Data fusion system for monitoring water quality and aquatic organisms based on Internet of Things

Abstract

The invention discloses a data fusion system for water quality and aquatic organism monitoring based on the Internet of Things, and relates to the technical field of lake monitoring. When water quality parameters fluctuate, an analysis module analyzes the grey correlation degree of fluctuating water quality parameters and aquatic organisms, calculates aquatic organism similarity characteristics between sub-regions, clusters all the sub-regions, and obtains a clustering result; the grey correlation degree in each category is subjected to integral analysis, an attention sorting module is combined with a clustering result and historical changes of the water quality parameters, after influence indexes of all the water quality parameters relative to the lake are searched, a water quality parameter attention sorting table is generated and sent to a lake management platform. According to the fusion system, the similarity characteristics of the aquatic organisms among the sub-regions are calculated, the sub-regions are clustered on the basis of the characteristics, lake regions with similar ecology are divided, the pertinence of lake management is improved, the affected aquatic organisms can be quickly identified, and a scientific basis is provided for ecological protection and pollution abatement.

Description

Technical Field

[0001] The present invention relates to the technical field of lake monitoring, and particularly to a data fusion system for water quality and aquatic organism monitoring based on the Internet of Things. Background Art

[0002] Traditional water quality monitoring mainly relies on manual sampling and laboratory analysis, which has problems such as poor timeliness and insufficient spatial coverage. The data fusion system for water quality and aquatic organism monitoring based on the Internet of Things (IoT) realizes real-time and accurate monitoring of the water environment and biological ecology through sensor networks, wireless communication, big data analysis, and artificial intelligence technology, providing a scientific basis for water environmental protection and ecological restoration.

[0003] The existing technologies have the following defects:

[0004] 1. Existing water quality monitoring systems mainly focus on abnormal changes in water quality parameters themselves (such as pH value, dissolved oxygen, ammonia nitrogen concentration, etc.), but lack direct observation and impact assessment of aquatic biological communities, and fail to establish a quantitative relationship between water quality parameters and the dynamics of aquatic biological populations. Water quality changes often have complex chain reactions on lake ecosystems. For example, changes in certain water quality factors may affect the reproduction of phytoplankton, thereby affecting the survival of fish communities;

[0005] 2. As a complex ecosystem, lakes have significant differences in hydrodynamic conditions, aquatic biological communities, pollution source distributions, etc. in different regions, resulting in obvious spatial heterogeneity in water quality changes. Existing monitoring systems often lack systematic analysis of the hydroecological characteristics of different lake areas, making it difficult to adopt management measures according to local conditions. For example, some regions may be more prone to eutrophication, while other regions may be more sensitive to pollution. However, due to the lack of sub-region division and clustering analysis, it is difficult for managers to formulate refined control strategies.

[0006] Based on this, the present invention proposes a data fusion system for water quality and aquatic organism monitoring based on the Internet of Things. By calculating the similarity characteristics of aquatic organisms between sub-regions and clustering the sub-regions based on these characteristics, lake areas with similar ecology are divided, improving the pertinence of lake management, quickly identifying affected aquatic organisms, and providing a scientific basis for ecological protection and pollution control. Summary of the Invention

[0007] The object of the present invention is to provide a data fusion system for water quality and aquatic organism monitoring based on the Internet of Things to solve the deficiencies in the background art.

[0008] To achieve the above object, the present invention provides the following technical solution: A data fusion system for water quality and aquatic organism monitoring based on the Internet of Things, including a data acquisition module, a monitoring module, an analysis module, and a focus ranking module;

[0009] Data acquisition module: Obtain information about the lake based on the lake management platform, and divide the lake into several sub-regions according to the lake area;

[0010] Monitoring module: Real-time monitor the changes in water quality parameters of each sub-region through Internet of Things sensing devices;

[0011] Analysis module: When the water quality parameters fluctuate, analyze the grey correlation degree between the fluctuating water quality parameters and aquatic organisms, calculate the similarity characteristics of aquatic organisms between sub-regions, cluster all sub-regions, and conduct an overall analysis of the grey correlation degree in each category;

[0012] Attention ranking module: Combine the clustering results with the historical changes of water quality parameters, find the influence index of all water quality parameters relative to the lake, generate a water quality parameter attention ranking table and send it to the lake management platform.

[0013] In a preferred embodiment, the analysis module calculates the grey correlation degree between the water quality parameters and aquatic organisms based on the water quality parameter values and influence index, and the expression is: , where, is the grey correlation degree, is the number of time points, is the grey correlation coefficient at the

[0014] In a preferred embodiment, the calculation expression of the grey correlation coefficient is:

[0015] , where, is the grey correlation coefficient, is the absolute difference between the water quality parameter and the th type of aquatic organism at time , and , is the value of the water quality parameter at time , is the th type of aquatic organism's influence factor at time , represents the double minimum operator, represents the double maximum operator, is the resolution coefficient, and .

[0016] In a preferred embodiment, after the analysis module obtains the types of aquatic organisms in all sub-regions, it calculates the similarity characteristics of aquatic organisms between each sub-region and other sub-regions;

[0017] After obtaining the similarity characteristics of aquatic organisms between each sub-region and other sub-regions, construct a correlation matrix based on the similarity characteristics of aquatic organisms;

[0018] Compare the aquatic similarity features between all sub - regions with a preset feature threshold, where the feature threshold is used to determine whether a sub - region can be classified into a cluster, and classify all sub - regions with aquatic similarity features greater than the feature threshold into a cluster.

[0019] In a preferred embodiment, the expression of the correlation matrix is:

[0020] , where is the correlation matrix, is the number of sub - regions.

[0021] In a preferred embodiment, calculate the aquatic similarity features of each sub - region with other sub - regions, and the expression is: , where is the sub - region and the sub - region 's aquatic similarity feature, , are respectively the average values of the aquatic population numbers in the sub - regions and the sub - region , is the density of the th type of aquatic organism in the sub - region , is the density of the th type of aquatic organism in the sub - region .

[0022] In a preferred embodiment, the concerned sorting module obtains the average value of the grey relational degrees of each cluster, and obtains the historical fluctuation occurrence frequency of the fluctuating water quality parameters, and calculates the influence index based on the average value of the grey relational degrees and the historical fluctuation occurrence frequency. The expression is: , where is the influence index, is the average value of the grey relational degrees, is the historical fluctuation occurrence frequency, , are weight coefficients, and ;

[0023] After obtaining the influence indices of all water quality parameters, sort all water quality parameters from large to small according to the influence indices to generate a concern sorting table.

[0024] In a preferred embodiment, when the analysis module analyzes fluctuations in water quality parameters, it obtains the density decline rate and mortality rate of aquatic organisms, normalizes the density decline rate and mortality rate so that the value ranges of the density decline rate and mortality rate are mapped between [0, 1], and sums the normalized density decline rate and mortality rate to obtain an impact factor.

[0025] In a preferred embodiment, the analysis module compares the real-time monitored water quality parameters with the corresponding parameter thresholds of the water quality parameters, obtains the difference by subtracting the corresponding parameter threshold from the real-time monitored water quality parameter value, takes the absolute value of the difference as the deviation value of the water quality parameter, and if the deviation value of the water quality parameter is greater than the preset deviation threshold, it analyzes that the water quality parameter fluctuates.

[0026] In a preferred embodiment, the monitoring module detects the acidity and alkalinity of the water body through a pH sensor, monitors the oxygen content in the water through a dissolved oxygen sensor, monitors the ammonia nitrogen, total phosphorus, and total nitrogen contents in the water through ammonia nitrogen, total phosphorus, and total nitrogen sensors, monitors the water transparency through a turbidity sensor, monitors the water temperature through a temperature sensor, and detects the water conductivity through a conductivity sensor.

[0027] In the above technical solution, the technical effects and advantages provided by the present invention are as follows:

[0028] The present invention divides a lake into several sub-regions according to the lake area by a data acquisition module. When the water quality parameters fluctuate, the analysis module analyzes the grey correlation degree between the fluctuating water quality parameters and aquatic organisms, calculates the similarity characteristics of aquatic organisms between sub-regions, clusters all sub-regions, and conducts an overall analysis of the grey correlation degree in each category. After the attention ranking module combines the clustering results with the historical changes of the water quality parameters, searches for the influence indexes of all water quality parameters relative to the lake, generates a water quality parameter attention ranking table and sends it to the lake management platform. The fusion system calculates the similarity characteristics of aquatic organisms between sub-regions, clusters the sub-regions based on these characteristics, divides the lake areas with similar ecology, improves the pertinence of lake management, can quickly identify the affected aquatic organisms, and provides a scientific basis for ecological protection and pollution control. Brief Description of the Drawings

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0030] Figure 1 It is the method flow chart of the present invention. Detailed Embodiments

[0031] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. 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.

[0032] Embodiment

[0033] Please refer to Figure 1 As shown, the data fusion system for water quality and aquatic organism monitoring based on the Internet of Things in this embodiment includes a data acquisition module, a monitoring module, an analysis module, and a focus ranking module;

[0034] Data acquisition module: Obtain the basic information of the lake based on the lake management platform. The basic information includes the lake area, location, historical water quality data, and aquatic organism species. Divide the lake into several sub-regions according to the lake area, and send the sub-regions to the analysis module and the monitoring module;

[0035] The data acquisition module is mainly responsible for obtaining the basic information of the lake from the lake management platform and dividing sub-regions according to the lake area. The divided sub-region data will be sent to the analysis module and the monitoring module to support subsequent water quality monitoring and ecological analysis. The specific process of this module is as follows:

[0036] Extract the basic information related to lake ecological monitoring from the lake management platform, including but not limited to:

[0037] Lake area (for area division);

[0038] Lake geographical location (for positioning and GIS analysis);

[0039] Historical water quality data (for trend analysis and water quality change assessment);

[0040] Aquatic organism species and distribution information (for biological similarity analysis);

[0041] According to the total area of the lake, combined with factors such as hydrodynamic characteristics, pollution source distribution, and aquatic organism habitats, divide the lake into multiple sub-regions. The division method can adopt the following methods:

[0042] Grid division: Divide the lake into equally sized grid cells, and each cell is used as a sub-region.

[0043] Natural zoning: Based on the topographic features of the lake, the positions of inlet and outlet, and the distribution characteristics of aquatic organisms, delimit natural sub-regions.

[0044] Dynamic adjustment: Adjust the sub-region boundaries according to historical water quality data and aquatic biological monitoring results, making the regional division more in line with ecological characteristics.

[0045] The basic attribute information that each sub-region should contain includes:

[0046] Region number (unique identifier);

[0047] Latitude and longitude range (for convenient GIS analysis);

[0048] Area size (for subsequent data normalization processing);

[0049] Historical water quality characteristics (summarize the water quality change trend of the region based on past data);

[0050] Distribution of aquatic organisms (main species, population density, etc.);

[0051] The sub-region information is transmitted to the analysis module for subsequent:

[0052] Grey relational analysis of water quality parameters and aquatic organisms;

[0053] Calculation of aquatic organism similarity between sub-regions;

[0054] Assessment of the impact index of water quality parameters on lake ecology;

[0055] The sub-region information is transmitted to the monitoring module to deploy Internet of Things sensing devices in each sub-region for real-time water quality monitoring. The monitoring module will dynamically allocate monitoring devices and optimize the sensor layout; adjust the monitoring frequency according to the water quality characteristics of the sub-region; obtain real-time water quality data and feedback it to the data fusion system;

[0056] The main function of the data acquisition module is to integrate the basic information of the lake management platform, scientifically divide the lake sub-regions, and provide data support to the analysis module and the monitoring module. Through reasonable regional division, the accuracy of water quality monitoring can be improved, and basic data for subsequent water ecological analysis can be provided.

[0057] Monitoring module: Real-time monitor the changes in water quality parameters of each sub-region through Internet of Things sensing devices, and send the changes in water quality parameters to the analysis module;

[0058] The monitoring module detects the acidity and alkalinity of the water body through a pH sensor, monitors the oxygen content in the water through a dissolved oxygen (DO) sensor, monitors the ammonia nitrogen, total phosphorus, and total nitrogen content in the water through ammonia nitrogen, total phosphorus, and total nitrogen sensors, monitors the water transparency through a turbidity sensor, monitors the water temperature through a temperature sensor, and detects the water conductivity through a conductivity sensor.

[0059] To ensure comprehensive monitoring of the lake ecological environment, it is necessary to deploy various types of water quality sensing devices, and each sensor is responsible for monitoring different water quality parameters, specifically including:

[0060] pH sensor: Used to detect the acidity and alkalinity of water bodies. Changes in pH values may be related to factors such as pollutant emissions and algal blooms.

[0061] Dissolved oxygen (DO) sensor: Used to monitor the oxygen content in water. The dissolved oxygen level is a key indicator of the aquatic biological living environment. Low oxygen may lead to fish death or water eutrophication.

[0062] Ammonia nitrogen, total phosphorus, and total nitrogen sensors: These parameters are core indicators for measuring the degree of water pollution. When exceeding the standard, they may cause algal blooms and affect the survival of aquatic organisms.

[0063] Turbidity sensor: Monitors water transparency. Higher turbidity may be caused by sediment, suspended particles, or microorganisms, affecting the aquatic ecosystem.

[0064] Temperature sensor: Water temperature affects the metabolism of aquatic organisms, the dissolved oxygen content, and microbial activities.

[0065] Conductivity sensor: Detects the conductivity of water bodies and can be used to judge salinity changes, especially suitable for ecological monitoring in the brackish water interface area.

[0066] The device deployment should fully consider the geographical characteristics, hydrodynamic characteristics, pollution source distribution, and aquatic biological habitats of the lake to ensure the representativeness and accuracy of monitoring data. The main strategies include:

[0067] Uniform distribution: Suitable for lakes with a small area and a relatively uniform ecological environment. The same number of sensing devices are deployed in each sub-region.

[0068] Key monitoring: For key areas such as around pollution sources and ecologically sensitive areas (such as fish spawning grounds), increase the sensor density to improve the monitoring accuracy.

[0069] Dynamic adjustment: Combining historical water quality data, dynamically optimize the sensor deployment locations to ensure that the devices can effectively monitor key water quality change areas.

[0070] Sensor calibration: All devices need to be precisely calibrated before going online to eliminate drift errors and ensure accurate measurement data.

[0071] Device connection test: Confirm the stability of the connection between the sensor and the data transmission network (such as LoRa, NB-IoT, 4G / 5G, or satellite communication) to ensure that the monitoring data can be uploaded in real time.

[0072] Power management: For solar-powered or battery-powered devices, conduct power consumption assessment to ensure that the devices can operate for a long time.

[0073] Self-check mechanism: The device needs to perform self-checks regularly, including data acquisition accuracy, transmission signal strength, battery power, etc., to ensure the stable operation of the system.

[0074] Conventional sampling frequency: Under stable water quality conditions, collect data every 5 minutes or 1 hour to ensure data continuity.

[0075] High-frequency sampling in abnormal situations: When the system detects fluctuations in water quality parameters (such as a sudden drop in pH or a sharp decrease in dissolved oxygen), immediately increase the sampling frequency (such as once a minute) and continuously monitor the change trend in this area.

[0076] Fixed-point sampling: The sensing devices in each sub-region regularly collect the current water quality parameters.

[0077] Mobile monitoring (optional): In some lake areas, unmanned boats or underwater robots can be used to carry sensing devices for mobile monitoring to make up for the limitations of fixed-point monitoring.

[0078] The data collected by the sensors includes water quality parameter values, timestamps, device numbers, geographical locations, etc. It is necessary to unify the data format to ensure that the data collected by different devices can be processed compatibly. The data format example is:

[0079] {

[0080] "timestamp":"2025-03-07T10:30:00Z",

[0081] "device_id":"sensor_001",

[0082] "location":{"latitude":39.9042,"longitude":116.4074},

[0083] "pH":7.2,

[0084] "DO":6.5,

[0085] "NH3-N":0.3,

[0086] "turbidity":12.0

[0087] }。

[0088] Analysis module: When the water quality parameters fluctuate, analyze the grey relational degree between the fluctuating water quality parameters and aquatic organisms. After calculating the similarity characteristics of aquatic organisms between sub-regions, cluster all sub-regions, and conduct an overall analysis of the grey relational degree in each category. Send the clustering results to the attention ranking module;

[0089] After clustering the sub-regions, the overall analysis can identify the main water quality parameters affecting the entire clustering cluster, helping managers adjust water quality management measures targeted. For example, if all sub-regions in a certain clustering cluster are sensitive to dissolved oxygen fluctuations, then dissolved oxygen can be monitored with emphasis and measures can be taken in advance to prevent the deterioration of the aquatic ecosystem in the entire region.

[0090] The analysis module compares the real-time monitored water quality parameters with the corresponding parameter thresholds. Obtain the difference by subtracting the corresponding parameter threshold from the real-time monitored water quality parameter value, and take the absolute value of the difference as the deviation value of the water quality parameter. If the deviation value of the water quality parameter is greater than the preset deviation threshold, it is analyzed that the water quality parameter has fluctuated;

[0091] When the analysis module analyzes that the water quality parameters have fluctuated, obtain the density decline rate and mortality rate of aquatic organisms, normalize the density decline rate and mortality rate, map the value range of the density decline rate and mortality rate to between [0,1], and sum the normalized density decline rate and mortality rate to obtain the impact factor;

[0092] The calculation logic of the density decline rate is: When the water quality parameters fluctuate, obtain the density of aquatic organisms at the moment when the water quality parameters fluctuate and the density of aquatic organisms at the current moment. Subtract the density of aquatic organisms at the current moment from the density of aquatic organisms at the moment when the water quality parameters fluctuate to obtain the density decline value. Obtain the monitoring duration by subtracting the moment when the water quality parameters fluctuate from the current moment. Divide the density decline value by the monitoring duration to obtain the density decline rate. The greater the density decline rate, the greater the impact of the water quality parameters on aquatic organisms.

[0093] The acquisition logic of the mortality rate is: After the water quality parameters have fluctuated for a period of time, obtain the number of extinct aquatic organisms (i.e., disappearance and death), divide the number of extinct organisms by the duration of the fluctuation to obtain the mortality rate. The greater the mortality rate, the greater the impact of the water quality parameters on aquatic organisms.

[0094] The calculation expression of the grey correlation coefficient is: , where is the grey correlation coefficient, is the water quality parameter and the th type of aquatic organism at time absolute difference, and , is the value of the water quality parameter at time , is the impact factor of the th aquatic organism at time . represents the double minimum operator, represents the double maximum operator, is the resolution coefficient, and .

[0095] In this application, the processing logic of the double minimum operator is as follows: First, obtain the th aquatic organism to find the absolute difference at all times t, that is , and then take the smallest one from these minimum values, which represents the minimum absolute difference between all aquatic organisms and water quality parameters at all times, reflecting the smallest degree of difference between sequences.

[0096] The processing logic of the double maximum operator is as follows: First, obtain the th aquatic organism to find the absolute difference at all times t, that is , and then take the largest one from these maximum values, which represents the maximum absolute difference between all aquatic organisms and water quality parameters at all times, reflecting the largest degree of difference between sequences.

[0097] The analysis module calculates the grey correlation degree between the water quality parameter and the aquatic organism based on the water quality parameter value and the impact index. The expression is: , where is the grey correlation degree, is the number of time points, is the th grey correlation coefficient at the time point.

[0098] When the water quality parameter changes, the species distribution and quantity of aquatic organisms may be affected. To analyze the lake ecosystem more comprehensively, the analysis module first calculates the similarity characteristics of aquatic organisms between sub-regions, and then clusters all sub-regions based on the similarity.

[0099] After the analysis module calculates the similarity characteristics of aquatic organisms between sub-regions, it clusters all sub-regions and conducts an overall analysis of the grey correlation degree in each category. The clustering result is sent to the attention ranking module.

[0100] The analysis module first obtains the aquatic organism population information of each sub-region, mainly including:

[0101] Aquatic organism species (such as fish, plankton, benthos, etc.);

[0102] The quantity of each species (individuals / L);

[0103] Biomass of each species (g / L or mg / L);

[0104] Diversity index of species (such as Shannon-Wiener index, Pielou evenness index, etc.);

[0105] The data sources include real-time monitored aquatic organism data and historical aquatic organism distribution data to accurately measure the dynamic changes of the biological community when water quality parameters change.

[0106] After the analysis module obtains the aquatic organism species in all sub-regions, it calculates the similarity characteristics of aquatic organisms between each sub-region and other sub-regions. The expression is: , where, is the similarity characteristic of aquatic organisms between sub-region and sub-region , , are respectively the average values of the aquatic organism population numbers in sub-region and sub-region , is the density of the th type of aquatic organism in sub-region , is the density of the th type of aquatic organism in sub-region .

[0107] After obtaining the similarity characteristics of aquatic organisms between each sub-region and other sub-regions, a correlation matrix is constructed based on the similarity characteristics of aquatic organisms. The correlation matrix is:

[0108] , where, is the correlation matrix, is the number of sub-regions.

[0109] The similarity characteristics of aquatic organisms between all sub-regions are compared with a preset characteristic threshold. The characteristic threshold is used to judge whether a sub-region can be classified into a cluster. All sub-regions with similarity characteristics of aquatic organisms greater than the characteristic threshold are classified into a cluster. If the similarity characteristics of aquatic organisms of a sub-region with all other sub-regions are less than or equal to the characteristic threshold, then this sub-region forms a separate cluster.

[0110] Suppose the characteristic threshold is 0.6, and the lake is divided into 3 sub-regions. The similarity characteristics of aquatic organisms in each sub-region are calculated by the Pearson correlation coefficient and form a 3×3 correlation matrix;

[0111] Suppose according to the calculation of the Pearson correlation coefficient, the obtained 3×3 correlation matrix is shown in Table 1 (example data):

[0112] Table 1 Correlation matrix

[0113] sub-region 1 2 3 1 1 0.75 0.40 2 0.75 1 0.55 3 0.40 0.55 1

[0114] Clustering is performed according to the characteristic threshold of 0.6. Sub-region 1 and sub-region 2 form a cluster because the similarity characteristic value between them, 0.75, is > 0.6. The similarity characteristic values of sub-region 3 are 0.40 and 0.55 respectively, both ≤ 0.6. Therefore, sub-region 3 alone forms a clustering cluster;

[0115] Finally, the 3 sub-regions are divided into 2 clustering clusters:

[0116] Cluster 1: {1, 2};

[0117] Cluster 2: {3} (a single sub-region).

[0118] After the analysis module clusters all sub-regions to obtain at least one clustering cluster, it calculates the mean value of the grey relational degree of each clustering cluster. That is, it sums up the grey relational degrees of all sub-regions in the clustering cluster to obtain the total grey relational degree value, and divides the total grey relational degree value by the number of sub-regions in the clustering cluster to obtain the mean value of the grey relational degree.

[0119] Attention ranking module: Combining the clustering results with the historical changes of water quality parameters, after finding the influence index of all water quality parameters relative to the lake, it generates a water quality parameter attention ranking table and sends it to the lake management platform;

[0120] The attention ranking module obtains the mean value of the grey relational degree of each clustering cluster, and obtains the historical fluctuation occurrence frequency of the fluctuating water quality parameters. It calculates the influence index based on the mean value of the grey relational degree and the historical fluctuation occurrence frequency. The expression is: , where, is the influence index, is the mean value of the grey relational degree, is the historical fluctuation occurrence frequency, 、 are weight coefficients, and ;

[0121] The larger the influence index of the water quality parameter, the greater the impact of the change of this water quality parameter on the aquatic organisms in the lake, the wider the influence range, and the greater the historical fluctuation occurrence frequency. Therefore, after obtaining the influence indexes of all water quality parameters, all water quality parameters are sorted from large to small according to the influence index to generate an attention ranking table.

[0122] This application divides a lake into several sub - regions according to the lake area through a data acquisition module. When the water quality parameters fluctuate, the analysis module analyzes the grey correlation degree between the fluctuating water quality parameters and aquatic organisms. After calculating the similarity characteristics of aquatic organisms between sub - regions, all sub - regions are clustered, and an overall analysis of the grey correlation degree in each category is carried out. The attention - ranking module combines the clustering results with the historical changes of water quality parameters, finds the influence index of all water quality parameters relative to the lake, generates a water quality parameter attention - ranking table and sends it to the lake management platform. The fusion system calculates the similarity characteristics of aquatic organisms between sub - regions and clusters the sub - regions based on these characteristics, dividing the lake areas with similar ecology, improving the pertinence of lake management, quickly identifying the affected aquatic organisms, and providing a scientific basis for ecological protection and pollution control.

[0123] The working process of the fusion system is as follows:

[0124] The fusion system obtains the basic information of the lake based on the lake management platform. The basic information includes the lake area, location, historical water quality data, and aquatic organism species. The lake is divided into several sub - regions according to the lake area. The water quality parameter changes in each sub - region are monitored in real - time through Internet of Things sensing devices. When the water quality parameters fluctuate, the grey correlation degree between the fluctuating water quality parameters and aquatic organisms is analyzed. After calculating the similarity characteristics of aquatic organisms between sub - regions, all sub - regions are clustered, and an overall analysis of the grey correlation degree in each category is carried out. Combining the clustering results with the historical changes of water quality parameters, the influence index of all water quality parameters relative to the lake is found, and a water quality parameter attention - ranking table is generated and sent to the lake management platform.

[0125] The above formulas are all calculated by removing the dimension and taking their numerical values. The formula is obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0126] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0127] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only the specific embodiments. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A data fusion system for water quality and aquatic organism monitoring based on the Internet of Things, characterized in that: It includes data acquisition module, monitoring module, analysis module and attention sorting module; Data acquisition module: obtain lake information based on the lake management platform and divide the lake into several sub-areas according to the lake area; Monitoring module: Real-time monitoring of water quality parameter changes in each sub-area through IoT sensor devices; Analysis module: When water quality parameters fluctuate, the grey correlation between the fluctuating water quality parameters and aquatic organisms is analyzed. After calculating the similarity characteristics of aquatic organisms between sub-areas, all sub-areas are clustered, and the grey correlation in each category is analyzed holistically. Attention ranking module: Combining the clustering results with the historical changes of water quality parameters, after finding the impact index of all water quality parameters relative to the lake, a water quality parameter attention ranking table is generated and sent to the lake management platform.

2. The data fusion system for water quality and aquatic organism monitoring based on the Internet of Things according to claim 1, characterized in that: The analysis module calculates the grey correlation degree between water quality parameters and aquatic organisms based on the water quality parameter values and the influence index, and the expression is: , where is the grey correlation degree, is the number of time points, is the grey correlation coefficient at the 3. The data fusion system for water quality and aquatic organism monitoring based on the Internet of Things according to claim 2, characterized in that: The calculation expression of the grey relational coefficient is: , where is the grey correlation coefficient, is the absolute difference between the water quality parameter and the th aquatic organism at time , and , is the value of the water quality parameter at time , is the influence factor of the th aquatic organism at time , represents the double minimum operator, represents the double maximum operator, is the resolution coefficient, and .

4. The data fusion system for water quality and aquatic organism monitoring based on the Internet of Things according to claim 3, wherein: After the analysis module obtains the aquatic species in all sub-regions, it calculates the similarity characteristics of aquatic species in each sub-region with those in other sub-regions; After obtaining the similarity characteristics of aquatic organisms between each sub-region and other sub-regions, a correlation matrix is ​​constructed based on the similarity characteristics of aquatic organisms; The aquatic biological similarity characteristics between all sub-areas are compared with the preset characteristic threshold. The characteristic threshold is used to determine whether the sub-area can be classified into a cluster. All sub-areas with aquatic biological similarity characteristics greater than the characteristic threshold are classified into a cluster.

5. The data fusion system for water quality and aquatic organism monitoring based on the Internet of Things according to claim 4, characterized in that: The expression of the correlation matrix is: , where is the correlation matrix, is the number of sub-regions.

6. The data fusion system for water quality and aquatic organism monitoring based on the Internet of Things according to claim 5, characterized in that: Calculate the aquatic similarity characteristics between each sub-region and other sub-regions, with the expression: , where is the aquatic similarity characteristic between sub-region and sub-region . , are respectively the mean values of the aquatic population numbers in sub-region and sub-region . is the density of the th type of aquatic organisms in sub-region , is the density of the th type of aquatic organisms in sub-region .

7. The data fusion system for water quality and aquatic organism monitoring based on the Internet of Things according to claim 6, wherein: The concerned sorting module obtains the mean value of the grey relational grade of each clustering cluster, and obtains the historical fluctuation occurrence frequency of the fluctuating water quality parameters. The influence index is calculated based on the mean value of the grey relational grade and the historical fluctuation occurrence frequency. The expression is as follows: , where is the influence index, is the mean value of the grey relational grade, is the historical fluctuation occurrence frequency, , are the weight coefficients, and ; After obtaining the impact index of all water quality parameters, all water quality parameters are sorted from large to small according to the impact index to generate a ranking table of attention.

8. The data fusion system for water quality and aquatic organism monitoring based on the Internet of Things according to claim 7, characterized in that: When the analysis module analyzes the fluctuation of water quality parameters, it obtains the density decrease rate and mortality rate of aquatic organisms, normalizes the density decrease rate and mortality rate, maps the value range of the density decrease rate and mortality rate to [0,1], and sums the normalized density decrease rate and mortality rate to obtain the influencing factor.

9. The data fusion system for water quality and aquatic organism monitoring based on the Internet of Things according to claim 8, characterized in that: The analysis module compares the water quality parameters monitored in real time with the parameter thresholds corresponding to the water quality parameters, obtains the difference by subtracting the corresponding parameter thresholds from the water quality parameter values ​​monitored in real time, and takes the absolute value of the difference as the deviation value of the water quality parameter. If the deviation value of the water quality parameter is greater than the preset deviation threshold, the water quality parameter analysis shows fluctuations.

10. The data fusion system for water quality and aquatic organism monitoring based on the Internet of Things according to claim 9, characterized in that: The monitoring module detects the acidity and alkalinity of the water body through a pH sensor, monitors the oxygen content in the water through a dissolved oxygen sensor, monitors the ammonia nitrogen, total phosphorus and total nitrogen content in the water through ammonia nitrogen, total phosphorus and total nitrogen sensors, monitors the transparency of the water body through a turbidity sensor, monitors the temperature of the water body through a temperature sensor, and detects the conductivity of the water body through a conductivity sensor.

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