A comprehensive toxicity monitoring system for coastal water quality based on multi-source environmental data

Abstract

The present invention relates to the field of water quality monitoring and discloses a coastal water quality comprehensive toxicity monitoring system based on multi-source environmental data, comprising a multi-living organism monitoring unit, a multi-source environmental parameter acquisition unit, a data processing and analysis unit and an early warning unit; the multi-living organism monitoring unit is provided with a variety of sensitive living organisms to collect their behavior data in real time; the environmental parameter acquisition unit synchronously obtains environmental parameters; the data processing unit uses a preset toxicity analysis model and combines the environmental parameters to correct the living organism behavior analysis results to judge the comprehensive toxicity of water quality; the early warning unit adopts a graded early warning mechanism, divides different warning levels according to the comprehensive toxicity index and the area of ​​the toxic area, performs graded warnings through sound and light, and implements differentiated control measures for the toxic area; the present invention realizes multi-dimensional precise monitoring of the comprehensive toxicity of coastal water quality, intelligent dynamic early warning and efficient regional control, and effectively improves the coastal ecological environment protection capability.

Description

Technical Field

[0001] The present invention relates to the field of water quality monitoring, and in particular to a coastal zone water quality integrated toxicity monitoring system based on multi-source environmental data. Background Art

[0002] Coastal water environments are complex and volatile, susceptible to industrial emissions, agricultural runoff, ship pollution, and unexpected accidents. Traditional physical and chemical monitoring methods struggle to fully capture unknown toxic pollutants and their combined toxic effects. Online toxicity early warning systems based on living organisms, such as fish, can provide early, comprehensive toxicity alerts by sensing abnormal behavior in real time, becoming an important supplement to water quality safety monitoring.

[0003] However, the existing living organism toxicity monitoring system still has some defects:

[0004] First, relying on behavioral analysis of a single species, such as fish, can easily miss non-sensitive pollutants and fail to identify specific toxicants.

[0005] Secondly, fluctuations in typical coastal zone parameters such as ambient water temperature, salinity, turbidity, and dissolved oxygen can easily trigger abnormal non-toxic behaviors of living organisms, leading to false positive alarms. Summary of the Invention

[0006] The purpose of the present invention is to provide a coastal water quality comprehensive toxicity monitoring system based on multi-source environmental data to solve the above technical problems.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A comprehensive coastal water quality toxicity monitoring system based on multi-source environmental data, including:

[0009] A multi-living organism monitoring unit is equipped with a variety of living organisms that are sensitive to different pollutants and is used to monitor the behavioral data of living organisms in real time;

[0010] A multi-source environmental parameter acquisition unit is used to collect coastal water environmental parameters in real time, including water temperature, salinity, turbidity, and dissolved oxygen;

[0011] a data processing and analysis unit, connected to the multi-living organism monitoring unit and the multi-source environmental parameter acquisition unit, respectively, for receiving behavioral data of the living organisms and environmental parameters, analyzing the behavioral data of the living organisms based on a preset toxicity analysis model, and correcting the analysis results in combination with the environmental parameters to determine the comprehensive toxicity of coastal water quality;

[0012] The early warning unit is connected to the data processing and analysis unit, and issues an early warning when the data processing and analysis unit determines that the coastal water quality has comprehensive toxicity.

[0013] As a further technical solution, the preset toxicity analysis model includes the following steps:

[0014] For each living organism, calculate the ratio of the quantitative value of the abnormal behavior degree to the historical maximum abnormal value, and multiply it by the sensitivity coefficient of the living organism to the pollutant to obtain the basic toxicity index;

[0015] For each environmental parameter, the environmental correction factor is calculated using the formula: environmental correction factor = 1 - correction coefficient × normalized parameter value;

[0016] The final comprehensive toxicity index is obtained by adding the product of the basic toxicity index of all living organisms and the corresponding environmental correction factor;

[0017] Among them, the sensitivity coefficient is determined by fitting historical experimental data, the correction coefficient is determined by experiments on the relationship between environmental parameters and living biological behaviors, and the normalized parameter value is calculated based on the current parameter value and historical extreme values.

[0018] As a further technical solution, the method for analyzing the behavioral data of living organisms includes:

[0019] For each living organism, the swimming speed, trajectory deviation distance and breathing frequency are collected in real time;

[0020] Calculate the relative change rate or ratio of swimming speed, trajectory deviation distance and respiratory rate to the baseline value under normal conditions;

[0021] Based on the weight coefficients determined in advance through training, the relative change rates or ratios of swimming speed, trajectory deviation distance, and respiratory rate are weighted and summed, and then multiplied by the comprehensive group behavior index to obtain a quantitative value of the degree of behavioral abnormality of the living organism;

[0022] The weight coefficient is determined through experimental data training, and the sum of the weights of swimming speed, trajectory offset distance and breathing frequency is 1.

[0023] As a further technical solution, the method for correcting the analysis results in combination with environmental parameters includes:

[0024] When the comprehensive toxicity index exceeds the basic warning threshold, the correction factors of each environmental parameter are recalculated;

[0025] Multiply the basic toxicity index by the product of all environmental correction factors to obtain the corrected toxicity index;

[0026] The revised toxicity index is compared with the revised warning threshold determined based on historical data. If it exceeds the threshold, a warning is triggered; otherwise, no warning is triggered.

[0027] As a further technical solution, the group behavior comprehensive index is determined by the following method:

[0028] The group aggregation degree is obtained by calculating the inverse of the average distance between all individuals in the unit monitoring area, and then comparing it with the baseline aggregation degree to obtain the relative change rate of aggregation degree;

[0029] Behavioral regularity was determined by analyzing the statistical variance of the angle of change in the direction of individual movement within consecutive time periods. A larger variance indicated a worse behavioral regularity.

[0030] Reaction sensitivity is obtained by measuring the inverse of the response latency of living organisms to burst light stimulation, and then comparing it with the baseline sensitivity to obtain the relative change rate of sensitivity;

[0031] The relative change rates of group aggregation, behavioral regularity, and reaction sensitivity are nonlinearly combined: the relative change rate of aggregation is exponentially increased, the variance of behavioral regularity is negatively increased, and the relative change rate of sensitivity is exponentially increased. The three are then multiplied to obtain a preliminary group behavior index.

[0032] Modify the preliminary group behavior indicators: adjust the influence of each parameter through the adjustment coefficient determined by the experiment, and finally obtain the comprehensive group behavior indicators.

[0033] As a further technical solution, the early warning unit adopts a graded early warning mechanism, and the early warning unit divides different early warning levels according to the comprehensive toxicity index output by the data processing and analysis unit:

[0034] When the warning threshold is less than the comprehensive toxicity index ≤ When a yellow warning is triggered, a low-frequency alarm sound is emitted through the sound warning module and a yellow light flashes;

[0035] when <Comprehensive toxicity index≤ When an orange warning is triggered, a medium-frequency alarm sound is emitted through the sound warning module and an orange light flashes;

[0036] When the comprehensive toxicity index> When a red warning is triggered, the sound warning module emits a high-frequency alarm sound and the red light is continuously on;

[0037] in, is the mild toxicity warning threshold, is the severe toxicity warning threshold, and Determined through a joint analysis of historical toxicity data and environmental parameters.

[0038] As a further technical solution, the early warning unit includes a spatial analysis module for calculating the area of ​​the toxic zone based on the geographical coordinates of the early warning point:

[0039] When the distance between adjacent warning points is less than the preset spatial threshold, the warning points are connected to form a closed polygonal area;

[0040] Calculate the area A of the closed polygon using the Gauss-Green formula;

[0041] Warning levels are divided according to area size:

[0042] When A≤A1, a yellow zone warning is triggered, and only the zone boundaries are monitored;

[0043] When A1<A≤A2, the orange zone warning is triggered and the area is monitored in a grid-like manner. The grid size is automatically adjusted to , k is the empirical coefficient, is the grid side length for grid monitoring;

[0044] When A>A2, a red zone warning is triggered, drone inspection is started, and three-dimensional modeling of the area is carried out. At the same time, the area is divided into core area, buffer area and impact area for hierarchical management and control.

[0045] As a further technical solution, each time a toxic area warning is generated:

[0046] Record the geographical coordinates, area size, warning level and generation time of the area;

[0047] Establish a spatiotemporal index database to store the evolution data of toxicity areas at different time points;

[0048] Dynamically adjust the data sampling frequency according to the area size.

[0049] Beneficial effects of the present invention:

[0050] (1) Through the coordinated work of multiple living organism monitoring units and multi-source environmental parameter collection units, the behavioral data of various living organisms that are sensitive to different pollutants are monitored in real time, and environmental parameters such as water temperature and salinity are collected simultaneously. Combined with the preset toxicity analysis model, the behavioral data of living organisms are deeply analyzed, and the results of environmental parameter correction are used to achieve high-precision judgment of the comprehensive toxicity of coastal water quality. Compared with a single monitoring method, the accuracy and comprehensiveness of monitoring are significantly improved, and changes in water quality in complex environments can be captured in a timely manner;

[0051] (2) The early warning unit adopts a graded early warning mechanism, which divides different warning levels according to the comprehensive toxicity index, and gradually increases the warning intensity from yellow to red. In combination with the spatial analysis module, the early warning strategy is dynamically adjusted according to the area of ​​the toxic area, such as grid monitoring and drone inspection. At the same time, by recording the evolution data of the toxic area, establishing a spatiotemporal index database and dynamically adjusting the sampling frequency, the real-time tracking of the toxic spread trend is ensured, effectively improving the timeliness and pertinence of the early warning, and buying valuable time for coastal environmental protection.

[0052] (3) When a regional warning is triggered, the core area, buffer zone and impact zone can be automatically divided according to the area of ​​the toxic area for hierarchical management and control; differentiated monitoring and control measures can be taken for different areas, such as focusing on monitoring boundaries in small areas and launching drone inspections and three-dimensional modeling in large areas, so as to achieve rational allocation and efficient utilization of resources, greatly improve the efficiency and effectiveness of coastal pollution control, and reduce ecological and environmental risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The present invention will be further described below with reference to the accompanying drawings.

[0054] Figure 1 This is a system structure diagram of the present invention. DETAILED DESCRIPTION

[0055] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making any creative efforts shall fall within the scope of protection of the present invention.

[0056] See also Figure 1 As shown, the present invention is a coastal water quality integrated toxicity monitoring system based on multi-source environmental data, comprising:

[0057] A multi-living organism monitoring unit is equipped with a variety of living organisms that are sensitive to different pollutants and is used to monitor the behavioral data of living organisms in real time;

[0058] A multi-source environmental parameter acquisition unit is used to collect coastal water environmental parameters in real time, including water temperature, salinity, turbidity, and dissolved oxygen;

[0059] a data processing and analysis unit, connected to the multi-living organism monitoring unit and the multi-source environmental parameter acquisition unit, respectively, for receiving behavioral data of the living organisms and environmental parameters, analyzing the behavioral data of the living organisms based on a preset toxicity analysis model, and correcting the analysis results in combination with the environmental parameters to determine the comprehensive toxicity of coastal water quality;

[0060] The early warning unit is connected to the data processing and analysis unit, and issues an early warning when the data processing and analysis unit determines that the coastal water quality has comprehensive toxicity.

[0061] In this embodiment, through the collaborative operation of multiple living organism monitoring units and multi-source environmental parameter collection units, multi-dimensional real-time collection of water quality information is achieved; the data processing and analysis unit combines environmental parameters to correct the living organism behavior analysis results, overcoming the limitations of single organism or parameter monitoring; the early warning unit responds to toxicity judgment results in a timely manner, providing rapid early warnings for abnormal coastal water quality, significantly improving the comprehensiveness and reliability of the monitoring system, and building a strong data defense line for coastal ecological protection.

[0062] The preset toxicity analysis model includes the following steps:

[0063] For each living organism, calculate the ratio of the quantitative value of the abnormal behavior degree to the historical maximum abnormal value, and multiply it by the sensitivity coefficient of the living organism to the pollutant to obtain the basic toxicity index;

[0064] For each environmental parameter, the environmental correction factor is calculated using the formula: environmental correction factor = 1 - correction coefficient × normalized parameter value;

[0065] The final comprehensive toxicity index is obtained by adding the product of the basic toxicity index of all living organisms and the corresponding environmental correction factor;

[0066] Among them, the sensitivity coefficient is determined by fitting historical experimental data, the correction coefficient is determined by experiments on the relationship between environmental parameters and living biological behaviors, and the normalized parameter value is calculated based on the current parameter value and historical extreme values.

[0067] In this embodiment, the preset toxicity analysis model combines the sensitivity of living organisms to pollutants with the interference of environmental factors by calculating basic toxicity indicators and environmental correction factors to form a comprehensive toxicity index; the above method can scientifically quantify the impact of different pollutants on living organisms, and effectively eliminate the interference of environmental fluctuations such as water temperature and salinity on monitoring results, making water quality toxicity assessment closer to the actual pollution situation, greatly improving the accuracy and stability of toxicity judgment.

[0068] For example, the preset toxicity analysis model is calculated using the following formula:

[0069]

[0070] in, is the comprehensive toxicity index of coastal water quality. is the number of species of living organisms in the multi-living organism monitoring unit, For the living organisms, For the The sensitivity coefficient of living organisms to the toxicity of pollutants is obtained by fitting historical experimental data; It is a quantitative value of the degree of abnormal behavior of the living organism, which is calculated by weighting the current behavioral parameters of the living organism, namely the swimming speed change rate, trajectory deviation, and respiratory rate fluctuation value; For the The maximum quantitative value of the degree of abnormal behavior of a living organism under the known maximum toxic environment; The number of types of environmental parameters collected by the multi-source environmental parameter collection unit, For the Environmental parameters, For the The effect of environmental parameters on The correction coefficient for the influence of living organisms’ behavior is determined through experiments on the relationship between environmental parameters and living organisms’ behavior; For the Normalized values ​​of environmental parameters, , is the current parameter value, 、 are the minimum and maximum values ​​in the historical data of the current parameter, respectively. The parameter value is mapped to the interval [0, 1] to eliminate the dimension effect.

[0071] is the environmental correction factor for each environmental parameter.

[0072] The above preset toxicity analysis model is used to calculate the comprehensive toxicity index of coastal water quality The calculation comprehensively considers the types of living organisms in the multi-living organism monitoring unit and the environmental parameters collected by the multi-source environmental parameter collection unit; Specifically, for each living organism, the quantified value is calculated based on the degree of abnormal behavior of the current living organism. , the maximum quantitative value of behavioral abnormality under the known maximum toxic environment , and the sensitivity coefficient of the living organism to the toxicity of the pollutant , get a basic value; then for each environmental parameter, according to its correction coefficient of the effect on the behavior of living organisms , and the normalized value of the environmental parameter , adjust the basic value; finally, add up the basic values ​​of all living organisms after environmental parameter adjustment to obtain the final coastal water quality comprehensive toxicity index .

[0073] Taking water temperature as an example, we conducted an experiment on the relationship between environmental parameters and the behavior of living organisms to obtain the effect of water temperature on the The process of calculating the correction coefficient for the influence of the behavior of living organisms is:

[0074] Establish a controlled experimental environment, such as setting up a series of different water temperature gradients to cover the actual water temperature range that may occur in the coastal zone, such as 5°C to 35°C. Under each water temperature gradient, other environmental parameters such as salinity, turbidity, and dissolved oxygen are maintained at normal levels.

[0075] Place selected living organisms and continuously monitor and record their behavioral data, including swimming speed, staying area, and breathing rate. Each behavioral data item is standardized.

[0076] The changing patterns of biological behavior data under different water temperatures were analyzed, and the correction coefficient of the effect of water temperature on the behavior of the living organisms was calculated using the linear regression method.

[0077] The fitting equation is B = aT + b, where B is the quantitative value of biological behavior and is calculated as follows: B = q1 × swimming speed + q2 × staying area + q3 × breathing rate, and q1, q2, and q3 are influence coefficients determined based on experiments;

[0078] T is the water temperature, a is the correction coefficient; b is the reference offset, which is used to correct the model intercept to ensure that when T = Tref (reference water temperature), B corresponds to the quantitative result of normal biological behavior;

[0079] Similar single-variable control experiments were used for other environmental parameters such as salinity, turbidity, and dissolved oxygen to obtain their corresponding correction coefficients.

[0080] The method for analyzing the behavior data of living organisms includes:

[0081] For each living organism, the swimming speed, trajectory deviation distance and breathing frequency are collected in real time;

[0082] Calculate the relative change rate or ratio of swimming speed, trajectory deviation distance and respiratory rate to the baseline value under normal conditions;

[0083] Based on the weight coefficients determined in advance through training, the relative change rates or ratios of swimming speed, trajectory deviation distance, and respiratory rate are weighted and summed, and then multiplied by the comprehensive group behavior index to obtain a quantitative value of the degree of behavioral abnormality of the living organism;

[0084] The weight coefficient is determined through experimental data training, and the sum of the weights of swimming speed, trajectory offset distance and breathing frequency is 1.

[0085] In this embodiment, the analysis method for the behavioral data of living organisms comprehensively captures the abnormal performance of living organisms in a polluted environment by collecting key behavioral indicators such as swimming speed, trajectory offset distance, and breathing rate, and combining weight coefficients with comprehensive indicators of group behavior. Compared with single indicator monitoring, the above-mentioned multi-indicator weighted analysis method can more sensitively and meticulously reflect the impact of water quality toxicity on organisms, thereby enhancing the sensitivity and credibility of toxicity judgment.

[0086] For example, the process of analyzing the behavioral data of living organisms is as follows:

[0087] For each living organism, obtain the swimming speed per unit time , the average offset distance between the swimming trajectory and the normal trajectory , respiratory rate , the quantitative value of the degree of behavioral abnormality is calculated by the following formula :

[0088]

[0089] in, 、 、 are the average swimming speed, average displacement distance, and average breathing frequency of the living organism in normal water quality environment. 、 、 are the weight coefficients of swimming speed change, trajectory deviation, and respiratory frequency fluctuation in the quantitative calculation of behavioral abnormality, which are determined through experimental data training and meet the requirements. , It is a comprehensive indicator of group behavior.

[0090] The analysis of the behavioral data of living organisms clarified the quantitative value of the degree of behavioral abnormality The calculation method is to obtain the swimming speed of each living organism in unit time. , the average offset distance between the swimming trajectory and the normal trajectory , respiratory rate These three key behavioral parameters are then calculated separately, along with the corresponding parameters of the living organism in normal water quality, and the average swimming speed. , average offset distance , average respiratory rate The weight coefficients of swimming speed change, trajectory deviation, and respiratory rate fluctuation in the quantitative calculation of behavioral abnormality were used to calculate the degree of difference. 、 、 After weighted summation, the overall and group behavior comprehensive indicators are multiplied to obtain the quantitative value of the degree of behavioral abnormality .

[0091] The method for correcting the analysis results in combination with environmental parameters includes:

[0092] When the comprehensive toxicity index exceeds the basic warning threshold, the correction factors of each environmental parameter are recalculated;

[0093] Multiply the basic toxicity index by the product of all environmental correction factors to obtain the corrected toxicity index;

[0094] The revised toxicity index is compared with the revised warning threshold determined based on historical data. If it exceeds the threshold, a warning is triggered; otherwise, no warning is triggered.

[0095] In this embodiment, a correction method for the analysis results is combined with environmental parameters. When the comprehensive toxicity index exceeds the threshold, the correction factor is dynamically adjusted to avoid misjudgment or missed judgment due to changes in environmental parameters. By combining the basic toxicity index with the environmental correction factor, the toxicity level is re-evaluated to ensure the accuracy and reliability of the early warning results, and to maintain efficient early warning capabilities in the complex and changeable coastal environment.

[0096] For example, the process of correcting the analysis results in combination with environmental parameters is as follows:

[0097] When the calculated coastal water quality comprehensive toxicity index Exceeding the basic warning threshold When, according to the formula: right Corrected to obtain the corrected toxicity index ;

[0098] like >Revised warning threshold , warning threshold Determined by the joint analysis of historical environmental parameters and toxicity data; then the early warning unit is triggered to issue an early warning. If ≤ revised warning threshold , no warning will be issued.

[0099] In terms of correcting the analysis results by combining environmental parameters, when the comprehensive toxicity index of coastal water quality calculated by the toxicity analysis model is Exceeding the pre-set basic warning threshold When Correction is made by reusing the correction coefficient of the environmental parameters on the behavior of living organisms , and the normalized values ​​of environmental parameters , the modified toxicity index is calculated by a specific formula ; Then, the modified toxicity index and the revised warning threshold Compare; if Still greater than , then the early warning unit is triggered to issue an early warning, indicating that there is a comprehensive toxicity risk in the coastal water quality; if Less than or equal to , no warning will be issued, and the current water quality risk is considered to be within a controllable range.

[0100] The comprehensive index of group behavior Determined by:

[0101] The group aggregation degree is obtained by calculating the inverse of the average distance between all individuals in the unit monitoring area, and then comparing it with the baseline aggregation degree to obtain the relative change rate of aggregation degree;

[0102] Behavioral regularity was determined by analyzing the statistical variance of the angle of change in the direction of individual movement within consecutive time periods. A larger variance indicated a worse behavioral regularity.

[0103] Reaction sensitivity is obtained by measuring the inverse of the response latency of living organisms to burst light stimulation, and then comparing it with the baseline sensitivity to obtain the relative change rate of sensitivity;

[0104] The relative change rates of group aggregation, behavioral regularity, and reaction sensitivity are nonlinearly combined: the relative change rate of aggregation is exponentially increased, the variance of behavioral regularity is negatively increased, and the relative change rate of sensitivity is exponentially increased. The three are then multiplied to obtain a preliminary group behavior index.

[0105] Modify the preliminary group behavior indicators: adjust the influence of each parameter through the adjustment coefficient determined by the experiment, and finally obtain the comprehensive group behavior indicators.

[0106] In this embodiment, the method for determining the comprehensive index of group behavior evaluates the changes in the behavior of biological groups from multiple dimensions such as group aggregation, behavioral regularity and reaction sensitivity. Through nonlinear combination and parameter adjustment, the coordinated response characteristics of biological groups under pollution stress are fully considered. The above method can more realistically reflect the health status of biological groups, provide a richer and more reliable basis for water quality toxicity judgment, and enhance the biological relevance and analytical depth of the monitoring system.

[0107] For example: The calculation formula of the comprehensive index of group behavior is: ;

[0108] in, is the group aggregation degree, which is expressed as: , For individuals With individuals The distance between is the total number of individuals in the monitoring area; The angle of change in the direction of movement within a continuous time period The variance of ,characterizes the regularity of behavior; For reaction sensitivity, , is the response latency to burst light stimulation; 、 is the reference value of the corresponding parameter, 、 、 is the adjustment coefficient determined by experiment.

[0109] The early warning unit adopts a graded early warning mechanism, and the early warning unit divides different early warning levels according to the comprehensive toxicity index output by the data processing and analysis unit:

[0110] When the warning threshold is less than the comprehensive toxicity index ≤ When a yellow warning is triggered, a low-frequency alarm sound is emitted through the sound warning module and a yellow light flashes;

[0111] when <Comprehensive toxicity index≤ When an orange warning is triggered, a medium-frequency alarm sound is emitted through the sound warning module and an orange light flashes;

[0112] When the comprehensive toxicity index> When a red warning is triggered, the sound warning module emits a high-frequency alarm sound and the red light is continuously on;

[0113] in, is the mild toxicity warning threshold, is the severe toxicity warning threshold, and Determined through a joint analysis of historical toxicity data and environmental parameters.

[0114] In this embodiment, the graded warning mechanism of the warning unit is divided into different levels according to the comprehensive toxicity index, and adopts differentiated warning methods of sound and light to enable staff to quickly identify the degree of toxic hazard; the above-mentioned graded warning mode avoids a one-size-fits-all warning method, which not only improves the targeted nature of the warning, but also can reasonably allocate emergency resources according to the severity of toxicity, thereby achieving efficient pollution emergency response.

[0115] The early warning unit includes a spatial analysis module for calculating the area of ​​the toxic zone based on the geographical coordinates of the early warning point:

[0116] When the distance between adjacent warning points is less than the preset spatial threshold, the warning points are connected to form a closed polygonal area;

[0117] Calculate the area A of the closed polygon using the Gauss-Green formula;

[0118] Warning levels are divided according to area size:

[0119] When A≤A1, a yellow zone warning is triggered, and only the zone boundaries are monitored;

[0120] When A1<A≤A2, the orange zone warning is triggered and the area is monitored in a grid-like manner. The grid size is automatically adjusted to , k is the empirical coefficient, is the grid side length for grid monitoring;

[0121] When A>A2, a red zone warning is triggered, drone inspection is started, and three-dimensional modeling of the area is carried out. At the same time, the area is divided into core area, buffer area and impact area for hierarchical management and control.

[0122] For example: The specific process of hierarchical control is as follows:

[0123] Core Zone: This zone is defined as the 30% of areas with the highest comprehensive toxicity index within the toxic zone. Pollutant concentrations in this area are high, posing the greatest threat to coastal ecosystems. For management and control, high-frequency sampling and monitoring, along with the deployment of high-density monitoring equipment, ensure real-time monitoring of water quality changes. Professional emergency response teams and efficient treatment equipment are prioritized for rapid implementation of targeted treatment measures, such as pollutant adsorption and degradation, to minimize pollution hazards.

[0124] The buffer zone covers the central 40% of the toxic zone. While less toxic than the core zone, it still presents a risk of pollutant spread. During management, a grid-based, regular inspection system is implemented, combining drone aerial photography with ground inspections to dynamically monitor changes in the pollution boundary. Based on the progress of treatment in the core zone and pollution spread trends, treatment plans are flexibly adjusted, and preventive measures such as interception and dilution are implemented in advance to prevent further spread of pollution.

[0125] Impact zone: The 30% of the area outside the toxic zone may be indirectly affected by the pollution. Regarding management and control measures, appropriately reduce monitoring frequency and utilize a combination of satellite remote sensing and ground-based monitoring to gain a macroscopic understanding of regional environmental changes. Strengthen environmental early warning and develop emergency response plans to rapidly initiate emergency responses should pollution spread. Simultaneously, carry out ecological restoration preparations to maintain regional ecological stability.

[0126] In this embodiment, the spatial analysis module of the early warning unit dynamically adjusts the early warning strategy according to the area of ​​the toxic zone, from boundary monitoring to grid management, and then to drone inspections and three-dimensional modeling, taking adaptive control measures for polluted areas of different sizes. The above scheme realizes the precise allocation of resources, avoids the waste of resources in large-scale area monitoring, and ensures the rapid location and disposal of small-scale pollution at the same time, significantly improving the efficiency and effectiveness of coastal pollution control.

[0127] Each time a toxic zone alert is generated:

[0128] Record the geographical coordinates, area size, warning level and generation time of the area;

[0129] Establish a spatiotemporal index database to store the evolution data of toxicity areas at different time points;

[0130] Dynamically adjust the data sampling frequency according to the area size.

[0131] The specific method of dynamically adjusting the data sampling frequency according to the area size is:

[0132] When the area A≤A1, the basic sampling frequency of environmental parameters and living organism behavior data is maintained at Y0; when A1<A≤A2, the sampling frequency is adjusted to Y1=Y0×2; when A>A2, the sampling frequency is increased to Y2=Y0×4;

[0133] If the area of ​​the region is reduced, when the reduced area A' satisfies A1<A'≤A2, the sampling frequency is reduced to Y1; when A'≤A1, it is restored to the basic sampling frequency Y0.

[0134] In this embodiment, by recording the evolution data of toxic areas and establishing a spatiotemporal index database, the dynamic change process of the polluted area can be fully tracked, providing historical data support for pollution source tracing and governance effect evaluation; the mechanism of dynamically adjusting the data sampling frequency ensures that the data density is increased in the critical stage of pollution spread and resource consumption is reduced in the stable stage, achieving a balance between data collection efficiency and resource utilization, and enhancing long-term operation adaptability and data management capabilities.

[0135] It should be noted that the calculation formulas and various parameters involved in the calculations in the present invention have been dimensionally processed in advance, and the process of dimensionless processing is well known in the industry and will not be described here.

[0136] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A coastal water quality integrated toxicity monitoring system based on multi-source environmental data, characterized by: include: A multi-living organism monitoring unit is equipped with a variety of living organisms that are sensitive to different pollutants and is used to monitor the behavioral data of living organisms in real time; A multi-source environmental parameter acquisition unit is used to collect coastal water environmental parameters in real time, including water temperature, salinity, turbidity, and dissolved oxygen; a data processing and analysis unit, connected to the multi-living organism monitoring unit and the multi-source environmental parameter acquisition unit, respectively, for receiving behavioral data of the living organisms and environmental parameters, analyzing the behavioral data of the living organisms based on a preset toxicity analysis model to obtain a comprehensive toxicity index, and correcting the analysis results in combination with the environmental parameters to determine the comprehensive toxicity of coastal water quality; The method for correcting the analysis results in combination with environmental parameters includes: When the comprehensive toxicity index exceeds the basic warning threshold, the correction factors of each environmental parameter are recalculated; the current comprehensive toxicity index is used as the basic toxicity index; Multiply the basic toxicity index by the product of all environmental correction factors to obtain the corrected comprehensive toxicity index; Compare the revised comprehensive toxicity index with the revised warning threshold determined based on historical data. If it exceeds the threshold, an early warning is triggered; otherwise, no early warning is triggered. an early warning unit connected to the data processing and analysis unit, and issuing an early warning when the data processing and analysis unit determines that the coastal water quality has comprehensive toxicity; The preset toxicity analysis model includes the following steps: For each living organism, calculate the ratio of the quantitative value of the abnormal behavior degree to the historical maximum abnormal value, and multiply it by the sensitivity coefficient of the living organism to the pollutant to obtain the basic toxicity index; For each environmental parameter, the environmental correction factor is calculated using the formula: environmental correction factor = 1 - correction coefficient × normalized parameter value; The final comprehensive toxicity index is obtained by adding the product of the basic toxicity index of all living organisms and the corresponding environmental correction factor; Among them, the sensitivity coefficient is determined by fitting historical experimental data, the correction coefficient is determined by experiments on the relationship between environmental parameters and living biological behaviors, and the normalized parameter value is calculated based on the current parameter value and historical extreme values.

2. The coastal water quality integrated toxicity monitoring system based on multi-source environmental data according to claim 1 is characterized in that: The method for analyzing the behavior data of living organisms includes: For each living organism, the swimming speed, trajectory deviation distance and breathing frequency are collected in real time; Calculate the relative change rate or ratio of swimming speed, trajectory deviation distance and respiratory rate to the baseline value under normal conditions; Based on the weight coefficients determined in advance through training, the relative change rates or ratios of swimming speed, trajectory deviation distance, and respiratory rate are weighted and summed, and then multiplied by the comprehensive group behavior index to obtain a quantitative value of the degree of behavioral abnormality of the living organism; The weight coefficient is determined through experimental data training, and the sum of the weights of swimming speed, trajectory offset distance and breathing frequency is 1.

3. The coastal water quality integrated toxicity monitoring system based on multi-source environmental data according to claim 2 is characterized in that: The group behavior comprehensive index is determined by the following method: The group aggregation degree is obtained by calculating the inverse of the average distance between all individuals in the unit monitoring area, and then comparing it with the baseline aggregation degree to obtain the relative change rate of aggregation degree; Behavioral regularity was determined by analyzing the statistical variance of the angle of change in the direction of individual movement within consecutive time periods. A larger variance indicated a worse behavioral regularity. Reaction sensitivity is obtained by measuring the inverse of the response latency of living organisms to burst light stimulation, and then comparing it with the baseline sensitivity to obtain the relative change rate of sensitivity; The relative change rate of group aggregation, the variance of behavioral regularity, and the relative change rate of reaction sensitivity are nonlinearly combined: the relative change rate of aggregation is exponentially raised, the variance of behavioral regularity is negatively exponentially raised, and the relative change rate of sensitivity is exponentially raised. Then, the three are multiplied together to obtain the preliminary group behavior index. Modify the preliminary group behavior indicators: adjust the influence of each parameter through the adjustment coefficient determined by the experiment, and finally obtain the comprehensive group behavior indicators.

4. The coastal water quality integrated toxicity monitoring system based on multi-source environmental data according to claim 1 is characterized in that: The early warning unit adopts a graded early warning mechanism, and the early warning unit divides different early warning levels according to the comprehensive toxicity index output by the data processing and analysis unit: When the warning threshold is less than the comprehensive toxicity index ≤ When the yellow warning is triggered, a low-frequency alarm sound is emitted through the sound warning module and the yellow light flashes; when <Comprehensive toxicity index≤ When an orange warning is triggered, a medium-frequency alarm sound is emitted through the sound warning module and the orange light flashes; When the comprehensive toxicity index> When a red warning is triggered, the sound warning module emits a high-frequency alarm sound and the red light is continuously on; in, is the mild toxicity warning threshold, is the severe toxicity warning threshold, and Determined through a joint analysis of historical toxicity data and environmental parameters.

5. The coastal water quality integrated toxicity monitoring system based on multi-source environmental data according to claim 4 is characterized in that: The early warning unit includes a spatial analysis module for calculating the area of ​​the toxic zone based on the geographical coordinates of the early warning point: When the distance between adjacent warning points is less than the preset spatial threshold, the warning points are connected to form a closed polygonal area; Calculate the area A of the closed polygon using the Gauss-Green formula; Warning levels are divided according to area size: When A≤A1, a yellow zone warning is triggered, and only the zone boundaries are monitored; When A1<A≤A2, the orange zone warning is triggered and the area is monitored in a grid-like manner. The grid size is automatically adjusted to , k is the empirical coefficient, is the grid side length for grid monitoring; When A>A2, a red zone warning is triggered, drone inspection is started, and three-dimensional modeling of the area is carried out. At the same time, the area is divided into core area, buffer area and impact area for hierarchical management and control.

6. The coastal water quality integrated toxicity monitoring system based on multi-source environmental data according to claim 5 is characterized in that: Each time a toxic zone alert is generated: Record the geographical coordinates, area size, warning level and generation time of the area; Establish a spatiotemporal index database to store the evolution data of toxicity areas at different time points; Dynamically adjust the data sampling frequency according to the area size.

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