5544 results about "Water area" patented technology

The invention discloses an intelligent water quality monitoring system and method based on a turbidity sensor, and relates to the technical field of water quality monitoring. Data acquisition nodes are arranged in a water area to be monitored, data are acquired by using the turbidity sensor and an intelligent water quality monitoring equipment group, an environment data set S is constructed, and preprocessing is performed; acquiring a turbidity change coefficient # imgabs0 # according to a turbidity data set in the preprocessed environment data set S, performing primary evaluation, marking abnormal data acquisition nodes, acquiring a particulate matter dynamic index KL and a pollution index WR according to a particulate matter data set and an organic pollutant data set in the environment data set S, and performing secondary evaluation according to the turbidity change coefficient # imgabs1 # and the turbidity change coefficient # imgabs0 # in combination with the particulate matter dynamic index KL and the pollution index WR. Acquiring a comprehensive water quality score Q, performing secondary evaluation, and constructing a water quality prediction model to predict the water quality change and regulate and control the water quality for the data acquisition nodes which are normally evaluated for the second time; the intelligent level of water quality monitoring is improved, the accuracy of water quality monitoring is enhanced, and the safety of water quality is ensured.

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 discloses an underground water pollution traceability analysis and ecological restoration decision support system, and relates to the technical field of computer models, the system comprises a data monitoring and graph network construction part, a pollution diffusion simulation analysis part and an ecological restoration part; the data monitoring and graph network construction part is used for setting a plurality of monitoring points in a target underground water area, and completing the construction of a graph network by taking the reciprocal of the distance between nodes as an edge weight; the pollution diffusion simulation analysis part is used for performing feature extraction on the graph network to obtain graph network features and pollutant concentration distribution of each position in the target underground water area; and the ecological restoration part is used for setting a target pollutant concentration distribution, and screening out an optimal ecological restoration operation by utilizing the knowledge graph according to the difference. According to the invention, the monitoring and repairing capabilities of underground water pollution are obviously improved, and the repairing efficiency is improved.

The invention relates to a multi-site water quality prediction method based on spatio-temporal feature fusion. Compared with the prior art, the multi-site water quality prediction method solves the defect of low water quality prediction accuracy. The method comprises the following steps: acquiring and preprocessing multi-site water quality basic data; performing feature screening on the water area water quality data; a # imgabs0 # model is constructed; a # imgabs 1 # model is trained; and obtaining a water quality prediction result. According to the method, modeling is carried out on multi-site water quality index data from three angles of causality, space and semantics, and more related factors are considered for a complex water area scene; on the basis of the Shenchang differential equation, a more interpretable modeling scheme is provided for the water quality propagation process, and compared with a traditional Shenchang differential modeling and multi-hop propagation mode, the utilization rate and the propagation rate of information are 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 maritime traffic flow analysis method based on big data, and relates to the technical field of information, and the method comprises the steps: collecting data related to maritime traffic, and carrying out the preprocessing; performing adaptive dynamic division on the sea area by using the processed data to generate a real-time updated water area boundary; based on the self-adaptive division result, analyzing a ship behavior mode to obtain an anomaly detection result; analyzing interaction among the ship groups according to an abnormal detection result, and predicting potential congestion and conflicts; based on the group behavior prediction result, ship path planning is optimized, and an optimal route suggestion is generated; by constructing a space-time diagram convolutional network model and comprehensively considering relative positions, speeds and rendezvous frequencies of ships, potential navigation conflicts can be accurately predicted; according to the method, the optimal route suggestion based on multi-objective optimization is provided for the ship by optimizing path planning, so that the sailing efficiency and the economical efficiency are improved to the maximum extent on the premise of ensuring the safety.

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.

The invention discloses a water quality comprehensive regulation and control intelligent model based on water quality hydrodynamic force, and relates to the technical field of water quality comprehensive regulation and control, which comprises the following steps: constructing a comprehensive meteorological monitoring network, capturing key meteorological parameters of air temperature, rainfall, humidity and wind speed in real time, and formulating water area monitoring paths of an unmanned aerial vehicle and an unmanned ship according to detected environment data; the method comprises the following steps: setting an unmanned aerial vehicle monitoring team to monitor the water surface condition in real time, setting an unmanned ship formation to run according to a formulated route, carrying out key monitoring and sampling on a specific polluted area, constructing a water quality standard-reaching early warning system, and carrying out prediction and early warning simulation calculation and correction, and analyzing and evaluating the water quality condition. According to the water quality comprehensive regulation and control intelligent model based on the water quality hydrodynamic force, water area pollution can be quickly found, pollutants can be traced and researched in time, pollution expansion is prevented, meanwhile, pollution early warning can be conducted according to data, potential environmental risks can be found, and powerful technical support is provided for environmental protection work.

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 discloses a water area surface flow velocity area monitoring method based on intelligent visual analysis, and relates to the technical field of intelligent visual analysis. According to the specific implementation scheme, continuous image collection is conducted on a target water area, dynamic images flowing on the surface of the water area are obtained, and image preprocessing is conducted on video frames of the dynamic images; directly comparing global feature similarities through feature extraction, feature matching and optical flow optimization by using an optical flow estimation algorithm of a convolutional neural network, and outputting optical flow field data of the water area surface flow velocity; after the optical flow field data are obtained, pixel displacement information of optical flow estimation is converted into a flow velocity field on an actual physical unit, and meanwhile regional distribution of the flow velocity on the surface of the water body is analyzed. According to the invention, the optical flow estimation algorithm of the convolutional neural network is utilized to directly compare global feature similarities and output water area surface flow velocity data.

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 discloses a KAN network-based water quality parameter remote sensing inversion method and system, and the method comprises the following steps: 1, obtaining field detection water quality parameters of a set region, and carrying out the classification according to time; 2, acquiring and preprocessing a multispectral remote sensing image; 3, constructing a KAN model architecture, inputting the waveband reflectivity of the remote sensing image at a certain point, outputting the water quality parameter at the point, taking the label as the real detection data of the water quality parameter, and taking the loss function as the mean square error of the output and the label; 4, taking the water quality parameters and the wave band reflectivity data set as a training set, and training the KAN network by using a loss function; and 5, inputting the wave band reflectivity of the water quality parameter value to be detected into the trained KAN network to obtain the water quality parameter value of the whole water area. According to the method, the calculation cost and the model complexity can be effectively reduced, and the model result can be subjected to interpretable operation so as to better adapt to actual application requirements.

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 discloses a drainage basin flood risk assessment method and system based on a particle recognition algorithm, and belongs to the technical field of flood early warning. Although the real-time flow of a river can be measured through an existing scheme, how to deduce and evaluate the flood inundation condition is not involved, and consequently the flood inundation condition around a river bank cannot be rapidly known. According to the drainage basin flood risk assessment method based on the particle recognition algorithm, the drainage basin spatial discrete model, the flow velocity calculation model, the flow simulation model, the two-dimensional hydrodynamic model and the flood risk assessment model are constructed, so that the section flow of the drainage basin can be calculated, and the flood inundation condition can be deduced and assessed; therefore, the flood inundation risk and inundation data around the water area can be quickly known, the requirement of flood risk analysis of the drainage basin can be met, the accuracy of flood disaster early warning is improved, and more data supplementation and management bases are provided for flood control of the drainage basin.

The invention discloses a floating type photovoltaic platform device capable of actively adjusting the angle of a photovoltaic module, which comprises floating bodies, a photovoltaic mechanism, a central control system, connecting floating bodies and gravity anchors, and is characterized in that the photovoltaic mechanism is mounted on the floating bodies, the floating bodies are connected with the gravity anchors, the gravity anchors are placed in a water area, the plurality of floating bodies are connected through the connecting floating bodies, and the central control system is connected with the central control system. The central control system is installed on the connecting floating body, the central control system is respectively connected with the floating body and the photovoltaic mechanism, the floating body comprises an adjusting air cushion and a supporting floating body, the adjusting air cushion is arranged on the supporting floating body, the photovoltaic mechanism is arranged on the adjusting air cushion, and the supporting floating body is respectively connected with the connecting floating body and the gravity anchor. The sun position and the illumination intensity are monitored in real time through the central control system, the angle of the photovoltaic mechanism is actively adjusted through the adjusting air cushion, it is ensured that the photovoltaic assembly can receive sunlight at the optimal angle all the time, and therefore the photoelectric conversion efficiency is remarkably 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 provides a sick juvenile fish identification method based on an improved YOLOv10 model, and the method comprises the steps: collecting a fish image in a water area as a to-be-detected image, and carrying out the preprocessing of the to-be-detected image, and obtaining an image data set; the method comprises the following steps: constructing a YOLOv10-UDFishNet model; the image data set is input into the YOLOv10-UFFishNet model, the YOLOv10-UFFishNet model is trained through the image data set, and the trained YOLOv10-UFFishNet model is obtained; and inputting a fish image in a water area to be detected into the trained YOLOv10-UFFishNet model to obtain a fish detection result and position information of a fish individual target. According to the method, the sick juvenile fish in the turbid underwater environment can be accurately and efficiently identified.

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.