. In addition, the risk of disease transmission cannot be ignored. The prevention and control of marine diseases is becoming increasingly complex. Once an epidemic breaks out, it will cause huge economic losses to marine ranches. In addition, marine ranches are also facing other risks such as natural disasters and man-made destruction. These risks together constitute a huge challenge to the sustainable development of marine ranches.

[0004] At present, marine ranch water quality risk assessment is facing many challenges. The assessment standards are not unified, and there are differences in the assessment methods and standards adopted by different regions and institutions, which makes it difficult to compare and apply the assessment results. The weight distribution of each parameter is unreasonable, and some key parameters may be ignored or over-emphasized, which affects the accuracy and reliability of the assessment results. The risk level division is unclear, and there is a lack of scientific division standards and basis, which makes it difficult for the assessment results to accurately reflect the actual situation of marine ranch water quality risks. Summary of the invention

[0005] The purpose of the present invention is to provide a method for assessing the water quality risk of marine ranches to solve the technical problems in the prior art that the assessment of the water quality risk of marine ranches is inconsistent in assessment standards and the weight distribution of each parameter is unreasonable, which affects the accuracy and reliability of the assessment results. To achieve the above purpose, the present invention proposes a method for assessing the water quality risk of marine ranches, and the specific scheme is as follows:

[0006] A method for assessing marine ranch water quality risk comprises the following steps:

[0007] Step 1: determining the water quality grade of the marine ranch according to multiple water quality parameters of the marine ranch and the risk interval corresponding to each water quality parameter;

[0008] Step 2: When the water quality level belongs to the standard risk level, a risk fuzzy matrix is ​​constructed according to the allocation weight of each water quality parameter and the membership degree of each water quality parameter in the risk interval, and the comprehensive membership degree of each risk interval is determined according to the risk fuzzy matrix;

[0009] Step 3: Determine a comprehensive evaluation index of the marine ranch water quality based on the comprehensive membership and the measurement value of each risk interval, thereby evaluating the marine ranch water quality risk.

[0010] Preferably, before step 2, the method further includes:

[0011] Determine whether the water quality level belongs to the standard risk level;

[0012] If not, the water quality level is determined to be an extreme risk level, and corresponding management measures are taken according to the extreme risk level.

[0013] Preferably, determining the allocation weight of each water quality parameter specifically includes:

[0014] Each water quality parameter is scored and the allocation weight of each water quality parameter is determined based on the scoring results.

[0015] Preferably, the Delphi method is used to score the importance of each water quality parameter.

[0016] Preferably, the allocation weight of each water quality parameter is determined according to the scoring result, specifically including:

[0017] Determine the authority coefficient of the scoring expert based on his / her professional title coefficient;

[0018] The allocation weight of each water quality parameter is determined according to the authority coefficient and the scoring result.

[0019] Preferably, determining the degree of membership of each water quality parameter in the risk interval specifically includes:

[0020] The membership of each water quality parameter in each risk interval is calculated based on the membership function.

[0021] Preferably, the multiple water quality parameters include water temperature, salinity, dissolved oxygen, pH value, ammonia nitrogen, nitrate and hydrogen sulfide.

[0022] Preferably, determining whether the water quality level belongs to the standard risk level specifically includes:

[0023] The preset rules are used to determine whether the water quality level belongs to the standard risk level according to the multiple water quality parameters and their corresponding risk intervals.

[0024] Preferably, the risk interval includes an optimal interval, a warning value interval and a limit value interval.

[0025] Preferably, the preset rules are specifically:

[0026] When at least one water quality parameter is within a limit value interval or a preset number of water quality parameters are within a warning value interval, it is determined that the water quality level of the ocean ranch is at an extreme risk level.

[0027] Beneficial effects: This application adopts the fuzzy comprehensive evaluation method, and realizes the scientific assessment of water quality risks through reasonable weight allocation and fuzzy relationship matrix construction. The evaluation system divides the risk level into four levels: low risk, medium risk, high risk and extreme risk, and establishes judgment rules based on the principle of extreme risk priority, and formulates corresponding risk response measures. This evaluation method not only takes into account the independent influence of various indicators, but also reflects the combined effect of multiple factors. Through this evaluation system, water quality risks can be discovered and warned in a timely manner so that corresponding management measures can be taken to ensure the sustainable development of marine ranches. Future research directions will focus on optimizing model accuracy, improving the automated monitoring system, and further expanding the scope of application of the evaluation model to provide a reference for water quality risk management in more marine ranches. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a schematic diagram of the flow chart of the marine ranch water quality risk assessment method of the present invention. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solution and advantages of the present invention more clear, the present invention is further described in detail below in conjunction with specific implementation methods. It should be understood that the specific implementation methods described here are only used to explain the present invention and do not limit the scope of protection of the present invention.

[0030] A method for assessing water quality risk in marine ranches

[0031] In view of the important position of marine ranches in the marine economy and the many risks they face, this paper proposes a water quality risk assessment method based on fuzzy mathematics theory. This method aims to provide accurate data support for water quality management of marine ranches through a scientific and comprehensive assessment system, thereby ensuring the health of marine organisms and the sustainable development of marine ranches.

[0032] The specific implementation is as follows:

[0033] Step 1: determining the water quality grade of the marine ranch according to multiple water quality parameters of the marine ranch and the risk interval corresponding to each water quality parameter;

[0034] First, the water quality level is preliminarily judged based on multiple key water quality parameters of the marine ranch and their corresponding risk intervals. These risk intervals usually include optimal intervals, warning value intervals, and limit value intervals, which are set based on a large amount of experimental data and expert experience. Among them, the optimal interval means that the water quality is beneficial to both fish and shellfish, and the water quality remains within this range without affecting growth and health. The warning value interval means that the water quality is close to the risk interval, which may cause a decrease in growth rate and cause disease, and requires strengthened monitoring. The limit value interval means that when the water quality is in this range, it will cause fish or shellfish to stagnate in growth or even die, which is a high risk and needs to be dealt with immediately.

[0035] By real-time monitoring of these water quality parameters, we can gain a preliminary understanding of the water quality status of the marine ranch and provide basic data for subsequent risk assessment. Tables 1 and 2 show the risk indicators for fish and shellfish, respectively.

[0036] In this embodiment, the multiple water quality parameters include water temperature, salinity, dissolved oxygen, pH value, ammonia nitrogen, nitrate and hydrogen sulfide.

[0037] Specifically, in this embodiment, water quality parameters are continuously monitored, and environmental factors such as monitoring time and location are recorded. A database is established based on the recorded information, and the database also includes water quality parameter exceeding the standard event and treatment measures.

[0038] Table 1 Fish risk indicators

[0039] Water quality parameters Optimal range Warning value range Limit value range Water temperature (℃) 20℃-30℃ 10℃-15℃ <10℃ (fish death) salinity(‰) 20‰-25‰ 16‰-20‰ or 25‰-34‰ <16‰ or >34‰ Dissolved oxygen (mg / L) >5mg / L 3mg / L-5mg / L <3mg / L (fish death) pH 7.0-9.0 6.5-7.0 or 9.0-9.5 <6.5 or >9.5 Ammonia nitrogen (NH3-N) <0.2mg / L 0.2mg / L-0.5mg / L >0.5mg / L Nitrate (NO3-) <50mg / L 50mg / L-75mg / L >75mg / L Hydrogen sulfide (H2S) 0mg / L 0mg / L-0.01mg / L >0.01mg / L

[0040] Table 2 Shellfish risk indicators

[0041] Water quality parameters Optimal range Warning value range Limit value range Water temperature (℃) 16℃-30℃ 10℃-16℃ or 30℃-36℃ <10°C or >36°C salinity(‰) 30‰-40‰ <30‰ or >40‰ - Dissolved oxygen (mg / L) >5mg / L 3mg / L-5mg / L <3mg / L (shellfish death) pH 7.0-9.0 6.5-7.0 or 9.0-9.5 <6.5 or >9.5 Ammonia nitrogen (NH3-N) <0.2mg / L 0.2mg / L-0.5mg / L >0.5mg / L Nitrate (NO3-) <50mg / L 50mg / L-75mg / L >75mg / L Hydrogen sulfide (H2S) 0mg / L 0mg / L-0.01mg / L >0.01mg / L

[0042] Specifically, in this embodiment, the specific data of the water quality parameters obtained by monitoring and the risk intervals to which they belong are shown in Table 3:

[0043] Table 3 Water quality data of a fish species in the case

[0044] parameter Measurements Risk range Water temperature 13℃ Warning value range salinity 26‰ Warning value range Dissolved oxygen 6.2mg / L Optimal range pH 8.2 Optimal range Ammonia nitrogen 0.15mg / L Optimal range Nitrates 45mg / L Optimal range Hydrogen sulfide 0.00mg / L Optimal range

[0045] Furthermore, it also includes: determining whether the water quality level belongs to the standard risk level; if not, determining that the water quality level belongs to the extreme risk level, and taking corresponding management measures according to the extreme risk level.

[0046] In the risk assessment process, once a specific water quality parameter exceeds its limit value, it will pose a fatal threat to organisms cultured in marine ranches. However, if other water quality parameters are maintained within the optimal range, it may be difficult to fully reveal this potential fatal risk based on multi-parameter comprehensive risk assessment alone. In view of this, the present invention adopts the following key strategies when assessing water quality risks: First, prioritize identifying and excluding situations where water quality is at extreme risk, that is, judging that the water quality level belongs to the standard risk level. Once the water quality risk is judged to be at an extreme risk level, promptly take corresponding management measures to ensure the safety of cultured organisms.

[0047] Further, determining whether the water quality level belongs to the standard risk level specifically includes: using preset rules to determine whether the water quality level belongs to the standard risk level according to the multiple water quality parameters and their corresponding risk intervals.

[0048] Furthermore, the preset rule is specifically: when at least one water quality parameter is in a limit value range or a preset number of water quality parameters are in a warning value range, it is determined that the water quality level of the marine ranch is at an extreme risk level.

[0049] Specifically, as shown in Table 1 and Table 2, each water quality parameter has an "optimal range", "warning value range" and "limit value range". When the dissolved oxygen is less than 3 mg / L, it will cause the death of fish and shellfish; when parameters such as water temperature and pH value exceed the limit value range, it will directly threaten the survival of farmed organisms. At the same time, the warning value also has a cumulative effect, that is, when the parameter is in the warning value range, it will lead to a decrease in growth rate and may cause disease, which requires strengthened monitoring. In summary, when a single parameter reaches the limit value, it may cause the death of farmed organisms. When two parameters reach the limit value at the same time, a synergistic effect may occur, and the degree of risk increases exponentially. Therefore, priority is given to identifying and eliminating extreme risks, which is based on the scientific thresholds of water quality parameters and takes into account actual management needs.

[0050] Specifically, in this embodiment, if any of the following conditions is met, it is directly determined that the water quality of the marine ranch is at an extreme risk level.

[0051] Condition 1: Two or more parameters are within the limit value range;

[0052] When a single water quality parameter reaches its limit, it may pose a fatal threat to farmed organisms, such as when dissolved oxygen is lower than 3 mg / L or when pH value deviates seriously from the normal range. When multiple parameters are within the limit range at the same time, the hazards will have a synergistic effect, and the risk level will increase exponentially, which may lead to large-scale death of farmed organisms.

[0053] Condition 2: Six or more parameters are within the warning value range;

[0054] If six or more of the seven monitoring parameters are in the warning range, it means that the water quality system has deteriorated as a whole. Although a single parameter at the warning value may only lead to a decrease in growth rate or an increased risk of disease, when the vast majority of parameters (more than 85%) are in the warning state at the same time, it indicates that the water environment is highly unstable, and a chain reaction may occur between the parameters, and the system may collapse at any time.

[0055] Condition 3: One limit value parameter and three or more warning value parameters;

[0056] This situation reflects a serious imbalance in the water quality system. On the one hand, potentially fatal risk factors (limit value parameters) have appeared, and on the other hand, there is a significant deterioration of multiple parameters (multiple warning values). In this case, the hazards of limit value parameters may be amplified by other parameters at warning values, accelerating the deterioration of the water quality environment, so immediate risk intervention is needed.

[0057] When the water quality level is determined according to the above steps and it is confirmed that the water quality has not reached the extreme risk level but is at the standard risk level, the risk assessment process is immediately initiated.

[0058] Step 2: When the water quality level belongs to the standard risk level, a risk fuzzy matrix is ​​constructed according to the allocation weight of each water quality parameter and the membership degree of each water quality parameter in the risk interval, and the comprehensive membership degree of each risk interval is determined according to the risk fuzzy matrix;

[0059] Furthermore, the allocation weight of each water quality parameter is determined, specifically including: scoring each water quality parameter, and determining the allocation weight of each water quality parameter according to the scoring result.

[0060] Specifically, when the water quality level belongs to the standard risk level, each water quality parameter is scored, and the allocation weight of each water quality parameter is determined based on the scoring result.

[0061] Furthermore, the Delphi method was used to score the importance of each water quality parameter.

[0062] Furthermore, the allocation weight of each water quality parameter is determined according to the scoring result, specifically including: determining the authority coefficient of the scoring expert according to the professional title coefficient; and determining the allocation weight of each water quality parameter according to the authority coefficient and the scoring result.

[0063] Specifically, the Delphi method was used for expert consultation, experts were selected from the fields of marine ecology, aquaculture, etc., and the research target was limited to water quality parameters. In this embodiment, the water quality parameters were specifically the seven key parameters in marine ranch water quality monitoring: dissolved oxygen (DO), temperature (T), salinity (S), pH value, chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP).

[0064] Expert consultation was divided into three rounds. In the first round, experts were asked to rate the importance of the seven parameters (1-5 points) and collect preliminary suggestions. In the second round, a preliminary weight ranking was formed based on the results of the first round, and experts were asked to review and revise. In the third round, the final confirmation was made based on the results of the second round to form the final weight allocation plan.

[0065] Among them, when allocating weights, the authority coefficient of the expert is first determined. The authority coefficient is determined based on the expert's professional title coefficient and the expert's understanding of water quality parameters. The formula for calculating the expert's authority coefficient is as follows:

[0066]

[0067] Where:

[0068] A n is the authority coefficient of the nth expert;

[0069] K n is the coefficient of the nth expert’s understanding of the parameter (1-5 points);

[0070] S n is the professional title coefficient of the nth expert;

[0071] n represents the number of experts, n = 1, 2, ..., N;

[0072] N represents the total number of experts, that is, the number of experts participating in the scoring;

[0073] It represents the sum of the products of all experts’ understanding degree coefficient and professional title coefficient.

[0074] Subsequently, the allocation weight of each water quality parameter is determined based on the authority coefficient and the scoring results. The weight calculation formula of the parameter is as follows:

[0075]

[0076] Where:

[0077] W i is the final weight of the i-th water quality parameter;

[0078] Q ni is the score of the nth expert on the i-th parameter;

[0079] i represents a water quality parameter sequence, and in this embodiment, i=1, 2, ..., 7.

[0080] In this embodiment, the allocation weights of the seven water quality parameters are finally determined as shown in the following table:

[0081] Table 4 Weight distribution table of single water quality parameters

[0082] parameter Weight (W) Water temperature 0.158 salinity 0.141 Dissolved oxygen (DO) 0.214 pH 0.153 <![CDATA[Ammonia nitrogen (NH 3 -N)]]> 0.148 <![CDATA[Nitrate (NO 3 -)]]> 0.094 <![CDATA[Hydrogen sulfide (H 2 S)]]> 0.092

[0083] The weight distribution results of these seven water quality parameters reflect the consensus of experts on the importance of water quality parameters. Among them, dissolved oxygen, as a basic element for the survival of aquatic organisms, has the highest weight, and water temperature and pH value, as key factors affecting biological physiological activities, also have high weights.

[0084] Furthermore, determining the degree of membership of each water quality parameter in the risk interval specifically includes: calculating the degree of membership of each water quality parameter in each risk interval according to the membership function.

[0085] Specifically, the change from a safe state to a dangerous state of water quality parameters is a gradual process, not an instantaneous change. In order to more accurately capture the transition state of this parameter between different risk levels, a membership function is designed. This function is specifically used to determine the degree of belonging of any given water quality parameter indicator in different risk intervals. The definition of the membership function is as follows:

[0086]

[0087] Where:

[0088] x represents the specific value of the water quality parameter;

[0089] μ j (x) represents the membership of the value to the jth risk interval. According to this definition, if the value of the water quality parameter falls within a certain risk interval, its membership to the corresponding risk interval is 1, otherwise it is 0;

[0090] j represents a risk interval sequence, and in this embodiment, j=1, 2, 3.

[0091] That is, determine the specific risk interval for each water quality parameter. These risk intervals can be derived from a combination of factors such as water quality parameter thresholds, historical data, expert opinions, etc. Then, for each water quality parameter, substitute its actual value into the above-mentioned membership function to calculate the membership of the value for each risk interval. This step can be implemented by programming or calculated manually, depending on the amount and complexity of the data. Finally, the membership of each water quality parameter in different risk intervals is obtained, and these memberships will serve as an important basis for subsequent risk assessment and weight allocation. By comparing the membership of different water quality parameters in the same risk interval, we can understand which parameters contribute more to the risk interval, so as to more accurately assess the water quality status and formulate corresponding management measures.

[0092] Specifically, since the water quality parameters of marine ranches have mutual influences and the evaluation process often contains fuzzy uncertainty, the fuzzy comprehensive evaluation method can better reflect the actual situation of the evaluation object. Therefore, a risk fuzzy matrix is ​​constructed based on the distribution weights of water quality parameters and the membership degree of the risk interval to which they belong. Specifically, the expression of the fuzzy matrix is ​​as follows:

[0093] R=[r ij ] 7×3

[0094] Where:

[0095] r ij is an element in the matrix, which indicates the membership of the i-th water quality parameter to the risk interval of the j-th water quality parameter. The membership is a value between 0 and 1, which is used to quantify the degree to which an element belongs to a set. By constructing the fuzzy relationship matrix, the membership of the seven water quality parameters to the three risk intervals is quantified.

[0096] In view of the significant differences in the degree of influence of different water quality parameters on aquaculture organisms, a weight vector A is introduced to quantify the importance of each parameter. In order to combine the weight vector A with the risk fuzzy matrix R representing the membership of each water quality parameter in different risk intervals, a fuzzy synthesis operation is used. The purpose of the fuzzy synthesis operation is to calculate a new vector B based on the weight vector A and the risk fuzzy matrix R. Vector B = (b 1 , b 2 , b 3 ) for each element b j Represents the comprehensive membership value of the j-th risk interval in the comprehensive evaluation results.

[0097] Specifically, the comprehensive membership degree b j The calculation formula is as follows:

[0098] B=A·R=(b 1 , b 2 , b 3 )

[0099] Where:

[0100] A represents a weight vector composed of the distribution weights of multiple water quality parameters;

[0101] R represents the risk fuzzy matrix;

[0102] b j represents the comprehensive membership value of the j-th risk interval. In this embodiment, j=1, 2, 3.

[0103] Among them, the comprehensive membership degree b of each risk interval is j The specific calculation formula is as follows:

[0104]

[0105] Where:

[0106] b j represents the comprehensive membership value of the j-th risk interval;

[0107] a i is the allocation weight of the i-th water quality parameter;

[0108] r ij It represents the membership of the i-th water quality parameter in the j-th risk interval.

[0109] Step 3: Determine a comprehensive evaluation index of the marine ranch water quality based on the comprehensive membership and the measurement value of each risk interval, thereby evaluating the marine ranch water quality risk.

[0110] After the calculation of the comprehensive membership of the marine ranch water quality parameters is completed, the comprehensive evaluation index of the marine ranch water quality is determined according to the comprehensive membership and the preset measurement value of each risk interval, thereby evaluating the water quality risk.

[0111] First, set the measurement value d for each risk interval j , these metrics reflect the severity of different risk intervals. Specifically, in this embodiment:

[0112] Optimal interval d 1 It is set to 0.25, based on the consideration that this range is most beneficial to the growth and development of aquaculture organisms and has the lowest risk. This value is set as the baseline value, indicating that the water quality is in a normal and controllable state.

[0113] Warning value interval d 2 It was set at 0.50, which is twice the optimal interval measure, indicating that water quality is at a moderate risk level that requires concern but is not yet dangerous.

[0114] Limit value interval d 3 It is set to 1.00, the maximum value, which is four times the optimal range metric and twice the warning range metric, reflecting that the water quality parameters have reached potentially lethal levels and the risk is the highest.

[0115] This measurement value setting maintains a 2-fold progressive relationship between each level, which is not only convenient for calculation and risk accumulation, but also complies with the gradient principle of risk assessment, and can clearly reflect the gradual increase in risk levels.

[0116] Next, the comprehensive evaluation index calculation formula is used to calculate the final comprehensive evaluation index P. The specific formula is as follows:

[0117]

[0118] Where:

[0119] P represents the comprehensive evaluation index;

[0120] b j represents the comprehensive membership value of the j-th risk interval;

[0121] d j Represents the metric value of the j-th risk interval.

[0122] By calculating the comprehensive evaluation index, the qualitative description of water quality parameters can be converted into a quantitative evaluation index, so as to more objectively and accurately assess the water quality risk of marine ranches. This comprehensive evaluation index not only provides a quantitative basis for the determination of risk levels, but also provides an important reference for subsequent risk management and formulation of measures.

[0123] Furthermore, the present invention also includes determining a comprehensive risk level of water quality based on a comprehensive evaluation index, and determining management measures based on the comprehensive risk level.

[0124] Specifically, in this embodiment, a risk determination system is constructed with the comprehensive evaluation index P as the core, and four levels of comprehensive risk levels are established. The comprehensive evaluation index of this application comprehensively considers the distribution weights and membership of various water quality parameters, and comprehensively reflects the overall risk level of water quality conditions. The division of comprehensive risk levels strictly follows the benchmark setting of water quality parameter measurement values ​​to ensure the accuracy and effectiveness of the evaluation.

[0125] Specifically, the quantized value of the optimal interval (d 1 =0.25) as the low risk limit, the quantitative value of the warning value interval (d 2 =0.50) is used as an important reference for determining high risk. At the same time, we set a reasonable transition interval between each level to ensure the continuity and accuracy of risk determination.

[0126] After obtaining the comprehensive evaluation index P, the comprehensive risk level is determined according to the range of the index, and corresponding management measures are taken. The following is a table of specific risk level determination and corresponding measures:

[0127] Table 5 Risk level determination and corresponding measures based on comprehensive evaluation method

[0128] Comprehensive evaluation index range Comprehensive risk level Suggested Actions P≤0.25 Low risk Maintain routine monitoring 0.25<P≤0.40 Medium risk Encryption monitoring frequency 0.40<P≤0.60 High risk Activate early warning mechanism P>0.60 Extreme Risk Take emergency measures immediately

[0129] The setting of the transition interval of the comprehensive risk level fully considers the number of water quality parameters exceeding the standard and the principle of combined effect. In this embodiment, the medium-risk interval (0.25-0.40) serves as a buffer zone for the transition from the optimal interval to the warning value interval, providing managers with a time window for preventive intervention, which helps to timely discover and deal with potential risks. The high-risk interval (0.40-0.60) is centered on the warning value of 0.50, taking into account the cumulative effects that may be caused by the interaction of multiple parameters, ensuring that effective measures are taken before the risk escalates. Setting 0.60 as the judgment threshold for extreme risk is based on the system's tolerance when water quality parameters fluctuate between the warning value and the limit value, ensuring a rapid response in extreme cases to ensure water quality safety.

[0130] This interval division method based on the cumulative effect of parameters not only takes into account the gradual evolution of risks, but also conforms to the response law of ecosystems to environmental stress, providing managers with a scientific and reasonable decision-making basis.

[0131] According to the monitoring data of the water quality parameters of this embodiment and the risk intervals to which they belong, the shared fuzzy matrix and the metric values ​​corresponding to each risk interval are as follows:

[0132]

[0133] According to the above table, the vector B corresponding to the comprehensive membership of each risk interval in this embodiment is (0.701, 0.299, 0.000)

[0134] Therefore, the comprehensive evaluation index of the marine ranch in this embodiment is P = 0.25 × 0.701 + 0.5 × 0.299 + 1.0 × 0.000 = 0.32475

[0135] According to Table 5, the water quality of this embodiment belongs to the medium risk level.

[0136] This example conducts a water quality risk assessment for the Nan'ao Island Ocean Ranch in Shantou. By building a complete assessment system, it achieves comprehensive monitoring and scientific assessment of key water quality parameters. The system covers seven important indicators, including water temperature, salinity, dissolved oxygen, pH value, ammonia nitrogen, nitrate and hydrogen sulfide, to ensure the comprehensiveness and accuracy of the assessment. By establishing a water quality indicator database and formulating risk indicator standards applicable to fish and shellfish, a solid basic data support is provided for risk assessment.

[0137] In terms of assessment methods, this application adopts a fuzzy comprehensive evaluation method, and realizes a scientific assessment of water quality risks through reasonable weight allocation and fuzzy relationship matrix construction. The assessment system divides the risk level into four levels: low risk, medium risk, high risk and extreme risk, and establishes a judgment rule based on the principle of extreme risk priority, and formulates corresponding risk response measures. This assessment method not only takes into account the independent influence of each indicator, but also reflects the combined effect of multiple factors. Through this assessment system, water quality risks can be discovered and warned in a timely manner so that corresponding management measures can be taken to ensure the sustainable development of marine ranches. Future research directions will focus on optimizing model accuracy, improving the automated monitoring system, and further expanding the scope of application of the assessment model to provide a reference for water quality risk management in more marine ranches.

[0138] The above are only several embodiments of the present invention and are not intended to limit the present invention in any form. Although the present invention is disclosed as above in the form of a preferred embodiment, it is not intended to limit the present invention. Any technician familiar with the profession, without departing from the scope of the technical solution of the present invention, using the above disclosed technical content to make slight changes or modifications are equivalent to equivalent implementation cases and fall within the scope of the technical solution.

Claims

1. A method for assessing marine ranch water quality risk, characterized in that: The following steps are involved: Step 1: determining the water quality grade of the marine ranch according to multiple water quality parameters of the marine ranch and the risk interval corresponding to each water quality parameter; Step 2: When the water quality level belongs to the standard risk level, a risk fuzzy matrix is ​​constructed according to the allocation weight of each water quality parameter and the membership degree of each water quality parameter in the risk interval, and the comprehensive membership degree of each risk interval is determined according to the risk fuzzy matrix; Step 3: Determine a comprehensive evaluation index of the marine ranch water quality based on the comprehensive membership and the measurement value of each risk interval, thereby evaluating the marine ranch water quality risk.

2. The marine ranch water quality risk assessment method according to claim 1, characterized in that: The step 2 also includes: Determine whether the water quality level belongs to the standard risk level; If not, the water quality level is determined to be an extreme risk level, and corresponding management measures are taken according to the extreme risk level.

3. The marine ranch water quality risk assessment method according to claim 1 is characterized in that: Determine the weights assigned to each water quality parameter, including: Each water quality parameter is scored and the allocation weight of each water quality parameter is determined based on the scoring results.

4. The marine ranch water quality risk assessment method according to claim 3 is characterized in that: The Delphi method was used to score the importance of each water quality parameter.

5. The marine ranch water quality risk assessment method according to claim 3 is characterized in that: The weights assigned to each water quality parameter are determined based on the scoring results, including: Determine the authority coefficient of the scoring expert based on his / her professional title coefficient; The allocation weight of each water quality parameter is determined according to the authority coefficient and the scoring result.

6. The marine ranch water quality risk assessment method according to claim 1, characterized in that: Determine the degree of membership of each water quality parameter in the risk range, including: The membership of each water quality parameter in each risk interval is calculated based on the membership function.

7. The marine ranch water quality risk assessment method according to claim 1 is characterized in that: The multiple water quality parameters include water temperature, salinity, dissolved oxygen, pH value, ammonia nitrogen, nitrate and hydrogen sulfide.

8. The marine ranch water quality risk assessment method according to claim 2 is characterized in that: Determine whether the water quality level belongs to the standard risk level, including: The preset rules are used to determine whether the water quality level belongs to the standard risk level according to the multiple water quality parameters and their corresponding risk intervals.

9. The marine ranch water quality risk assessment method according to claim 8, characterized in that: The risk interval includes an optimal interval, a warning value interval and a limit value interval.

10. The marine ranch water quality risk assessment method according to claim 9, characterized in that: The preset rules are specifically: When at least one water quality parameter is within a limit value interval or a preset number of water quality parameters are within a warning value interval, it is determined that the water quality level of the ocean ranch is at an extreme risk level.

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