4203 results about "Water area" patented technology

The invention relates to the technical field of water photovoltaic flexible support pile foundation construction data processing, in particular to a process for a water photovoltaic flexible support pile foundation, which comprises the following steps: fusing meteorological, hydrological and pile deformation multi-source data through a dynamic weight distribution mechanism, and generating a space-time associated dynamic load spectrum by adopting wavelet packet transformation and entropy feature extraction; a collaborative analysis architecture of a finite element simulation and machine learning agent model is constructed, an anchoring depth mapping relation and a real-time design parameter adjustment instruction are output in parallel, and macroscopic fluid parameters and microscopic material degradation characteristics are associated through a data cascade interaction correction mechanism; full-life-cycle data version management is achieved through the block chain technology, and finite element model re-calibration and protection strategy dynamic updating are triggered. The dynamic load response analysis precision and long-term stability of the pile foundation are improved, and the deformation coordination requirement of the flexible support in the complex water area environment is met.

The invention relates to the field of water quality pollution traceability, in particular to a water quality pollution traceability analysis method and system based on an intelligent unmanned ship. The method comprises the following steps: acquiring a multi-mode network monitoring parameter of a real-time position based on a multi-mode communication module, performing a dynamic multi-mode network intelligent switching decision, and constructing an intelligent multi-mode network switching strategy; real-time water area multi-dimensional water quality monitoring parameters of the intelligent unmanned ship are obtained according to the multi-dimensional water quality sensor, abnormal pollution parameter change detection is carried out, and abnormal change pollution parameters are marked; performing reverse water quality spatial change analysis and gradient descent iterative traceability calculation on the abnormal change pollution parameters to generate a potential pollution diffusion path; and performing intelligent traceability cruise according to the potential pollution diffusion path, and tracking and positioning the polluted water source to obtain a polluted water source point. According to the invention, efficient and accurate water quality pollution traceability is realized.

The embodiment of the invention provides a BIM-based hydraulic engineering digital management method and system. The method comprises the following steps: performing multi-dimensional monitoring system deployment on a to-be-monitored area to obtain multi-dimensional hydrological data; constructing a first BIM based on the topographic data of the to-be-monitored area, the real-time work area image, the hydraulic engineering construction information of the design stage and the equipment deployment information; fusing the multi-dimensional hydrological data, the water area change data, the construction progress data and the operation and maintenance monitoring data into the first BIM, and constructing a second BIM including a full life cycle; and through the equipment characteristic curve and the water conservancy project physical model, in combination with the multi-dimensional hydrological data and the water area change data, predicting a water conservancy project structure change trend and potential risk factors in the second BIM, fusing the water conservancy project structure change trend and the potential risk factors into the second BIM, and displaying an implementation effect and improvement suggestions in real time in the second BIM. A user is assisted to realize hydraulic engineering digital management, and the engineering management efficiency is improved.

The invention provides a water area safety patrol method and system based on multi-modal feature analysis, and relates to the technical field of image recognition. The method comprises the steps of collecting a visible light image, an infrared image and laser radar data corresponding to a target water area, and performing time synchronization and space alignment to obtain visible light alignment data, infrared alignment data and laser radar alignment data; in response to the condition that the definition of the visible light image is higher than a quality reference value, carrying out edge detection and region segmentation processing on the visible light alignment data, and generating a candidate region set containing a target contour; for the infrared alignment data corresponding to the candidate region set, screening out a thermal anomaly region from the candidate region set; and carrying out three-dimensional space feature extraction and motion state feature extraction on the laser radar alignment data corresponding to the thermal anomaly region, and carrying out anomaly identification on the thermal anomaly region. According to the scheme, high-precision safety patrol of the target water area in various environments can be realized.

The invention discloses an unmanned ship navigation path obstacle avoidance method and system, and particularly relates to the technical field of unmanned ships. The method comprises the following steps: acquiring information of a fixed obstacle, a dynamic target and environmental factors, predicting the motion track of the dynamic target by using a deep learning algorithm, improving the track prediction accuracy, calculating an optimal obstacle avoidance path in real time based on a Dijkstra algorithm in combination with the navigation state of the unmanned ship and hydrodynamic environmental factors, and obtaining the optimal obstacle avoidance path. The trajectory prediction deviation is evaluated through a hydrodynamic disturbance factor and a motion inertia deviation factor; when the deviation exceeds a threshold value, the system adaptively adjusts a path, preferentially adopts a minimum steering angle strategy and an energy consumption optimization strategy, ensures the navigation stability of the unmanned ship, and meanwhile, continuously optimizes the strategy in combination with historical obstacle avoidance data, so that the intelligent obstacle avoidance capability, the navigation stability and the energy consumption efficiency of the unmanned ship in a complex dynamic water area can be improved; the system is suitable for application scenes such as water area patrol, environment monitoring and ocean detection.

The invention discloses a multi-temporal-spatial-scale cyanobacterial bloom early warning method for middle and large lake and reservoir water areas, and belongs to the field of cyanobacterial bloom prediction and risk monitoring. The method comprises the following steps: generating a pixel-level FAI index based on target water area remote sensing data, resampling meteorological data into a pixel level, then constructing a spatial-temporal distribution data set, training an Autoformer-ST-GNN time sequence model to realize FAI index prediction, dividing cyanobacterial bloom levels according to the FAI index prediction, and obtaining a global change trend; water quality monitoring points are arranged in key areas to collect data, historical water quality and meteorological data are utilized to train a DMC-PatchTST model fused with a blue-green algae migration period, and multi-time-scale prediction of the density of blue-green algae at the monitoring points is achieved; and finally, combining the global trend with a monitoring point prediction result to construct a space-time multi-scale cyanobacterial bloom comprehensive early warning system. According to the method, multi-source data and multiple models are fused, so that cyanobacterial bloom time-space multi-scale comprehensive early warning is realized, and the method is accurate and comprehensive.

Provided is a detection method of spaceborne GNSS-R original intermediate frequency coherent reflection signals in ocean, polar and inland water areas, including: acquiring spaceborne GNSS-R original intermediate frequency signal data of TDS-1 or CYGNSS and preprocessing the data; selecting coherent detection feature engineering; setting data labels of different scenes and coherent and incoherent reflected signals; dividing a training set and a test set; and training and testing a multimode-oriented hybrid model for coherent and incoherent detection and classification of spaceborne GNSS-R signals, using the training set to train a model, applying a trained detection model to a test data set, and comparing and evaluating obtained detection results with a classical coherent detection algorithm.

The invention provides a wetland vegetation intelligent monitoring method and system based on multi-dimensional data fusion, and the method comprises the following steps: collecting a water area unmanned plane image of a wetland water area through a monitoring unmanned plane, and obtaining a water area remote sensing image of the wetland water area through a remote sensing satellite; the water area unmanned aerial vehicle image and the water area remote sensing image are fused into a water area fusion image, the submerged vegetation distribution condition is recognized, and a submerged vegetation monitoring route is planned; the underwater monitoring robot is controlled to continuously detect a wetland water area, and a submerged vegetation standard image and a submerged vegetation multispectral image are collected at the same time; performing image enhancement on the submerged vegetation standard image through a submerged vegetation identification model, and identifying and marking target submerged vegetation from the submerged vegetation enhanced image; selecting a target multispectral region containing target submerged vegetation from the submerged vegetation multispectral image; and vegetation feature data of the target submerged vegetation are calculated based on the target multispectral region. According to the invention, related data of submerged vegetation can be continuously and accurately monitored.

The invention relates to a method, device and product for monitoring river flow in real time, and the method comprises the steps: obtaining tower footing video data of a target river reach which comprises a target section and a pier buoy; inputting the tower footing video data into a water level and flow velocity identification model, and obtaining the water level elevation and the surface flow velocity of at least part of the cross section in the target section through the water level and flow velocity identification model; inputting the water surface elevation into a three-dimensional digital river channel model to obtain a section water passing area of a corresponding cross section; and obtaining the cross section flow of the target cross section according to the cross section water passing area and the surface flow velocity. According to the embodiment of the invention, automatic flow estimation based on the tower footing video can be realized, the manual measurement cost is reduced, the spatial-temporal resolution of flow monitoring is improved, and the method can be used for large-range river monitoring, especially in areas lacking hydrometric stations, and can provide data support for drainage basin water resource management and flood prevention early warning.

The invention provides a water quality heavy metal pollution detection method and system based on Raman spectrum. The method comprises the following steps: collecting a water sample through a water area to be detected, and scanning the water sample by adopting a Raman spectrometer to generate an original Raman spectrogram; based on the original Raman spectrogram, using a wavelet transform algorithm to perform de-noising processing, reducing background interference, using a principal component analysis technology to extract characteristic peaks, and generating a heavy metal preliminary prediction result; based on the preliminary heavy metal prediction result, constructing a heavy metal concentration prediction model by applying a partial least squares regression algorithm, performing quantitative analysis, evaluating prediction accuracy by adopting a cross validation technology, and generating a heavy metal actual concentration result; and based on the actual heavy metal concentration result, comparing with a water quality safety standard, evaluating a pollution condition, and generating a water quality heavy metal pollution detection report. The technical scheme provided by the invention has high sensitivity, can efficiently denoise and accurately predict, and provides a scientific means for monitoring and treating heavy metal pollution of water.

The invention relates to the technical field of water quality monitoring, in particular to a water quality monitoring analysis method and system of a water conservancy system. The method comprises the following steps: acquiring area scale and water area type distribution, and carrying out sensing planning layout and water quality index real-time monitoring to obtain a water conservancy system water quality index data set; performing regional water quality index distribution coupling and regional time step division on the water conservancy system to obtain a corresponding water conservancy pH value, water conservancy dissolved oxygen concentration, water conservancy turbidity, water conservancy heavy metal concentration and water conservancy microorganism concentration; performing microorganism growth efficiency evaluation and microorganism pollution attenuation analysis on the water conservancy microorganism concentration to obtain a water conservancy area microorganism pollution degradation attenuation rate; and performing water quality pollution anomaly quantification and anomaly category decision management on the water quality index data set of the water conservancy system to generate a water quality anomaly category management strategy so as to execute corresponding regional water quality category management work of the water conservancy system. The accuracy and efficiency of water quality monitoring can be improved.

The invention relates to the technical field of topographic survey, in particular to an overwater and underwater topographic survey method based on cooperation of an unmanned ship and an unmanned aerial vehicle. The method comprises the following steps: acquiring platform parameter data of an unmanned ship and an unmanned aerial vehicle; according to the platform parameter data, isomerization grouping based on the residual electric quantity, the communication signal strength, the cruising ability and the measurement precision is carried out, a collaborative formation scheme is determined according to a preset water area measurement task sequence, and collaborative parameter data is obtained; acquiring environment data of a measurement area; and performing regional division modeling based on water area topographic features and environmental complexity according to the environmental data of the measurement region, and applying the collaborative parameter data to a regional model to obtain a topological model of the measurement region. The unmanned ship is used as a mobile charging platform, real-time electric quantity monitoring and docking charging mechanisms are combined, the endurance guarantee of the unmanned aerial vehicle is realized through energy supply strategy optimization, and the continuity and stability of tasks are ensured.

The invention provides a hydro-junction water level adjusting method and system. Wherein the sediment deposition space-time atlas containing the sedimentary gradient is constructed and periodically corrected through scanning of an underwater robot and synchronous acquisition of satellite remote sensing data. A radar array is deployed at a bend section of a flood discharge channel, and vector distribution of a three-dimensional sediment flow velocity field is analyzed by using multi-frequency pulse signals. Through correlation analysis of the dynamic coupling sedimentary gradient and the flow velocity field, a correlation area of flow velocity sudden change and sediment deposition is accurately identified, and a constraint condition set including coordinate positioning and a flow-sediment matching rule is established. And in combination with real-time water level monitoring data, calculating and generating a dynamic adjustment parameter sequence of the opening degree of the flood discharge gate by using a constraint condition set, thereby realizing accurate regulation and control of the water level. According to the technical scheme, the flood discharge parameters are intelligently optimized, the water level of the hydro-junction can be accurately regulated and controlled, the sediment deposition problem of a high-sediment-content water area is effectively relieved, and safe operation and flood control efficiency of the junction are guaranteed.

The invention belongs to the technical field of pollution tracing, and discloses a distributed water body pollution tracing method and system, and the method comprises the steps: constructing a plurality of directed node pairs according to a topological structure of each partition node in a monitored water area; according to the pollutant concentration data in the monitored water area, determining a time delay interval of a pollutant concentration peak value between each directed node pair; determining an effective node pair of which the time delay interval meets the space-time constraint from the plurality of directed node pairs, and constructing a plurality of backtracking paths according to the effective node pair; performing particle tracking simulation on the pollutant concentration data to obtain a plurality of simulation paths, and determining a particle intersection area of pollutants according to the plurality of simulation paths; and determining a pollution source area according to the plurality of backtracking paths and the space intersection of the particle intersection area, and further determining traceability positioning. According to the method, the backtracking path is constructed through dynamic time-delay analysis, high-precision identification of the pollution source is realized by combining particle tracking and gridding intersection positioning, and the problems of insufficient space-time dynamics and large positioning deviation of a traditional method are solved.

The invention discloses a complex water area garbage intelligent detection and collection method based on multi-modal perception, and the method comprises the steps: obtaining image perception data and semantic environment data of a complex water area; the method comprises the following steps: performing preprocessing, performing feature extraction and semantic coding, and then performing multi-modal feature alignment fusion, feature extraction and multi-task detection to obtain a garbage detection result, thereby effectively realizing deep fusion of semantic priori and visual features, and improving the accuracy and robustness of garbage recognition in a complex water area environment; then task importance evaluation is carried out to construct a garbage disposal priority queue, a path dynamic planning strategy oriented to energy consumption constraint and task priority collaborative optimization is constructed to carry out path generation, navigation energy consumption is reduced on the premise that task benefits are considered, and garbage detection and collection tasks in a complex water area are achieved; the system adapts to various intelligent devices such as unmanned ships, unmanned aerial vehicles and shore-based platforms, and has high autonomy, environmental adaptability and engineering deployability.

The invention discloses an agricultural water utilization right distribution and transaction management method suitable for a high-water area, and particularly relates to the technical field of agricultural water utilization right management. A self-adaptive water right scheduling model is constructed, the water surplus and shortage conditions in the future 2-24 hours are calculated, the agricultural water quota is dynamically adjusted through a regional water right redistribution mechanism, and meanwhile, a water right transaction is divided into a long-term contract transaction and a short-term instant transaction based on an intelligent contract system of a block chain, so that the water right transaction efficiency is improved. The peasant households are allowed to match and dispense water resources in real time through the intelligent water right transaction terminal, the water right transaction matching rule is further adjusted in combination with historical transaction data, a water transfer execution effect and peasant household water consumption behavior analysis, and a regional water resource scheduling strategy is adaptively adjusted; accurate irrigation, efficient water right circulation and intelligent scheduling are achieved, the utilization efficiency of agricultural water resources is effectively improved, stable production of farmlands is guaranteed, meanwhile, a water right market circulation mechanism is adjusted, and the flexibility and the intelligent level of water resource regulation and control are improved.

The invention relates to the technical field of unmanned systems, and discloses an unmanned ship directional operation system cooperating with unmanned aerial vehicle cruise, and the system comprises an unmanned aerial vehicle module which carries a high-precision sensor and is used for scanning a water area environment in real time and generating a high-precision map; the unmanned ship module is provided with a navigation system and an operation device and is used for executing water surface orientation operation according to the high-precision map; the cloud platform module is connected with the unmanned aerial vehicle module and the unmanned ship module through a wireless communication network and is used for data interaction, task scheduling and remote control; and the intelligent charging shore base is used for providing automatic charging support for the unmanned ship module. A high-precision water area map is generated in real time through the unmanned aerial vehicle module and is transmitted to the unmanned ship module, so that the unmanned ship can optimize the route and the obstacle avoidance operation according to the dynamically updated environment data, and the task execution precision is remarkably improved; therefore, the effectiveness of patrolling, cleaning and monitoring tasks is improved.

The invention discloses a ship management method and system based on digital twinborn scene construction. The method comprises the following steps: acquiring multi-source scene data of a target water area in real time, and constructing a corresponding ship management digital twin model; performing integrity evaluation on the constructed digital twinborn model, and performing defect compensation according to an evaluation result to obtain an optimized digital twinborn model; further collecting ship density and weather condition data of each sub-region of the target water area, constructing a network transmission delay prediction model, analyzing related data based on the model, and generating a network delay coefficient distribution diagram of the target water area; and according to the delay distribution diagram, determining the real-time updating lag amount of each subunit of the optimization model, and correcting the ship position information in the digital twin model. According to the method, the real-time performance and accuracy of the digital twin model can be remarkably improved, and the ship operation management efficiency and safety in a complex water area environment are enhanced.

The invention relates to the technical field of environment assessment, in particular to a coastal zone culture area multi-dimensional environment assessment method and a cloud platform. The method comprises the following steps: continuously recording dissolved oxygen concentration, pH value, centigrade temperature, salinity unit, turbidity scattering unit, chlorophyll fluorescence intensity, ammonia nitrogen milligram per liter and nitrite milligram per liter, synchronously calling multi-source remote sensing images covering a culture area and an adjacent water area, extracting remote sensing reflectivity data through image correction and atmospheric correction, and calculating to obtain water color parameters. According to the method, the water quality sensor array is deployed at the key point of the culture area, continuous multi-parameter environmental data acquisition is carried out, multi-source remote sensing images are synchronously integrated, water color information is extracted in real time, and the timeliness and data accuracy of culture water area environmental monitoring are improved; trend decomposition and dynamic baseline construction are carried out based on the data sequence, so that the environmental fluctuation evaluation of the breeding area is more objective and accurate.

The invention relates to the technical field of water quality pollution detection, in particular to a water quality pollution rapid detection method driven by a quantum sensing technology. A quantum sensor array is deployed in a water area to be detected, and quantum signal response data of the water area is collected; constructing a pollutant quantum recognition model, and inputting quantum signal response data into the model to obtain an initial predicted value of pollutant concentration; generating an initial detection parameter of the quantum detection instrument based on the initial predicted value, executing primary pollution detection by the instrument according to the parameter, and collecting primary deviation data of an actual detection value and an expected detection value; determining a detection error according to the deviation data, further correcting the initial detection parameter, and generating an optimized detection parameter; and finally, the instrument performs secondary pollution detection based on the optimized detection parameters. According to the method, complex water sample pretreatment is not needed, water quality pollution detection can be rapidly and accurately completed, detection requirements of different water areas are met, and the detection efficiency and accuracy are improved.

The invention provides an unmanned ship path planning method in a complex water area, and aims to solve the problem that an existing path planning method cannot be adaptively adjusted in a dynamic hydrological environment. A sensor (RTK-GPS, laser radar) on an unmanned ship is used for environment information collection, a hydrological state vector is constructed, and a global path is preliminarily planned based on a multi-agent reinforcement learning framework. Then, local path fine adjustment is carried out in combination with an improved A * algorithm, a risk compensation item is dynamically adjusted, and it is ensured that the unmanned ship can realize efficient and safe path planning in different hydrological environments. According to the method, the weight of the reward function is adjusted in real time, so that the unmanned ship can flexibly deal with different hydrological environments and adapt to complex water area conditions, and the accuracy and efficiency of path planning are improved.

The invention discloses a lake algae vertical distribution daily change monitoring method based on a satellite remote sensing image. The invention relates to the technical field of environmental engineering and image processing, and solves the problem that vertical distribution daily variation of algae is difficult to obtain through single observation. The method comprises the following steps: collecting data of algae samples at different depths of a lake, establishing satellite-ground synchronous data, establishing a vertically distributed mathematical model, and parameterizing distribution characteristics of algae in a water depth direction; the remote sensing image is preprocessed, and an inversion model is established based on the relation between the surface chlorophyll concentration in the in-situ data and the remote sensing reflectivity. Utilizing machine learning to optimize model parameters, and combining with a vertical distribution parameterization result to construct an algae vertical distribution remote sensing monitoring model; the method integrates remote sensing data to invert lake algae vertical distribution, performs time sequence analysis on data obtained by inversion, estimates algae vertical distribution and spatial change conditions of a target water area, and provides important scientific basis and technical support for algae bloom prevention and control and environmental protection.

The invention discloses a boundary detection and positioning method suitable for trans-boundary water pollution, and relates to the technical field of river water quality monitoring. According to data obtained at the longitudinal and transverse positions of a river channel of each monitoring point of a selected drainage basin, a two-dimensional hydrodynamic model of the selected drainage basin is established; then, combining with the obtained target pollutant concentration to construct a diffusion model of the target pollutant, identifying and integrating a to-be-positioned region set in each time period, and screening out the concentration abrupt change regions which are spatially adjacent and consistent in concentration gradient change trend through clustering analysis by utilizing the spatial distribution positions and concentration gradient values of the concentration abrupt change regions; and the boundary contour of the pollutant is generated by fitting in combination with the direction information of the flow velocity field. According to the method, after concentration abrupt change areas identified in each time period are integrated, clustering analysis is carried out according to spatial distribution and gradient change trends of the concentration abrupt change areas, pollution boundary contour fitting is optimized in combination with flow velocity field direction information, and pollution boundary detection requirements in a complex water area environment are met.

The invention discloses a bridge ship collision early warning system based on artificial intelligence, and relates to the field of intelligent traffic infrastructure safety protection. The method is used for solving the problems of difficulty in multi-source data fusion and low risk identification accuracy in a complex water area. The method comprises the following steps: firstly, uniformly mapping a radar point cloud, an AIS signal and a visual trajectory to a coordinate system taking a pier as a center through a Lie group transformation algorithm, and constructing a ship motion state tensor; then, ship behavior characteristics are extracted through a space-time diagram convolutional network, and intention probability distribution and behavior deviation degree are output; thirdly, an orthogonal constraint variational auto-encoder is adopted to decouple the autonomous control behavior and the environment disturbance factor, and a collision risk quantized value is generated according to the norm ratio of the autonomous control behavior and the environment disturbance factor; and finally, constructing a dynamic alarm threshold value according to the behavior deviation degree and the disturbance characteristics, realizing graded early warning, and driving a behavior pattern library to update through a feedback mechanism to form a self-adaptive closed-loop early warning system.

The invention discloses a water area detection method based on multi-modal fusion perception, so as to improve the accuracy and real-time performance of urban waterlogging monitoring. The method comprises the following steps: combined feature extraction of optical flow and visual attributes: collecting continuous image frames, and extracting visual and motion features by using an optical flow estimation network and a convolutional neural network (CNN) in a combined manner to realize accurate perception of water area changes; feature fusion driven by a space-time attention mechanism: constructing the space-time attention mechanism, fusing multi-level features, and forming unified space-time feature representation so as to enhance the detection capability of the ponding area; constructing an end-to-end waterlogging detection model: designing a deep learning network fusing classification and regression tasks, and realizing accurate identification and positioning of a waterlogging ponding area; real-time ponding range tracking and change analysis: based on a dynamic monitoring technology, analyzing an expansion trend of a ponding area, and providing accurate early warning information to support emergency decision making; according to the method, static visual features and dynamic motion features of a water body are extracted in parallel, a multi-modal feature fusion strategy is adopted, and an end-to-end intelligent detection model is constructed. Experimental verification shows that the method has high detection precision and real-time performance in a complex electromagnetic environment, the reliability and intelligent early warning capability of urban waterlogging monitoring are remarkably improved, and powerful technical support is provided for urban water area management and disaster prevention and control.

The invention relates to the technical field of water quality detection, and discloses a water quality sampling monitoring method and system for an intelligent water station based on ultraviolet spectrum, and the method comprises the steps: carrying out the regional division of the intelligent water station, and obtaining a plurality of water areas; performing water quality analysis according to the ultraviolet spectrum data of the water sample at the central sampling point of each water area to obtain a normal water area and an abnormal water area; performing water quality analysis on all sampling point water samples in the abnormal water area to obtain pollution source sampling points; the intelligent water station is subjected to regional treatment, so that the water sample collection quantity is reduced, and the water quality detection process is accelerated; different water quality analysis methods are adopted in different water quality analysis stages, dynamic adjustment of the analysis process is achieved, a sampling point closest to a pollution source is determined, and water station water quality treatment is facilitated; the model parameters of the water quality detection model are optimized by using a multi-objective optimization method, so that local optimum is effectively avoided when a multi-output objective problem is processed, and the accuracy of a water quality detection result is improved.

The invention discloses an inland water body turbidity satellite remote sensing method based on optical classification and spectral simulation, which comprises the following steps: S1, acquiring water body spectral data, water body turbidity, satellite remote sensing images and meteorological data of a to-be-detected area, and calculating water body remote sensing reflectivity; s2, preprocessing the satellite remote sensing image to obtain a water body area; performing equivalent calculation on the remote sensing reflectivity data of the water body actual measurement spectrum to obtain the remote sensing reflectivity of each channel of the satellite sensor; performing water body optical classification by combining the actually measured water body spectral shape and turbidity distribution characteristics of the research area; s3, performing correlation analysis on different optical types of the actually measured spectrum according to the actually measured turbidity and the remote sensing reflectivity of multiple channels of the satellite, selecting channel remote sensing reflectivity data with high turbidity correlation as a sample, and constructing a turbidity remote sensing model; the turbidity remote sensing model is trained and tested, and the optimal turbidity remote sensing model is screened out. The method can improve the precision of remote sensing inversion of the turbidity of the inland water body.

The invention discloses a dynamic water quality monitoring system based on an unmanned ship and a data acquisition and distribution algorithm. Comprising an unmanned ship platform module, an initial navigation path setting module, a water quality data acquisition module, a dynamic adjustment and optimization module, a control interaction module and a dynamic adjustment and optimization module, wherein the dynamic adjustment and optimization module dynamically adjusts the navigation strategy and sampling frequency of the unmanned ship in a target to-be-detected water area through a second branch of a first neural network model; the unmanned ship can sail autonomously, continuous and global water quality monitoring is achieved, through an efficient data processing algorithm, the accuracy and reliability of data are improved, a scientific basis is provided for water quality management, a distributed algorithm model is achieved to conduct predictive analysis on each specific target sampling point, the accurate predictability of unmanned ship monitoring is improved, and the water quality monitoring efficiency is improved. The monitoring strategy is dynamically adjusted according to actual requirements, optimal configuration of monitoring resources is achieved, the monitoring cost is reduced, the water quality change trend is found in advance through prediction analysis of the neural network model, and powerful support is provided for early warning and emergency response.

The invention provides a water level and water quality monitoring method based on an AI technology energizing intelligent central pivot rod, which takes an AI intelligent central pivot sensing rod as a core and integrates various components such as a radar static pressure composite monitoring water level terminal, a water quality five-parameter monitoring terminal, a camera acquisition terminal and a cloud analysis full-amount meteorological sensing station. Through specific structure assembly and field installation, the device is deployed in different water areas such as inland rivers, lakes and rivers. The system collects data according to a set time interval and records environment and scene characteristic parameters. After collected data are preprocessed, adaptive lightweight model combinations are selected according to different scenes and data characteristics, a training set and a test set are divided for model training and fusion, and then the data are deployed to a monitoring terminal and verified by the test set. The system realizes intelligent data processing by means of the AI technology, has flexibility and reliability, can efficiently and accurately collect and stably transmit data, provides accurate technical support for water level and water quality monitoring, and has great significance for flood control and water body ecological protection.

The invention relates to the technical field of underwater detection, and discloses an underwater comprehensive detection system. The system collects multi-dimensional acoustic data including sound wave reflection intensity, propagation time delay and frequency dispersion characteristics of a target water area through an underwater sonar array; dividing the detection area into a plurality of layered units according to a water attenuation coefficient and a substrate reflection characteristic of the acoustic data, and extracting an environment intrinsic parameter of each layered unit; constructing an acoustic propagation path model based on the environmental intrinsic parameters, and generating adaptive filtering parameters in combination with a preset noise suppression strategy; calling sonar array hardware configuration data, matching filtering parameters with an array beam forming mode, and calculating a detection sensitivity threshold value of each layered unit; and integrating the sensitivity threshold and the acoustic data, generating a three-dimensional acoustic characteristic spectrum of the target water area, and marking the space coordinates and confidence level of the abnormal area. The system can improve the comprehensiveness and accuracy of underwater detection, and meets the requirement of high-precision detection.