1677 results about "Observation point" patented technology

The invention discloses an adaptive genetic algorithm multi-satellite cooperative task dynamic scheduling planning method based on fusion and merging observation, and the method comprises the steps: firstly, decomposing regional tasks through equal-ratio sampling, and constructing discrete observation points; secondly, tasks are converted into a weighted meta-task set, and selectable satellites and time windows are calculated in combination with satellite resources, tasks and attitude constraints; further, detecting and combining the meta-tasks meeting conditions, and constructing a combined observation model taking the maximum observation benefit as a target; and finally, solving is optimized by adopting a self-adaptive genetic algorithm, constraint is ensured to be met, and an executable multi-satellite collaborative observation scheduling scheme is generated. In the regional task planning and scheduling process, a regional discrete adaptive sampling method is adopted to quickly decompose regional tasks into point tasks, and the completion rate of regional observation tasks is increased; in addition, an effective meta-task merging observation method is designed, and the multi-satellite dynamic task planning benefit is effectively improved.

The invention provides a regional soil moisture dynamic distribution prediction method, belongs to the field of soil moisture content prediction, and provides a sequential hybrid drive method (mRestart-EnKF-ML), the method combines a modified ensemble Kalman filter (mRestart-EnKF) and a machine learning model (ML), and the dynamic distribution of the regional soil moisture is predicted. And dynamically updating soil hydraulic parameters by assimilating data of a plurality of observation stations in real time, and generating a soil moisture profile data set with any temporal-spatial resolution. Through machine learning model training, the method can accurately predict the soil water content SMC at no observation point, and has high adaptability and expandability. Therefore, the technology provided by the invention has remarkable advantages in the aspects of improving SMC prediction precision, expanding temporal-spatial resolution, coping with complex terrains and meteorological conditions and the like, and an efficient and practical solution can be provided for dynamic monitoring and management of soil moisture.

The invention relates to an IMU / GNSS fusion method based on Kalman filtering, computer equipment and a storage medium. The method comprises the following steps: acquiring discrete observation points according to sampling data; obtaining a reference sampling section according to the integrity of the grouping interval, and comparing forward and reverse data obtained by forward recursion and reverse recursion to obtain a recursion boundary; analyzing the Kalman gain matrix to obtain a prediction state value; fusing the IMU data and the GNSS data according to the double integration result and the mapping conversion result to obtain an initial fusion quantity; obtaining a mean square error value according to a test criterion and the change characteristics; processing the corrected recursion boundary through recursion optimization to obtain a corrected fusion amount; analyzing to obtain a state quantity according to the reconstructed correction fusion quantity, the characteristics of the IMU data and the GNSS data and a reconstruction rule; the state quantity is input into the extended Kalman filter, fusion of IMU data and GNSS data is completed through a prediction-update-feedback mechanism, and the positioning precision and reliability of the integrated navigation system are effectively improved.

The invention discloses a matrix layout illumination state detection method, device and equipment, and the method comprises the steps: collecting and analyzing a power supply bus current waveform and a single lamp feedback signal, and generating a node feature cluster; extracting a power consumption difference value of adjacent lamps, identifying an abnormal observation point and constructing a fault diffusion chain; marking a timestamp for the fault diffusion chain, determining a fault source, and marking current by injecting characteristics to delimit an influence domain; deploying monitoring anchor points at the boundary of the influence domain, collecting the light color parameter variation and predicting the residual life; extracting the oscillation envelope of the starting current peak, evaluating the health degree of the driver and generating a maintenance density field; and a three-dimensional detection field is constructed by fusing multi-dimensional detection data, singular points are searched, and coordinates of a fault lamp are accurately analyzed through fault positioning pulses. According to the invention, three-dimensional monitoring from electrical characteristics and optical performance to driver states is realized, a single lamp fault can be accurately positioned, a life trend can be predicted, fault propagation can be tracked, and technical support is provided for intelligent operation and maintenance of a large-scale illumination system.

The invention discloses a river and lake water level and flow prediction method and system in the field of remote sensing hydrology and hydraulic engineering, and the method comprises the steps: obtaining satellite radar altimeter data, river and lake water system data, rainfall inversion data and surface temperature inversion data based on a target river, a target lake and a watershed range file; taking the cross interval as a virtual hydrometric station observation point, extracting and processing waveform data, calculating water surface elevation information through a wavelet tracking algorithm, constructing an elevation profile group to establish an initial water level time sequence, and generating a river and lake water level inversion data set through fitting and elevation reselection. Meanwhile, NDWI data are obtained from satellite radar altimeter data, water surface width data are extracted, an inversion data set is formed by combining actually-measured river channel section data and a Manning formula inversion river channel flow rate, and finally various kinds of data are input into a pre-constructed intelligent prediction model to obtain future river and lake water level and flow rate change conditions. According to the invention, the precision of river and lake water level and flow prediction in areas lacking data is effectively improved.

The invention provides a laser and inertia fusion point cloud map construction method and equipment. Mobile laser point cloud data and inertial measurement data which are subjected to hardware time synchronization are obtained through a laser radar and an inertial measurement unit sensor; performing motion distortion correction on the laser point cloud in one frame according to inertial measurement data; projecting the laser observation point after distortion correction to a map coordinate system for feature association according to the position and attitude obtained by inertial measurement data integration, and calculating the position and attitude of a carrier by considering the laser positioning degradation condition; dividing the point cloud sub-graphs according to the time span, constructing the relative pose constraint between the sub-graphs, and optimizing the poses of the sub-graphs to obtain the high-precision point cloud map. The three-dimensional sparse sampling of the environment is directly obtained through the laser radar, the discrete inertia pose is corrected by using the continuous time model to obtain more accurate point cloud distortion correction, the carrier pose is updated by considering laser degradation, and the robustness and precision of point cloud mapping in the moving process are improved.

A slope monitoring method based on optical flow estimation and binocular vision belongs to the technical field of deformation monitoring and measurement, and adopts the technical scheme that a binocular stereoscopic vision system is built, and camera acquisition parameters are calibrated; transmitting the image to a server in real time through the Internet of Things; distortion correction and stereo matching are carried out to obtain a depth map; monitoring points are manually or automatically arranged on the corrected image, and dense optical flow estimation tracking pixel point movement is carried out; world coordinates of observation points and movement conditions of each frame are obtained, and displacement changes of slope monitoring points are obtained by combining coordinates before and after displacement; and obtaining a displacement rate by combining monitoring time and displacement, judging slope safety, obtaining a displacement track, and performing linear interpolation to obtain a slope deformation cloud picture, thereby realizing the monitoring purpose. The slope displacement monitoring method has the advantages that slope monitoring can be carried out more flexibly and efficiently, the safety of workers is improved, the slope monitoring cost is reduced, real-time slope displacement monitoring is achieved, and the intensive degree of slope monitoring points is greatly improved.

The invention provides a mobile robot active positioning method and device for an indoor dynamic scene, and the method comprises the steps: collecting indoor environment data, and constructing an indoor global map which comprises the geometric structure and semantic information of an environment; sensing data collected by the mobile robot in real time are matched with an indoor global map, the pose of the mobile robot in the three-dimensional space is estimated in real time, and the uncertainty index of pose estimation is calculated; evaluating information gains of different potential observation points based on uncertainty indexes of pose estimation, and selecting the observation point with the maximum information gain as a candidate target point; planning a path to the candidate target point; the mobile robot collects sensing data at the candidate target points, the sensing data are matched with the indoor global map again, and the pose of the mobile robot is updated; the indoor environment change is monitored in real time, the indoor global map is dynamically updated, and the indoor global map is regularly corrected and optimized.

The invention belongs to the field of remote sensor calibration, and provides an ROLO model improvement method combining foundation and meteorological satellite lunar observation data, which comprises the following steps of: acquiring a meteorological satellite lunar observation image data set and a foundation lunar observation image data set, and respectively correcting the meteorological satellite lunar observation image data set and the foundation lunar observation image data set; obtaining standard meteorological satellite observation moon radiance and foundation observation moon radiance, and calculating observation moon irradiance; calculating geometric parameters of observation points, the earth, the moon and the sun at the observation moments of the foundation and the meteorological satellite, and calculating the moon irradiance of the ROLO model; polynomial fitting is adopted for the relative deviation between the observed moon irradiance of different wave bands and different moon phase angles and the moon irradiance of the ROLO model, and the ROLO model is improved. The method is not affected by the atmosphere, and errors caused by atmospheric correction of ROLO model foundation observation data are directly avoided; and the phase angle dependence of the ROLO model can be corrected, and the prediction precision of the ROLO model can be improved.

The invention relates to the technical field of geophysics, and particularly discloses a comprehensive inversion method and system based on bidirectional magnetic susceptibility weighted constraint, and the method comprises the steps: dividing an underground three-dimensional space, and building two different cuboid models; determining a total magnetic field observed on the ground and gradient anomaly thereof based on the observation point coordinates and the cuboid model position, and establishing a kernel function matrix; determining an inversion objective function reflecting the relationship between the total magnetic field and the gradient anomaly thereof and the physical property parameters; adding the bidirectional magnetic susceptibility weighted constraint function into an inversion objective function; the optimal solution of the comprehensive inversion target function is solved based on the conjugate gradient method, the high-level resolution capability of the magnetic gradient data and the deep detection advantage of the total magnetic field data are fused, the precision of fine depiction of the superficial anomalous body boundary and the reconstruction precision of the deep field source structure are remarkably improved, a vector cross multiplication regularization constraint mechanism is introduced, and the method has the advantages of high resolution and high resolution. The inversion multiplicity under the complex geological condition is greatly inhibited, and the reliability of the model result and the geological rationality are enhanced.

The invention provides a transformer substation unmanned aerial vehicle three-dimensional route real-time planning and autonomous obstacle avoidance system, and relates to the technical field of non-electrical variable control or adjustment systems. Through cooperative work of a navigation mode switching module and a near-field navigation control module, an electric field of equipment is used as a natural navigation beacon; and high-precision autonomous flight is realized in a strong electromagnetic interference area with RTK signal failure, and the dependence on external positioning is eliminated, so that the inspection blind area is eliminated. Meanwhile, a fusion environment map containing multi-physics field information is constructed, the value of observation points is scientifically quantified according to waypoint inspection efficiency indexes, and a path planning algorithm aims at optimizing a comprehensive target fusing energy consumption, risks and inspection efficiency. The design not only ensures that the unmanned aerial vehicle can safely get close to the core equipment, but also enables the planned route to be the most valuable path rather than the shortest path, and finally fundamentally improves the safety, the operation efficiency and the data validity of substation inspection.

The invention discloses an automatic data processing method and device for a flat beach river channel and a medium. The automatic data processing method for the flat beach river channel comprises the following steps: acquiring historical observation data of a target section in a target river reach; for each historical observation frequency, determining the beach lip position of the target section at the historical observation frequency by using the observation point where the lowest point elevation is located and the riverbed elevations of the plurality of observation points at the historical observation frequency; based on the riverbed elevation of the beach lip position of the target section at the historical observation times, determining the flat beach water level of the target section at the historical observation times; and determining the actual flat beach water level of the target section according to the flat beach water levels of the target section at the plurality of historical observation times. The flat beach water level identification efficiency and accuracy can be improved.

Provided is an image generation device which detects an obstacle that is present in the surrounding space, on the basis of a captured image obtained by a stereo camera of a head-mounted display, and sets a boundary 72 of a play area in which a user wearing the head-mounted display is allowed to move. When a new obstacle is detected inside the play area, the image generation device generates tangent lines 82a and 82 from an observation point H on a floor surface to a contour 80 of the obstacle and uses the tangent lines and a portion which is a part of the contour 80 and which is between tangent points AB to set a new boundary 86 of the play area.

The invention discloses a real-time dynamic trajectory tracking method and system for millimeter wave radar gesture recognition, and relates to the technical field of gesture recognition tracking, and the method comprises the steps: receiving an echo signal reflected by a gesture, extracting a potential target point cloud, and carrying out the clustering generation of a gesture point cloud sequence; establishing a multi-modal motion model library, dynamically selecting an optimal motion model by adopting graph matching, and generating a prediction state in combination with a gesture point cloud sequence; on the basis of a Poisson multi-Bernoulli hybrid filtering framework, according to the signal-to-noise ratio and spatial distribution of the current gesture point cloud sequence, dynamically adjusting the observation weight, optimizing the observation point cloud, and carrying out optimal association by combining Mahalanobis distance with dynamic time warping; a multi-hypothesis tracking strategy is adopted to maintain trajectory hypothesis, an optimal trajectory is selected through a trajectory scoring mechanism, and Kalman filtering smoothing processing is performed on the optimal trajectory. According to the method, high-precision and low-delay tracking of gesture motion is realized, gesture habits of different users and complex environment interference can be adapted, and meanwhile, relatively high track precision is kept.

The invention discloses an urban low-altitude wind field monitoring method and system, and relates to the field of urban environment forecasting, and the method comprises the steps: building a four-dimensional variation assimilation system based on a four-dimensional variation target extensive function formula by taking a wind field state variable of a target urban region as a state variable in the four-dimensional variation target extensive function formula; inputting the wind field observation data of each observation point in the target city area into a four-dimensional variational assimilation system for initial field assimilation to obtain assimilated wind field data; taking the assimilated wind field data as initial meteorological field data, performing wind field simulation calculation by using a preset WRF-LES mode, and obtaining wind field forecast data in the target urban area; real-time monitoring data of each observation point in a target city area is assimilated into an initial and boundary meteorological condition input field of a preset WRF-LES mode through a four-dimensional variational assimilation system, so that wind field information in the target city area is reflected, and city low-altitude wind field forecast data can be acquired in near real time with high resolution.

The embodiment of the invention provides a multi-element deep learning correction method and device for numerical mode forecasting. The method is applied to the technical field of data processing, and comprises the following steps: marking observation points, and performing loss function construction on the positions of the observation points to obtain a model optimization target; carrying out iterative updating on model parameters, and carrying out training process optimization by adopting a dynamic learning rate adjustment strategy to obtain a forecast field correction model; and applying the prediction field correction model to target numerical mode prediction data acquired in real time, performing prediction effect evaluation, and performing standardization processing and anti-standardization processing to obtain a correction prediction result. According to the method, the information extraction capability of a deep learning visual algorithm model is utilized, fine deviation correction of data of a numerical mode forecast space grid by meteorological station observation data is completed, meanwhile, multiple types of meteorological elements are fused, the deep learning model is utilized to learn mutual influences and restrictions between related elements, and the prediction precision of the numerical mode forecast space grid is improved. And finer deviation correction is completed.

The invention discloses an oil reservoir production dynamic prediction method fusing discrete gradient information, and belongs to the technical field of oil reservoir development and artificial intelligence crossing, and the method comprises the steps: building a heterogeneous oil reservoir oil-water two-phase flow numerical simulation data set based on a numerical simulation method; designing a double-branch network structure and extracting spatial and physical characteristics of input field data in parallel, wherein the spatial and physical characteristics comprise a main characteristic coding branch and a differential operator branch; designing a backbone network to carry out deep nonlinear modeling; an efficient pressure and saturation field prediction neural network model is constructed based on a double-branch network structure and a backbone network, in a model training stage, spatial region observation points of part of time steps are used to participate in data item loss calculation, and meanwhile, physical control equation residuals are introduced into all time steps and a whole space to serve as physical loss items; a trained efficient pressure and saturation field prediction neural network model is obtained, and high-precision prediction of a full-time-sequence pressure field and a saturation field is achieved.

The invention discloses a pollution source dynamic tracing method and equipment for industrial park hidden gas leakage. Comprising the following steps: acquiring meteorological monitoring data at each observation point through fixed monitoring equipment and / or mobile monitoring equipment; correcting the coordinates of the observation points; predicting a first gas pollutant predicted concentration corresponding to each observation point after coordinate correction according to a Gaussian plume diffusion model; inputting the first gas pollutant predicted concentration and meteorological monitoring data into a CatBoost model to obtain a second gas pollutant predicted concentration; taking the difference between the actual gas pollutant concentration at each observation point and the second gas pollutant predicted concentration as a target function, and solving the target function to obtain the pollutant source intensity and the source position; completing one iteration; and setting the observation point near the current predicted pollutant source position, and so on, and obtaining the final pollutant source intensity and source position after n iterations.

The invention discloses a dual-band InSar and GNSS fused high-precision three-dimensional deformation adaptive monitoring method, and the method comprises the following steps: constructing a dual-band observation equation, and decomposing the dual-band observation equation to obtain a dual-band InSAR resolving residual error; calculating the comprehensive weight of each GNSS observation point through an entropy weight method, selecting a plurality of GNSS observation points with high comprehensive weights as an optimal GNSS reference point combination, and converting the three-dimensional deformation observed by the optimal GNSS reference points into an LOS direction; expanding a two-dimensional radial basis function to a three-dimensional radial basis function, optimizing the number and distribution of the three-dimensional radial basis function according to the residual drop rate in the expansion process, and performing basis function encryption on the key area by combining the LOS direction result of the reference point; an original two-dimensional state equation in a Kalman filtering algorithm is expanded to be three-dimensional, meanwhile, a GNSS three-dimensional observation value serves as a strong constraint condition of the Kalman filtering algorithm, and a final three-dimensional deformation rate field is output through iteration and 3D-Va analysis.

The invention provides a chip testability design program code generation device and method, and the device comprises a chip design file processing module which is used for receiving a chip design file; the testability design circuit generating and marking module is used for adding a test point and an observation point in a circuit of the chip according to a testability design rule of the chip; the resource dynamic calculation module is used for dynamically generating a pseudo netlist according to the flattening rule; the virtual device modeling module is used for modeling a virtual device and recording an FPGA model generated when the pseudo netlist is generated; the simulation and testability design collaborative optimization module is used for generating a simulation code of the verification platform according to the program code completion degree of the chip design file and performing simulation; and the design data sharing module is used for storing the shared chip design file. The invention further provides a method for implementing the device. According to the method, the efficiency of designing the chip test circuit can be improved, and the flexibility and usability of collaborative design of tools in different design stages of the chip are improved.

The invention relates to the technical field of photovoltaic power generation, in particular to a photovoltaic power station ultra-short-term power prediction method based on double foundation cloud atlas, which comprises the following steps: collecting real-time foundation cloud atlas of two imagers of a photovoltaic power station, and carrying out image preprocessing; dividing the preprocessed foundation cloud picture into different cloud cluster areas by using an image segmentation algorithm; according to a binocular vision principle, calculating cloud bottom height and horizontal distance of each cloud cluster relative to an observation point; and simulating the ground cloud shadow coverage condition of the photovoltaic power station, obtaining an attenuation coefficient, correcting the irradiation intensity by using the attenuation coefficient, and inputting into the power prediction model to obtain an ultra-short-term power prediction value. Through the prediction method, the shielding condition of the cloud layer projection on each area of the photovoltaic power station can be accurately judged, and the ultra-short-term power prediction precision and accuracy of the large-scale photovoltaic power station under the cloudy weather condition are improved.

To solve conventional problems, that is, to provide a bridge displacement monitoring system capable of continuously measuring a displacement of a bridge member and determining whether or not the displacement amount is appropriate after subtracting a displacement due to a temperature.SOLUTION: A bridge displacement monitoring system according to the present invention is a system for monitoring the displacement of a bridge, and includes temperature measurement means, positioning means, observation point displacement calculation means, smooth displacement calculation means, temperature-based displacement generation means, correlation value calculation means, processing method selection means, temperature displacement calculation means, displacement difference calculation means, and first warning output means. The smooth displacement calculation means performs two or more types of noise processing on the observation point displacement to obtain two or more types of "smooth displacements", and the temperature-based displacement generation means generates two or more types of "temperature-based displacement data sets" based on the "time-based temperature data set" and the two or more types of "time-based displacement data sets".SELECTED DRAWING: Figure 2

The invention discloses a bridge tower section attitude intelligent prediction method and system based on multi-point cooperative localization, and the method comprises the steps: S100, selecting a plurality of observation points on two adjacent side surfaces of an installed section, obtaining the space coordinates of the observation points through a laser reflector, and carrying out the cooperative measurement through a plurality of total stations; s200, performing attitude change prediction on the to-be-installed segment based on the rigid body transformation mathematical model, calculating the optimal attitude of the to-be-installed segment by using a rotation matrix and a translation vector, optimizing attitude estimation by excessive-point redundant information, and controlling an accumulative error; s300, performing BIM modeling on the bridge tower structure, performing high-precision three-dimensional rendering, converting complex monitoring data into a visual graphical interface, and displaying a segment state in real time; s400, according to data comparison, analysis is carried out in combination with errors, and if the errors with design data are large, corresponding adjustment is carried out; and S500, the steps are repeated till all the bridge tower sections are accurately positioned and installed. And the erection precision and the construction efficiency of the bridge tower sections are improved.

The invention provides a laser array type soil surface displacement field identification method, which comprises the following steps of: determining a plurality of observation points according to a laser array and a target on the soil surface, establishing a coordinate system by taking a soil body mark point without load as an original point, and recording the original coordinates of each observation point; after the soil body displaces, the laser array records coordinates of the observation points after displacement according to a scanning period; extracting coordinates of a plurality of observation points after soil displacement according to the target displacement field of the soil surface and the spatial change surface data; and interpolating and outputting the plurality of coordinates into a surface by using an interpolation method. According to the invention, high-precision and non-contact scanning is carried out on the land surface by utilizing laser displacement sensor equipment through a laser array type soil surface displacement field identification technology, and the displacement condition of the land surface can be monitored in real time through processing and analyzing scanned data, so that potential geological disaster risk points can be identified. Therefore, the purpose of continuously monitoring the soil surface displacement in real time is achieved.

The invention discloses a natural resource supervision method based on three-dimensional GIS scene and video fusion, and relates to the technical field of intelligent natural supervision. Comprising the steps of collecting a video stream through a preset observation point, extracting a continuous frame sequence, and calling a space parameter set; performing dynamic region detection on the video frame sequence to generate change feature data; mapping the change characteristic data into a topological structure to be updated based on the projection matrix; calling a pre-constructed three-dimensional GIS scene model, and positioning an update area; according to the type identifier of the change feature data, executing an operation of deleting the associated geometry or constructing and fusing a new geometry; adding a state attribute label to the updated geometry, and storing an update model; the resource change rate is calculated, and when the change rate exceeds a preset industry threshold value, an early warning instruction is generated and pushed to the control terminal. And the space accuracy, the model updating efficiency and the early warning reliability of resource change identification in a complex natural scene are remarkably improved.

The invention discloses a short-term power prediction method and system for a regional wind power cluster, and the method comprises the steps: S1, collecting and processing wind power plant data of each wind power generator; s2, calculating wind power plant data of non-observation points of the wind power plant by using a spatial interpolation method, obtaining a wind speed and wind direction distribution diagram of the whole wind power plant based on the data of known observation points, and extracting the wind power plant data; s3, performing similarity capture on the fans, and classifying the similar fans into one group; s4, integrating the wind power plant data, and enhancing the wind power plant data; a graph attention mechanism advanced feature learning method is utilized to deeply excavate the mutual relation among the fans in the wind power plant; s5, in combination with a FoecastNet deep learning model, constructing a deep learning model; and S6, based on wind power plant data, performing short-term regional wind power cluster power prediction by using the constructed deep learning model.

The utility model belongs to the technical field of settlement point observation, and discloses a concealed settlement observation point device which comprises a wall body, a mounting hole is formed in the side wall of the wall body, a settlement observation device is mounted in the mounting hole, and the settlement observation device comprises a mounting rod mounted in the mounting hole and a measuring head fixedly arranged on the side wall of the mounting rod. A signboard is installed on the side wall of the wall body and located above the settlement observation device, and a protection device used for protecting the settlement observation device is installed on the wall body. The effect of protecting the detection device is achieved.

The invention relates to the technical field of radio, and particularly discloses a high-precision electromagnetic spectrum map construction method and system for a topographic relief environment, and the method comprises the steps: obtaining spectrum data in a to-be-constructed region, the spectrum data comprising signal intensity and geographic position; combining terrain digital elevation and topographic relief elevation information in a to-be-constructed region with the signal intensity, and matching with an observation point according to resolution to obtain topographic relief elevation information of the observation point; establishing a model for the topographic relief elevation information of the observation point based on a Gaussian regression process, and performing parameter optimization on the model by using hyper-parameters of a kernel function; and according to a posterior probability estimation formula of the Gaussian regression process, constructing a spectrum map of the to-be-constructed region by using the monitoring value of the spectrum data in the to-be-constructed region as input.

The invention relates to the technical field of grassland monitoring, in particular to an adjustable grassland remote sensing monitoring system and method, and the system comprises a data preprocessing module, a variation trend analysis module, a self-adaptive remote sensing parameter configuration module, a grassland monitoring network layout optimization module and a remote sensing observation and intelligent estimation module. The data preprocessing module integrates historical remote sensing and ground actual measurement data and provides a data basis for subsequent analysis; the change trend analysis module calculates the change rate and the standard deviation of the key features and identifies the change trend of the grassland ecological environment; the adaptive remote sensing parameter configuration module adaptively adjusts remote sensing monitoring parameters, and the monitoring efficiency is improved; the grassland monitoring network layout optimization module optimizes the layout of the remote sensing detection unit and improves the efficiency of the monitoring network; and remote sensing observation and intelligent estimation use a spatial interpolation method to estimate the value of a future observation point, so that the workload of field measurement is reduced. The method is used for solving the technical problem that the current grassland monitoring management effect is poor.

The invention relates to a photoelectric pod air target correction method based on target information, which comprises the following steps: before an aircraft takes off, measuring the geographic position of a selected target point, after the aircraft enters an airline, observing and aiming a target in the intervals of 0-90 degrees, 90-180 degrees,-90-0 degree and-180--90 degrees of the azimuth of a photoelectric pod respectively, and after the aircraft enters the airline, observing and aiming the target in the intervals of 0-90 degrees, 90-180 degrees,-90-0 degree and-180--90 degrees of the azimuth of the photoelectric pod; the current inertial navigation information and the GPS geographic position are recorded at each observation point through a ground snapshot instruction, target correction acquisition point collection is completed after target observation of each azimuth angle under each quadrant is completed, the installation deviation angle of the photoelectric pod and the aircraft inertial navigation is calculated, the installation deviation angle is written into a pod EEPROM to be stored, and the installation deviation angle of the photoelectric pod and the aircraft inertial navigation is obtained. In a word, the method is suitable for all ground station systems and airplanes with air boresight conditions, and has the advantages of low cost, short time, convenience in use and capability of ensuring the correction precision of the installation deviation angle.