5325 results about "Large range" patented technology

The invention discloses a landslide prediction and early warning system and method based on a remote sensing technology, and is applied to the technical field of landslide early warning. Comprising a data acquisition module for acquiring multi-source data in a monitoring area; the cloud computing module is used for large-range landslide risk assessment and deformation monitoring; the edge calculation module is used for landslide risk assessment and deformation monitoring of a specific area; the end-cloud cooperative communication module is used for realizing data interaction between the cloud computing module and the edge computing module; the federal learning module is used for realizing cooperative training under data privacy protection of different areas; and the landslide early warning module is used for carrying out early warning based on landslide risk assessment and deformation monitoring results of the cloud computing module and the edge computing module. According to the invention, by using the deep learning model, the end-cloud collaborative architecture and the federal learning driven data fusion framework, efficient, low-cost and high-precision landslide risk prediction is realized.

The invention belongs to the technical field of digital restoration of computer vision and cultural heritage, and particularly relates to a traditional picture restoration method fusing low-resolution prior and efficient visual selection, which comprises the following steps: constructing a multi-source image data set, taking images in the multi-source image data set as high-resolution images, preprocessing the high-resolution images to obtain low-resolution images, and carrying out high-resolution priori and high-efficiency visual selection on the low-resolution images. The high-resolution image and the low-resolution image are respectively masked to generate simulated damage mask images, and the simulated damage mask images comprise a regular damage mask image and an irregular damage mask image; taking the multi-source image data set and the preprocessed multi-source image data set as training data, and training a multi-source image model; the dual-stage repair network comprises a coarse repair network and a fine repair network; according to the method, the problems of structural semantic loss, high priori information dependency and insufficient global and local coordination when an existing image restoration method is used for processing a complex scene and a large-range missing region are solved.

The invention relates to a multi-machine cooperative yaw control method and device considering a dynamic wind direction and a medium. The multi-machine cooperative yaw control method comprises the steps that S1, the dynamic relation between the wind direction and a wake flow area is established based on a Gaussian-curl hybrid model; s2, constructing a single-machine yaw optimization model based on model predictive control, optimizing single-machine yaw by taking maximum generating capacity of a wind power plant and reduction of fatigue load as optimization objectives, and obtaining a single-machine optimal yaw angle through rolling optimization and feedback correction; and S3, a serial optimization method is adopted to carry out multi-round iterative optimization on one fan by one fan, and an optimal multi-fan cooperative yaw control result is obtained. Compared with the prior art, the method has the advantages that higher total generating capacity and more balanced fatigue load can be realized, so that the whole wind power plant reaches the optimal state of power and load in a large range.

The invention relates to the technical field of laser material processing, and discloses a laser annealing temperature control method and device.The method comprises the steps that the specific heat capacity, the heat conductivity coefficient, the heat diffusivity, the heat radiation rate, the heat reflectivity and the heat loss coefficient are obtained, and a material thermal characteristic model is established; performing grid division according to the material thermal characteristic model to obtain a laser energy density parameter; extracting a thermal strain coefficient according to the laser energy density parameter, and establishing a temperature-thermal strain mapping relation prediction model; dynamically adjusting a laser power proportion and a pulse width time sequence according to the temperature-thermal strain mapping relation prediction model to obtain temperature field distribution data; according to the temperature field distribution data, carrying out shortest path analysis to obtain a temperature regulation and control decision; and according to the temperature regulation and control decision, inputting a preset control regulation model, and combining reinforcement learning iteration optimization parameters to obtain a temperature control scheme. According to the method, large-range temperature crossing and fine adjustment in an extremely short time can be realized.

The invention relates to the technical field of path planning, and discloses an unmanned aerial vehicle path planning method based on large-range cruise, which comprises the steps of geographic information acquisition, geographic information processing, regional discretization, starting point and target point setting, feasible path search and path optimization. Geographic information collection is comprehensive and accurate, data of terrains, obstacles, no-fly zones and the like are covered, and a foundation is laid for path planning; regional discretization fits the characteristics of an unmanned aerial vehicle, cells are divided according to the minimum turning radius and the effective range of a sensor, and flight safety and perception are guaranteed. Feasible path searching is efficient and reasonable, and the optimal path is searched by well-designed cost and heuristic functions and considering multiple factors; path optimization is effective, based on a B spline curve smoothing method, curvature and length are reduced, energy consumption is reduced, and abrasion is reduced; safety verification is added, collision and cross-zone risks can be accurately judged, and safety guarantee is enhanced.

The invention discloses an absolute type planar two-dimensional time grating displacement sensor based on an alternating electric field and a measuring method. Excitation signals are applied to X excitation pairs in sequence, when an induction electrode generates an induction signal, the excitation signals are stopped from being applied to the remaining X excitation pairs, and four paths of electric signals generated by an induction group are processed, so that the absolute type planar two-dimensional time grating displacement sensor based on the alternating electric field is obtained. Obtaining the X excitation antipode of the movable ruler base body and the displacement position of the movable ruler base body in the X excitation antipode so as to obtain the two-dimensional time grating absolute displacement value of the movable ruler base body relative to the fixed ruler base body in the X direction; similarly, a two-dimensional time grating absolute displacement value of the movable ruler base body in the Y direction is obtained; and then taking the two-dimensional time grating absolute displacement values in the XY direction as the coordinates of the movable ruler base body in the XY direction to obtain the absolute displacement of the movable ruler base body. The absolute positions in the X direction and the Y direction are obtained in a scanning mode so as to achieve planar two-dimensional absolute displacement measurement, accumulated errors and random errors can be reduced, the measurement precision can be improved, and wide-range high-precision measurement can be achieved.

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 discloses an unmanned aerial vehicle airport site selection method suitable for large-scale electric power line inspection, and the method comprises the steps: S1, carrying out the primary selection of a geographic space, constructing a geographic constraint evaluation model, carrying out the processing of elements and historical disaster data through discrete coding, generating a safe and suitable region through the multi-factor weighted stacking space analysis, and carrying out the calculation of an evaluation result; and a K-Means clustering algorithm based on Euclidean distance is combined to screen a primary selection region. S2, risk assessment preselection: constructing an entropy weight-TOPSIS risk assessment model, determining a risk factor weight by using an entropy evaluation method, calculating a scheme closeness degree through a TOPSIS algorithm, and screening a low-risk preselection region; and S3, constructing a binary relationship between a demand center set and a pre-selected site set by combining an electric power inspection target and taking an electric power inspection task target as a demand node, measuring weighted traffic cost through Euclidean distance, designing a double-layer nested 0-1 decision variable, and realizing optimization of an airport deployment area by taking minimization of a total weighted distance as a target function.

The invention discloses a forest carbon reserve and carbon sink monitoring and evaluating method based on a multi-source remote sensing technology. The method comprises the following steps: multi-source remote sensing data acquisition: respectively acquiring optical remote sensing data and radar remote sensing data; tree species classification and identification: carrying out tree species classification on the forest in the research area to realize tree species scale space distribution information extraction; for different tree species, biomass model parameters obtained through field investigation are combined, a biomass estimation model based on the tree species is established, model input parameters are biomass model parameters, and vegetation biomass is output; calculating the carbon reserve: calculating the vegetation carbon reserve by utilizing the inverted vegetation biomass and combining the carbon content coefficients of different tree species; estimating the soil carbon reserve, and finally obtaining the total carbon reserve of the forest ecosystem: calculating the carbon reserve variation of the forest ecosystem at different time points based on the multi-source remote sensing data of the long-term sequence. According to the invention, large-range, high-precision and continuous forest carbon reserve and carbon sink monitoring and evaluation considering tree species difference is realized.

The invention discloses an automatic welding device for a rotary alloy furnace tube, and belongs to the technical field of arc welding equipment.The automatic welding device for the rotary alloy furnace tube comprises a base and a U-shaped bin located at the top of the base, and the two ends of the interior of the U-shaped bin are jointly provided with a centering clamping mechanism used for clamping the outer side of the furnace tube; a driving mechanism for providing power for the centering clamping mechanism to clamp the furnace tube is arranged on the outer side of the centering clamping mechanism, and a welding mechanism is arranged on the inner top of the U-shaped bin. A local sealing design is adopted for the inflation mechanism, the annular air bag is attached to the inner wall of the furnace tube after being inflated, an independent cavity communicated with the outside only through a welding seam gap is formed near the welding seam, the protection requirement can be met only by filling the local space with argon, the whole furnace tube or a large-range external space does not need to be filled, and compared with traditional overall argon filling, the argon filling efficiency is greatly improved. The argon consumption is greatly reduced, the industrial gas purchase cost is remarkably reduced, and the production cost investment is reduced.

The invention provides a mining area deformation monitoring and risk assessment method, which comprises the following steps of: carrying out registration, de-leveling, filtering and phase unwrapping on acquired image data through InSAR (Interferometric Synthetic Aperture Radar), generating a coherence graph, a filtering interference graph and an unwrapping phase graph, carrying out phase decomposition by using a singular value algorithm, separating a deformation phase, an atmospheric delay phase and noise, and carrying out risk assessment. The method comprises the steps of expanding discrete deformation points into a continuous deformation field through Kriging interpolation, carrying out deformation grading by taking a deformation rate, an accumulated settlement amount and a deformation gradient as core indexes, identifying a deformation high-risk area, arranging measurement robot monitoring points in the high-risk area, and constructing a risk assessment model in combination with real-time deformation data of a measurement robot and geological parameters. And evaluating the risk grade of each part of the mining area through the dynamic risk index model, and triggering graded early warning. According to the method, the InSAR and measurement robot technology is combined to solve the data fusion problem of large-range deformation monitoring and local high-precision monitoring, and the dynamic risk assessment precision under the complex terrain of the mining area is improved.

The invention relates to the technical field of network monitoring, in particular to a method for constructing a dynamic self-adaptive alarm monitoring network based on LoRa and Ad-hoc ad hoc network technology, which comprises the following steps: (1) integrating a dynamic Ad-hoc ad hoc network routing protocol on each LoRa node, so that each node can automatically discover neighbor nodes, establish and maintain a dynamic routing table, and automatically discover the neighbor nodes; a non-centralized network architecture is realized; (2) through a multi-hop communication mode, the alarm information can be flexibly transmitted among the nodes, the coverage range of the network is expanded, and the system adapts to a complex geographical environment; and (3) by utilizing the characteristics of low power consumption and long-distance transmission of the LoRa technology, stable communication between the nodes in a large range is ensured, meanwhile, the energy consumption is reduced, and the working life of the nodes is prolonged. Through the dynamic self-adaptive Ad-hoc routing protocol, the network can automatically adjust the routing according to the node state and the environment change, the dynamic change of the network topology is adapted, and the reliable transmission of the alarm information is ensured.

The invention provides a visual navigation method and system based on an improved optical flow method, and relates to the technical field of computer vision and inertial navigation. A collaborative optimization framework of an inertial navigation system and visual feature navigation is constructed, carrier motion state parameters are obtained through dead reckoning of an inertial measurement unit, variance parameters of pose variation are calculated, and the layering depth of a pyramid LK optical flow method is dynamically adjusted based on the variance parameters. When the inertial navigation output variance exceeds a preset threshold value, the number of layers of an image pyramid is increased to cope with large-range motion, and otherwise, calculation levels are reduced to improve real-time performance. A dual-period optical flow / feature matching tracking mechanism is designed, improved sub-pixel-level optical flow tracking is adopted in a short period, the calculation complexity is reduced while the positioning precision is guaranteed, feature matching is switched to in a long period, accumulative errors are eliminated, and a period parameter N is determined by inertial navigation precision. According to the method, the calculation complexity can be reduced, and a better effect can be achieved on a low-calculation-performance platform.

The invention provides a sea ice area extraction method and device based on remote sensing, which are applied to the field of seawater detection, and the method comprises the steps: obtaining a remote sensing image of a target area; pixels of the remote sensing image are partitioned based on the normalized vegetation index, the pixels with the normalized vegetation index smaller than a preset vegetation index threshold value are divided into first type regions, and the first type regions comprise at least one of a water body region and a sea ice region; determining a normalized deicing index of the first type region; performing normalized deicing index threshold segmentation on the first type region through a maximum between-class variance method to obtain a sea ice classification threshold; and filtering and post-processing the remote sensing image based on the sea ice classification threshold, and taking the first type region of which the normalized deicing index is greater than the sea ice classification threshold as a sea ice region. The method is suitable for sea ice monitoring in a large area, and sea ice information can be efficiently and accurately extracted.

The invention provides an aero-engine wide-range component performance self-adaptive correction method and device, and the method comprises the steps: building an aero-engine component-level nonlinear model according to the aero-thermal characteristics of components of an aero-engine; a component characteristic self-adaptive correction factor is introduced to adjust the model performance, matching of a model simulation result and a target measurement parameter is taken as an optimization target, and component performance matching is converted into a self-adaptive optimization problem; establishing a curved surface fitting database, and constructing an adaptive correction factor curved surface fitting model through a moving least square method; the characteristics of the model component are adjusted through the self-adaptive correction factor curved surface, so that the output result of the aero-engine component-level simulation model is kept consistent with the measurement parameters under the large-range working condition, and the large-range correction of the component characteristic diagram is realized. According to the method, the simulation output of the corrected model can be kept consistent with the target engine within a large working condition range, and the deviation degree of the actual characteristics can be accurately represented through the self-adaptive correction result.

The invention discloses a water environment management system based on cloud computing, and relates to the technical field of environment monitoring. In order to solve the problems that in the prior art, water environment emergencies cannot be responded in real time, the requirement for rapid early warning cannot be met, regular manual data monitoring is limited by manpower, material resources and environmental conditions, high-frequency and large-range coverage cannot be achieved, and therefore the continuity and integrity of monitoring data are affected. Monitoring data are collected and transmitted to the cloud computing platform in real time, continuous and high-frequency monitoring of the water environment is achieved, the water quality change can be rapidly recognized through the instant analysis capacity of the cloud computing platform, abnormal key monitoring points are accurately recognized, the data collection and transmission time interval is dynamically adjusted according to the abnormity severity degree and the influence range, and the monitoring accuracy is improved. Therefore, key information can be acquired in time, unnecessary data transmission is avoided, resources are saved, abnormal conditions are tracked and managed, and problems can be effectively solved.

The invention discloses a wide-range weak feature scene measurement system based on multi-sensor fusion and structured light scanning, and relates to the technical field of wide-range scene scanning, in the system, an extended Kalman filtering algorithm is adopted, fusion is carried out based on positioning data provided by an inertial measurement module and a positioning sensing module, and a wide-range weak feature scene is obtained. And the distance measurement information provided by the laser distance measurement module is combined to correct the fused positioning data, so that the precision and robustness of subsequent point cloud registration are ensured. Then, according to position information and attitude information of the binocular structured light scanning camera during scene scanning, calculating a relative rotation matrix and a relative displacement matrix of each frame of scanning point cloud data relative to the first frame of scanning point cloud data, and performing coarse registration; in the coarse registration process, all frames of point clouds can be converted into a unified coordinate system by directly utilizing global pose information, and the efficiency of coarse registration is greatly improved. And finally, extracting laser marking point clouds projected by the non-contact laser marking device in each frame of scanning point cloud data, and performing iterative precise registration on each frame of scanning point cloud data by using color features and geometric distribution rules of the extracted laser marking point clouds. In conclusion, high-precision, high-efficiency and high-robustness point cloud registration in a large-scale weak feature scene is realized, and the method can be used for realizing high-precision non-contact measurement.

The invention discloses a model wide-range attack angle change tail and web support device suitable for an airplane wind tunnel test, which comprises two support modes, namely a curved knife support rod and a web support support rod, an attack angle change mechanism, a lifting compensation mechanism and a mounting base, and is characterized in that a wind tunnel airplane model is arranged at the end part of the support rod, and the support rod is in screw connection with the attack angle change mechanism; the attack angle changing mechanism is connected to the lifting compensation mechanism through the mounting base, and the lifting compensation mechanism is mounted in wind tunnel test equipment. According to the invention, the large-range attack angle change of the airplane model in the wind tunnel test can be realized, and the center is kept unchanged at the height of the wind tunnel test.

The invention discloses an SAR (Synthetic Aperture Radar) soil surface water content inversion method and device based on deep learning, and the method comprises the steps: constructing a deep learning regression model for predicting the volume content of soil water through combining SAR data, auxiliary data and in-situ sensor data; a ConvNeXt model is adopted to construct a parallel convolution encoder to solve multi-source data with different input resolutions, and back scattering coefficients, spectral information, digital elevation model high-resolution data and soil sand content, sludge content, clay content and stacking density low-resolution data are fused into soil moisture estimation. And the inference capability of unknown data is improved. The model obtained by training can realize large-range and high-resolution automatic estimation of the water content of the soil surface layer. According to the invention, the influence of radiation transmission noise is avoided, the inversion precision of a vegetation coverage area is improved, and large-range high-resolution automatic processing of soil humidity products is realized.

An estimation method and system for quasi-real-time monitoring of a moving velocity vector of a typhoon based on a synthetic aperture radar includes: (1) determining, using a threshold method, a position of a typhoon eye wall of a synthetic aperture radar (SAR) sea surface wind field of a typhoon; (2) extracting a final typhoon eye region using a maximum gradient method according to the position of the typhoon eye wall, and estimating an approximate ellipse of the typhoon eye wall and determining a center of the approximate ellipse as a typhoon center; (3) estimating large-scale background wind vector information using the estimated typhoon center and approximate ellipse; and (4) determining a moving velocity vector of the typhoon using the estimated background wind vector information and typhoon center.

The invention belongs to the technical field of live-action three-dimensional model construction, and particularly relates to a live-action three-dimensional model construction method based on ground multi-source heterogeneous point cloud fusion. The method comprises the steps of unmanned aerial vehicle image acquisition and three-dimensional modeling, ground multi-source heterogeneous point cloud data acquisition, ground multi-source heterogeneous point cloud registration, ground fusion point cloud processing, and unmanned aerial vehicle image three-dimensional model and ground fusion point cloud space registration and fusion modeling. According to the method, the unmanned aerial vehicle image and the ground multi-source heterogeneous point cloud are fused, so that the problem of detail missing in large-range scene modeling is solved. The high precision of the ground point cloud and the wide coverage of the unmanned aerial vehicle image are combined, and the integrity and fineness of the model are significantly improved. In addition, the point cloud color endowing technology enhances the sense of reality of the model, and provides efficient and reliable technical support for the fields of smart cities, cultural heritage protection and the like.

The invention relates to the field of river pollution treatment, and discloses a water quality treatment method, system, equipment and medium based on a remote sensing inversion technology, and the method comprises the following steps: S1, collecting a hyperspectral remote sensing image of a to-be-monitored water body, the hyperspectral remote sensing image comprising chlorophyll concentration, suspended matter concentration and dissolved oxygen; s2, based on the hyperspectral remote sensing image, preprocessing the hyperspectral remote sensing image, including geometric correction, atmospheric correction, noise removal and waveband fusion, to obtain a preprocessed image meeting an inversion precision requirement; and S3, inputting the preprocessed image into a water quality inversion model, and obtaining pixel-level chlorophyll, suspended solids and dissolved oxygen concentration data based on physical radiation transmission. By combining the hyperspectral remote sensing image and the water quality inversion model, large-range, real-time and high-precision water quality monitoring and pollution zoning are realized, model parameters are dynamically updated, and the timeliness and precision of water quality treatment are improved.

An assisted driving method and apparatus. The assisted driving method comprises: acquiring multi-modal perception information, wherein the multi-modal perception information is collected by a plurality of sensors provided in a vehicle (100); acquiring a plurality of groups of display information, wherein the display information comprises target areas to be displayed and grid granularities; performing perception prediction on the basis of the multi-modal perception information and the plurality of groups of display information, so as to obtain a plurality of pieces of occupancy grid information, wherein the occupancy grid information is used for describing the distribution of obstacles around the vehicle (100); and displaying a plurality of occupancy grid pictures on a human-computer interaction interface on the basis of the plurality of pieces of occupancy grid information. By means of the present application, not only can the panoramic perception capability of a driver for obstacles within a large range be improved, but the refined perception capability of the driver for obstacles within a short-distance range can also be improved, and the perception capability of the driver for surrounding environments can be improved by means of a combination of the panoramic perception capability and the refined perception capability, thereby avoiding scratching.

The utility model discloses a system for large-range high-efficiency detection of a building facade. The system comprises a wall-climbing robot and a remote controller, the wall-climbing robot comprises a robot body, a climbing module, a propeller propelling module, a detection module and a control module, the control module is in communication connection with the remote controller; and the control module is suitable for controlling the climbing module to drive the wall-climbing robot to climb or descend along the rope, controlling the propeller propelling module to propel the wall-climbing robot to be tightly attached to the building facade and controlling the detection module to carry out large-range damage detection on the building facade in the climbing or descending process of the wall-climbing robot under an instruction of the remote controller. According to the utility model, the wall-climbing robot is controlled to climb up and down stably on the vertical facade through the remote controller, and the large-range detection of the crack damage of the building facade is realized.

The invention discloses a wind turbine generator intelligent control system and method based on rotation laser radar and artificial intelligence fusion. The wind turbine generator intelligent control system comprises a dynamic scanning monitoring module which can carry out three-dimensional point cloud data collection on the azimuth angle range of 0-300 m and + / -90 degrees in front of a generator; the space-time feature processing module comprises a voxel grid denoising unit, a coordinate conversion unit and a space-time convolutional neural network ST-CNN; and a hybrid optimization control module. A variable-speed rotating multi-line laser radar is adopted, a dynamic scanning strategy is combined, and accurate monitoring of a large-range three-dimensional wind field in front of a unit is achieved. A space-time convolutional neural network ST-CNN is constructed, spatial features and time sequence information are creatively fused, and future wind conditions are accurately predicted. A hybrid control algorithm combining model predictive control MPC and deep reinforcement learning DRL is proposed, and control parameters are dynamically generated by taking power generation efficiency and unit load as optimization targets. From hardware deployment to software configuration, a complete and highly integrated intelligent control system is formed.

According to the optical carrier microwave stable transmission system based on the temperature control optical fiber delay line provided by the invention, accurate reverse compensation is carried out on delay jitter change through the delay compensation module, and excellent delay consistency is shown in ultra-wideband signal transmission; the defect that compensation devices such as a traditional piezoelectric ceramic optical fiber stretcher, an optical phase shifter, a cascade multi-stage optical switch and a motor space optical delay line cannot meet the requirements of large range, high precision, quick response and wide frequency spectrum compensation capability at the same time is overcome; that is to say, the link delay compensation scheme in the transmission system has large-range, high-precision, quick-response and wide-spectrum compensation capabilities, and is particularly suitable for optical fiber transmission links with high stability and consistency requirements on ultra-wideband signal transmission.

The invention relates to a remote sensing image target detection method fused with a multi-scene random field, and the method comprises the steps: building a model through a method combining multi-task learning and a conditional random field, extracting the context information near a suspected small target, assisting the model to judge a real target, integrating the effective results searched by the model, and outputting an image. Aiming at the problems of wide distribution range of remote sensing images and low model test analysis efficiency, a super-pixel segmentation method of the remote sensing images is designed according to the particularity of similar sizes and shapes of similar targets in the remote sensing images, gridding of a remote sensing large-range region is realized in a model reasoning test process, a retrieval region is divided into a plurality of small regions, and a remote sensing image is obtained. The network target searching efficiency is effectively improved; a target image data set containing different backgrounds is constructed, and the obtained effective feature information is input into a target detection network for training, so that the target detection accuracy of the electric power facility in the remote sensing image is realized, and the inspection efficiency is improved.

The invention discloses a multi-dimensional drainage port overflow risk assessment system and assessment method, and belongs to the technical field of drainage port overflow risk assessment systems, and the system integrates ground monitoring and satellite remote sensing multi-source data, and collects related information of a drainage port in an omnibearing manner. The ground monitoring data provides accurate local hydrology, water quality and drainage facility operation state information, the satellite remote sensing data realizes large-range monitoring, the advantages of the ground monitoring data and the satellite remote sensing data are complementary, the data are fused through a weighted average method and a Kalman filtering method, the data accuracy and reliability are improved, and the weighted average method distributes weights according to data characteristics for preliminary fusion, so that the accuracy and reliability of the data are improved. The Kalman filtering method adapts to data dynamic change, such as water level data fusion according to a state equation and an observation equation optimization result, combines the advantages of ground high-precision monitoring and satellite wide-range coverage, accurately reflects water level change, quantifies the overflow risk of a drainage port, and provides effective support for urban water fine supervision.

The invention discloses a large-area crop rapid mapping method integrating multi-source remote sensing data, and the method comprises the steps: obtaining a multi-source remote sensing satellite image, carrying out the preprocessing, amplifying a sample based on the spectrum similarity of a field collection sample point and an amplification sample point, and screening the amplification sample point based on a dynamic time warping distance; drawing a crop distribution reference map of the research subarea in cooperation with multi-source data; and based on the crop distribution reference diagram of the research sub-region, constructing a research region sub-pixel drawing sample set and predicting a sub-pixel distribution diagram of each crop type. According to the method, existing field collection sample points are fully utilized, the drawing potential of multi-source remote sensing data and different classifiers is explored, and crop sub-pixel rapid drawing of a large-scale research area is achieved.

The invention relates to an aquatic environment simulation monitoring method and device for evaluating the influence of pollutants, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring environmental parameters and dividing the range of a fine model based on the environmental parameters; constructing a simplified model based on the environmental parameters in the non-fine model range; constructing a fine model based on the environmental parameters in the fine model range; and outputting and displaying a corresponding simulation result based on the simplified model and the fine model. By the adoption of the method, through multi-scale coupling of the simplified model and the fine model, it can be guaranteed that the diffusion trend of pollutants can be rapidly simulated in a large range, detailed analysis can be conducted in the local key area through the high-precision model, and therefore the overall simulation efficiency and precision are improved.