Surface water pollution identification system and method based on multi-modal perception

Abstract

The invention discloses a surface water pollution identification system and method based on multi-modal sensing. The system comprises a multi-modal data acquisition module, a data fusion module, a pollution analysis module and a terminal module, the multi-modal data acquisition module is used for acquiring multi-modal data of surface water; the data fusion module is used for fusing the multi-modal data and extracting data features; the pollution analysis module analyzes the surface water pollution condition based on the data characteristics to obtain an analysis result; and the terminal module is used for displaying the analysis result and giving an alarm when the result is abnormal. Various monitoring technologies are integrated, chemical, physical and biological parameters of a water body are comprehensively covered, and the accuracy of a monitoring result is improved by adopting an advanced data processing and analysis algorithm; meanwhile, the artificial intelligence technology is applied, automatic monitoring, analysis and early warning are achieved, second-level data updating is achieved, and a water pollution event is quickly responded.

Description

Technical Field

[0001] The present invention belongs to the technical field of water quality monitoring, and in particular relates to a surface water pollution identification system and method based on multimodal sensing. Background Art

[0002] Surface water pollution is a global environmental problem that poses a serious threat to human health, ecosystem balance and economic development. With the acceleration of industrialization and urbanization, the problem of surface water pollution has become more complex and severe. Traditional water pollution monitoring methods mainly rely on laboratory analysis, which has the disadvantages of being time-consuming, costly, insufficiently representative and unable to monitor continuously. In order to overcome the above problems, a variety of online monitoring technologies have been developed in recent years, such as water quality sensors and remote sensing monitoring. These technologies have improved the efficiency and continuity of water pollution monitoring to a certain extent, but there are still some shortcomings.

[0003] In summary, the existing water pollution monitoring technology still needs to be improved in terms of real-time, comprehensiveness, accuracy and intelligence. Therefore, developing a water pollution monitoring system that integrates multiple monitoring methods, can comprehensively evaluate the water pollution status, and has highly intelligent and automated characteristics is of great significance for improving water quality monitoring efficiency and ensuring water environment safety. Summary of the invention

[0004] The present invention aims to solve the deficiencies of the prior art and provides the following solutions:

[0005] A surface water pollution identification system based on multimodal perception, comprising: a multimodal data acquisition module, a data fusion module, a pollution analysis module and a terminal module;

[0006] The multimodal data acquisition module is used to collect multimodal data of surface water;

[0007] The data fusion module is used to fuse the multimodal data and extract data features;

[0008] The pollution analysis module analyzes the surface water pollution situation based on the data features to obtain analysis results;

[0009] The terminal module is used to display the analysis results and issue an alarm when the results are abnormal.

[0010] Preferably, the multimodal data acquisition module comprises: a water quality sensor unit, an image monitoring unit and a satellite remote sensing unit;

[0011] The water quality sensor unit is used to monitor the chemical composition in the water body in real time to obtain water quality data;

[0012] The image monitoring unit is used to capture the water body surface image in real time to obtain water body image data;

[0013] The satellite remote sensing unit is used to obtain water body remote sensing images and obtain water body area data.

[0014] Preferably, the data fusion module comprises: a data receiving unit, a preprocessing unit, a fusion unit and a feature extraction unit;

[0015] The data receiving unit is used to receive the multimodal data;

[0016] The preprocessing unit is used to preprocess the multimodal data to obtain processed data;

[0017] The fusion unit is used to perform weighted fusion on the processed data to obtain a comprehensive data set;

[0018] The feature extraction unit is used to extract features from the comprehensive data set to obtain the data features.

[0019] Preferably, the pollution analysis module includes: a model building unit, a pollution analysis unit and a strategy generation unit;

[0020] The model building unit uses historical pollution data to build and train a water pollution analysis model;

[0021] The pollution analysis unit inputs the data features into the water pollution analysis model for analysis to obtain pollution situation analysis results;

[0022] The strategy generation unit is used to generate a processing strategy based on the pollution situation analysis result to obtain a strategy analysis result.

[0023] The present invention also provides a method for identifying surface water pollution based on multimodal perception, which is applied to any of the above-mentioned systems and comprises the following steps:

[0024] Collect multimodal data on surface water;

[0025] fusing the multimodal data and extracting data features;

[0026] Analyze the surface water pollution situation based on the data characteristics to obtain analysis results;

[0027] The analysis results are displayed and an alarm is issued if the results are abnormal.

[0028] Preferably, the method for collecting the multimodal data includes:

[0029] A water quality sensor is placed in the water, and the chemical composition of the water is monitored in real time by the water quality sensor to obtain water quality data;

[0030] Deploy monitoring equipment on the shore, and use the monitoring equipment to capture the surface image of the water body in real time to obtain water body image data;

[0031] Remote sensing satellites are used to obtain remote sensing images of water bodies and obtain water area data.

[0032] Preferably, the method for obtaining the data features includes:

[0033] receiving the multimodal data;

[0034] Preprocessing the multimodal data to obtain processed data;

[0035] Performing weighted fusion on the processed data to obtain a comprehensive data set;

[0036] Perform feature extraction on the comprehensive data set to obtain the data features.

[0037] Preferably, the method for obtaining the analysis result comprises:

[0038] Use historical pollution data to build and train water pollution analysis models;

[0039] Inputting the data features into the water pollution analysis model for analysis to obtain pollution situation analysis results;

[0040] A treatment strategy is generated based on the pollution situation analysis result to obtain a strategy analysis result.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] The present invention integrates multiple monitoring technologies, comprehensively covers the chemical, physical and biological parameters of water bodies, and adopts advanced data processing and analysis algorithms to improve the accuracy of monitoring results. At the same time, it applies artificial intelligence technology to realize automated monitoring, analysis and early warning, achieve data updates in seconds, and quickly respond to water pollution incidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0044] Figure 1 Schematic diagram of the system structure of an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0047] Embodiment 1

[0048] In this embodiment, if Figure 1 As shown, a surface water pollution identification system based on multimodal perception includes: a multimodal data acquisition module, a data fusion module, a pollution analysis module and a terminal module.

[0049] The multimodal data acquisition module is used to collect multimodal data of surface water.

[0050] The multimodal data acquisition module includes: a water quality sensor unit, an image monitoring unit and a satellite remote sensing unit. The water quality sensor unit is used to monitor the chemical composition of the water body in real time to obtain water quality data; the image monitoring unit is used to capture the surface image of the water body in real time to obtain water body image data; the satellite remote sensing unit is used to obtain water body remote sensing images to obtain water body area data.

[0051] In this embodiment, the water quality sensor unit includes but is not limited to a pH sensor, a dissolved oxygen sensor, an ammonia nitrogen sensor, a heavy metal sensor, and an organic pollutant sensor, which respectively collect the pH value, dissolved oxygen value, ammonia nitrogen pollution data, heavy metal data, and organic pollutant data of the water body. The image monitoring unit is a high-definition camera that captures the surface image of the water body in real time to analyze visible pollution information such as water color and floating objects.

[0052] The data fusion module is used to fuse multimodal data and extract data features.

[0053] The data fusion module includes: a data receiving unit, a preprocessing unit, a fusion unit and a feature extraction unit. The data receiving unit is used to receive multimodal data; the preprocessing unit is used to preprocess the multimodal data to obtain processed data; the fusion unit is used to perform weighted fusion on the processed data to obtain a comprehensive data set; the feature extraction unit is used to extract features from the comprehensive data set to obtain data features.

[0054] In this embodiment, the preprocessing unit includes: a filtering subunit, a standardization processing subunit, an alignment subunit and a data cleaning subunit. The filtering subunit is used to remove high-frequency noise or low-frequency trends in the data and retain useful signals. A low-pass filter is used to remove high-frequency noise, and a high-pass filter is used to remove low-frequency noise to obtain filtered data. The standardization processing subunit is used to normalize and standardize the data and convert it into a unified format and scale to facilitate comparison and fusion of data from different sources and types. Normalization usually scales the data to the interval [0,1], and then converts the data into a distribution with zero mean and unit variance to complete the standardization processing and obtain standardized data. The alignment subunit is used to perform consistency processing on the standardized data of different collection units and different monitoring points in time and space. A time synchronizer is used to calibrate the timestamp to ensure the consistency of data collection time, and then a spatial aligner is used to map data at different locations to a unified coordinate system through geographic information system (GIS) technology. The data cleaning subunit is used to identify and process outliers and missing values ​​in the data. First, outliers in the data are identified and removed through statistical analysis methods (such as standard deviation, box plot, etc.), and then missing values ​​in the data are filled through interpolation, mean replacement, etc. to obtain processed data.

[0055] The fusion unit includes: a weighted fusion subunit and a data integration subunit. The weighted fusion subunit first evaluates the accuracy and reliability of each data source, identifies errors and deviations in the data, calculates its confidence based on the historical performance and current performance of the data source, and then evaluates the amount of information in the data to determine the contribution of the data to the monitoring task. Then, it analyzes the correlation between different data sources, identifies the redundancy and complementarity of the data, and finally, according to the above evaluation results, assigns weights to each data source, comprehensively considers the weights of each data source, and generates a fusion result. The numerical integration subunit uses feature mapping and pooling technology to integrate the features of different data sources, and then uses the fusion algorithm to fuse the data according to the data characteristics and application requirements to obtain a comprehensive data set.

[0056] The feature extraction unit uses principal component analysis to extract key features that cause water pollution from the comprehensive data set, reduce data dimensions, remove redundant information, and retain the main changes and structures of the data. Among them, the key features of water pollution include: water quality pollution data and floating matter pollution data.

[0057] The pollution analysis module analyzes the surface water pollution situation based on data characteristics and obtains analysis results.

[0058] The pollution analysis module includes: a model building unit, a pollution analysis unit and a strategy generation unit. The model building unit uses historical pollution data to build and train a water pollution analysis model; the pollution analysis unit inputs data features into the water pollution analysis model for analysis to obtain pollution situation analysis results; the strategy generation unit is used to generate a treatment strategy based on the pollution situation analysis results to obtain a strategy analysis result.

[0059] In this embodiment, the model building unit is used to build and train a water pollution analysis model, which includes: a support vector machine model, a convolutional neural network model and a graph neural network model; the support vector machine model is used to identify the type of water pollution, and the support vector machine model is trained using historical water pollution data, and the water pollution is judged and classified based on the collected real-time water quality data to obtain the water pollution analysis result; the convolutional neural network model is used to identify the water surface, and the convolutional neural network model is trained using historical floating pollution data, and the floating objects in the water body are identified based on the water surface image to obtain the floating pollution analysis result; the graph neural network model analyzes the relationship and influence between water pollution by the original time series of water pollution data and floating pollution data, and identifies the propagation path of water pollution. The strategy generation unit automatically generates water pollution treatment measures based on the water pollution analysis results, floating pollution analysis results and water pollution propagation path, and obtains the strategy analysis result.

[0060] The terminal module is used to display the analysis results and issue an alarm when the results are abnormal.

[0061] In this embodiment, the terminal module displays the water quality pollution analysis results, floating matter pollution analysis results, water pollution transmission path and strategy analysis results to the monitoring personnel, and at the same time, when these analysis results are abnormal, an alarm is issued to the monitoring personnel.

[0062] Embodiment 2

[0063] In this embodiment, a surface water pollution identification method based on multimodal sensing includes the following steps:

[0064] S1. Collect multimodal data of surface water.

[0065] The method of collecting multimodal data includes: placing water quality sensors in the water, using the water quality sensors to monitor the chemical composition of the water in real time, and obtaining water quality data; placing monitoring equipment on the shore, using the monitoring equipment to capture water surface images in real time, and obtaining water image data; using remote sensing satellites to obtain water remote sensing images and obtain water area data.

[0066] In this embodiment, water quality sensors include but are not limited to pH sensors, dissolved oxygen sensors, ammonia nitrogen sensors, heavy metal sensors and organic pollutant sensors, which respectively collect pH values, dissolved oxygen values, ammonia nitrogen pollution data, heavy metal data and organic pollutant data of water bodies. High-definition cameras are used to capture images of water surface in real time to analyze visible pollution information such as water color and floating objects.

[0067] S2. Fuse multimodal data and extract data features.

[0068] The method for obtaining data features includes: receiving multimodal data; preprocessing the multimodal data to obtain processed data; performing weighted fusion on the processed data to obtain a comprehensive data set; and extracting features from the comprehensive data set to obtain data features.

[0069] In this embodiment, the preprocessing method includes: (1) removing high-frequency noise or low-frequency trends in the data and retaining useful signals, wherein a low-pass filter is used to remove high-frequency noise and a high-pass filter is used to remove low-frequency noise to obtain filtered data; (2) normalizing and standardizing the data and converting them into a unified format and scale to facilitate comparison and fusion of data from different sources and types. Normalization usually scales the data to the interval [0, 1] and then converts the data into a distribution with zero mean and unit variance to complete the standardization process and obtain standardized data; (3) performing standardization on different sampling methods. The standardized data of the collection units and different monitoring points are processed for consistency in time and space. The time synchronizer is used to calibrate the timestamp to ensure the consistency of the data collection time. Then, the spatial aligner is used to map the data at different locations to a unified coordinate system through the geographic information system (GIS) technology; (4) Identify and process outliers and missing values ​​in the data. First, the outliers in the data are identified through statistical analysis methods (such as standard deviation, box plot, etc.) and the outliers are removed. Then, the missing values ​​in the data are filled by interpolation, mean replacement and other methods to obtain processed data.

[0070] The method for obtaining a comprehensive data set includes: (1) firstly, evaluating the accuracy and reliability of each data source, identifying errors and deviations in the data, and calculating its confidence based on the historical performance and current performance of the data source. Then, the amount of information in the data is evaluated to determine the contribution of the data to the monitoring task. Then, the correlation between different data sources is analyzed to identify the redundancy and complementarity of the data. Finally, based on the above evaluation results, a weight is assigned to each data source, and the weight of each data source is comprehensively considered to generate a fusion result. (2) The numerical integration subunit uses feature mapping and pooling technology to integrate the features of different data sources. Then, according to the data characteristics and application requirements, the data is fused using a fusion algorithm to obtain a comprehensive data set.

[0071] The feature extraction method includes: using principal component analysis to extract the key features that cause water pollution from the comprehensive data set, reducing data dimensions, removing redundant information, and retaining the main changes and structures of the data. Among them, the key features of water pollution include: water quality pollution data and floating matter pollution data.

[0072] S3. Analyze the surface water pollution situation based on data characteristics and obtain analysis results.

[0073] The method for obtaining the analysis results includes: using historical pollution data to build and train a water pollution analysis model; inputting data features into the water pollution analysis model for analysis to obtain pollution situation analysis results; generating a treatment strategy based on the pollution situation analysis results to obtain a strategy analysis result.

[0074] In this embodiment, a water pollution analysis model is constructed and trained, and the water pollution analysis model includes: a support vector machine model, a convolutional neural network model, and a graph neural network model; the support vector machine model is used to identify the type of water pollution, and the support vector machine model is trained using historical water pollution data, and the water pollution is judged and classified based on the collected real-time water quality data to obtain the water pollution analysis result; the convolutional neural network model is used to identify the water surface, and the convolutional neural network model is trained using historical floating pollution data, and the floating objects in the water body are identified based on the water surface image to obtain the floating pollution analysis result; the graph neural network model analyzes the relationship and influence between water pollution in the original time series of water pollution data and floating pollution data, and identifies the propagation path of water pollution. Then, based on the water pollution analysis results, floating pollution analysis results, and water pollution propagation paths, water pollution treatment measures are automatically generated to obtain strategy analysis results.

[0075] S4. Display the analysis results and issue an alarm if the results are abnormal.

[0076] In this embodiment, the water quality pollution analysis results, floating matter pollution analysis results, water pollution transmission path and strategy analysis results are displayed to the monitoring personnel. At the same time, when these analysis results are abnormal, an alarm is issued to the monitoring personnel.

[0077] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A surface water pollution identification system based on multimodal perception, characterized in that: include: Multimodal data acquisition module, data fusion module, pollution analysis module and terminal module; The multimodal data acquisition module is used to collect multimodal data of surface water; The data fusion module is used to fuse the multimodal data and extract data features; The pollution analysis module analyzes the surface water pollution situation based on the data features to obtain analysis results; The terminal module is used to display the analysis results and issue an alarm when the results are abnormal.

2. A surface water pollution identification system based on multimodal perception according to claim 1, characterized in that: The multimodal data acquisition module includes: a water quality sensor unit, an image monitoring unit and a satellite remote sensing unit; The water quality sensor unit is used to monitor the chemical composition in the water body in real time to obtain water quality data; The image monitoring unit is used to capture the water body surface image in real time to obtain water body image data; The satellite remote sensing unit is used to obtain water body remote sensing images and obtain water body area data.

3. The surface water pollution identification system based on multimodal perception according to claim 1 is characterized in that: The data fusion module includes: a data receiving unit, a preprocessing unit, a fusion unit and a feature extraction unit; The data receiving unit is used to receive the multimodal data; The preprocessing unit is used to preprocess the multimodal data to obtain processed data; The fusion unit is used to perform weighted fusion on the processed data to obtain a comprehensive data set; The feature extraction unit is used to extract features from the comprehensive data set to obtain the data features.

4. The surface water pollution identification system based on multimodal perception according to claim 1 is characterized in that: The pollution analysis module includes: a model building unit, a pollution analysis unit and a strategy generation unit; The model building unit uses historical pollution data to build and train a water pollution analysis model; The pollution analysis unit inputs the data features into the water pollution analysis model for analysis to obtain pollution situation analysis results; The strategy generation unit is used to generate a processing strategy based on the pollution situation analysis result to obtain a strategy analysis result.

5. A method for identifying surface water pollution based on multimodal perception, the method being applied to the system according to any one of claims 1 to 4, characterized in that: The following steps are involved: Collect multimodal data on surface water; fusing the multimodal data and extracting data features; Analyze the surface water pollution situation based on the data characteristics to obtain analysis results; The analysis results are displayed and an alarm is issued if the results are abnormal.

6. A surface water pollution identification method based on multimodal perception according to claim 5, characterized in that: The method for collecting the multimodal data includes: A water quality sensor is placed in the water, and the chemical composition of the water is monitored in real time by the water quality sensor to obtain water quality data; Deploy monitoring equipment on the shore, and use the monitoring equipment to capture the surface image of the water body in real time to obtain water body image data; Remote sensing satellites are used to obtain remote sensing images of water bodies and obtain water area data.

7. The surface water pollution identification method based on multimodal perception according to claim 5 is characterized in that: The method for obtaining the data features includes: receiving the multimodal data; Preprocessing the multimodal data to obtain processed data; Performing weighted fusion on the processed data to obtain a comprehensive data set; Perform feature extraction on the comprehensive data set to obtain the data features.

8. The surface water pollution identification method based on multimodal perception according to claim 5 is characterized in that: The method for obtaining the analysis result includes: Use historical pollution data to build and train water pollution analysis models; Inputting the data features into the water pollution analysis model for analysis to obtain pollution situation analysis results; A treatment strategy is generated based on the pollution situation analysis result to obtain a strategy analysis result.

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