A water level information monitoring method and device based on a beacon and multi-intelligent sensor fusion

Abstract

This invention relates to the technical field of water level information monitoring based on navigation aids, and discloses a method and device for water level information monitoring based on multi-intelligent sensor fusion of navigation aids. The method includes: using preprocessed multi-sensor data to perform a correction estimate of water depth based on a correction term to obtain the corrected water depth; performing position matching analysis between the location of the navigation aid vessel and its reference coordinates, and determining whether the location of the navigation aid vessel meets navigation standards based on the water depth; if the location of the navigation aid vessel does not meet navigation standards, generating an optimal adjustment path to drive the navigation aid vessel to the recommended location. This invention utilizes flow velocity, temperature, and attitude angle to construct a correction term, improving the accuracy of water depth estimation. It combines BeiDou position information with reference coordinates to determine whether the navigation aid vessel deviates from navigation standards. Once deviated, path planning drives the navigation aid vessel to adjust its position, achieving integrated water level monitoring and navigation aid management.

Description

Technical Field

[0001] This invention relates to the field of water level information monitoring based on navigation marks, and more particularly to a method and apparatus for water level information monitoring based on the fusion of multiple intelligent sensors of navigation marks. Background Technology

[0002] In navigable waterways such as rivers, harbors, and lakes, accurate monitoring of water level information is crucial for ensuring navigation safety, guiding vessel passage, and optimizing scheduling and management. With the continuous development of intelligent navigation aids and Internet of Things (IoT) technologies, floating navigation aid platforms have gradually acquired capabilities such as data acquisition, edge computing, and remote communication, becoming an important carrier for the digitalization of water infrastructure. Especially in complex hydrodynamic environments, single sensors are easily affected by flow velocity, temperature differences, and wave disturbances, making it difficult to provide stable and accurate water level data. Therefore, integrating multi-source sensors, dynamically sensing environmental changes, and achieving intelligent judgment and path control has become an important research direction for improving the intelligence level of navigation aid systems.

[0003] In the prior art, patent CN111103038B proposes a method and system for monitoring hydrological information. This method primarily acquires hydrological information (water level, area) from measurement points, plots a water level-area time relationship curve, and inversely infers topographic information to estimate the water storage capacity of the reservoir area. When the water level or water storage capacity exceeds a preset threshold, it triggers the drainage system to regulate. This method emphasizes dynamic modeling of water level-area within a macroscopic water body, is suitable for water storage management scenarios in reservoirs or static lakes, has fixed measurement point locations, and a relatively stable water environment. It does not involve the movement or drift of monitoring nodes and lacks multi-dimensional environmental perception capabilities.

[0004] In addition, traditional water level monitoring systems mostly rely on radar or ultrasonic water level gauges deployed at fixed stations on the shore, or obtain hydrological information through manual inspections, remote sensing images and other means (such as CN106228579B, CN111142165A). However, they have defects such as high deployment rigidity, poor adaptability and weak data real-time performance. Especially under floating navigation platforms and complex flow conditions, it is difficult to achieve efficient and reliable water level sensing and regulation.

[0005] To address this issue, this patent proposes a water level monitoring method based on the fusion of multiple intelligent sensors from navigation marks. By fusing data from multiple sensors to perform water depth correction estimation, the accuracy and real-time performance of water level monitoring are improved. Summary of the Invention

[0006] This invention provides a water level monitoring method based on the fusion of multiple intelligent sensors for navigation aids, effectively solving the technical problems of insufficient accuracy, failure to detect position deviations in real time, difficulty in correcting the impact of hydrological anomalies, and lack of monitoring of equipment operating status in traditional water level monitoring methods. Traditional methods often rely on a single radar or pressure level gauge, which is easily affected by fluctuations, water flow disturbances, and attitude changes, resulting in large data errors and making it difficult to adapt to dynamic and complex aquatic environments. This application significantly improves the stability and accuracy of water level estimation by deploying multiple types of intelligent sensors (such as radar flow meters and temperature sensors), fusing multi-source data, and introducing water level correction terms based on physical factors such as temperature, flow velocity, and attitude angle. Simultaneously, this application possesses the ability to detect navigation aid vessel deviation and determine navigation standards, dynamically generating adjustment paths and controlling the navigation aid vessel to return to its mooring position by fusing water level information, thereby improving the stability and safety of navigation guidance. Furthermore, this application also solves the problems of invisible on-site equipment status and delayed fault diagnosis by achieving digitalization and visualization of water level information monitoring through intelligent sensor operating status reporting and full-process data uploading.

[0007] To achieve the above objectives, the present invention provides a method for monitoring water level information based on the fusion of multiple intelligent sensors of navigation marks, comprising the following steps:

[0008] S1: Deploy smart sensors on the hull of the navigation beacon to collect multi-sensor data and preprocess it to obtain preprocessed multi-sensor data;

[0009] S2: Use the preprocessed multi-sensor data to perform a correction estimate of the water depth based on the correction term, and obtain the corrected water depth;

[0010] S3: Perform position matching analysis between the pre-processed multi-sensor data of the buoy vessel's location and the buoy vessel's reference coordinates, calculate the drift distance, and determine whether the buoy vessel's location meets navigation standards based on the water depth.

[0011] S4: If the location of the buoy vessel does not meet the navigation standards, an optimal adjustment path is generated to drive the buoy vessel to the recommended location. The status information of the smart sensors, the preprocessed multi-sensor data, the corrected estimated water depth, drift distance, and the location information of the buoy vessel are uploaded to the management platform for visual management. The status information of the smart sensors includes the battery level and running time of the smart sensors.

[0012] As a further improvement of the present invention:

[0013] Optionally, data from multiple sensors is acquired and preprocessed, including:

[0014] The intelligent sensor includes a temperature sensor, a Beidou positioning module, a level gauge, and a radar flow meter. The temperature sensor is used to monitor the water temperature, the Beidou positioning module is used to locate the position of the navigation beacon, where the position of the navigation beacon is in latitude and longitude coordinates, the level gauge is used to monitor the water pressure, and the radar flow meter is used to monitor the water depth, the attitude angle of the navigation beacon, and the flow velocity and direction of the water.

[0015] The deployed smart sensors monitor the hydrological information of the buoy vessel's location at fixed time periods each day, obtaining daily multi-sensor data. This multi-sensor data is in the form of multi-dimensional data sequences, including water temperature data sequences, buoy vessel location data sequences, water pressure data sequences, water depth data sequences, buoy vessel attitude angle data sequences, water flow velocity, and flow direction sequences.

[0016] The data sequences in the multi-sensor data are filtered and interpolated sequentially, and the last data value of the data sequence is extracted to form the preprocessed multi-sensor data.

[0017] The preprocessed multi-sensor data consists of water temperature, buoy vessel location, water pressure, water depth, buoy vessel attitude angle, water velocity, and flow direction. The water temperature, buoy vessel location, water pressure, water depth, buoy vessel attitude angle, water velocity, and flow direction are, in order, the last data value in the filtered and interpolated data sequences of water temperature, buoy vessel location, water pressure, water depth, buoy vessel attitude angle, and water velocity and flow direction.

[0018] Optionally, the water depth is estimated using a correction term based on the preprocessed multi-sensor data, including:

[0019] Water depth is extracted from the preprocessed multi-sensor data. The system integrates water temperature, the attitude angle of the navigation mark vessel, and water flow velocity to generate correction terms, which are used to correct and estimate the extracted water depth. These correction terms include flow correction terms and height measurement deviation correction terms.

[0020] The formula for calculating the flow correction term is:

[0021] ;

[0022] in, This is a flow correction term constructed based on water temperature and flow velocity, where V represents the flow velocity. This indicates the standard water density at the set standard water temperature. Indicates water temperature, Indicates water temperature The density of the water below;

[0023] The formula for calculating the height measurement deviation correction term is:

[0024] ;

[0025] in, This indicates the height measurement deviation correction term, where h represents the installation height of the radar flow meter. These are all attitude angles of the buoy vessel hull from preprocessed multi-sensor data. These are, in order, the pitch angle and roll angle of the buoy vessel;

[0026] The flow correction term and the altimeter deviation correction term are weighted and processed to generate the correction term. Water depth in preprocessed multi-sensor data A corrected estimate is performed to obtain the corrected water depth. .

[0027] Optionally, a position matching analysis is performed between the installed position of the navigation aid vessel and the reference coordinates of the navigation aid vessel, and the drift distance is calculated, including;

[0028] Extracting the location of navigational aid vessels from preprocessed multi-sensor data And obtain the reference coordinates of the navigation mark hull. The elliptical area centered on the reference coordinates of the navigational aid vessel is set as the recommended navigation area, and the location of the navigational aid vessel is calculated. Deviation coefficient of whether it deviates from the recommended air traffic area As a result of the location matching analysis, among which ,like A value of 0 indicates that the navigation mark vessel is in position. If not deviating from the recommended airspace, A value of 1 indicates the location of the navigation aid vessel. Deviating from the recommended navigation area, including the location of the navigational aid vessel. Relative coordinates of the navigation beacon hull All coordinates are latitude and longitude coordinates;

[0029] The position of the navigational aid vessel was calculated using the Haversine formula. Relative coordinates of the navigation beacon hull The distance between them serves as the location of the navigational aid vessel. The drift distance.

[0030] Optionally, based on the water depth and the location matching analysis results, it is determined whether the location of the navigation aid vessel meets navigation standards, including:

[0031] Obtain the corrected water depth ,like The difference is greater than the preset minimum water depth, and the location matching analysis results are... Then determine the location of the navigation beacon vessel. It does not meet navigation standards, where H represents the critical safe water level. Specifically, H is set to 10 meters, and the preset minimum water depth difference is 5 meters.

[0032] Optionally, if the location of the buoy vessel does not meet navigation standards, an optimal adjustment path is generated, including:

[0033] If the location of the buoy vessel does not meet navigation standards, a recommended location corresponding to the buoy vessel's current location is selected from a set of candidate locations. This set of candidate locations consists of multiple buoy vessel locations suitable for temporary mooring, and the buoy vessel's mooring location is defined by latitude and longitude coordinates. The corresponding formula for selecting the recommended position is:

[0034] ;

[0035] in, Indicates the location of the navigational aid vessel. The corresponding recommended position Represents the set of candidate positions. , where p represents any mooring position of the beacon hull in the candidate position set. Indicates the mooring position of the navigational aid vessel. Location of navigation aid vessel The evaluation results of the evaluation index at the k-th position, where , The smaller the value, the more likely it is that the buoy vessel is moored. The better the performance of the evaluation metric at the k-th position, the better. Let the evaluation coefficient of the k-th position evaluation index be set. Specifically, set The values ​​are 0.4, 0.4, and 0.2 respectively.

[0036] Indicates from the set of candidate positions The location where the buoy hull moors with the best comprehensive evaluation result based on the location evaluation indicators will be selected as the buoy hull installation location. Corresponding recommended position ;

[0037] Indicates the mooring position of the navigational aid vessel. Location of navigation aid vessel The distance cost index characterizes the mooring position of the navigational aid vessel. Location of navigation aid vessel The distance between them Indicates the mooring position of the navigational aid vessel. Location of navigation aid vessel The water flow direction interference index characterizes the mooring position of the navigation aid vessel. Location of navigation aid vessel The difference between the azimuth angles and the direction of water flow. Indicates the mooring position of the navigational aid vessel. Location of navigation aid vessel The berthing popularity index represents the berthing position of the navigation mark vessel. The number of berthed navigation aid vessels;

[0038] The waters where the buoy is located are divided into multiple continuous square areas, and each square area is used as a grid. The grid where the buoy is located and the grid where the recommended location is located are obtained, which are used as the starting point and the target point, respectively. The optimal adjustment path between the starting point and the target point is obtained by using an improved A* search algorithm. The buoy is deployed in each of the grids.

[0039] Optionally, the improved A* search algorithm is used to solve for the optimal adjustment path between the starting point and the target point, including:

[0040] The improved A* search algorithm integrates water level information monitored by all navigational aids and introduces differences in water depth and current direction between adjacent grids to optimize the path cost function in the A* search algorithm, making the water level change of the optimal adjustment path smoother and safer. The water level information includes the corrected estimated water depth and current direction of each grid.

[0041] To address the above problems, the present invention also provides an electronic device, the electronic device comprising:

[0042] Memory, storing at least one instruction;

[0043] Communication interfaces enable communication between electronic devices; and

[0044] The processor executes the instructions stored in the memory to implement the above-described method for monitoring water level information based on the fusion of multiple intelligent sensors of navigation marks.

[0045] To address the aforementioned problems, the present invention also provides a computer-readable storage medium storing at least one instruction, which is executed by a processor in an electronic device to implement the above-described method for monitoring water level information based on the fusion of multiple intelligent sensors of navigation marks.

[0046] Compared with existing technologies, this invention proposes a water level information monitoring method based on the fusion of multiple intelligent sensors from navigation marks. This technology has the following beneficial effects:

[0047] First, the flow correction term and the height deviation correction term significantly improve the measurement accuracy and system stability of radar flowmeters in dynamic hydrological environments. In natural water bodies, the influence of water temperature on liquid density can cause slight uplift or subsidence of the radar wave reflection interface, especially in areas with high flow velocity or significant temperature differences. The flow correction term can effectively compensate for the deviation of the water level system caused by thermal expansion and contraction and changes in fluid viscosity, enhancing the seasonal consistency and lateral comparability of measurement data. Furthermore, the attitude angle changes of the buoy vessel under wave disturbances can cause the radar flowmeter's ranging direction to deviate from the vertical axis, resulting in an underestimation of the actual measurement value. Through a cosine correction model based on attitude angle calculation, real-time dynamic compensation for radar antenna height projection errors is achieved, effectively eliminating high-frequency noise interference from short-period fluctuations on water depth data, significantly improving the continuity and smoothness of the water depth curve, especially in scenarios with large waves and frequent surface disturbances, where ranging errors can be controlled within the centimeter level. These two correction mechanisms work synergistically to provide the buoy water depth measurement system with multi-factor dynamic stability enhancement capabilities for real-world water environments.

[0048] Meanwhile, regarding the recommended location selection method, the distance cost term is based on the Haversine distance metric, which can accurately assess the geographical offset between the buoy vessel's setting location and its mooring location, ensuring that the recommended location will not cause large-scale channel deviations; the current direction interference cost is based on a similarity function constructed according to the angle between the main current direction and the candidate location direction, which is particularly effective in turbulent or complex hydrological areas, reducing the drift or rotation phenomenon caused by the impact of current on the setting point; the mooring heat cost can intelligently avoid high-frequency navigation areas, reducing the buoy body deviation caused by human interference or collisions. Attached Figure Description

[0049] Figure 1 This is a flowchart illustrating a method for monitoring water level information based on the fusion of multiple intelligent sensors from navigation marks, provided in an embodiment of the present invention.

[0050] Figure 2 A table showing the correspondence between instructions and hydrological parameters for an HQ1601 radar flowmeter, provided as an embodiment of the present invention;

[0051] Figure 3 This is a schematic diagram illustrating a radar flow meter deployment example according to an embodiment of the present invention;

[0052] Figure 4 as well as Figure 5 A visual management diagram for water level information monitoring provided in an embodiment of the present invention;

[0053] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0054] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0055] This application provides a method for monitoring water level information based on the fusion of multiple intelligent sensors using navigational aids. The executing entity of this method includes, but is not limited to, at least one electronic device that can be configured to execute the method provided in this application, such as a server or a terminal. In other words, the method can be executed by software or hardware installed on a terminal device or a server device, and the software may be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0056] Reference Figure 1 Embodiment 1 of the present invention is as follows:

[0057] S1: Deploy smart sensors on the hull of the navigation beacon to collect multi-sensor data and preprocess it to obtain preprocessed multi-sensor data.

[0058] Collect and preprocess data from multiple sensors, including:

[0059] The intelligent sensor includes a temperature sensor, a Beidou positioning module, a level gauge, and a radar flow meter. The temperature sensor is used to monitor the water temperature, the Beidou positioning module is used to locate the position of the navigation beacon, where the position of the navigation beacon is in latitude and longitude coordinates, the level gauge is used to monitor the water pressure, and the radar flow meter is used to monitor the water depth, the attitude angle of the navigation beacon, and the flow velocity and direction of the water.

[0060] Specifically, the level gauge is an HQ-P1000 pressure level gauge, which uses a high-performance diffused silicon pressure sensor as the measuring element to measure the static pressure of the liquid, which is proportional to the liquid depth, and converts it into a standard current, voltage or digital signal output through a signal conditioning circuit to achieve accurate measurement of liquid pressure. The HQ-P1000 pressure level gauge uses the MODBUS communication protocol.

[0061] The radar flow meter is an HQ1601 radar flow meter. It utilizes MODBUS-RTU commands based on the MODBUS communication protocol to control the radar flow meter and collect information such as water depth, the attitude angle of the buoy vessel, water velocity, and flow direction. Figure 2 This is a table showing the correspondence between commands and hydrological parameters for the HQ1601 radar flow meter;

[0062] It should be noted that when installing a radar flow meter on the inner side of the vessel near the river channel, the sampling surface should face the direction of the flow velocity. The level on top of the radar flow meter should be installed horizontally. Data transmission is achieved through the ZB-CT310S navigation beacon telemetry and control terminal and the 485 expansion interface, as per [reference needed]. Figure 3 The diagram shows a sample deployment of a radar flow meter. The temperature sensor and level gauge are deployed in the submerged part of the water body, and the Beidou positioning module is deployed on the top of the buoy hull.

[0063] The deployed smart sensors monitor the hydrological information of the buoy vessel's location at fixed time periods each day, obtaining daily multi-sensor data. This multi-sensor data is in the form of multi-dimensional data sequences, including water temperature data sequences, buoy vessel position data sequences, water pressure data sequences, water depth data sequences, buoy vessel attitude angle data sequences, water flow velocity, and flow direction sequences. As an embodiment of this application, the fixed time period for monitoring hydrological information is set from 9:45 AM to 9:50 AM daily.

[0064] The data sequences in the multi-sensor data are sequentially filtered and interpolated for completion, and the last data value of the data sequence is extracted to form the preprocessed multi-sensor data. Specifically, the filtering method is median filtering based on a sliding window, and the interpolation completion method includes linear interpolation, Lagrange interpolation, and cubic spline interpolation. Lagrange interpolation is suitable for the position data sequence of the buoy, cubic spline interpolation is suitable for the attitude angle data sequence of the buoy, and linear interpolation is suitable for other data sequences besides the position data sequence and attitude angle data sequence.

[0065] The preprocessed multi-sensor data consists of water temperature, buoy vessel location, water pressure, water depth, buoy vessel attitude angle, water velocity, and flow direction. Specifically, the water temperature, buoy vessel location, water pressure, water depth, buoy vessel attitude angle, water velocity, and flow direction are, in order, the last data value in the filtered and interpolated sequences of water temperature, buoy vessel location, water pressure, water depth, buoy vessel attitude angle, and water velocity and flow direction. This preprocessed multi-sensor data is used as the daily transmitted water depth, water temperature, and flow velocity data to determine whether the buoy vessel location meets navigation standards. The buoy vessel's position is adjusted promptly to prevent accidental grounding of passing vessels due to improper buoy installation, ensuring smooth navigation.

[0066] S2: Use the preprocessed multi-sensor data to perform a correction estimate of the water depth based on the correction term, and obtain the corrected water depth.

[0067] Water depth is estimated using preprocessed multi-sensor data based on correction terms, including:

[0068] Water depth is extracted from the preprocessed multi-sensor data. The system integrates water temperature, the attitude angle of the navigation mark vessel, and water flow velocity to generate correction terms, which are used to correct and estimate the extracted water depth. These correction terms include flow correction terms and height measurement deviation correction terms.

[0069] The formula for calculating the flow correction term is:

[0070] ;

[0071] in, This is a flow correction term constructed based on water temperature and flow velocity, where V represents the flow velocity. This indicates the standard water density at the set standard water temperature. Indicates water temperature, Indicates water temperature The water density at that temperature; it should be noted that the standard water temperature was set at 4 degrees Celsius, and the standard water density at 4 degrees Celsius is approximately 10,000 kg per cubic meter;

[0072] Since changes in water temperature significantly affect the density of water, they indirectly affect the velocity distribution of the water body and the stability of water surface fluctuations measured by the radar flowmeter. For example, at higher temperatures, the viscosity of the water decreases, turbulence intensifies, and the amplitude of water surface fluctuations increases, leading to instability in the measurement of water depth by the radar flowmeter. Flow disturbance correction is required. Therefore, in the flow correction proposed in this application, thermal expansion and contraction and viscosity changes are taken into account. When water expands due to heat, the mass per unit volume decreases (i.e., the density decreases), but the overall volume increases. Therefore, under the same water volume, the water depth may increase slightly.

[0073] The formula for calculating the height measurement deviation correction term is:

[0074] ;

[0075] in, This indicates the height measurement deviation correction term, where h represents the installation height of the radar flow meter. These are all attitude angles of the buoy vessel hull from preprocessed multi-sensor data. These are, in order, the pitch angle and roll angle of the buoy vessel;

[0076] Because the hull of the navigation aid vessel is often slightly tilted due to the influence of waves, currents, or wind, such tilting will cause changes in the emission angle of the radar flow meter mounted on it, which in turn affects the vertical accuracy of water depth measurement. Therefore, it is necessary to correct the vertical accuracy by taking into account the attitude angle of the navigation aid vessel.

[0077] The flow correction term and the altimeter deviation correction term are weighted and processed to generate the correction term. Water depth in preprocessed multi-sensor data A corrected estimate is performed to obtain the corrected water depth. .

[0078] It should be noted that the weighting of the flow correction term represents the amplification effect of unit density change on measurement interference, depending on the radar height, detection angle, and floating body motion characteristics (the setting range is 0.3-0.8), while the weighting of the height deviation correction term is set within the range of 0.2-0.4.

[0079] S3: Perform position matching analysis between the pre-processed multi-sensor data and the reference coordinates of the buoy vessel, calculate the drift distance, and determine whether the buoy vessel's position meets navigation standards based on the water depth.

[0080] The location of the navigation aid vessel is matched with its reference coordinates, and the drift distance is calculated, including:

[0081] Extracting the location of navigational aid vessels from preprocessed multi-sensor data And obtain the reference coordinates of the navigation mark hull. The elliptical area centered on the reference coordinates of the navigational aid vessel is set as the recommended navigation area, and the location of the navigational aid vessel is calculated. Deviation coefficient of whether it deviates from the recommended air traffic area As a result of the location matching analysis, among which ,like A value of 0 indicates that the navigation mark vessel is in position. If not deviating from the recommended airspace, A value of 1 indicates the location of the navigation aid vessel. Deviating from the recommended navigation area, including the location of the navigational aid vessel. Relative coordinates of the navigation beacon hull All coordinates are latitude and longitude coordinates; specifically, the formula for calculating the deviation coefficient is:

[0082] ;

[0083] in, The ellipse parameters represent the elliptical region (default a is 10 meters, b is 5 meters), where a represents the semi-major axis of the elliptical region in the precision direction (unit: meters), and b represents the semi-major axis of the elliptical region in the latitude direction (unit: meters).

[0084] Represents a sign function, if ,but ,otherwise ;

[0085] The position of the navigational aid vessel was calculated using the Haversine formula. Relative coordinates of the navigation beacon hull The distance between them serves as the location of the navigational aid vessel. The drift distance.

[0086] Based on the water depth and the location matching analysis results, determine whether the location of the navigation aid vessel meets navigation standards, including:

[0087] Obtain the corrected water depth ,like The difference is greater than the preset minimum water depth, and the location matching analysis results are... Then determine the location of the navigation beacon vessel. It does not meet navigation standards, where H represents the critical safe water level. Specifically, H is set to 10 meters, and the preset minimum water depth difference is 5 meters.

[0088] S4: If the location of the buoy vessel does not meet the navigation standards, an optimal adjustment path is generated to drive the buoy vessel to the recommended location. The status information of the smart sensors, the preprocessed multi-sensor data, the corrected estimated water depth, drift distance, and the location information of the buoy vessel are uploaded to the management platform for visual management.

[0089] If the location of the buoy vessel does not meet navigation standards, an optimal adjustment path is generated, including:

[0090] If the location of the buoy vessel does not meet navigation standards, a recommended location corresponding to the buoy vessel's current location is selected from a set of candidate locations. This set of candidate locations consists of multiple buoy vessel locations suitable for temporary mooring, and the buoy vessel's mooring location is defined by latitude and longitude coordinates. The corresponding formula for selecting the recommended position is:

[0091] ;

[0092] ;

[0093] ;

[0094] ;

[0095] in, Indicates the location of the navigational aid vessel. The corresponding recommended position Represents the set of candidate positions. , where p represents any mooring position of the beacon hull in the candidate position set. Indicates the mooring position of the navigational aid vessel. Location of navigation aid vessel The evaluation results of the evaluation index at the k-th position, where , The smaller the value, the more likely it is that the buoy vessel is moored. The better the performance of the evaluation metric at the k-th position, the better. Let the evaluation coefficient of the k-th position evaluation index be set. Specifically, set The values ​​are 0.4, 0.4, and 0.2 respectively.

[0096] Indicates from the set of candidate positions The location where the buoy hull moors with the best comprehensive evaluation result based on the location evaluation indicators will be selected as the buoy hull installation location. Corresponding recommended position ;

[0097] Indicates the mooring position of the navigational aid vessel. Location of navigation aid vessel The distance cost index characterizes the mooring position of the navigational aid vessel. Location of navigation aid vessel The distance between them Indicates the mooring position of the navigational aid vessel. Location of navigation aid vessel The water flow direction interference index characterizes the mooring position of the navigation aid vessel. Location of navigation aid vessel The difference between the azimuth angles and the direction of water flow. Indicates the mooring position of the navigational aid vessel. Location of navigation aid vessel The berthing popularity index represents the berthing position of the navigation mark vessel. The number of berthed navigation aid vessels;

[0098] The waters where the buoy is located are divided into multiple continuous square areas, and each square area is used as a grid. The grid where the buoy is located and the grid where the recommended location is located are obtained, which are used as the starting point and the target point, respectively. The optimal adjustment path between the starting point and the target point is obtained by using an improved A* search algorithm. The buoy is deployed in each of the grids.

[0099] This indicates the use of the Haversine formula to calculate the mooring position of the navigational aid vessel. Location of navigation aid vessel The distance between them This indicates the maximum distance the ship can travel (default is 60km).

[0100] This indicates the direction of water flow in the preprocessed multi-sensor data. Indicates the mooring position of the navigational aid vessel. Location of navigation aid vessel The azimuth angle between them;

[0101] Indicates the mooring position of the navigational aid vessel. The number of navigational aid vessels already moored at the location. Indicates the maximum number of vessels that can be moored at the designated location (preset 20).

[0102] The proposed location selection method, when faced with a buoy vessel's current location not meeting navigation standards, can intelligently select the optimal location from multiple available candidate points, thus improving the scientific rigor and automation of buoy deployment. By constructing a weighted cost function based on multi-dimensional evaluation indicators, it comprehensively considers three key factors: distance cost from the current location, water flow direction interference cost, and berthing popularity cost, significantly enhancing the environmental adaptability and navigation safety assurance capabilities of berthing location decisions.

[0103] Specifically, the distance cost, based on the Haversine distance metric, can accurately assess the geographical offset between the buoy's placement location and its mooring location, ensuring that the recommended location does not cause large-scale channel deviations; the current direction interference cost constructs a similarity function based on the angle between the main current direction and the candidate location direction, which is particularly effective in turbulent or complex hydrological areas, reducing the drift or rotation phenomenon caused by the impact of current on the placement point; and the mooring heat cost can intelligently avoid high-frequency navigation areas, reducing the buoy's deviation caused by human interference or collisions.

[0104] The improved A* search algorithm is used to find the optimal adjustment path between the starting point and the target point, including:

[0105] The improved A* search algorithm integrates water level information monitored by all navigational aids, introduces differences in water depth and current direction between adjacent grids, and optimizes the path cost function in the A* search algorithm. This makes the water level change of the optimal adjustment path smoother and safer. The water level information includes the corrected estimated water depth and current direction of each grid. The optimization formula for the path cost between adjacent grids is as follows:

[0106] ;

[0107] ;

[0108] ;

[0109] in, This represents the path cost between the nth and mth cells, where the nth and mth cells are adjacent cells. Q represents the total number of grid cells, and ;

[0110] This represents the distance between the center of the nth grid cell and the center of the mth grid cell. This represents the difference in water depth between the nth and mth grid cells. This represents the difference in water flow direction between the nth and mth grid cells; specifically, the distance... The calculation method is the Haversine distance metric;

[0111] The corrected water depth for the nth grid cell. H represents the corrected estimated water depth of the m-th grid cell, and H represents the critical safe water level.

[0112] This represents the azimuth angle from the center of the nth grid cell to the center of the mth grid cell. This indicates the direction of water flow from the center of the nth grid cell to the center of the mth grid cell;

[0113] This path cost function introduces water level difference and current disturbance as environmental constraint variables into the A* algorithm, effectively breaking through the single perspective of traditional A* algorithms that only use Euclidean or Manhattan distance as the path cost. It exhibits significant advantages in perception-driven and dynamic adaptation. In traditional methods, path planning often ignores the non-uniformity of the hydrological environment, leading to problems such as unstable drift, increased energy consumption, and even navigation interference when navigational aids are maneuvering in high-water fluctuation zones or counter-current zones. This method, by introducing a water level change term... This significantly improves the ability to avoid waterlogged areas or shallow waters; differences in water flow direction This method enhances the natural alignment of the path with the downstream direction, reducing propulsion energy consumption and the frequency of anchor adjustments. Field results show that this method reduces the average repositioning time by 18% compared to the traditional A* algorithm in various typical estuarine waters, making it suitable for adjusting the position of highly reliable buoy vessels under complex hydrological conditions.

[0114] The system uploads the status information of the smart sensors, preprocessed multi-sensor data, corrected estimates of water depth and drift distance, and the position information of the navigation beacon to the management platform for further processing. Figure 4 as well as Figure 5 The visualization management shown includes smart sensor status information such as smart sensor battery level and runtime.

[0115] Example 2:

[0116] A water level information monitoring device includes a data acquisition device, a water depth correction module, and a navigation management module.

[0117] The data acquisition device is used to deploy smart sensors on the hull of the navigation beacon, collect multi-sensor data and preprocess it to obtain preprocessed multi-sensor data.

[0118] The water depth correction module is used to perform a correction estimate of the water depth based on a correction term using preprocessed multi-sensor data to obtain the corrected water depth.

[0119] The navigation management module is used to perform position matching analysis between the pre-processed multi-sensor data of the buoy vessel's location and the buoy vessel's reference coordinates, calculate the drift distance, and determine whether the buoy vessel's location meets the navigation standards based on the water depth. If the buoy vessel's location does not meet the navigation standards, an optimal adjustment path is generated to drive the buoy vessel to the recommended location. The module also uploads the smart sensor status information, pre-processed multi-sensor data, corrected estimated water depth, drift distance, and buoy vessel location information to the management platform for visual management.

[0120] It should be understood that the embodiments described are for illustrative purposes only and are not limited to this structure in the scope of the patent application.

[0121] It should be noted that the sequence numbers of the above embodiments of the present invention are merely for descriptive purposes and do not represent the superiority or inferiority of the embodiments. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, apparatus, article, or method. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0122] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0123] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A water level information monitoring method based on beacon-based multi-intelligent sensor fusion, characterized in that, The method includes: S1: Deploy smart sensors on the hull of the navigation beacon to collect multi-sensor data and preprocess it to obtain preprocessed multi-sensor data; S2: Use the preprocessed multi-sensor data to perform a correction estimate of the water depth based on the correction term, and obtain the corrected water depth; S3: Perform position matching analysis between the pre-processed multi-sensor data and the reference coordinates of the buoy vessel, calculate the drift distance, and combine the corrected estimated water depth to determine whether the buoy vessel's position meets the navigation standards. S4: If the location of the buoy vessel does not meet the navigation standards, the optimal adjustment path is generated to drive the buoy vessel to the recommended location. The status information of the smart sensors, the preprocessed multi-sensor data, the corrected estimated water depth, drift distance and the location information of the buoy vessel are uploaded to the management platform for visual management. The status information of the smart sensors includes the power of the smart sensors and the running time. If the navigation mark ship embodiment setting position does not meet the navigation standard, a recommended position corresponding to the navigation mark ship embodiment setting position is selected from a candidate position set, wherein the candidate position set is composed of a plurality of navigation mark ship docking positions available for temporary docking, the navigation mark ship docking position is a latitude and longitude coordinate, and the navigation mark ship embodiment setting position The corresponding recommended position selection formula is: ； wherein, represents a candidate position set, corresponding recommended position, represents a candidate position set, , p represents any position of the candidate position set, represents a position of the candidate position set, to the position of the candidate position set, evaluation result of the kth position evaluation index, wherein , the smaller, the better the performance of the position of the candidate position set, the kth position evaluation index, is the evaluation coefficient of the kth position evaluation index, set ; Indicates from the set of candidate positions The location where the buoy hull moors with the best comprehensive evaluation result based on the location evaluation indicators will be selected as the buoy hull installation location. Corresponding recommended position ; Indicates the mooring position of the navigational aid vessel. Location of navigation aid vessel The distance cost index characterizes the mooring position of the navigational aid vessel. Location of navigation aid vessel The distance between them Indicates the mooring position of the navigational aid vessel. Location of navigation aid vessel The water flow direction interference index characterizes the mooring position of the navigation aid vessel. Location of navigation aid vessel The difference between the azimuth angles and the direction of water flow. Indicates the mooring position of the navigational aid vessel. Location of navigation aid vessel The berthing popularity index represents the berthing position of the navigation mark vessel. The number of berthed navigation aid vessels; The waters where the buoy is located are divided into multiple continuous square areas, and each square area is used as a grid. The grid where the buoy is located and the grid where the recommended location is located are obtained, which are used as the starting point and the target point, respectively. The optimal adjustment path between the starting point and the target point is obtained by using an improved A* search algorithm. The buoy is deployed in each of the grids.

2. The water level information monitoring method based on multi-intelligent sensor fusion of navigation marks as described in claim 1, characterized in that, Collect and preprocess data from multiple sensors, including: The intelligent sensor includes a temperature sensor, a Beidou positioning module, a level gauge, and a radar flow meter. The temperature sensor is used to monitor the water temperature, the Beidou positioning module is used to locate the position of the navigation beacon, where the position of the navigation beacon is in latitude and longitude coordinates, the level gauge is used to monitor the water pressure, and the radar flow meter is used to monitor the water depth, the attitude angle of the navigation beacon, and the flow velocity and direction of the water. The deployed smart sensors monitor the hydrological information of the buoy vessel's location at fixed time periods each day, obtaining daily multi-sensor data. This multi-sensor data is in the form of multi-dimensional data sequences, including water temperature data sequences, buoy vessel location data sequences, water pressure data sequences, water depth data sequences, buoy vessel attitude angle data sequences, water flow velocity, and flow direction sequences. The data sequences in the multi-sensor data are filtered and interpolated sequentially, and the last data value of the data sequence is extracted to form the preprocessed multi-sensor data. The preprocessed multi-sensor data consists of water temperature, buoy vessel location, water pressure, water depth, buoy vessel attitude angle, water velocity, and flow direction. The water temperature, buoy vessel location, water pressure, water depth, buoy vessel attitude angle, water velocity, and flow direction are, in order, the last data value in the filtered and interpolated data sequences of water temperature, buoy vessel location, water pressure, water depth, buoy vessel attitude angle, and water velocity and flow direction.

3. The water level information monitoring method based on the fusion of multiple intelligent sensors of navigation marks as described in claim 2, characterized in that, Water depth is estimated using preprocessed multi-sensor data based on correction terms, including: Water depth is extracted from the preprocessed multi-sensor data. The system integrates water temperature, the attitude angle of the navigation mark vessel, and water flow velocity to generate correction terms, which are used to correct and estimate the extracted water depth. The correction terms include flow correction terms and height measurement deviation correction terms. The formula for calculating the flow correction term is: ； in, This is a flow correction term constructed based on water temperature and flow velocity, where V represents the flow velocity. This indicates the standard water density at the set standard water temperature. Indicates water temperature, Indicates water temperature The density of the water below; The formula for calculating the height measurement deviation correction term is as follows: ； in, This indicates the height measurement deviation correction term, where h represents the installation height of the radar flow meter. These are all attitude angles of the buoy vessel hull from preprocessed multi-sensor data. These are, in order, the pitch angle and roll angle of the buoy vessel; The flow correction term and the altimeter deviation correction term are weighted and processed to generate the correction term. Water depth in preprocessed multi-sensor data A corrected estimate is performed to obtain the corrected water depth. .

4. The water level information monitoring method based on multi-intelligent sensor fusion of navigation marks as described in claim 1, characterized in that, The location of the navigation aid vessel is matched with its reference coordinates, and the drift distance is calculated, including: Extracting the location of navigational aid vessels from preprocessed multi-sensor data And obtain the reference coordinates of the navigation beacon hull. The elliptical area centered on the reference coordinates of the navigational aid vessel is set as the recommended navigation area, and the location of the navigational aid vessel is calculated. Deviation coefficient of whether it deviates from the recommended air traffic area As a result of the location matching analysis, among which ,like A value of 0 indicates that the navigation mark vessel is in position. If it does not deviate from the recommended airspace, A value of 1 indicates the location of the navigation aid vessel. Deviating from the recommended navigation area, including the location of the navigational aid vessel. Relative coordinates of the navigation beacon hull All coordinates are latitude and longitude coordinates; The position of the navigational aid vessel was calculated using the Haversine formula. Relative coordinates of the navigation beacon hull The distance between them serves as the location of the navigational aid vessel. The drift distance.

5. The water level information monitoring method based on the fusion of multiple intelligent sensors of navigation marks as described in claim 4, characterized in that, Based on the water depth and the location matching analysis results, determine whether the location of the navigation aid vessel meets navigation standards, including: Obtain the corrected water depth ,like The difference is greater than the preset minimum water depth, and the location matching analysis results are... Then determine the location of the navigation beacon vessel. It does not meet navigation standards, where H represents the critical safe water level.

6. The water level information monitoring method based on multi-intelligent sensor fusion of navigation marks as described in claim 1, characterized in that, The improved A* search algorithm is used to find the optimal adjustment path between the starting point and the target point, including: The improved A* search algorithm integrates water level information monitored by all navigational aids and introduces differences in water depth and current direction between adjacent grids to optimize the path cost function in the A* search algorithm, making the water level change of the optimal adjustment path smoother and safer. The water level information includes the corrected estimated water depth and current direction of each grid.

7. A water level information monitoring device, characterized in that, The water level monitoring device includes a data acquisition device, a water depth correction module, and a navigation management module. The data acquisition device is used to deploy smart sensors on the hull of the navigation beacon, collect multi-sensor data and preprocess it to obtain preprocessed multi-sensor data. The water depth correction module is used to perform a correction estimate of the water depth based on a correction term using preprocessed multi-sensor data to obtain the corrected water depth. The navigation management module is used to perform position matching analysis between the pre-processed multi-sensor data of the buoy vessel's location and the buoy vessel's reference coordinates, calculate the drift distance, and determine whether the buoy vessel's location meets the navigation standards based on the water depth. If the buoy vessel's location does not meet the navigation standards, an optimal adjustment path is generated to drive the buoy vessel to the recommended location. The module also uploads the smart sensor status information, pre-processed multi-sensor data, corrected estimated water depth, drift distance, and buoy vessel location information to the management platform for visual management. To achieve the water level information monitoring method based on the fusion of multiple intelligent sensors for navigation marks as described in any one of claims 1-6.

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