Monitoring and early warning method for outbreak marine organism shell slugs

Abstract

The invention relates to the technical field of marine organism monitoring, in particular to a monitoring and early warning method for an outbreak marine organism slug. The method comprises the following steps: S1, determining a monitoring range and a monitoring station according to historical data for detecting the shell slugs and ecological habits of the shell slugs; s2, density monitoring is conducted on the shell slugs based on a diving monitoring algorithm and a bottom trawl monitoring algorithm; s3, performing influence evaluation through an economic loss algorithm and a loss degree algorithm; s4, performing early warning on outbreak of the shell slug based on a density-influence evaluation judgment rule, and taking a response action; and S5, generating an early warning report for monitoring outbreak of the shell slugs by adopting a comprehensive analysis method. According to the slug monitoring and early warning method, through a diving monitoring algorithm, a bottom trawl monitoring algorithm, an economic loss algorithm, a loss degree algorithm and a comprehensive analysis method, the biological density and the economic influence of slug outbreak are effectively evaluated, and the risk prediction and management efficiency is greatly improved.

Description

Technical Field

[0001] The invention relates to the technical field of marine biological monitoring, in particular to a monitoring and early warning method for explosive marine biological shell slugs. Background Art

[0002] The monitoring and early warning method for explosive marine organisms, shell slugs, is a technology that converts marine environmental data into quantitative predictions of shell slug outbreak risks based on the Lotka-Volterra prediction model and linear regression model. It aims to predict the outbreak potential of shell slugs through continuous monitoring of environmental factors such as water temperature, salinity, and pH value.

[0003] In view of the complexity of marine ecosystems and the diversity of ecological habits of shell slugs, the lack of accurate monitoring of shell slug density has affected the formulation of effective risk warning and response measures. Therefore, a monitoring and early warning method for explosive marine organisms such as shell slugs is provided. Summary of the invention

[0004] The purpose of the present invention is to provide a monitoring and early warning method for explosive marine organisms such as shell slugs, so as to solve the problem raised in the above background technology that due to the diversity of ecological habits of shell slugs, there is a lack of accurate monitoring of shell slug density, which affects the formulation of effective risk warnings and response measures.

[0005] To achieve the above object, the present invention aims to provide a monitoring and early warning method for an outbreak of marine biological shell slugs, comprising the following steps:

[0006] S1. Determine the monitoring scope, routine monitoring stations and emergency monitoring stations based on the historical data of shell slug detection and the ecological habits of shell slugs;

[0007] S2, density monitoring of shell slugs based on diving monitoring algorithm and bottom trawl monitoring algorithm;

[0008] S3. Conduct impact assessment on the results of density monitoring using economic loss algorithm and loss degree algorithm;

[0009] S4. Early warning of shell slug outbreaks and response actions based on density-impact assessment judgment rules;

[0010] S5. Use comprehensive analysis methods to generate early warning reports for monitoring shell slug outbreaks.

[0011] As a further improvement of the technical solution, in S1, the historical data includes the time, location, duration and impact of previous shell slug outbreaks; the ecological habits of shell slugs specifically include environmental preferences, food sources, reproductive habits and behavioral patterns;

[0012] Among them, the conventional monitoring stations are based on the distribution of sea areas and aquaculture areas where shell slugs have frequently broken out in history. Such a layout strategy aims to ensure that the monitoring stations can evenly cover the entire monitoring sea area and have sufficient representativeness to reflect the distribution and dynamics of shell slugs. The setting distance of conventional monitoring stations should be kept between 3km and 10km to ensure that the spatial resolution of the monitoring data can reasonably reflect the distribution of shell slugs. The number of monitoring stations should not be less than 5 to ensure the coverage of the monitoring network and the reliability of the data; the setting of emergency monitoring stations covers the high-value stations of conventional monitoring density of shell slugs, aquaculture areas and areas where fishermen report shell slugs gathering, to ensure that the early signs of shell slug outbreaks can be captured in time and effectively monitored and managed. The number of monitoring stations should not be less than 8 to ensure that there is sufficient data to support the assessment of the extent and scope of shell slug outbreaks.

[0013] As a further improvement of the technical solution, in S2, the diving monitoring algorithm is used to monitor the density of shell slugs in the diving area;

[0014] Among them, the diving monitoring algorithm adopts the sampling method for monitoring. Specifically, after arriving at the preset site, the diver carries the sample line to lay out a 5m×5m sample plot on site. In each sample plot, all shell slugs in the 150mm surface sediment within 5 250mm×250mm quantitative tools in the four corners and the center are collected. The underwater conditions are recorded synchronously using an underwater camera, and statistical analysis is performed indoors based on the image data. The calculation formula involved in diving monitoring of shell slug density is:

[0015] N=n A / S A ;

[0016] Where N is the density of shell slugs, ind / m 2 ;n A is the number of shell slugs in the survey plot, ind; S A is the survey sample area, in m 2 .

[0017] As a further improvement of the technical solution, the bottom trawl monitoring algorithm estimates the biological density of the outbreak shell slugs based on the swept sea area method. The swept sea area is the sea-sweeping width of the bottom trawl during the towing process in the sea multiplied by the distance the net is towed. The calculation formula involved in the shell slug density of the bottom trawl monitoring algorithm is:

[0018] ρ = C / (a × q);

[0019] Where ρ is the density of shell slugs, ind / m 2 ; C is the average number of shell slugs obtained per hour, ind / h; a is the sea area swept by the net per hour, in km 2 / h; q is the capture rate of the net, which is 0.8-1.

[0020] As a further improvement of the technical solution, in S3, the economic loss algorithm is used to calculate the loss caused by the impact on bottom-seeded shellfish aquaculture. The formula involved in the economic loss algorithm is as follows:

[0021] V a =δ a,b ×ρ b ×S a ×T a ×c a ;

[0022] Among them, V a is the economic loss of shellfish in the affected aquaculture area, dVCS value, in yuan; a,b is the feeding rate of shell slugs of different sizes on the aquaculture species in the affected aquaculture area, in kg / (ind·d); ρ b is the density of shell slugs in the affected aquaculture area, ind / m 2 ; S a is the area of ​​the affected breeding area, in m 2 ; T a is the disaster time, in days; c a is the average market price of farmed shellfish, in yuan / kg;

[0023] The loss degree algorithm is used to calculate the loss degree of shellfish in the affected aquaculture area. The formula involved in the loss degree algorithm is as follows:

[0024] P=(P a -P0) / P0×100%;

[0025] Where P is the degree of shellfish loss in the affected aquaculture area; P a is the production of the affected shellfish farming areas, in kg; P0 is the benchmark production of the affected shellfish farming areas, in kg.

[0026] As a further improvement of the technical solution, in S4, the density-impact assessment judgment rule is used for early warning of shell slug outbreaks, and the density-impact assessment judgment rule is specifically as follows:

[0027] Monitor the density and impact of shell slugs to determine the warning level, which is divided into yellow warning, orange warning and red warning from low to high, and then take corresponding response actions to the warning;

[0028] Among them, the yellow warning is a mild shell slug outbreak, and the shell slug monitoring density during the outbreak period is 1ind / m 2 ≤N<10ind / m 2The number of monitoring stations exceeds 20% of the total number of monitoring stations, and the impact on society and the environment is 10,000 mu ≤ S < 30,000 mu and 5 million yuan ≤ Va < 30 million yuan; P ≥ -10%; orange warning for moderate shell slug outbreaks, the shell slug monitoring density during the outbreak period is 10ind / m 2 ≤N<50ind / m 2 The number of monitoring stations exceeds 50% of the total number of monitoring stations, and the impact on society and the environment is 30,000 mu ≤ S < 100,000 mu and 30 million yuan ≤ Va < 100 million yuan; P ≥ -50%; the red alert is a severe shell slug outbreak, and the shell slug monitoring density during the outbreak period is N ≥ 50ind / m 2 The number of monitoring stations exceeds 80% of the total number of monitoring stations, and the impact on society and environment is S ≥ 100,000 mu and Va ≥ 100 million yuan; P < -50%.

[0029] As a further improvement of the technical solution, the early warning response action for the shell slug outbreak is divided into three action measures:

[0030] S7.1. For yellow alerts: Continue to monitor, scientifically adjust disposal measures, and pay close attention to changes in the shell slug outbreak alert level;

[0031] S7.2. For orange alerts: Conduct regular monitoring, with a frequency of 1-2 times / 2 months, pay attention to the outbreak and impact of shell slugs, organize professionals or fishermen and farmers to salvage and clean up shell slugs, and timely summarize information such as the fishing locations and catch volume of shell slugs;

[0032] S7.3. For red alerts: Increase the monitoring frequency to at least 1-2 times per month, depending on the actual situation, pay close attention to changes in the density and impact of shell slugs, organize professionals to salvage and clean up shell slugs, and increase cleaning efforts in areas with serious shell slug outbreaks. Timely summarize information such as shell slug fishing locations and catches to provide information for timely disposal.

[0033] As a further improvement of the technical solution, in S5, the comprehensive analysis method is a method for analyzing and generating an early warning report for shell slug outbreaks based on the Lotka-Volterra prediction model and the linear regression model. The early warning report is provided to decision makers so that they can take necessary preventive and response measures based on the latest monitoring data and analysis and prediction. The content of the early warning report includes monitoring methods and results, early warning methods, results and judgment levels, outbreak trend predictions, and response action recommendations;

[0034] The Lotka-Volterra prediction model is used to simulate the growth, reproduction and death process of shell slugs under specific environmental conditions, as well as their interactions with other species in the ecosystem. This type of model can help understand the ecological mechanism of shell slug outbreaks and predict the possibility of outbreaks and ecological consequences under different environmental conditions. The mathematical model expression involved in the simulation process of the Lotka-Volterra prediction model is:

[0035]

[0036] Among them, P represents the population density of shell slugs; Z represents the population density of natural enemies or competing species of shell slugs; r is the intrinsic growth rate of shell slugs; K is the carrying capacity of the environment; a is the probability of shell slugs being preyed on; e is the energy conversion efficiency; d is the mortality rate of natural enemies;

[0037] The linear regression model predicts the outbreak trend of shell slugs by analyzing historical monitoring data; the linear regression model is used to assess the outbreak risk under different environmental conditions, and provide a basis for taking preventive measures to deal with the outbreak of shell slugs, thereby improving the efficiency and effectiveness of responding to shell slug outbreaks; the formula involved in the linear regression model in the prediction process is:

[0038] Y=β0+β1X1+β2X2+...+βnXn+∈;

[0039] Among them, Y represents the predicted density of shell slugs; X1, X2…Xn represent factors affecting shell slug outbreaks, such as water temperature, salinity, and chlorophyll a concentration; β0, β1…βn are model parameters; ∈ is the error term.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] 1. In this monitoring and early warning method for an outbreak of marine organisms, shell slugs, the biological density of shell slugs is accurately calculated by combining a diving monitoring algorithm and a bottom trawl monitoring algorithm. At the same time, an economic loss algorithm and a loss degree algorithm are used to quantitatively evaluate the economic losses caused by the outbreak of shell slugs and their degree of impact, providing reliable data support for risk management of marine ecological protection and aquaculture.

[0042] 2. In this monitoring and early warning method for an outbreak of marine organisms, shell slugs, a comprehensive analysis method based on the Lotka-Volterra prediction model and the linear regression model is adopted to comprehensively analyze the marine environmental data and the biological characteristics of shell slugs to generate accurate shell slug outbreak early warning reports. This not only improves the accuracy and timeliness of the early warning, but also through model analysis, it can deeply understand the ecological mechanism and influencing factors of shell slug outbreaks, and provide a scientific basis for formulating more effective management strategies and response measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 The figure is a flow chart of the overall method of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] Example:

[0046] See also Figure 1 As shown, this embodiment provides a monitoring and early warning method for an outbreak of marine biological shell slugs, comprising the following steps:

[0047] S1. Determine the monitoring scope, routine monitoring stations and emergency monitoring stations based on the historical data of shell slug detection and the ecological habits of shell slugs;

[0048] In S1, the historical data include the time, location, duration and impact of previous shell slug outbreaks; the ecological habits of shell slugs specifically include environmental preferences, food sources, reproductive habits and behavioral patterns;

[0049] Among them, the conventional monitoring stations are based on the distribution of sea areas and aquaculture areas where shell slugs have frequently broken out in history. Such a layout strategy aims to ensure that the monitoring stations can evenly cover the entire monitoring sea area and have sufficient representativeness to reflect the distribution and dynamics of shell slugs. The setting distance of conventional monitoring stations should be kept between 3km and 10km to ensure that the spatial resolution of the monitoring data can reasonably reflect the distribution of shell slugs. The number of monitoring stations should not be less than 5 to ensure the coverage of the monitoring network and the reliability of the data; the setting of emergency monitoring stations covers the high-value stations of conventional monitoring density of shell slugs, aquaculture areas and areas where fishermen report shell slugs gathering, to ensure that the early signs of shell slug outbreaks can be captured in time and effectively monitored and managed. The number of monitoring stations should not be less than 8 to ensure that there is sufficient data to support the assessment of the extent and scope of shell slug outbreaks.

[0050] S2. Density monitoring of shell slugs based on a diving monitoring algorithm and a bottom trawl monitoring algorithm; the diving monitoring algorithm is used to monitor the density of shell slugs in the diving area,

[0051] Submersible monitoring can deeply explore the distribution details and ecological relationships of shell slugs in specific habitats, while bottom trawling monitoring can quickly obtain large-scale shell slug density data. The combination of the two algorithms can ensure a comprehensive understanding of shell slug outbreaks and accurately capture the detailed characteristics of local hotspots.

[0052] The data obtained by the two algorithm methods can verify each other, reducing the deviation that may be caused by a single method. The diving monitoring data can be used to correct the capture efficiency deviation caused by factors such as net selectivity and towing speed in bottom trawl monitoring, thereby improving the accuracy of overall density estimation.

[0053] Among them, the diving monitoring algorithm adopts the sampling method for monitoring. Specifically, after arriving at the preset site, the diver carries the sample line to lay out a 5m×5m sample plot on site. In each sample plot, all shell slugs in the 150mm surface sediment within 5 250mm×250mm quantitative tools in the four corners and the center are collected. The underwater conditions are recorded synchronously using an underwater camera, and statistical analysis is performed indoors based on the image data. The calculation formula involved in diving monitoring of shell slug density is:

[0054] N=n A / S A ;

[0055] Where N is the density of shell slugs, ind / m 2 ;n A is the number of shell slugs in the survey plot, ind; S A is the survey sample area, in m 2 .

[0056] In this embodiment, the bottom trawl monitoring algorithm estimates the biological density of the outbreak shell slug based on the swept sea area method. The swept sea area is the sea-sweeping width of the bottom trawl during the towing process in the sea multiplied by the distance the net is towed. The calculation formula involved in the shell slug density of the bottom trawl monitoring algorithm is:

[0057] ρ = C / (a × q);

[0058] Where ρ is the density of shell slugs, ind / m 2 ; C is the average number of shell slugs obtained per hour, ind / h; a is the sea area swept by the net per hour, in km 2 / h; q is the capture rate of the net, which is 0.8-1.

[0059] S3. The results of density monitoring are evaluated by economic loss algorithm and loss degree algorithm. In S3, the economic loss algorithm is used to calculate the loss caused by the impact on bottom-seeded shellfish aquaculture. The formula involved in the economic loss algorithm is as follows:

[0060] Va =δ a,b ×ρ b ×S a ×T a ×c a ;

[0061] Among them, V a is the economic loss of shellfish in the affected aquaculture area, dVCS value, in yuan; a,b is the feeding rate of shell slugs of different sizes on the aquaculture species in the affected aquaculture area, in kg / (ind·d); ρ b is the density of shell slugs in the affected aquaculture area, ind / m 2 ; S a is the area of ​​the affected breeding area, in m 2 ; T a is the disaster time, in days; c a is the average market price of farmed shellfish, in yuan / kg;

[0062] The loss degree algorithm is used to calculate the loss degree of shellfish in the affected aquaculture area. The formula involved in the loss degree algorithm is as follows:

[0063] P=(P a -P0) / P0×100%;

[0064] Where P is the degree of shellfish loss in the affected aquaculture area; P a is the production of the affected shellfish farming areas, in kg; P0 is the benchmark production of the affected shellfish farming areas, in kg.

[0065] S4. Early warning of shell slug outbreaks and taking responsive actions based on density-impact assessment judgment rules; in S4, the density-impact assessment judgment rules are used to early warning of shell slug outbreaks, and the density-impact assessment judgment rules are as follows:

[0066] Monitor the density and impact of shell slugs to determine the warning level, which is divided into yellow warning, orange warning and red warning from low to high, and then take corresponding response actions to the warning;

[0067] Among them, the yellow warning is a mild shell slug outbreak, and the shell slug monitoring density during the outbreak period is 1ind / m 2 ≤N<10ind / m 2 The number of monitoring stations exceeds 20% of the total number of monitoring stations, and the impact on society and the environment is 10,000 mu ≤ S < 30,000 mu and 5 million yuan ≤ Va < 30 million yuan; P ≥ -10%; orange warning for moderate shell slug outbreaks, the shell slug monitoring density during the outbreak period is 10ind / m 2 ≤N<50ind / m 2The number of monitoring stations exceeds 50% of the total number of monitoring stations, and the impact on society and the environment is 30,000 mu ≤ S < 100,000 mu and 30 million yuan ≤ Va < 100 million yuan; P ≥ -50%; the red alert is a severe shell slug outbreak, and the shell slug monitoring density during the outbreak period is N ≥ 50ind / m 2 The number of monitoring stations exceeds 80% of the total number of monitoring stations, and the impact on society and environment is S ≥ 100,000 mu and Va ≥ 100 million yuan; P < -50%.

[0068] In this embodiment, the early warning response action to the shell slug outbreak is divided into three action measures:

[0069] S7.1. For yellow alerts: Continue to monitor, scientifically adjust disposal measures, and pay close attention to changes in the shell slug outbreak alert level;

[0070] S7.2. For orange alerts: Conduct regular monitoring, with a frequency of 1-2 times / 2 months, pay attention to the outbreak and impact of shell slugs, organize professionals or fishermen and farmers to salvage and clean up shell slugs, and timely summarize information such as the fishing locations and catch volume of shell slugs;

[0071] S7.3. For red alerts: Increase the monitoring frequency to at least 1-2 times per month, depending on the actual situation, pay close attention to changes in the density and impact of shell slugs, organize professionals to salvage and clean up shell slugs, and increase cleaning efforts in areas with serious shell slug outbreaks. Timely summarize information such as shell slug fishing locations and catches to provide information for timely disposal.

[0072] S5. Generate an early warning report for monitoring shell slug outbreaks using a comprehensive analysis method. In S5, the comprehensive analysis method is a method for analyzing and generating an early warning report for shell slug outbreaks based on the Lotka-Volterra prediction model and the linear regression model. The early warning report is provided to decision makers so that they can take necessary preventive and response measures based on the latest monitoring data and analysis and prediction. The content of the early warning report includes monitoring methods and results, early warning methods, results and judgment levels, outbreak trend predictions, and response action recommendations;

[0073] Among them, the Lotka-Vo l terra prediction model is used to simulate the growth, reproduction and death process of shell slugs under specific environmental conditions, as well as their interactions with other species in the ecosystem; the Lotka-Vo l terra prediction model is used to help understand the ecological mechanism of shell slug outbreaks and predict the possibility of outbreaks and ecological consequences under different environmental conditions. The mathematical model expression involved in the simulation process of the Lotka-Vo l terra prediction model is:

[0074]

[0075] Among them, P represents the population density of shell slugs; Z represents the population density of natural enemies or competing species of shell slugs; r is the intrinsic growth rate of shell slugs; K is the carrying capacity of the environment; a is the probability of shell slugs being preyed on; e is the energy conversion efficiency; d is the mortality rate of natural enemies;

[0076] Among them, the linear regression model predicts the outbreak trend of shell slugs by analyzing historical monitoring data; the linear regression model is used to assess the outbreak risk under different environmental conditions, and provide a basis for taking preventive measures to deal with shell slug outbreaks, thereby improving the efficiency and effectiveness of responding to shell slug outbreaks; the formula involved in the linear regression model in the prediction process is:

[0077] Y=β0+β1X1+β2X2+...+βnXn+∈;

[0078] Among them, Y represents the predicted density of shell slugs; X1, X2…Xn represent factors affecting shell slug outbreaks, such as water temperature, salinity, and chlorophyll a concentration; β0, β1…βn are model parameters; ∈ is the error term.

[0079] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A monitoring and early warning method for outbreaks of marine biological shell slugs, characterized in that: The following steps are involved: S1. Determine the monitoring scope, routine monitoring stations and emergency monitoring stations based on the historical data of shell slug detection and the ecological habits of shell slugs; S2, density monitoring of shell slugs based on diving monitoring algorithm and bottom trawl monitoring algorithm; S3. Conduct impact assessment on the results of density monitoring using economic loss algorithm and loss degree algorithm; S4. Early warning of shell slug outbreaks and response actions based on density-impact assessment judgment rules; S5. Use comprehensive analysis methods to generate early warning reports for monitoring shell slug outbreaks.

2. The method for monitoring and early warning of outbreaks of marine biological shell slugs according to claim 1, characterized in that: In S1, the historical data include the time, location, duration and impact of previous shell slug outbreaks; the ecological habits of shell slugs specifically include environmental preferences, food sources, reproductive habits and behavioral patterns.

3. The method for monitoring and early warning of outbreaks of marine biological shell slugs according to claim 1, characterized in that: In S2, the diving monitoring algorithm is used to monitor the density of shell slugs in the diving area; Among them, the diving monitoring algorithm adopts the sampling method for monitoring. Specifically, after arriving at the preset site, the diver carries the sample line to lay out a 5m×5m sample plot on site. In each sample plot, all shell slugs in the 150mm surface sediment within 5 250mm×250mm quantitative tools in the four corners and the center are collected. The underwater conditions are recorded synchronously using an underwater camera, and statistical analysis is performed indoors based on the image data. The calculation formula involved in diving monitoring of shell slug density is: N=n A / S A ; Where N is the density of shell slugs, ind / m 2 ;n A is the number of shell slugs in the survey plot, ind; S A is the survey sample area, in m 2 .

4. The method for monitoring and early warning of outbreaks of marine biological shell slugs according to claim 1, characterized in that: The bottom trawl monitoring algorithm estimates the biological density of the outbreak shell slug based on the swept area method. The swept area is the swept width of the bottom trawl during the towing process in the sea multiplied by the distance the net is towed. The calculation formula involved in the shell slug density of the bottom trawl monitoring algorithm is: ρ = C / (a × q); Where ρ is the density of shell slugs, ind / m 2 ; C is the average number of shell slugs obtained per hour, ind / h; a is the sea area swept by the net per hour, in km 2 / h; q is the capture rate of the net, which is 0.8-1.

5. The method for monitoring and early warning of outbreaks of marine biological shell slugs according to claim 1, characterized in that: In S3, the economic loss algorithm is used to calculate the loss caused by the impact on bottom-seeded shellfish aquaculture. The formula involved in the economic loss algorithm is as follows: V a =d a,b ×r b ×S a ×T a ×c a ; Among them, V a is the economic loss of shellfish in the affected aquaculture area, dVCS value, in yuan; a,b is the feeding rate of shell slugs of different sizes on the aquaculture species in the affected aquaculture area, in kg / (ind·d); ρ b is the density of shell slugs in the affected aquaculture area, ind / m 2 ; S a is the area of ​​the affected breeding area, in m 2 ; T a is the disaster time, in days; c a is the average market price of farmed shellfish, in yuan / kg; The loss degree algorithm is used to calculate the loss degree of shellfish in the affected aquaculture area. The formula involved in the loss degree algorithm is as follows: P=(P a -P0) / P0×100%; Where P is the degree of shellfish loss in the affected aquaculture area; P a is the production of the affected shellfish farming areas, in kg; P0 is the benchmark production of the affected shellfish farming areas, in kg.

6. The method for monitoring and early warning of outbreaks of marine biological shell slugs according to claim 1, characterized in that: In S4, the density-impact assessment judgment rule is used for early warning of shell slug outbreaks, and the density-impact assessment judgment rule is as follows: Monitor the density and impact of shell slugs to determine the warning level, which is divided into yellow warning, orange warning and red warning from low to high, and then take corresponding response actions to the warning; Among them, the yellow warning is a mild shell slug outbreak, and the shell slug monitoring density during the outbreak period is 1ind / m 2 ≤N<10ind / m 2 The number of monitoring stations exceeds 20% of the total number of monitoring stations; the orange warning is a moderate shell slug outbreak, and the shell slug monitoring density during the outbreak period is 10ind / m 2 ≤N<50ind / m 2 The number of monitoring stations exceeds 50% of the total number of monitoring stations; the red alert is a severe shell slug outbreak, and the shell slug monitoring density during the outbreak period is N ≥ 50ind / m 2 The number of monitoring stations exceeds 80% of the total number of monitoring stations.

7. The method for monitoring and early warning of outbreaks of marine biological shell slugs according to claim 6, characterized in that: The early warning response to the shell slug outbreak is divided into three action measures: S7.

1. For yellow alerts: Continue to monitor, scientifically adjust disposal measures, and pay close attention to changes in the shell slug outbreak alert level; S7.2: For orange alert: Conduct regular monitoring, with a frequency of 1-2 times / 2 months, and pay attention to changes in the outbreak and impact of shell slugs; S7.

3. For red alerts: Increase monitoring frequency to at least 1-2 times per month, depending on actual conditions, and pay close attention to changes in shell slug density and impact.

8. The method for monitoring and early warning of outbreaks of marine biological shell slugs according to claim 1, characterized in that: In said S5, the comprehensive analysis method is a method for analyzing and generating an early warning report of shell slug outbreaks based on the Lotka-Volterra prediction model and the linear regression model; The Lotka-Volterra prediction model is used to simulate the growth, reproduction and death process of shell slugs under specific environmental conditions, as well as their interactions with other species in the ecosystem. The mathematical model expression involved in the simulation process of the Lotka-Volterra prediction model is: Among them, P represents the population density of shell slugs; Z represents the population density of natural enemies or competing species of shell slugs; r is the intrinsic growth rate of shell slugs; K is the carrying capacity of the environment; a is the probability of shell slugs being preyed on; e is the energy conversion efficiency; d is the mortality rate of natural enemies; The linear regression model predicts the outbreak trend of shell slugs by analyzing historical monitoring data. The formula involved in the prediction process of the linear regression model is: Y=β0+β1X1+β2X2+...+βnXn+∈; Among them, Y represents the predicted density of shell slugs; X1, X2…Xn represent factors affecting shell slug outbreaks, such as water temperature, salinity, and chlorophyll a concentration; β0, β1…βn are model parameters; ∈ is the error term.

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