River water pollution ecological monitoring management system

Abstract

The invention relates to the technical field of water pollution monitoring, and discloses a river water pollution ecological monitoring management system which comprises a biological behavior monitoring module, a population dynamic analysis module, a pollution early warning module and a data visualization module. Multi-module cooperative work is adopted, the biological behavior monitoring module and the population dynamic analysis module are used for monitoring biological individual behaviors and population levels, and river water quality information is comprehensively obtained; the fish swimming speed, the benthonic animal activity intensity, the population density and the structure change are analyzed by applying a scientific algorithm, and the data accuracy is guaranteed; the pollution early warning module calculates pollution risk indexes and divides pollution risk early warning levels according to the analysis results, early warning can be timely and accurately given out for pollution risks of different degrees, targeted coping strategies are provided for related departments, the scientificity and high efficiency of river water pollution monitoring management are effectively improved, and the economic benefit of river water pollution monitoring management is improved. River ecological protection and pollution treatment are facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of water pollution monitoring, and in particular to a river water pollution ecological monitoring and management system. Background Art

[0002] With the rapid advancement of industrialization and urbanization, river water pollution is becoming increasingly serious, posing a huge threat to the ecological environment and human health. As a key link in water pollution prevention and control, river water quality monitoring and management has received widespread attention.

[0003] Traditional methods for monitoring river water pollution rely primarily on chemical indicators, such as chemical oxygen demand (COD), biochemical oxygen demand (BOD), and heavy metal content. While these methods can directly reflect the types and concentrations of pollutants in water, they have limitations. Firstly, chemical testing requires specialized personnel and equipment, and sampling frequency is limited, making real-time, continuous monitoring difficult and unable to capture dynamic changes in water quality. Secondly, chemical indicators only reflect the pollutants themselves and cannot directly reflect the impact of pollution on the ecosystem, making it impossible to comprehensively assess the health of river ecosystems.

[0004] In recent years, biological monitoring technology has been gradually applied to river water pollution monitoring. By studying the responses of aquatic organisms to pollution, biological monitoring assesses water quality at the ecosystem level, partially addressing the shortcomings of chemical monitoring. However, existing biological monitoring technologies often focus on monitoring a single biological group or a single indicator, such as surveying only the species and abundance of fish or analyzing only the community structure of benthic animals. This single-dimensional monitoring approach cannot fully and systematically reflect the comprehensive impact of river water pollution on the ecosystem, making it difficult to accurately assess pollution risks and providing a comprehensive and effective basis for decision-making on water pollution control.

[0005] Therefore, there is an urgent need for an ecological monitoring and management system that can integrate multi-source data, comprehensively monitor river water pollution conditions from multiple dimensions such as biological behavior and population dynamics, and realize scientific assessment of pollution risks and effective early warning, so as to improve the scientificity and effectiveness of river water pollution monitoring and management, and provide strong support for river ecological protection and pollution control. Summary of the Invention

[0006] The purpose of the present invention is to provide a river water pollution ecological monitoring and management system to solve the technical problems raised in the background technology.

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

[0008] A river water pollution ecological monitoring and management system, comprising:

[0009] Biological behavior monitoring module, used to collect behavioral data of fish and benthic animals in designated rivers;

[0010] Population dynamics analysis module, used to statistically analyze population density and structural changes of different fish and benthic animals in a specified river;

[0011] The pollution early warning module is used to conduct pollution risk analysis based on the results obtained by the biological behavior monitoring module and the population dynamics analysis module;

[0012] The data visualization module is used to display the results of the biological behavior monitoring module, population dynamics analysis module, and pollution early warning module.

[0013] As a further solution of the present invention, the behavioral data is obtained as follows:

[0014] Step A1: Fish behavior data collection:

[0015] Underwater cameras and infrared sensors were deployed at different locations in the river;

[0016] Underwater cameras capture images of fish activity areas at a fixed frequency, while infrared sensors continuously monitor the movement of the fish's heat sources.

[0017] For the captured video, the fish position is identified frame by frame by manual or image recognition software. Then the sum of the change distance of the fish position between adjacent frames is calculated to obtain the fish movement distance D. At the same time, the shooting time interval T is recorded;

[0018] According to the formula Calculate the swimming speed V of the fish;

[0019] Step A2: Determination of abnormal fish behavior:

[0020] As for the swimming speed of fish, within a specified test period, by monitoring the swimming speed of fish in the same river section under normal water quality conditions, the minimum and maximum swimming speeds were extracted, and then the normal range of the swimming speed of fish in the river section was determined based on them. Vmin , V max ];

[0021] During a specified observation period, the swimming speed V of fish collected in the corresponding river section is compared with the normal range of fish swimming speed in the corresponding river section:

[0022] When V is in [V min ×α,V max ×(2-α)], then the behavior of the detected fish in this river section is judged to be normal;

[0023] When V is not in [V min ×α,Vmax ×(2-α)], then the behavior of the detected fish in this river section is judged to be abnormal;

[0024] Among them, α is a pre-set compensation factor;

[0025] During the specified observation period, the total number of fish detected in the river section is counted (Na), and the number of fish detected with abnormal behavior (Nb) is also counted.

[0026] Then by: formula Calculate the frequency FY of abnormal behavior of fish in this river section;

[0027] Step A3: Benthic animal behavior data collection:

[0028] The river section is divided into several sampling areas, and then a sediment sampler is used to sample different sampling areas at the bottom of the river;

[0029] Place the sampled sediment in a culture dish and observe the activities of benthic animals under a microscope or with the naked eye under suitable environmental conditions, while recording the number of benthic animal activities per unit time (N);

[0030] According to the formula Calculate the activity intensity A of benthic animals in the corresponding area of the sampling area; where S is the area of the sampling area;

[0031] Step A4: Determination of abnormal benthic animal behavior:

[0032] For the activity intensity of benthic animals, the activity intensity A of benthic animals collected in the corresponding river section during the specified observation period was compared with the normal range of the activity intensity of benthic animals in the corresponding river section:

[0033] When A is in [A min ×β,A max ×(2-β)], the behavior of the benthic animal under inspection in this river section is judged to be normal;

[0034] When A is not in [A min ×β,A max ×(2-β)], the behavior of the detected benthic animal in the river section is judged to be abnormal;

[0035] Among them, β is the pre-set compensation factor;

[0036] During the specified observation period, the total number of times benthic animals were detected in the river section (Nc) was counted, and the number of times benthic animals were detected to have abnormal behavior (Nd) was also counted;

[0037] Then by: formula Calculate the frequency FQ of abnormal behavior of benthic animals in this river section.

[0038] As a further solution of the present invention: in step A3, each sampling area is repeatedly sampled multiple times, and then the average value of the activity intensity obtained from each sampling is taken as the activity intensity of the point.

[0039] As a further solution of the present invention: the statistical analysis method is as follows:

[0040] Step B1: Population density statistics:

[0041] During the specified observation period, water samples are collected regularly at different sections of the river. Specifically, multiple sampling points are selected at each section, and water samples with a specified volume range VT are collected at each sampling point. The number of different fish and different benthic animals in the water samples at each sampling point is then counted. d (j), j = 1, 2, ... m, m refers to the number of different fish and different benthic animal species;

[0042] Then through: Calculate the population density ρ of different fish and different benthic animals in the water samples corresponding to each sampling point d (j);

[0043] Step B2: Population structure analysis:

[0044] In a specified observation period, for a fish or a different benthic animal, calculate the population density ρ of the fish or the different benthic animal counted at each sampling point in each section. d The average value ρ1 of (j) d (j);

[0045] Then the ρ1 of different fish and different benthic animals d (j), sort in descending order and extract ρ1 d The fish or benthic animal with the largest (j) value is regarded as the dominant species, and the ρ1 corresponding to the dominant species is d (j) is rewritten as ρ1 d (Y);

[0046] pass: Calculate the proportion of dominant species R(Y) within the specified observation period;

[0047] Extract the dominant species ratios in multiple historical observation periods and calculate their average value R1(Y). Then calculate the ratio difference between the dominant species ratios R(Y) and R1(Y) in the current observation period and record it as the dominant species ratio change rate RB. The dominant species ratio change rate RB is then compared with the pre-set ratio difference threshold RB0:

[0048] Where, RB = |R(Y)-R1(Y)|;

[0049] When the dominant species proportion change rate RB is greater than or equal to the proportion difference threshold RB0, it indicates that the population structure corresponding to the dominant species is abnormal;

[0050] When the dominant species proportion change rate RB is less than the proportion difference threshold RB0, it indicates that the population structure corresponding to the dominant species is normal.

[0051] As a further solution of the present invention: the pollution risk analysis method is as follows:

[0052] Extract the abnormal behavior frequency FY of fish, the abnormal behavior frequency FQ of benthic animals and the change rate of the dominant species ratio RB in the specified river section;

[0053] Then through: Z = FY × γ1 + FQ × γ2 + RB × γ3;

[0054] Calculate the pollution risk index Z in the designated river section;

[0055] Based on the calculated pollution risk index Z, the pollution risk warning level in the designated river section is divided into three levels: low risk, medium risk and high risk;

[0056] When Z≤Za, the pollution risk in the designated river section is judged to be low risk; low risk means that the river pollution situation is relatively light at this time and routine monitoring can continue;

[0057] When Za<Z≤Zb, the pollution risk in the designated river section is judged to be medium risk. Medium risk means that there may be a certain degree of pollution in the river section. The relevant departments need to increase the monitoring frequency and start to investigate the pollution source.

[0058] When Z>Zb, the pollution risk in the designated river section is judged to be high risk. High risk is used to immediately trigger an early warning, reminding relevant departments to take emergency measures to control pollution;

[0059] Among them, Za<Zb, and Za and Zb are both pre-set pollution risk thresholds.

[0060] Beneficial effects of the present invention:

[0061] Multi-dimensional precision monitoring: The ecological monitoring and management system constructed by the present invention covers multiple core modules such as biological behavior monitoring and population dynamics analysis, forming a comprehensive, multi-level monitoring system. The biological behavior monitoring module collects behavioral data of fish and benthic animals, and uses various technical means such as underwater cameras, infrared sensors, and sediment sampling to gain an in-depth understanding of the real-time activity status of aquatic organisms; the population dynamics analysis module conducts statistical analysis on the population density and structural changes of different fish and benthic animals, and grasps the evolution trend of the ecosystem from the population level. Compared with a single monitoring method, this multi-dimensional monitoring method can more comprehensively and accurately reflect the water quality of the river, greatly improving the reliability and accuracy of the monitoring results.

[0062] Scientific behavioral analysis and assessment: The system utilizes rigorous scientific methods to collect and analyze biological behavioral data. For fish behavioral data, the system accurately calculates swimming speed and, based on historically normal speed ranges under normal water quality conditions, introduces a compensation factor α to determine if fish behavior is abnormal. For benthic animal behavioral data, activity intensity is calculated, similarly based on the normal range and a compensation factor β to determine behavioral anomalies. This behavioral assessment method, based on quantitative data analysis, can keenly detect subtle behavioral differences in aquatic organisms caused by changes in water quality, enabling timely identification of potential pollution issues and providing a reliable basis for pollution early warning.

[0063] Ensuring Data Reliability: During data collection, the system implements a series of measures to ensure data reliability. For example, when collecting benthic animal behavior data, repeated sampling is performed in each sampling area, and the average is used as the activity intensity at that point, effectively reducing sampling errors. For population density statistics, water samples are collected at multiple sampling points across different sections of the river to ensure representativeness. These measures guarantee data quality from the source, making subsequent analysis results more credible and laying a solid foundation for accurate assessment of river water quality.

[0064] Dynamic Population Structure Assessment: The Population Dynamics Analysis module not only calculates population density but also assesses dynamic changes in population structure by analyzing the rate of change in the proportion of dominant species. By comparing the changes in the proportion of dominant species during the current observation period with historical periods, it is possible to promptly identify abnormal fluctuations in population structure and infer the impact of changes in river water quality on the ecosystem. This in-depth analysis of population structure helps to reveal the long-term impact of water pollution on ecosystems and provides important decision-making information for ecological protection and restoration.

[0065] Gradual Early Warning and Efficient Management: The pollution early warning module calculates the pollution risk index Z based on the results of biological behavior monitoring and population dynamics analysis, and categorizes pollution risk warning levels into low, medium, and high. This tiered early warning mechanism provides relevant departments with targeted response strategies based on the severity of the pollution risk. Routine monitoring is maintained when the risk is low; in medium-risk situations, monitoring is strengthened and pollution sources are investigated; and in high-risk situations, immediate early warnings are triggered and emergency control measures are implemented. This effectively improves the efficiency and targeted nature of water pollution control, reduces environmental losses, and achieves scientific and efficient management of river water pollution. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] The present invention will be further described below with reference to the accompanying drawings.

[0067] Figure 1 This is a system block diagram of a river water pollution ecological monitoring and management system of the present invention.

[0068] Figure 2 It is a flow chart of a biological behavior monitoring module in a river water pollution ecological monitoring and management system of the present invention.

[0069] Figure 3 The present invention is a schematic diagram of the flow of a population dynamics analysis module in a river water pollution ecological monitoring and management system. DETAILED DESCRIPTION

[0070] 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.

[0071] Example 1

[0072] See also Figure 1 、 Figure 2 、 Figure 3 As shown, the present invention is a river water pollution ecological monitoring and management system, comprising:

[0073] The biological behavior monitoring module is used to collect fish behavior data in a designated river; the specific method is as follows:

[0074] Fish behavior data collection:

[0075] Underwater cameras and infrared sensors were deployed at different locations in the river;

[0076] Underwater cameras capture fish activity areas at 30 frames per second, while infrared sensors continuously monitor the movement of fish heat sources;

[0077] For the captured video, the fish position is identified frame by frame by manual or image recognition software. Then the sum of the change distance of the fish position between adjacent frames is calculated to obtain the fish movement distance D. At the same time, the shooting time interval T is recorded;

[0078] According to the formula Calculate the swimming speed V of the fish;

[0079] Determination of abnormal fish behavior:

[0080] As for the swimming speed of fish, within a specified test period, by monitoring the swimming speed of fish in the same river section under normal water quality conditions, the minimum and maximum swimming speeds were extracted, and then the normal range of the swimming speed of fish in the river section was determined based on them. Vmin , V max ];

[0081] During a specified observation period, the swimming speed V of fish collected in the corresponding river section is compared with the normal range of fish swimming speed in the corresponding river section:

[0082] When V is in [V min ×α,V max ×(2-α)], then the behavior of the detected fish in this river section is judged to be normal;

[0083] When V is not in [V min ×α,V max ×(2-α)], then the behavior of the detected fish in this river section is judged to be abnormal;

[0084] Wherein, α is a pre-set compensation factor. In this embodiment, the value of α is 0.85;

[0085] During the specified observation period, the total number of fish detected in the river section is counted (Na), and the number of fish detected with abnormal behavior (Nb) is also counted.

[0086] Then by: formula Calculate the frequency FY of abnormal behavior of fish in this river section;

[0087] Compare the frequency of abnormal behavior of fish in this river section with the abnormal behavior frequency threshold pre-set based on the fish:

[0088] When the frequency of abnormal behavior is greater than or equal to the threshold value of abnormal behavior frequency set according to the fish species, the river section is judged to have a pollution risk; otherwise, the river section is not judged to have a pollution risk;

[0089] Example 1 deploys underwater cameras and infrared sensors to collect fish behavior data. Using precise calculations to determine fish swimming speeds, the system then determines whether fish behavior is abnormal based on scientifically defined normal ranges and compensation factors. The frequency of abnormal behavior is then used to determine the pollution risk of a river section. This method leverages fish's sensitivity to water quality changes, using their behavior as a pollution monitoring indicator. This enables real-time, dynamic monitoring of river pollution, enabling timely identification of potential pollution risks reflected by abnormal fish behavior. Compared to traditional manual inspections, this method offers advantages such as high monitoring efficiency, accurate data, and quantifiable analysis, providing an effective technical means for early warning of river pollution.

[0090] Example 2

[0091] See also Figure 1 、 Figure 2 、 Figure 3 As shown, as the second embodiment of the present invention, when the present application is specifically implemented, compared with the first embodiment, the technical solution of this embodiment is different from that of the first embodiment only in that in this embodiment, the biological behavior monitoring module is also used to collect behavioral data of benthic animals in a designated river; the specific method is as follows:

[0092] Benthic animal behavior data collection:

[0093] The river section is divided into several sampling areas, and then a sediment sampler is used to sample different sampling areas at the bottom of the river;

[0094] In this embodiment, the area S of each sampling region is fixed at 0.25 square meters;

[0095] Place the sampled sediment in a culture dish and observe the activities of benthic animals under a microscope or with the naked eye under suitable environmental conditions, while recording the number of benthic animal activities per unit time (N);

[0096] According to the formula Calculate the activity intensity A of benthic animals in the corresponding area of the sampling area; where S is the area of the sampling area;

[0097] In this embodiment, in order to ensure the accuracy of the data, each sampling area is repeatedly sampled multiple times, and the average value of the activity intensity obtained from each sampling is taken as the activity intensity at that point;

[0098] Determination of abnormal benthic animal behavior:

[0099] For the activity intensity of benthic animals, the activity intensity A of benthic animals collected in the corresponding river section during the specified observation period was compared with the normal range of the activity intensity of benthic animals in the corresponding river section:

[0100] When A is in [A min ×β,Amax ×(2-β)], the behavior of the benthic animal under inspection in this river section is judged to be normal;

[0101] When A is not in [A min ×β,A max ×(2-β)], the behavior of the detected benthic animal in the river section is judged to be abnormal;

[0102] Wherein, β is a pre-set compensation factor. In this embodiment, the value of β is 0.8;

[0103] During the specified observation period, the total number of times benthic animals were detected in the river section (Nc) was counted, and the number of times benthic animals were detected to have abnormal behavior (Nd) was also counted;

[0104] Then by: formula Calculate the frequency FQ of abnormal behaviors of benthic animals in this river section;

[0105] The frequency of abnormal behavior of benthic animals in this river section is compared with the abnormal behavior frequency threshold pre-set based on benthic animals:

[0106] When the frequency of abnormal behavior is greater than or equal to the abnormal behavior frequency threshold set based on benthic animals, the river section is judged to be at risk of pollution; otherwise, the river section is not judged to be at risk of pollution.

[0107] Example 2 builds on Example 1 by adding data on benthic animal behavior. By dividing the sampling area, repeating sampling, and calculating activity intensity, abnormal benthic animal behavior is determined based on a set normal range and compensation factor, and the pollution risk of the river section is assessed based on the frequency of abnormal behavior. As an important component of the river ecosystem, benthic animals' behavioral changes can intuitively reflect the state of the riverbed environment. This example broadens the scope of monitoring objects, providing data support for river pollution monitoring from different ecological levels, and overcoming the limitations of only monitoring fish behavior. This makes the monitoring results more comprehensive and reliable, and helps to more accurately judge the pollution status of the river.

[0108] Example 3

[0109] See also Figure 1 、 Figure 2 、 Figure 3 As shown, as the third embodiment of the present invention, when the present application is specifically implemented, compared with the first and second embodiments, the technical solution of this embodiment is different from the first and second embodiments only in that this embodiment further includes:

[0110] Population dynamics analysis module, used to statistically analyze population density and structural changes of different fish and benthic animals in a specified river;

[0111] The statistical analysis method is as follows:

[0112] Step B1: Population density statistics:

[0113] During the specified observation period, water samples are collected regularly at different sections of the river. Specifically, multiple sampling points are selected at each section, and water samples with a specified volume range VT are collected at each sampling point. The number of different fish and different benthic animals in the water samples at each sampling point is then counted. d (j), j = 1, 2, ... m, m refers to the number of different fish and different benthic animal species;

[0114] Then through: Calculate the population density ρ of different fish and different benthic animals in the water samples corresponding to each sampling point d (j);

[0115] Step B2: Population structure analysis:

[0116] In a specified observation period, for a fish or a different benthic animal, calculate the population density ρ of the fish or the different benthic animal counted at each sampling point in each section. d The average value ρ1 of (j) d (j);

[0117] Then the ρ1 of different fish and different benthic animals d (j), sort in descending order and extract ρ1 d The fish or benthic animal with the largest (j) value is regarded as the dominant species, and the ρ1 corresponding to the dominant species is d (j) is rewritten as ρ1 d (Y);

[0118] pass: Calculate the proportion of dominant species R(Y) within the specified observation period;

[0119] Extract the dominant species ratios in multiple historical observation periods and calculate their average value R1(Y). Then calculate the ratio difference between the dominant species ratios R(Y) and R1(Y) in the current observation period and record it as the dominant species ratio change rate RB. The dominant species ratio change rate RB is then compared with the pre-set ratio difference threshold RB0:

[0120] Where, RB = |R(Y)-R1(Y)|;

[0121] When the dominant species proportion change rate RB is greater than or equal to the proportion difference threshold RB0, it indicates that the population structure corresponding to the dominant species is abnormal, and it is determined that there is a pollution risk in the river section;

[0122] When the dominant species proportion change rate RB is less than the proportion difference threshold RB0, it indicates that the population structure corresponding to the dominant species is normal, and it is not determined that there is a pollution risk in this river section.

[0123] Example 3 introduces a population dynamics analysis module, which determines pollution risks by statistically analyzing population density and structural changes of different fish and benthic animals. This approach, from a population ecology perspective, not only focuses on the behavior of individual organisms but also delves deeper into the population level. By calculating population density, analyzing population structure, and the rate of change in the proportion of dominant species, it can identify the potential impact of pollution on the structure of biological populations. Compared with the previous two examples, this provides a more macroscopic and ecologically meaningful perspective on pollution monitoring, enabling early warning of ecosystem imbalances caused by pollution, and providing a more forward-looking decision-making basis for river ecological protection and pollution control.

[0124] Example 4

[0125] See also Figure 1 、 Figure 2 、 Figure 3 As shown, as the fourth embodiment of the present invention, when the present application is specifically implemented, compared with the first, second and third embodiments, the technical solution of this embodiment is to combine the solutions of the first, second and third embodiments. The difference between the technical solution of this embodiment and the first, second and third embodiments is that this embodiment also includes:

[0126] The pollution early warning module is used to conduct pollution risk analysis based on the results obtained by the biological behavior monitoring module and the population dynamics analysis module;

[0127] The pollution risk analysis method is as follows:

[0128] Extract the abnormal behavior frequency FY of fish, the abnormal behavior frequency FQ of benthic animals and the change rate of the dominant species ratio RB in the specified river section;

[0129] Then through: Z = FY × γ1 + FQ × γ2 + RB × γ3;

[0130] Calculate the pollution risk index Z in the designated river section;

[0131] Based on the calculated pollution risk index Z, the pollution risk warning level in the designated river section is divided into three levels: low risk, medium risk and high risk;

[0132] When Z≤Za, the pollution risk in the designated river section is judged to be low risk; low risk means that the river pollution situation is relatively light at this time and routine monitoring can continue;

[0133] When Za<Z≤Zb, the pollution risk in the designated river section is judged to be medium risk. Medium risk means that there may be a certain degree of pollution in the river section. The relevant departments need to increase the monitoring frequency and start to investigate the pollution source.

[0134] When Z>Zb, the pollution risk in the designated river section is judged to be high risk. High risk is used to immediately trigger an early warning, reminding relevant departments to take emergency measures to control pollution;

[0135] Among them, Za<Zb, and Za and Zb are both pre-set pollution risk thresholds;

[0136] Example 4 combines the solutions of the previous three examples and adds a pollution warning module. This module calculates a pollution risk index by comprehensively analyzing the frequency of abnormal fish and benthic animal behavior and the rate of change in the proportion of dominant species, thereby classifying pollution risk levels. This example integrates multi-dimensional monitoring data to construct a comprehensive and systematic pollution risk assessment system. Compared to a single monitoring method, it can more accurately and scientifically assess the extent of river pollution, providing a quantitative basis for developing targeted monitoring and control measures for different risk levels, effectively improving the scientific nature and effectiveness of river pollution monitoring and management.

[0137] Example 5

[0138] See also Figure 1 、 Figure 2 、 Figure 3 As shown in the figure, as the fifth embodiment of the present invention, the difference between this embodiment and the first, second, third and fourth embodiments is that this embodiment also includes:

[0139] The data visualization module is used to display the results of the biological behavior monitoring module, population dynamics analysis module, and pollution early warning module.

[0140] Example 5 adds a new data visualization module that can intuitively display the results from the monitoring module, population dynamics analysis module, and pollution warning module. This module breaks the abstractness of data and presents complex monitoring data in intuitive forms such as charts and graphs. This facilitates managers and researchers to quickly understand river pollution monitoring status and ecological trends, improves the readability and usability of data, and helps relevant personnel make timely and scientific decisions, greatly enhancing the practicality and application value of the entire monitoring and management system.

[0141] Example 6

[0142] See also Figure 1 、 Figure 2 、 Figure 3As shown, as the sixth embodiment of the present invention, when this application is specifically implemented, compared with the first, second, third, fourth and fifth embodiments, the technical solution of this embodiment is to combine and implement the solutions of the above-mentioned first, second, third, fourth and fifth embodiments.

[0143] Example 6 integrates all the solutions of the previous five examples to build a complete, comprehensive, and powerful river water pollution ecological monitoring and management system. The system integrates biological behavior monitoring, population dynamics analysis, pollution risk assessment and early warning, and data visualization, forming an organic whole from microscopic individual behavior to macroscopic population structure, from pollution monitoring to risk early warning, and then to data display. Through the collaborative work of multiple modules, it realizes all-round, multi-level monitoring and management of river water pollution, can quickly and accurately identify pollution problems, provide comprehensive data support and technical guarantees for scientific decision-making and effective governance, and significantly improve the efficiency and level of river ecological monitoring and management.

[0144] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0145] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A river water pollution ecological monitoring and management system, characterized in that: include: Biological behavior monitoring module, used to collect behavioral data of fish and benthic animals in designated rivers; Population dynamics analysis module, used to count and analyze population density and structural changes of different fish and benthic animals in a specified river; The pollution early warning module is used to conduct pollution risk analysis based on the results obtained by the biological behavior monitoring module and the population dynamics analysis module; The data visualization module is used to display the results of the biological behavior monitoring module, population dynamics analysis module, and pollution early warning module.

2. A river water pollution ecological monitoring and management system according to claim 1, characterized in that: Behavioral data include behavioral data of fish and benthic animals; among them: Fish behavior data were collected as follows: Underwater cameras and infrared sensors were deployed at different locations in the river; Underwater cameras capture images of fish activity areas at a fixed frequency, while infrared sensors continuously monitor the movement of the fish's heat sources. For the captured video, the fish's position is identified frame by frame, either manually or using image recognition software. The sum of the distances of the fish's position changes between adjacent frames is then calculated to obtain the fish's movement distance. The fish's swimming speed is then calculated by dividing the movement distance by the shooting time interval and recorded as V. The method for collecting benthic animal behavior data is as follows: The river section is divided into several sampling areas, and then a sediment sampler is used to sample different sampling areas at the bottom of the river; The sampled sediment is placed in a petri dish, and the number of benthic animal activities per unit time is recorded. Then divide the number of activities by the area of the sampling area to obtain the activity intensity of benthic animals in the corresponding area of the sampling area, and record it as A.

3. A river water pollution ecological monitoring and management system according to claim 2, characterized in that: The biological behavior monitoring module is also used to determine abnormal behavior of fish and benthic animals: Abnormal fish behavior is determined as follows: As for the swimming speed of fish, within a specified test period, by monitoring the swimming speed of fish in the same river section under normal water quality conditions, the minimum and maximum swimming speeds were extracted, and then the normal range of the swimming speed of fish in the river section was determined based on them. Vmin , V max ]; During a specified observation period, the swimming speed V of fish collected in the corresponding river section is compared with the normal range of fish swimming speed in the corresponding river section, and whether the behavior of the detected fish is abnormal is determined based on the comparison results; During the specified observation period, the total number of fish detected in the river section is counted, and the number of fish detected to have abnormal behavior is also counted; The frequency of abnormal behavior of fish in the river section was obtained by calculating the ratio of the number of times fish were detected to the total number of times fish were detected, and recorded as FY; The abnormal behavior of benthic animals is determined as follows: For benthic animal activity intensity, within a specified observation period, the activity intensity of benthic animals collected in the corresponding river section will be compared with the normal range of benthic animal activity intensity in the corresponding river section, and the behavior of the detected fish will be determined to be abnormal based on the comparison results; During the specified observation period, the total number of benthic animals detected in the river section was counted, and the number of times benthic animals were detected to have abnormal behavior was also counted; The frequency of abnormal behavior of benthic animals in the river section was calculated by calculating the ratio of the number of times benthic animals were detected to the total number of times benthic animals were detected, and it was recorded as FQ.

4. A river water pollution ecological monitoring and management system according to claim 3, characterized in that: When V is in [V min ×α,V max ×(2-α)], then the behavior of the detected fish in this river section is judged to be normal; When V is not in [V min ×α,V max ×(2-α)], then the behavior of the detected fish in this river section is judged to be abnormal; When A is in [A min ×β,A max ×(2-β)], the behavior of the benthic animal under inspection in this river section is judged to be normal; When A is not in [A min ×β,A max ×(2-β)], the behavior of the detected benthic animal in the river section is judged to be abnormal; Among them, α and β are corresponding pre-set compensation factors.

5. A river water pollution ecological monitoring and management system according to claim 3, characterized in that: in, Each sampling area is sampled repeatedly multiple times, and the average activity intensity obtained from each sampling is taken as the activity intensity at that point.

6. A river water pollution ecological monitoring and management system according to claim 3, characterized in that: The statistical method of population density is as follows: During the specified observation period, water samples are collected regularly at different sections of the river. Specifically, multiple sampling points are selected at each section, and water samples with a specified volume range VT are collected at each sampling point. The number of different fish and different benthic animals in the water samples at each sampling point is then counted. d (j), j = 1, 2, ... m, m refers to the number of different fish and different benthic animal species; Then through: Calculate the population density ρ of different fish and different benthic animals in the water samples corresponding to each sampling point d (j).

7. A river water pollution ecological monitoring and management system according to claim 6, characterized in that: The population structure is analyzed as follows: In a specified observation period, for a fish or a different benthic animal, calculate the population density ρ of the fish or the different benthic animal counted at each sampling point in each section. d The average value ρ1 of (j) d (j); Then the ρ1 of different fish and different benthic animals d (j), sort in descending order and extract ρ1 d The fish or benthic animal with the largest (j) value is regarded as the dominant species, and the ρ1 corresponding to the dominant species is d (j) is rewritten as ρ1 d (Y); pass: Calculate the proportion of dominant species R(Y) within the specified observation period; The dominant species proportions in multiple historical observation periods are extracted and their average value R1(Y) is calculated. The difference between the dominant species proportions R(Y) and R1(Y) in the current observation period is then calculated and recorded as the dominant species proportion change rate RB. The dominant species proportion change rate RB is compared with the pre-set proportion difference threshold RB0. Based on the comparison result, it is determined whether the population structure corresponding to the dominant species is normal.

8. A river water pollution ecological monitoring and management system according to claim 7, characterized in that: When the dominant species proportion change rate RB is greater than or equal to the proportion difference threshold RB0, it indicates that the population structure corresponding to the dominant species is abnormal; When the dominant species proportion change rate RB is less than the proportion difference threshold RB0, it indicates that the population structure corresponding to the dominant species is normal.

9. A river water pollution ecological monitoring and management system according to claim 7, characterized in that: The pollution risk analysis method is as follows: Extract the abnormal behavior frequency FY of fish, the abnormal behavior frequency FQ of benthic animals and the change rate of the dominant species ratio RB in the specified river section; Then through: Z = FY × γ1 + FQ × γ2 + RB × γ3; Calculate the pollution risk index Z in the designated river section; According to the calculated pollution risk index Z, the pollution risk warning level in the designated river section is divided into three levels: low risk, medium risk and high risk.

10. A river water pollution ecological monitoring and management system according to claim 9, characterized in that: The pollution risk warning levels are divided as follows: When Z≤Za, the pollution risk in the designated river section is judged to be low risk; When Za<Z≤Zb, the pollution risk in the designated river section is determined to be medium risk; When Z>Zb, the pollution risk in the designated river section is judged to be high risk; Among them, Za<Zb, and Za and Zb are both pre-set pollution risk thresholds.

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