Coastal zone water quality comprehensive toxicity monitoring system based on multi-source environmental data

Abstract

The invention relates to the field of water quality monitoring, and discloses a coastal zone water quality comprehensive toxicity monitoring system based on multi-source environmental data, which comprises 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 plurality of sensitive living organisms and acquires behavior data of the sensitive living organisms in real time; the environmental parameter acquisition unit synchronously acquires environmental parameters; the data processing unit corrects a living body biological behavior analysis result through a preset toxicity analysis model in combination with environmental parameters, and judges the comprehensive toxicity of the water quality; the early warning unit adopts a grading early warning mechanism, different early warning grades are divided according to the comprehensive toxicity index and the area of the toxic region, grading early warning is carried out through sound and light, and differentiated control measures are carried out on the toxic region; according to the invention, multi-dimensional accurate monitoring, intelligent dynamic early warning and efficient regional management and control of the comprehensive toxicity of the coastal zone water quality are realized, and the ecological environment protection capability of the coastal zone is effectively improved.

Description

Technical Field

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

[0002] The water environment in the coastal zone is complex and changeable, and is vulnerable to industrial emissions, agricultural runoff, ship pollution and emergencies. Traditional physical and chemical monitoring methods are difficult to comprehensively capture unknown toxic pollutants and combined toxicity effects. An online toxicity warning system based on living organisms such as fish can provide early comprehensive toxicity alarms by real-time sensing abnormal behaviors of living organisms, and has become an important supplement to water quality safety monitoring.

[0003] However, there are still some defects in the existing living organism toxicity monitoring systems: First, relying on the behavior analysis of a single species, such as fish, it is easy to miss the detection of insensitive pollutants and cannot identify the specific type of poison. Second, fluctuations in typical coastal zone parameters such as environmental water temperature, salinity, turbidity, and dissolved oxygen are likely to cause abnormal non-toxic behaviors of living organisms, resulting in false positive alarms. Summary of the Invention

[0004] The purpose of the present invention is to provide a comprehensive coastal water quality toxicity monitoring system based on multi-source environmental data to solve the above technical problems.

[0005] The purpose of the present invention can be achieved by the following technical solutions: A comprehensive coastal water quality toxicity monitoring system based on multi-source environmental data, comprising: A multi-living organism monitoring unit, provided with a variety of living organisms sensitive to different pollutants, for real-time monitoring of the behavior data of living organisms; A multi-source environmental parameter acquisition unit, for real-time acquisition of coastal water body environmental parameters, and the environmental parameters include water temperature, salinity, turbidity and dissolved oxygen; A data processing and analysis unit, respectively connected to the multi-living organism monitoring unit and the multi-source environmental parameter acquisition unit, for receiving the behavior data of living organisms and environmental parameters, analyzing the behavior data of living organisms based on a preset toxicity analysis model, and correcting the analysis result in combination with environmental parameters to judge the comprehensive toxicity of coastal water quality; An early warning unit, connected to the data processing and analysis unit, for giving an early warning when the data processing and analysis unit judges that there is comprehensive toxicity in the coastal water quality.

[0006] As a further technical solution, the preset toxicity analysis model includes the following steps: For each living organism, calculate the ratio of the quantified 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, calculate the environmental correction factor through the formula: environmental correction factor = 1 - correction coefficient × normalized parameter value; Accumulate the products of the basic toxicity indices of all living organisms and the corresponding environmental correction factors to obtain the final comprehensive toxicity index; 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 the behavior of living organisms, and the normalized parameter value is calculated based on the current parameter value and historical extreme values.

[0007] As a further technical solution, the method for analyzing the behavior data of living organisms includes: For each living organism, collect the swimming speed, trajectory deviation distance, and breathing frequency in real time; Calculate the relative change rate or ratio of the swimming speed, trajectory deviation distance, and breathing frequency to the reference values in the normal state; According to the weight coefficients determined by pre-training, perform weighted summation on the relative change rates or ratios of the swimming speed, trajectory deviation distance, and breathing frequency, and then multiply by the comprehensive index of group behavior to obtain the quantified value of the abnormal behavior degree of the living organism; The weight coefficients are determined by training with experimental data, and the sum of the weights of the swimming speed, trajectory deviation distance, and breathing frequency is 1.

[0008] As a further technical solution, the method for correcting the analysis result by combining environmental parameters includes: When the comprehensive toxicity index exceeds the basic warning threshold, recalculate the correction factors of each environmental parameter; Multiply the basic toxicity index by the product of all environmental correction factors to obtain the corrected toxicity index; Compare the corrected toxicity index with the corrected warning threshold determined based on historical data. If it exceeds, trigger a warning; otherwise, do not give a warning.

[0009] As a further technical solution, the comprehensive index of group behavior is determined in the following way: The group aggregation degree is obtained by calculating the reciprocal of the average distance between all individuals in the unit monitoring area, and then comparing it with the reference aggregation degree to obtain the relative change rate of the aggregation degree; The behavior regularity is determined by analyzing the statistical variance of the angle of change of the individual movement direction within a continuous time period. The larger the variance, the worse the behavior regularity; The reaction sensitivity is obtained by measuring the reciprocal of the response latency of a living organism to a sudden light stimulus, and then comparing it with the reference sensitivity to obtain the relative change rate of sensitivity; Non-linearly combine the relative change rates of population aggregation degree, behavioral regularity, and reaction sensitivity: take the exponential power of the relative change rate of aggregation degree, take the negative exponent of the variance of behavioral regularity, take the exponential power of the relative change rate of sensitivity, and then multiply the three to obtain the preliminary population behavior index; Modify the preliminary population behavior index: adjust the influence degree of each parameter through the adjustment coefficient determined by experiments to finally obtain the comprehensive population behavior index.

[0010] As a further technical solution, the warning unit adopts a hierarchical warning mechanism, and the warning unit divides different warning levels according to the comprehensive toxicity index output by the data processing and analysis unit: When the warning threshold < comprehensive toxicity index ≤ a yellow warning is triggered, and a low-frequency alarm sound is emitted through the sound warning module, and the yellow light flashes; When < comprehensive toxicity index ≤ an orange warning is triggered, and a medium-frequency alarm sound is emitted through the sound warning module, and the orange light flashes; When the comprehensive toxicity index > a red warning is triggered, and the sound warning module emits a high-frequency alarm sound, and the red light continuously lights up; Among them, is the warning threshold for mild toxicity, is the warning threshold for severe toxicity, and are determined by the joint analysis of historical toxicity data and environmental parameters.

[0011] As a further technical solution, the warning unit includes a spatial analysis module for calculating the area of the toxicity region according to the geographical location coordinates of the warning points: When the distance between adjacent warning points is less than the preset spatial threshold, connect the warning points to form a closed polygon area; Calculate the area A of the closed polygon through the Gauss-Green formula; Divide the warning level according to the area size: When A ≤ A1, a yellow area warning is triggered, and only the area boundary is monitored key; When A1 < A ≤ A2, an orange area warning is triggered, and the area is monitored in a grid pattern, and the grid size is automatically adjusted to , k is an empirical coefficient, is the side length of the grid for grid monitoring; When A > A2, trigger a warning in the red area, start the drone inspection and conduct 3D modeling of the area. At the same time, divide the area into a core area, a buffer area, and an impact area for hierarchical control.

[0012] As a further technical solution, each time a warning for the toxic area is generated: Record the geographical location coordinates, area size, warning level, and generation time of the area; Establish a spatio-temporal index database to store the evolution data of the toxic area at different time points; Dynamically adjust the data sampling frequency according to the area size of the area.

[0013] Advantages of the present invention: (1) Through the collaborative work of multiple live biological monitoring units and multiple-source environmental parameter collection units, real-time monitoring behavior data of multiple live organisms sensitive to different pollutants are utilized, and environmental parameters such as water temperature and salinity are synchronously collected. Combined with a preset toxicity analysis model, in-depth analysis of the live biological behavior data is carried out, and based on the environmental parameter correction results, high-precision judgment of the comprehensive toxicity of the coastal water quality is achieved. Compared with a single monitoring method, the accuracy and comprehensiveness of the monitoring are significantly improved, and the water quality changes in a complex environment can be captured in a timely manner; (2) The warning unit adopts a hierarchical warning mechanism, divides different warning levels according to the comprehensive toxicity index, and gradually enhances the warning intensity from yellow to red. Combined with the spatial analysis module, the 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 spatio-temporal index database, and dynamically adjusting the sampling frequency, the real-time tracking of the toxic diffusion trend is ensured, effectively improving the timeliness and pertinence of the warning, and winning precious time for the environmental protection of the coastal zone; (3) When a regional warning is triggered, the core area, buffer area, and impact area can be automatically divided according to the area of the toxic area for hierarchical control; different monitoring and treatment measures are taken for different areas, such as focusing on monitoring the boundary of a small-area region, starting drone inspection and 3D modeling for a large-area region, realizing the reasonable allocation and efficient utilization of resources, greatly improving the efficiency and effect of coastal zone pollution control, and reducing the ecological environment risk. Brief Description of the Drawings

[0014] The present invention will be further described below with reference to the drawings.

[0015] Figure 1 It is the system structure block diagram of the present invention. Detailed Embodiment

[0016] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0017] Please refer to Figure 1 As shown, the present invention is a comprehensive toxicity monitoring system for coastal water quality based on multi-source environmental data, including: A multi-living organism monitoring unit, which is provided with a variety of living organisms sensitive to different pollutants and is used to monitor the behavior data of the living organisms in real time; A multi-source environmental parameter acquisition unit, which is used to acquire the coastal water body environmental parameters in real time, and the environmental parameters include water temperature, salinity, turbidity and dissolved oxygen; A data processing and analysis unit, which is respectively connected to the multi-living organism monitoring unit and the multi-source environmental parameter acquisition unit, and is used to receive the behavior data of the living organisms and the environmental parameters, analyze the behavior data of the living organisms based on a preset toxicity analysis model, and correct the analysis result in combination with the environmental parameters to judge the comprehensive toxicity of the coastal water quality; An early warning unit, which is connected to the data processing and analysis unit, and when the data processing and analysis unit judges that there is comprehensive toxicity in the coastal water quality, it gives an early warning.

[0018] In this embodiment, through the collaborative operation of the multi-living organism monitoring unit and the multi-source environmental parameter acquisition unit, multi-dimensional real-time acquisition of water quality information is realized; the data processing and analysis unit corrects the living organism behavior analysis result in combination with the environmental parameters, overcoming the limitations of single-organism or parameter monitoring; the early warning unit responds in time to the toxicity judgment result, provides a rapid early warning for abnormal coastal water quality, significantly improves the comprehensiveness and reliability of the monitoring system, and builds a data defense line for coastal ecological protection.

[0019] The preset toxicity analysis model includes the following steps: For each living organism, calculate the ratio of the behavior abnormality degree quantization value to the historical maximum abnormality value, and multiply it by the sensitivity coefficient of the living organism to pollutants to obtain the basic toxicity index; For each environmental parameter, calculate the environmental correction factor through the formula: environmental correction factor = 1 - correction coefficient × normalized parameter value; Accumulate the products of the basic toxicity indexes of all living organisms and the corresponding environmental correction factors to obtain the final comprehensive toxicity index; 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 the behavior of living organisms, and the normalized parameter value is calculated based on the current parameter value and historical extreme values.

[0020] 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 the monitoring results, making the water quality toxicity assessment closer to the actual pollution situation and greatly improving the accuracy and stability of toxicity judgment.

[0021] For example, the preset toxicity analysis model is calculated using the following formula: Where, is the comprehensive toxicity index of coastal water quality, is the number of species of living organisms in the multi-living organism monitoring unit, is the th species of living organism, is the th species of living organism's sensitivity coefficient to pollutant toxicity, obtained by fitting historical experimental data; is the quantified value of the current abnormal behavior degree of the living organism, calculated by weighting the behavior parameters of the current living organism, namely the swimming speed change rate, trajectory deviation degree, and breathing frequency fluctuation value; is the th species of living organism's maximum quantified value of abnormal behavior degree in the known maximum toxicity environment; is the number of types of environmental parameters collected by the multi-source environmental parameter acquisition unit, is the th type of environmental parameter, is the th type of environmental parameter's correction coefficient for the behavior of the th species of living organism, determined by experiments on the relationship between environmental parameters and the behavior of living organisms; is the th species of environmental parameter's normalized value, , is the current parameter value, , and

[0022] are the minimum and maximum values in the historical data of the current parameter respectively, mapping the parameter value to the [0, 1] interval to eliminate the influence of dimension.

[0023] The above-mentioned 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, according to its current quantification value of abnormal behavior , the maximum quantification value of abnormal behavior in the known maximum toxicity environment , and the sensitivity coefficient of the living organism to pollutant toxicity , a basic value is obtained; then for each environmental parameter, according to its correction coefficient for the behavior of living organisms , and the normalized value of this environmental parameter , the basic value is adjusted; finally, the basic values of all living organisms adjusted by environmental parameters are accumulated to obtain the final comprehensive toxicity index of coastal water quality .

[0024] Taking the environmental parameter of water temperature as an example, the process of conducting an experiment on the relationship between environmental parameters and the behavior of living organisms to obtain the correction coefficient of water temperature on the behavior of the th living organism is as follows: Build a controllable experimental environment, such as setting a series of different water temperature gradients, covering the water temperature range that may actually occur in the coastal zone, such as 5°C to 35°C, and maintaining other environmental parameters such as salinity, turbidity, and dissolved oxygen stable at normal levels under each water temperature gradient; Put the selected living organisms, continuously monitor and record the biological behavior data, including swimming speed, staying area, and breathing frequency, and each behavior data is standardized.

[0025] Analyze the variation law of biological behavior data at different water temperatures, and use the linear regression method to calculate the correction coefficient of water temperature on the behavior of this living organism.

[0026] The fitting equation is B = aT + b, where B is the quantification value of biological behavior, and the calculation formula is: B = q1×swimming speed + q2×staying area + q3×breathing frequency, and q1, q2, and q3 are influence coefficients determined based on the experiment; T is the water temperature, a is the correction coefficient; b is the reference offset used to correct the model intercept to ensure that when T = Tref (reference water temperature), B corresponds to the quantification result of normal biological behavior; For other environmental parameters such as salinity, turbidity, and dissolved oxygen, similar single-variable control experiments are used to obtain their respective corresponding correction coefficients.

[0027] The analysis method for the biological behavior data of living organisms includes: For each living organism, the swimming speed, trajectory offset distance, and breathing frequency are collected in real time; Calculate the relative change rates or ratios of the swimming speed, trajectory deviation distance, and breathing frequency to the reference values under normal conditions; According to the weight coefficients determined by pre-training, after weighted summing the relative change rates or ratios of the swimming speed, trajectory deviation distance, and breathing frequency, and then multiplying by the comprehensive index of group behavior, obtain the quantification value of the abnormal behavior degree of the living organism; The weight coefficients are determined by training with experimental data, and the sum of the weights of the swimming speed, trajectory deviation distance, and breathing frequency is 1.

[0028] In this embodiment, for the analysis method of the behavior data of living organisms, by collecting the key behavior indicators of the swimming speed, trajectory deviation distance, and breathing frequency, combined with the weight coefficients and the comprehensive index of group behavior, comprehensively capture the abnormal performance of living organisms in the polluted environment; the above multi-index weighted analysis method can more sensitively and meticulously reflect the impact of water quality toxicity on organisms compared with single-index monitoring, enhancing the sensitivity and credibility of toxicity judgment.

[0029] Example: The process of analyzing the behavior data of living organisms is as follows: For each type of living organism, obtain the swimming speed within a unit time and the average deviation distance between the swimming trajectory and the normal trajectory and the breathing frequency , and calculate the quantification value of the abnormal behavior degree through the following formula : where , , are the average swimming speed, average deviation distance, and average breathing frequency of the living organism in the normal water quality environment respectively, , , are the weight coefficients of the swimming speed change, trajectory deviation, and breathing frequency fluctuation in the quantification calculation of the abnormal behavior degree respectively, determined by training with experimental data, and satisfy , is the comprehensive index of group behavior.

[0030] For the analysis of the behavior data of living organisms, the calculation method of the quantification value of the abnormal behavior degree is clarified; specifically, for each type of living organism, obtain its swimming speed within a unit time and the average deviation distance between the swimming trajectory and the normal trajectory and the breathing frequency these three key behavior parameters; then, calculate the corresponding parameters of these three parameters and the living organism in the normal water quality environment respectively, the average swimming speed , average offset distance , average breathing rate Degree of difference, and based on the weight coefficients of swimming speed change, trajectory deviation, and breathing rate fluctuation in the quantification calculation of abnormal behavior degree , , After weighted summation and then multiplication by the comprehensive index of overall and group behavior, the quantification value of abnormal behavior degree is obtained .

[0031] The method for correcting the analysis result by combining environmental parameters includes: When the comprehensive toxicity index exceeds the basic warning threshold, recalculate the correction factors of each environmental parameter; Multiply the basic toxicity index by the product of all environmental correction factors to obtain the corrected toxicity index; Compare the corrected toxicity index with the corrected warning threshold determined based on historical data. If it exceeds, trigger a warning; otherwise, do not give a warning.

[0032] In this embodiment, the method for correcting the analysis result by combining environmental parameters dynamically adjusts the correction factors when the comprehensive toxicity index exceeds the threshold, avoiding misjudgment or missed judgment caused by changes in environmental parameters; by combining the basic toxicity index with environmental correction factors, re-evaluating the toxicity level, ensuring the accuracy and reliability of the warning result, and maintaining high-efficiency warning ability in the complex and changeable coastal zone environment.

[0033] Example: The process of correcting the analysis result by combining environmental parameters is as follows: When the calculated comprehensive toxicity index of coastal zone water quality exceeds the basic warning threshold , according to the formula: Correct to obtain the corrected toxicity index ; If > the corrected warning threshold , the warning threshold is determined by the joint analysis of historical environmental parameters and toxicity data; then trigger the warning unit to give a warning. If ≤ the corrected warning threshold , then no warning is given.

[0034] In terms of correcting the analysis result by combining environmental parameters, when the comprehensive toxicity index of coastal zone water quality calculated by the toxicity analysis model exceeds the preset basic warning threshold , it is necessary to correct ; the correction method is to use the correction coefficient of the influence of environmental parameters on the behavior of living organisms again , and the normalized values of environmental parameters , the corrected toxicity index is calculated through a specific formula ; After that, the corrected toxicity index is compared with the corrected warning threshold ; If is still greater than , the warning unit is triggered to give a warning, indicating that there is a comprehensive toxicity risk in the coastal water quality; If is less than or equal to , no warning is given, and it is considered that the current water quality risk is within the controllable range.

[0035] The comprehensive index of group behavior is determined in the following way: The group aggregation degree is obtained by calculating the reciprocal of the average distance between all individuals in the unit monitoring area, and then the relative change rate of the aggregation degree is obtained by comparing it with the reference aggregation degree; The behavioral regularity is determined by analyzing the statistical variance of the angle change of the individual movement direction within a continuous time period. The larger the variance, the worse the behavioral regularity; The reaction sensitivity is obtained by measuring the reciprocal of the response latency of the living organism to the sudden light stimulus, and then the relative change rate of the sensitivity is obtained by comparing it with the reference sensitivity; The relative change rates of the group aggregation degree, behavioral regularity and reaction sensitivity are non-linearly combined: the exponential power is taken for the relative change rate of the aggregation degree, the negative exponential is taken for the variance of the behavioral regularity, and the exponential power is taken for the relative change rate of the sensitivity, and then the three are multiplied to obtain the preliminary group behavior index; The preliminary group behavior index is corrected: the influence degree of each parameter is adjusted by the adjustment coefficient determined through experiments, and finally the comprehensive index of group behavior is obtained.

[0036] 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 degree, behavioral regularity and reaction sensitivity. Through non-linear combination and parameter adjustment, the cooperative reaction characteristics of biological groups under pollution stress are fully considered; The above method can more truly reflect the health status of biological groups, provide a richer and more reliable basis for water quality toxicity judgment, and improve the biological relevance and analysis depth of the monitoring system.

[0037] Example: The calculation formula of the comprehensive index of group behavior is: ; Among them, is the group aggregation degree, and the expression is: , is the individual and the individual The distance between is the total number of individuals in the monitoring area; is the variance of the angle of change of the movement direction within a continuous time period which characterizes the behavioral regularity; is the response sensitivity, , is the response latency to the sudden light stimulus; , is the reference value of the corresponding parameter, , , is the adjustment coefficient determined through experiments.

[0038] The warning unit adopts a hierarchical warning mechanism, and the warning unit divides different warning levels according to the comprehensive toxicity index output by the data processing and analysis unit: When the warning threshold < comprehensive toxicity index ≤ , a yellow warning is triggered, and a low-frequency alarm sound is emitted through the sound warning module, and the yellow light flashes; When < comprehensive toxicity index ≤ , an orange warning is triggered, and a medium-frequency alarm sound is emitted through the sound warning module, and the orange light flashes; When the comprehensive toxicity index > , a red warning is triggered, and the sound warning module emits a high-frequency alarm sound, and the red light stays on continuously; Among them, is the mild toxicity warning threshold, is the severe toxicity warning threshold, and are determined through the joint analysis of historical toxicity data and environmental parameters.

[0039] In this embodiment, the hierarchical warning mechanism of the warning unit divides different levels according to the comprehensive toxicity index, and adopts a differential warning method of sound and light, enabling the staff to quickly identify the degree of toxicity hazard; the above-mentioned hierarchical warning mode avoids the one-size-fits-all warning method, improves the pertinence of the warning, and can reasonably allocate emergency resources according to the severity of the toxicity, realizing an efficient pollution emergency response.

[0040] The warning unit includes a spatial analysis module for calculating the toxicity area according to the geographical location coordinates of the warning points: When the distance between adjacent warning points is less than the preset spatial threshold, the warning points are connected to form a closed polygon area; The area A of the closed polygon is calculated through the Gauss-Green formula; The warning level is divided according to the area size: When A ≤ A1, a yellow - area warning is triggered, and only the area boundary is monitored intensively; When A1 < A ≤ A2, an orange - area warning is triggered, and the area is monitored in a grid pattern. The grid size is automatically adjusted to , where k is an empirical coefficient, and is the side length of the grid for grid - pattern monitoring; When A > A2, a red - area warning is triggered. Drone patrol is started and 3D modeling of the area is carried out. At the same time, the area is divided into a core area, a buffer area, and an impact area for hierarchical control.

[0041] For example: The specific process of hierarchical control is as follows: Core area: Designated as the 30% area with the highest comprehensive toxicity index within the toxicity area. This area has a high pollutant concentration and poses the greatest threat to the coastal - zone ecosystem. In terms of control, high - frequency sampling monitoring is adopted, high - density monitoring equipment is deployed to grasp the water - quality changes in real time; professional emergency - treatment teams and efficient treatment equipment are preferentially allocated to quickly carry out targeted treatment measures such as pollutant adsorption and degradation to minimize the pollution hazard.

[0042] Buffer area: Covers the middle 40% area of the toxicity area. Although the toxicity is relatively weaker than that of the core area, there is a risk of pollutant diffusion. In management, grid - pattern regular patrol is implemented, combined with drone aerial photography and ground patrol to dynamically monitor the change of the pollution boundary; according to the treatment progress in the core area and the pollution - diffusion trend, the treatment plan is flexibly adjusted, and preventive measures such as interception and dilution are taken in advance to prevent further pollution diffusion.

[0043] Impact area: As the 30% area outside the toxicity area, it may be indirectly affected by pollution. In terms of control methods, the monitoring frequency is appropriately reduced, and a combination of satellite remote sensing and ground monitoring is used to macroscopically grasp the regional environmental changes; environmental warning is strengthened, and an emergency plan is formulated. Once pollution spreads, the emergency response is quickly activated, and at the same time, ecological - restoration preparation work is carried out to maintain the ecological stability of the area.

[0044] In this embodiment, the spatial - analysis module of the warning unit dynamically adjusts the warning strategy according to the area of the toxicity area. From boundary monitoring to grid management, and then to drone patrol and 3D modeling, appropriate control measures are taken for pollution areas of different scales; the above - mentioned scheme realizes the precise allocation of resources, avoids the waste of resources in large - area monitoring, and at the same time ensures the rapid positioning and disposal of small - scale pollution, significantly improving the efficiency and effectiveness of coastal - zone pollution control.

[0045] When generating a toxicity - area warning each time: Record the geographical location coordinates, area size, warning level, and generation time of the area; Establish a spatio - temporal index database to store the evolution data of the toxicity area at different time points; Dynamically adjust the data sampling frequency according to the size of the area.

[0046] The specific method for dynamically adjusting the data sampling frequency according to the size of the area is as follows: When the area A ≤ A1, maintain the basic sampling frequency of environmental parameters and living organism behavior data as Y0; when A1 < A ≤ A2, adjust the sampling frequency to Y1 = Y0 × 2; when A > A2, increase the sampling frequency to Y2 = Y0 × 4; If the area of the region shrinks, when the reduced area A' satisfies A1 < A' ≤ A2, reduce the sampling frequency to Y1; when A' ≤ A1, restore to the basic sampling frequency Y0.

[0047] In this embodiment, by recording the evolution data of the toxic area and establishing a spatio-temporal index database, the dynamic change process of the polluted area can be completely traced, providing historical data support for pollution source tracing and treatment effect evaluation; the mechanism of dynamically adjusting the data sampling frequency ensures that the data density is increased in the critical stage of pollution diffusion and the resource consumption is reduced in the stable stage, achieving the balance between data collection efficiency and resource utilization, and enhancing the long-term operation adaptability and data management ability.

[0048] It should be noted that: the calculation formulas and each parameter participating in the operation in the present invention are all pre-dimensionless processed, and the process of dimensionless processing is well known in the industry and will not be described here.

[0049] The above has described a specific embodiment of the present invention in detail, but the content described is only the preferred embodiment of the present invention and cannot be considered as limiting the implementation scope of the present invention. All equivalent changes and improvements made according to the scope of the present invention application should still fall within the patent coverage scope of the present invention.

Claims

1. A comprehensive toxicity monitoring system for coastal water quality based on multi-source environmental data, characterized in that, Including: A multi-living organism monitoring unit, which is provided with a variety of living organisms 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, which is used to collect coastal water environmental parameters in real time, and the environmental parameters include water temperature, salinity, turbidity and dissolved oxygen; A data processing and analysis unit, which is respectively connected to the multi-living organism monitoring unit and the multi-source environmental parameter acquisition unit, and is used to receive the behavioral data of living organisms and environmental parameters, analyze the behavioral data of living organisms based on a preset toxicity analysis model to obtain a comprehensive toxicity index, and correct the analysis result in combination with environmental parameters to judge the comprehensive toxicity of coastal water quality; The method for correcting the analysis result in combination with environmental parameters includes: When the comprehensive toxicity index exceeds the basic warning threshold, recalculate the correction factors of each environmental parameter; Multiply the basic toxicity index by the product of all environmental correction factors to obtain the corrected toxicity index; Compare the corrected toxicity index with the corrected warning threshold determined based on historical data. If it exceeds, trigger a warning; otherwise, do not give a warning; A warning unit, which is connected to the data processing and analysis unit, and gives a warning when the data processing and analysis unit judges that there is comprehensive toxicity in the coastal water quality.

2. The integrated coastal water quality toxicity monitoring system based on multi-source environmental data according to claim 1, characterized in that, The preset toxicity analysis model includes the following steps: For each living organism, calculate the ratio of the quantification value of the behavior abnormality degree to the historical maximum abnormality value, and multiply it by the sensitivity coefficient of the living organism to pollutants to obtain the basic toxicity index; For each environmental parameter, calculate the environmental correction factor through the formula: environmental correction factor = 1 - correction coefficient × normalized parameter value; Accumulate the products of the basic toxicity indexes of all living organisms and the corresponding environmental correction factors to obtain the final comprehensive toxicity index; 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 the behavior of living organisms, and the normalized parameter value is calculated based on the current parameter value and historical extreme values.

3. The integrated coastal water quality toxicity monitoring system based on multi-source environmental data according to claim 2, characterized in that, The analysis method for the behavioral data of living organisms includes: For each living organism, collect the swimming speed, trajectory deviation distance and breathing frequency in real time; Calculate the relative change rate or ratio of the swimming speed, trajectory deviation distance and breathing frequency to the reference value in the normal state; According to the weight coefficients determined by pre-training, perform weighted summation on the relative change rates or ratios of the swimming speed, trajectory deviation distance and breathing frequency, and then multiply by the comprehensive index of group behavior to obtain the quantification value of the behavior abnormality degree of the living organism; The weight coefficients are determined by training with experimental data, and the sum of the weights of the swimming speed, trajectory deviation distance and breathing frequency is 1.

4. The integrated toxicity monitoring system for coastal water quality based on multi-source environmental data according to claim 3, wherein The comprehensive index of group behavior is determined by the following method: Group aggregation degree, which is obtained by calculating the reciprocal of the average distance between all individuals in the unit monitoring area, and then comparing it with the reference aggregation degree to obtain the relative change rate of aggregation degree; Behavior regularity, which is determined by analyzing the statistical variance of the angle of change of individual movement directions within a continuous time period. The larger the variance, the worse the behavior regularity; The reaction sensitivity is obtained by measuring the reciprocal of the response latency of a living organism to a sudden light stimulus, and then comparing it with the reference sensitivity to obtain the relative change rate of sensitivity; The relative change rates of population aggregation degree, behavioral regularity, and reaction sensitivity are non-linearly combined: taking the exponential power of the relative change rate of aggregation degree, taking the negative exponent of the variance of behavioral regularity, taking the exponential power of the relative change rate of sensitivity, and then multiplying the three to obtain the preliminary population behavior index; The preliminary population behavior index is corrected: the influence degree of each parameter is adjusted by the adjustment coefficient determined through experiments to finally obtain the comprehensive population behavior index.

5. The integrated toxicity monitoring system for coastal water quality based on multi-source environmental data according to claim 1, wherein The warning unit adopts a hierarchical warning mechanism, and the warning unit divides different warning levels according to the comprehensive toxicity index output by the data processing and analysis unit: When the warning threshold < comprehensive toxicity index ≤ a yellow warning is triggered, and a low-frequency alarm sound is emitted through the sound warning module, and the yellow light flashes; When <Comprehensive toxicity index ≤ a medium-frequency alarm sound is triggered through the sound warning module and the orange light flashes to issue an orange warning When the comprehensive toxicity index > , a red warning is triggered, and the sound warning module emits a high-frequency alarm sound while continuously turning on the red light; Among them, is the warning threshold for mild toxicity, is the warning threshold for severe toxicity, and are determined by the joint analysis of historical toxicity data and environmental parameters.

6. The integrated coastal water quality toxicity monitoring system based on multi-source environmental data according to claim 5, wherein The warning unit includes a spatial analysis module for calculating the area of the toxicity region according to the geographical location coordinates of the 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 polygon area; Calculate the area A of the closed polygon through the Gauss-Green formula; Divide the warning level according to the area size: When A ≤ A1, trigger a yellow area warning and only focus on monitoring the area boundary; When A1 < A ≤ A2, an orange area warning is triggered, and grid monitoring is carried out inside the area. The grid size is automatically adjusted to , where k is an empirical coefficient, is the side length of the grid for grid monitoring; When A > A2, trigger a red area warning, start drone patrol and conduct 3D modeling of the area, and at the same time divide the area into a core area, a buffer area, and an impact area for hierarchical management and control.

7. The integrated coastal water quality toxicity monitoring system based on multi-source environmental data according to claim 6, wherein, When generating a toxicity area warning each time: Record the geographical location coordinates, area size, warning level, and generation time of the area; Establish a spatio-temporal index database to store the evolution data of the toxicity area at different time points; Dynamically adjust the data sampling frequency according to the area size of the area.

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