Underwater environment monitoring method based on intelligent underwater vehicle

Abstract

The invention relates to the technical field of environment monitoring, and discloses an underwater environment monitoring method based on an intelligent underwater vehicle, which comprises the following steps: S1, applying the intelligent underwater vehicle to the remote sensing monitoring of the underwater environment of an inland river, and taking the intelligent underwater vehicle as a platform, the method comprises the following steps: S1, carrying water environment sensor equipment including a multi-beam sonar, a multispectral imager, a hyperspectral water quality parameter instrument and the like, and S2, taking an intelligent underwater vehicle as a monitoring platform in an internal river underwater environment monitoring process, and controlling through a shore-based remote control end. According to the underwater environment monitoring method based on the intelligent underwater vehicle, a whole multi-sensor integration platform integrates a multi-beam sonar, a multi-spectral imager and a hyperspectral water quality parameter instrument into the same platform, so that multi-dimensional underwater environment monitoring is realized, the comprehensiveness and accuracy of data acquisition are improved, and the underwater environment monitoring precision is improved. The whole monitoring method can be flexibly applied in different inland river environments, is high in adaptability, and can meet various monitoring requirements, such as pollution source tracking and resource evaluation.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and in particular to an underwater environmental monitoring method based on an intelligent underwater vehicle. Background Art

[0002] With the development of the economy and the improvement of living standards, people are paying more and more attention to environmental protection. Human production and life activities have led to changes in the physical and chemical characteristics of water bodies, causing water quality deterioration and causing serious harm to human life and health. Water environment monitoring is an important aspect of environmental protection. The degree of water pollution can be reflected by the spectral characteristics of water. The content and composition of impurities in water will affect the spectral characteristics of water.

[0003] Manual sampling requires collecting water samples in the water and analyzing them, which has certain limitations in accuracy and time efficiency. Hydrological measurement requires installing water level and flow measurement equipment in the water, which is complex to operate and requires professional technical support. At the same time, measurement accuracy is easily affected by the external environment. At present, hydrological measurement mainly relies on fixed hydrological stations for hydrological measurement, which is difficult to meet the demand for obtaining real-time data at any point. Although aerial remote sensing technology can obtain water quality information over a large area, its resolution is low and it is difficult to achieve in-depth monitoring of the underwater ecological environment. The monitoring results can only represent the water quality conditions at a specific time and place, and cannot achieve real-time continuous monitoring. As a result, some enterprises avoid the time and area of water quality monitoring and discharge pollution illegally. Management enterprises find it difficult to track down and lock down illegal enterprises and lack a comprehensive understanding of the entire water body, which increases the operating costs and management burden of management enterprises. The complexity of the underwater environment due to the influence of factors such as water depth, flow rate, underwater topography, and aquatic organisms also increases the difficulty of water quality monitoring. Therefore, in order to better solve the problems in water quality monitoring, it is necessary to develop new technical means to improve the efficiency and accuracy of water quality monitoring. Therefore, an underwater environment monitoring method based on an intelligent underwater vehicle is proposed to solve the above problems. Summary of the Invention

[0004] (1) Technical problems solved In response to the shortcomings of the existing technology, the present invention provides an underwater environment monitoring method based on an intelligent submersible, which has the advantages of efficient and accurate monitoring of the underwater environment, and solves the problem of difficult to meet the requirements of underwater monitoring points and monitoring continuity.

[0005] (2) Technical solution To achieve the above-mentioned purpose of efficient and accurate monitoring of the underwater environment, the present invention provides the following technical solution: an underwater environment monitoring method based on an intelligent underwater vehicle, comprising the following steps: S1. By applying intelligent submersibles to remote sensing monitoring of inland river underwater environments, using intelligent submersibles as platforms and equipped with water environment sensor equipment including multi-beam sonar, multispectral imagers, and hyperspectral water quality parameter meters, underwater terrain mapping, underwater feature identification, and quantitative inversion of water quality elements can be achieved; S2. During inland river underwater environment monitoring, the intelligent submersible serves as a monitoring platform and is controlled by a shore-based remote control terminal to achieve data collection and transmission functions: During the data transmission process, wireless water depth monitoring nodes floating on the water surface in the form of buoys form a sensor network. All sub-nodes are deployed in the area to be monitored. The sub-nodes periodically collect water depth data and transmit the data to the aggregation gateway node in a wireless multi-hop manner. The aggregation gateway node then transmits the measured data to the server through the shore-based base station or directly stores it locally, so that users can view the underwater topography of the water area detected by the intelligent submersible through the remote control terminal; S3, the multi-beam sonar in step S1 is used to obtain underwater terrain data and achieve high-precision terrain mapping. The multi-beam sonar measures underwater terrain by emitting sound wave pulses and receiving echoes. The sound wave pulses spread in a fan-shaped manner, covering a large water area. When the sound waves encounter underwater objects or terrain surfaces, they are reflected. The sensor receives the echo signal and calculates the distance and position of the object by measuring the transmission time and intensity of the sound waves; S4. In order to obtain stable detection data, it is also necessary to eliminate some other interfering factors, such as eliminating the influence of waves, river water and tidal changes. Among them, eliminating tidal changes is the most important issue. Tidal correction technology is also called water level correction. It is helpful to improve measurement accuracy in acoustic beam monitoring: 1) Scour terrain simulation: The simulation process of underwater terrain scouring is to develop data using limited feature points, and then transform it into the development process of the overall scouring by constructing a numerical model. Therefore, it is necessary to select multiple representative interpolation points and test points to determine the curve of the scouring process change and verify the authenticity of the data. It is necessary to consider the data range of the interpolation points and test points: it is necessary to select points in key locations with appropriate areas and a moderate number. In the process of physical simulation, an initial terrain should be determined first, and then the underwater terrain should be measured regularly to obtain the maximum scouring terrain, so as to determine a final terrain. Then, MATLAB is used to simulate the scouring of the underwater terrain. 2) Curve fitting: The key to curve fitting is to find the relationship between the independent variable and the dependent variable. When solving a problem that is difficult to solve based on experience alone, mathematical fitting can be used to solve the problem. Therefore, an original recursive function formula is used to fit the data to improve the accuracy of underwater terrain simulation: Let the dependent variable and independent variables , , ,…, And experimental data ={ , ,…, },in =( , , ,…, ), = ( , , ,…, ) is the fitting function; ①Use the recursive theory of linear function to fit the numerical value and target limit ; ② Reorganize the data to form 2 new samples and ; ③Use 、 Limited quantity 、 Go to step 1, the fitting functions are ( ,…, ), ( ,…, , ); ④ General ( ,…, , ) All ( , ,..., ) to obtain The specific form: (0, ,..., )= ( ,…, ); ( , ,…, )= [ , ,..., , ( , ,…, ) 3) Scour process simulation: After determining the fitting function ( , ,..., ) After that, the underwater terrain scouring process was simulated using MATLAB tools; S5. The multispectral imager in step S1 is used to identify underwater objects and obtain underwater ecological information. The multispectral imager captures the reflectivity or emissivity of underwater objects under different wavelengths of light, which usually include multiple bands such as visible light and near-infrared light. Each band corresponds to a different spectral characteristic of underwater objects, which helps to identify the type of underwater objects. By analyzing multispectral data, underwater objects such as vegetation, rocks or sediments can be identified. Different objects have different reflective characteristics under different spectra. The multispectral imager can use these differences to establish a ground object classification map to facilitate ecological monitoring and resource assessment. S6. The hyperspectral water quality parameter meter in step S1 is used to monitor water quality elements such as turbidity, dissolved oxygen, and nutrients to achieve quantitative inversion of water quality. The hyperspectral water quality parameter meter monitors water quality parameters by capturing spectral information of each wavelength in the water body. Its working process is to decompose light into hundreds or even thousands of bands and analyze the spectral reflectance or absorption rate of each band. Since different components in the water body (such as suspended particulate matter, chlorophyll a, total phosphorus and total nitrogen, etc.) have different absorption or reflection characteristics for specific wavelengths of light, the inversion algorithm combined with hyperspectral data can accurately invert parameters such as turbidity, chlorophyll concentration, dissolved oxygen, and nutrients in the water body. Especially in inland river environments, this method can quickly and contactlessly provide multi-dimensional data on water pollution levels and ecological health.

[0006] Preferably, the data collected in steps S3 to S6 are networked and transmitted to a convergence gateway, and the nodes form a multi-hop communication network through the IEEE802.15.4 protocol to transmit the information data to the convergence gateway as much as possible.

[0007] Preferably, in step S2, the aggregation gateway mainly completes data aggregation and forwarding and communication protocol parsing and conversion. The gateway parses the data received from the perception layer according to the designed protocol field and re-encapsulates it. Considering the characteristics of a large number of sensor nodes, high data collection frequency and tolerance of occasional packet loss, the UDP protocol is used to forward the data packets to the central server.

[0008] Preferably, the central server in step S2 serves as a bridge between the monitoring system and the outside world. The server program parses the data obtained by the system according to the protocol and stores it in the corresponding database table, which users can access through a web interface. This structure allows users to remotely view changes in underwater terrain via the network.

[0009] (3) Beneficial effects Compared with the existing technology, the present invention provides an underwater environment monitoring method based on an intelligent underwater vehicle, which has the following beneficial effects: 1. This underwater environment monitoring method based on an intelligent submersible includes a multi-sensor integrated platform: multi-beam sonar, multispectral imager, and hyperspectral water quality parameter meter are integrated into the same platform to achieve multi-dimensional underwater environment monitoring and improve the comprehensiveness and accuracy of data collection.

[0010] 2. This underwater environmental monitoring method based on intelligent submersibles can be flexibly applied in different inland river environments, has strong adaptability, and can meet various monitoring needs, such as pollution source tracking and resource assessment.

[0011] 3. This underwater environment monitoring method based on intelligent submersibles uses buoys in conjunction with central servers and aggregation gateways to ensure the continuity, stability and image clarity of data transmission during the entire monitoring data transmission process. By processing data using multi-beam water level correction technology, the monitoring of acoustic beams is conducive to improving measurement accuracy, thereby improving the accuracy of the entire method in underwater environment monitoring. DETAILED DESCRIPTION

[0012] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described 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 creative efforts are within the scope of protection of the present invention.

[0013] A method for underwater environment monitoring based on an intelligent underwater vehicle comprises the following steps: S1. By applying intelligent submersibles to remote sensing monitoring of inland river underwater environments, using intelligent submersibles as platforms and equipped with water environment sensor equipment including multi-beam sonar, multispectral imagers, and hyperspectral water quality parameter meters, underwater terrain mapping, underwater feature identification, and quantitative inversion of water quality elements can be achieved; S2. During inland river underwater environment monitoring, the intelligent submersible serves as a monitoring platform and is controlled by a shore-based remote control terminal to achieve data collection and transmission functions: During the data transmission process, wireless depth monitoring nodes, floating on the water surface like buoys, form a sensor network. All sub-nodes are deployed in the area to be monitored. These sub-nodes periodically collect depth data and transmit it to a converged gateway node via wireless multi-hop communication. The converged gateway node then transmits the measured data via a shore-based base station to a server or stores it locally, allowing users to remotely monitor the underwater topography of the area being surveyed by the intelligent submersible. The central server acts as a communication bridge between the monitoring system and the outside world. The server program parses the data acquired by the system according to the protocol and stores it in the corresponding database table, which users can access via a web interface. This architecture allows users to remotely monitor changes in underwater topography over the network. S3, the multi-beam sonar in step S1 is used to obtain underwater terrain data and achieve high-precision terrain mapping. The multi-beam sonar measures underwater terrain by emitting sound pulses and receiving echoes. The sound pulses spread in a fan-shaped manner, covering a large water area. When the sound waves encounter underwater objects or terrain surfaces, they are reflected. The sensor receives the echo signal and calculates the distance and position of the object by measuring the transmission time and intensity of the sound waves. For the collected data, the network transmits the data to the aggregation gateway. The nodes form a multi-hop communication network through the IEEE802.15.4 protocol, and transmit the information data to the aggregation gateway in step S2 as much as possible; S4. In order to obtain stable detection data, it is also necessary to eliminate some other interfering factors, such as eliminating the influence of waves, river water and tidal changes. Among them, eliminating tidal changes is the most important issue. Tidal correction technology is also called water level correction. It is helpful to improve measurement accuracy in acoustic beam monitoring: 1) Scour terrain simulation: The simulation process of underwater terrain scouring is to develop data using limited feature points, and then transform it into the development process of the overall scouring by constructing a numerical model. Therefore, it is necessary to select multiple representative interpolation points and test points to determine the curve of the scouring process change and verify the authenticity of the data. It is necessary to consider the data range of the interpolation points and test points: it is necessary to select points in key locations with appropriate areas and a moderate number. In the process of physical simulation, an initial terrain should be determined first, and then the underwater terrain should be measured regularly to obtain the maximum scouring terrain, so as to determine a final terrain. Then, MATLAB is used to simulate the scouring of the underwater terrain. 2) Curve fitting: The key to curve fitting is to find the relationship between the independent variable and the dependent variable. When solving a problem that is difficult to solve based on experience alone, mathematical fitting can be used to solve the problem. Therefore, an original recursive function formula is used to fit the data to improve the accuracy of underwater terrain simulation: Let the dependent variable and independent variables , , ,…, And experimental data ={ , ,…, },in =( , , ,…, ), = ( , , ,…, ) is the fitting function; ①Use the recursive theory of linear function to fit the numerical value and target limit ; ② Reorganize the data to form 2 new samples and ; ③Use 、 Limited quantity 、 Go to step 1, the fitting functions are ( ,…, ), ( ,…, , ); ④ General ( ,…, , ) All ( , ,..., ) to obtain The specific form: (0, ,..., )= ( ,…, ); ( , ,…, )= [ , ,..., , ( , ,…, ) 3) Scour process simulation: After determining the fitting function ( , ,..., ) After that, the underwater terrain scouring process was simulated using MATLAB tools; S5. The multispectral imager in step S1 is used to identify underwater objects and obtain underwater ecological information. The multispectral imager captures the reflectivity or radiance of underwater objects under different wavelengths of light, which usually include multiple bands such as visible light and near-infrared. Each band corresponds to a different spectral characteristic of underwater objects, which helps to identify the type of underwater objects. By analyzing multispectral data, underwater objects such as vegetation, rocks or sediments can be identified. Different objects have different reflective characteristics under different spectra. The multispectral imager can use these differences to establish a feature classification map to facilitate ecological monitoring and resource assessment. For the collected data, the network is formed to transmit the data to the aggregation gateway. The nodes form a multi-hop communication network through the IEEE802.15.4 protocol to transmit the information data to the aggregation gateway in step S2 as much as possible. S6. The hyperspectral water quality parameter meter in step S1 is used to monitor water quality elements such as turbidity, dissolved oxygen, and nutrients to achieve quantitative inversion of water quality. The hyperspectral water quality parameter meter monitors water quality parameters by capturing spectral information of each wavelength in the water body. Its working process is to decompose light into hundreds or even thousands of bands and analyze the spectral reflectance or absorbance of each band. Since different components in the water body (such as suspended particulate matter, chlorophyll a, total phosphorus and total nitrogen, etc.) have different absorption or reflection characteristics for specific wavelengths of light, the inversion algorithm combined with hyperspectral data can accurately invert parameters such as turbidity, chlorophyll concentration, dissolved oxygen, and nutrients in the water body. Especially in inland river environments, this method can quickly and contactlessly provide multi-dimensional data on water pollution levels and ecological health. For the collected data, the network is formed to transmit the data to the aggregation gateway. The nodes form a multi-hop communication network through the IEEE802.15.4 protocol to transmit the information data to the aggregation gateway in step S2 as much as possible.

[0014] The beneficial effects of the present invention are: Remote control operation: The submersible is controlled by a shore-based remote control to navigate underwater and conduct monitoring according to the set path; Data collection: During navigation, sensors collect underwater topography, ecology, and water quality data in real time. During data collection and transmission, buoys work with central servers and aggregation gateways to create a stable and fast data transmission environment to ensure continuity, stability, and image clarity. Data processing and analysis: The collected data is processed through the software system to generate underwater topographic maps, land feature distribution maps and water quality analysis reports. At the same time, the data is processed in conjunction with the water level correction technology of multi-beam sounding, and machine learning algorithms are used to improve the accuracy of water quality parameter inversion.

[0015] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for underwater environment monitoring based on an intelligent underwater vehicle, characterized in that: The following steps are involved: S1. By applying intelligent submersibles to remote sensing monitoring of inland river underwater environments, using intelligent submersibles as platforms and equipped with water environment sensor equipment including multi-beam sonar, multispectral imagers, and hyperspectral water quality parameter meters, underwater terrain mapping, underwater feature identification, and quantitative inversion of water quality elements can be achieved; S2. During inland river underwater environment monitoring, the intelligent submersible serves as a monitoring platform and is controlled by a shore-based remote control terminal to achieve data collection and transmission functions: During the data transmission process, wireless water depth monitoring nodes floating on the water surface in the form of buoys form a sensor network. All sub-nodes are deployed in the area to be monitored. The sub-nodes periodically collect water depth data and transmit the data to the aggregation gateway node in a wireless multi-hop manner. The aggregation gateway node then transmits the measured data to the server through the shore-based base station or directly stores it locally, so that users can view the underwater topography of the water area detected by the intelligent submersible through the remote control terminal; S3, the multi-beam sonar in step S1 is used to obtain underwater terrain data and achieve high-precision terrain mapping. The multi-beam sonar measures underwater terrain by emitting sound wave pulses and receiving echoes. The sound wave pulses spread in a fan-shaped manner, covering a large water area. When the sound waves encounter underwater objects or terrain surfaces, they are reflected. The sensor receives the echo signal and calculates the distance and position of the object by measuring the transmission time and intensity of the sound waves; S4. In order to obtain stable detection data, it is also necessary to eliminate some other interfering factors, such as eliminating the influence of waves, river water and tidal changes. Among them, eliminating tidal changes is the most important issue. Tidal correction technology is also called water level correction. It is helpful to improve measurement accuracy in acoustic beam monitoring: 1) Scour terrain simulation: The simulation process of underwater terrain scouring is to develop data using limited feature points, and then transform it into the development process of the overall scouring by constructing a numerical model. Therefore, it is necessary to select multiple representative interpolation points and test points to determine the curve of the scouring process change and verify the authenticity of the data. It is necessary to consider the data range of the interpolation points and test points: it is necessary to select points in key locations with appropriate areas and a moderate number. In the process of physical simulation, an initial terrain should be determined first, and then the underwater terrain should be measured regularly to obtain the maximum scouring terrain, so as to determine a final terrain. Then, MATLAB is used to simulate the scouring of the underwater terrain. 2) Curve fitting: The key to curve fitting is to find the relationship between the independent variable and the dependent variable. When solving a problem that is difficult to solve based on experience alone, mathematical fitting can be used to solve the problem. Therefore, an original recursive function formula is used to fit the data to improve the accuracy of underwater terrain simulation: Let the dependent variable and independent variables , , ,…, And experimental data ={ , ,…, },in =( , , ,…, ), = ( , , ,…, ) is the fitting function; ①Use the recursive theory of linear function to fit the numerical value and target limit ; ② Reorganize the data to form 2 new samples and ; ③Use 、 Limited quantity 、 Go to step 1, the fitting functions are ( ,…, ), ( ,…, , ); ④ General ( ,…, , ) All ( , ,..., ) to obtain The specific form: (0, ,..., )= ( ,…, ); （ ， ，…， ）＝ ［ ， ，...， ， （ ， ，…， ）］ 3) Scour process simulation: After determining the fitting function ( , ,..., ) After that, the underwater terrain scouring process was simulated using MATLAB tools; S5. The multispectral imager in step S1 is used to identify underwater objects and obtain underwater ecological information. The multispectral imager captures the reflectivity or emissivity of underwater objects under different wavelengths of light, which usually include multiple bands such as visible light and near-infrared light. Each band corresponds to a different spectral characteristic of underwater objects, which helps to identify the type of underwater objects. By analyzing multispectral data, underwater objects such as vegetation, rocks or sediments can be identified. Different objects have different reflective characteristics under different spectra. The multispectral imager can use these differences to establish a ground object classification map to facilitate ecological monitoring and resource assessment. S6. The hyperspectral water quality parameter meter in step S1 is used to monitor water quality elements such as turbidity, dissolved oxygen, and nutrients to achieve quantitative inversion of water quality. The hyperspectral water quality parameter meter monitors water quality parameters by capturing spectral information of each wavelength in the water body. Its working process is to decompose light into hundreds or even thousands of bands and analyze the spectral reflectance or absorption rate of each band. Since different components in the water body (such as suspended particulate matter, chlorophyll a, total phosphorus and total nitrogen, etc.) have different absorption or reflection characteristics for specific wavelengths of light, the inversion algorithm combined with hyperspectral data can accurately invert parameters such as turbidity, chlorophyll concentration, dissolved oxygen, and nutrients in the water body. Especially in inland river environments, this method can quickly and contactlessly provide multi-dimensional data on water pollution levels and ecological health.

2. The underwater environment monitoring method based on an intelligent underwater vehicle according to claim 1, characterized in that: The data collected in steps S3 to S6 are networked and transmitted to the aggregation gateway. The nodes form a multi-hop communication network through the IEEE802.15.4 protocol to transmit the information data to the aggregation gateway as much as possible.

3. The underwater environment monitoring method based on an intelligent underwater vehicle according to claim 1, characterized in that: In step S2, the aggregation gateway mainly completes data aggregation and forwarding and communication protocol parsing and conversion. The gateway parses the data received from the perception layer according to the designed protocol field and re-encapsulates it. Considering the characteristics of a large number of sensor nodes, high data collection frequency and tolerance of occasional packet loss, the UDP protocol is used to forward the data packets to the central server.

4. The underwater environment monitoring method based on an intelligent underwater vehicle according to claim 1, characterized in that: The server in step S2 acts as a bridge between the monitoring system and the outside world. The server program parses the data obtained by the system according to the protocol and stores it in the corresponding database table, which users can access through a web interface. This structure allows users to remotely monitor underwater terrain changes over the network.

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