133results about How to "Improve engineering practicability" patented technology

The invention provides a trajectory tracking control method and system for a high-load omni-directional AGV, the omni-directional AGV is driven by a plurality of steering wheel mechanisms which are independently steered and driven, and the trajectory tracking control method is characterized by comprising the following steps: acquiring vehicle state information and expected trajectory information of the omni-directional AGV in real time; on the basis of the vehicle state information and the expected track information, core parameters of a model prediction controller are optimized online through a chaos simulated annealing algorithm, and the core parameters comprise a state weight matrix, a control increment weight matrix, a prediction time domain and a control time domain; wherein the model prediction controller performs state prediction based on a longitudinal-transverse decoupling dynamic model adaptive to a high-load working condition; performing rolling horizon optimization through the model prediction controller by utilizing the optimized core parameters, and solving to obtain an optimal control increment; and converting the optimal control increment into a driving instruction for a multi-steering-wheel mechanism, and controlling the omnidirectional AGV to track an expected trajectory.

The invention provides a clothing pattern intelligent generation method and system based on multi-modal visual identification, and relates to the technical field of visual identification. The method comprises the following steps: firstly, acquiring clothing image data and text description data, performing unified preprocessing, obtaining a style category label, an appearance attribute label and a local contour key point set through multi-modal visual identification, generating structure semantic elements and geometric anchor points by utilizing a semantic-to-structure mapping knowledge base, and constructing a plate type structure diagram; further analyzing the structural constraint and executing constraint solution to generate a candidate pattern; and in combination with manufacturability verification and constraint correction, closed-loop optimization is formed, a target plate type file meeting the structure and manufacturing requirements is output, and effective connection from semantic recognition to plate type structure generation is realized.

This invention provides a radiometric calibration and temperature inversion method for a cooled infrared thermal imager, comprising: constructing a temperature calibration device, acquiring multiple calibration temperature points and their corresponding blackbody radiance to obtain corresponding datasets, acquiring two sets of response image grayscale value datasets to obtain a radiometric calibration model; plotting response curves to simultaneously solve for unknown parameters in the radiometric calibration model to obtain a near-range radiometric calibration model; using the modified radiometric calibration model for back-calculation to obtain the actual radiance of the target; pre-setting the constructed lookup table in the embedded processing unit of the infrared thermal imager to perform online temperature inversion output to obtain the absolute temperature of the measured target, and then outputting the absolute temperature of the measured target to a host computer display module. This invention can significantly reduce the calibration workload, enabling the infrared thermal imager to flexibly adjust the integration time and maintain high-precision temperature measurement in complex scenarios, significantly improving its engineering practicality and environmental adaptability.

The application relates to a deformation field prediction method based on image quality evaluation and adaptive pre-training. Wavelet packet decomposition is performed on a reference image and a deformation image of a collected component to obtain coefficients of each subband of each layer of the images, and Pearson correlation coefficients of each subband are calculated based on all the coefficients corresponding to the subband. The subbands are screened based on L2 norms of coefficient vectors of the subbands and the Pearson correlation coefficients, and the screened coefficient vectors of the subbands are reconstructed to obtain reconstructed reference images and reconstructed deformation images. Quality evaluation indexes of each reconstructed sample pair are calculated. Training branches to which the corresponding reconstructed sample pairs belong are determined based on the quality evaluation indexes, a U-Net model is preliminarily trained by using a preset supervised training strategy based on the reconstructed sample pairs of different training branches, the U-Net model is unsupervisedly trained, and displacement fields and strain fields are predicted based on the trained U-Net model.

The application discloses a V-shaped groove weld deviation detection method based on a YOLO model and groove reconstruction in the field of welding. First, a single YOLO model is trained to synchronously identify and intercept the left and right edge windows of a groove and the center window of a welding torch in a globally collected image of a welding area. Second, the groove edge window image is subjected to a gray scale transformation and a local adaptive threshold method, and the left and right edges of the groove are reconstructed in combination with a random sample consensus algorithm and a multi-level rollback mechanism. Then, the center of the groove is located by a midpoint recursion method based on the reconstructed groove edge, and the center of the welding torch is determined according to the center window of the welding torch. Finally, the deviation between the center of the groove and the center of the welding torch is calculated, and a two-level filtering mechanism combining Kalman filtering and moving average filtering is adopted to obtain a stable weld deviation value. The synchronous positioning of the double groove edges is realized by a single model, the arc light interference is effectively inhibited by combining the local adaptive threshold and the reconstruction method, and the robustness and precision of the weld deviation detection are improved.

The application discloses a variable working condition cross-component causal coupling modeling and fault diagnosis method and device, and relates to the technical field of rotating machinery fault diagnosis. The method comprises the following steps: collecting and preprocessing multi-component signals under variable rotating speed working conditions of a rotating machinery; based on multivariate Granger causal modeling, prior information and dynamic disturbance information are fused to construct a dynamic causal diagram; a double-view structure of a main view of the dynamic causal diagram and an auxiliary view of a correlation diagram is constructed, multi-scale coupling features are extracted through a multi-order spectrum filter and an attention mechanism, a symmetric InfoNCE contrast learning loss and a supervised classification loss are combined to optimize and train a model, and fault classification is realized. The device is correspondingly provided with signal acquisition, dynamic causal diagram construction, feature fusion, model training and fault prediction modules. The application improves the accuracy of fault feature representation and the robustness of diagnosis under variable working conditions, is suitable for complex industrial scenes with multi-component coupling and frequent rotating speed switching, and has strong engineering practicability.

This application provides a method for predicting the wear state of steel-based ceramic wear-resistant components, relating to the field of material life assessment technology. The method includes the following steps: emitting excitation eddy currents containing a first frequency and a second frequency into a steel substrate, and receiving the response signal modulated by the state of the steel substrate and the ceramic-steel interface; processing the response signals corresponding to the first and second frequencies respectively, extracting the first phase change of the first reference signal corresponding to the initial health state of the component relative to the first frequency, and the second phase change of the second reference signal corresponding to the second frequency; calculating the difference between the first and second phase changes to characterize the change in thermal conductivity state of the ceramic-steel interface; obtaining the real-time operating temperature of the wear-resistant component, and based on the real-time operating temperature and the difference, assessing the current state level of the ceramic-steel interface, and predicting the remaining safe service time of the ceramic wear-resistant layer by combining the trend of the difference over time.

The invention belongs to the technical field of electronic countermeasures, and particularly relates to a distributed jammer closed-loop coherent control method based on synthesis effect feedback, which comprises the following steps of: modeling signal coherent synthesis efficiency, acquiring coherent synthesis efficiency of a jammer A and a jammer B under different phase differences under an error-free condition, and calculating the coherent synthesis efficiency of the jammer A and the jammer B; establishing a phase difference-coherent combination efficiency mapping relation reference database; a-B jammer phase correction is carried out; and jammer A-C correction is carried out. According to the method, the robustness and the engineering practicability are remarkably improved; the cost, the power consumption and the complexity are effectively reduced; according to the invention, by taking a synthesis effect as a guide, the harsh requirement on the absolute space-time precision of a single node is relaxed, common commercial devices with lower cost and lower precision are allowed to be used for construction, and meanwhile, the complexity of initial calibration and setting is simplified, so that the hardware cost, power consumption and deployment and maintenance difficulty of the whole distributed mode are greatly reduced; and the dynamic self-correction and long-term stability capabilities are endowed.

This invention discloses a self-cleaning radiative cooling coating based on biomass carbon quantum dots, its preparation method, and its application. The coating comprises a polymer film-forming resin, carbon quantum dots, and hollow glass microspheres; wherein the mass ratio of the polymer film-forming resin to the filler is 1:(0.1-0.5), and the mass ratio of the hollow glass microspheres to the carbon quantum dots is 1:(0.05-0.2). This invention synergistically enhances solar reflectivity through the photoluminescence properties of carbon quantum dots and the scattering properties of hollow glass microspheres, while simultaneously utilizing the synergistic effect of fluorinated resin and modified hollow glass microspheres to endow the coating with superhydrophobic self-cleaning function. A single layer of this coating can achieve a solar reflectivity >92%, mid-infrared emissivity >94%, and contact angle >150°, possessing both efficient daytime radiative cooling and long-lasting self-cleaning performance. It is particularly suitable for passive heat dissipation of outdoor equipment such as new energy vehicle charging piles, and features simple process, environmental friendliness, and excellent performance.

The application discloses a kind of photovoltaic power probability prediction method and system, it is related to photovoltaic power generation power prediction field, including: obtaining historical meteorological data and photovoltaic power data and pre-processing and division, constructs daily meteorological feature vector and carries out density peak clustering, generates the soft assignment vector of daily meteorological feature vector;Based on the input feature matrix of pre-processed historical meteorological data, variational autoencoder is input with the input feature matrix and soft assignment vector, and the decoupled initial hidden variable is obtained;SDE-Net is constructed, and the photovoltaic power prediction mean is solved to obtain with initial hidden variable, and final prediction distribution is generated;Variational autoencoder and SDE-Net are jointly trained, and photovoltaic power prediction model is obtained;Obtain the sample to be predicted, and predict using photovoltaic power prediction model, and obtain photovoltaic power probability prediction result.The application greatly improves the reliability and engineering practicability of photovoltaic power probability prediction.

The invention discloses a vehicle safety assessment method and device based on an AI-assisted attack tree, equipment and a medium, and belongs to the technical field of computers. The method comprises the following steps: automatically generating an attack tree containing a dynamic risk level by adopting a hierarchical agent workflow based on a vehicle type architecture; extracting a threat scene from the attack tree and generating a structured description; matching a historical knowledge base by utilizing a retrieval enhancement generation technology, and generating an asset list and a threat score through a large language model; and synthesizing the attack tree and the scoring result to generate an evaluation conclusion containing security disposal suggestions. According to the technical scheme, the attack tree can be automatically generated based on the vehicle model architecture, intelligent scoring is linked, finally, the safety evaluation conclusion containing specific disposal suggestions is output, and the evaluation efficiency, accuracy and engineering practicability are remarkably improved.

The invention relates to a radiation meter dense feed source array directional diagram optimization method based on a generative adversarial network. The method comprises the following steps: constructing a training data set, a condition vector containing a target directional diagram performance index and a corresponding optimal amplitude-phase weight vector; designing a conditional generative adversarial network model; constructing a physical constraint loss function by embedding a differentiable electromagnetic calculation module; loading the constructed training data set, and training a complete generative adversarial network model based on a loss function embedded with a differentiable electromagnetic calculation module; and when the system antenna pattern obtained according to the amplitude-phase adjustment parameters output by the generative adversarial network model meets the antenna pattern evaluation index, completing dense feed source array antenna pattern optimization. According to the method, the target weight is dynamically adjusted and optimized on orbit, so that a single model can adapt to beam forming requirements in different observation scenes, and flexible switching of multiple task modes such as high-resolution detection and wide-range general survey is realized.

The application discloses a kind of quadrotor unmanned aerial vehicle formation anti-saturation flexible preset time performance tracking control systems, solve the stability of existing system is influenced by input saturation, the problem that performance boundary is fixed and cannot be flexibly adjusted.System includes quadrotor unmanned aerial vehicle system module, flexible preset performance technology module, anti-saturation formation controller design module and stability analysis module.Through the establishment of unmanned aerial vehicle dynamics standard model containing input saturation, the saturation-tolerant flexible preset performance function that can be adjusted according to system operating state is constructed, the fuzzy adaptive backstepping controller with anti-saturation compensation is designed in combination with the leader-follower strategy, and the system stability is proved by using Lyapunov theory.Simulation shows that the system can effectively offset the influence of input saturation, flexibly adjust the performance boundary, make the formation tracking error quickly converge and the closed-loop system signal is bounded, realize the high-precision, high-stability tracking control of quadrotor unmanned aerial vehicle formation.

The invention discloses a variable-force pressing method and device of a seeder based on vision and sensor fusion. The method comprises the following steps: acquiring an earth surface soil image through an industrial camera, extracting soil structure characteristics, and constructing a multi-source information fusion model to calculate a target ballast force in combination with ballast force and soil water content information acquired by a pressure sensor and a humidity sensor; and through feedforward calculation of the electric push rod and PID closed-loop control, self-adaptive adjustment of the ballast force is realized. And meanwhile, a model optimization and self-learning mechanism is introduced, so that the accuracy and adaptability of ballast force adjustment are improved. The matched device comprises a rack, a press wheel, an electric push rod, a camera, a pressure sensor, a humidity sensor and other structures, and the problems that an existing seeder press device is insufficient in press force adjusting precision and low in intelligent level are solved. The pressing force can be adjusted in a self-adaptive mode according to different soil conditions, the soil pressing uniformity and the sowing quality are improved, and the device is suitable for intelligent pressing operation of agricultural machines such as a sowing machine.

This utility model relates to the field of sensor technology, specifically a spring terminal plug-in connector for sensors. It includes a terminal base for connecting to a sensor housing, with three bracket slots on its outer side wall; a terminal bracket with a bracket body, having three bracket pins along its outer side wall, which plug into the three bracket slots to achieve connection between the terminal bracket and the terminal base; each bracket pin has a terminal insertion hole into which a spring terminal is inserted; the outer and inner connecting ends of the spring terminal extend out of the terminal insertion hole, with the outer connecting end contacting a spring in the component to be connected for electrical connection. This solution effectively solves the problems of "mismatched connection surfaces and unstable assembly" in traditional spring terminals, adapts to three-wire sensors, improves connection reliability and assembly efficiency, and is suitable for sensors in industrial, automotive, and other fields.

The invention discloses a direct current charging interface identification and positioning method, and belongs to the technical field of automatic charging of electric vehicles. The method comprises a coarse positioning stage and a fine positioning stage. In the coarse positioning stage, the initial position of the charging port is quickly determined through image preprocessing, contour screening and ellipse fitting. In the fine positioning stage, a multi-factor cluster matching algorithm is adopted, and firstly, a matching preselected area of each feature hole is determined according to a preset geometric cluster relationship among a plurality of feature holes in the charging port; secondly, dynamically calculating scaling and rotation coefficients of the template to adapt to different postures; and finally, constructing a matching degree evaluation function in combination with a central gravitation and edge gravitation principle, and performing matching calculation on an original image and a gradient image template so as to realize accurate positioning of the feature hole. The method solves the problem that the recognition precision and efficiency of the charging port are difficult to consider at the same time in a complex light field environment, and has the advantages of high positioning precision, high processing speed, high environmental adaptability and easy engineering deployment.

The invention provides a mobile robot unknown static target surrounding control method based on image vision, which comprises the following steps: acquiring an image containing an unknown static target, and extracting pixel coordinates of target feature points in an image plane; based on the internal reference of the monocular camera, converting the pixel coordinate into a normalized image plane coordinate, and constructing an image error signal according to the normalized image plane coordinate and an expected steady-state coordinate corresponding to the expected surrounding radius; calculating a forward linear velocity control quantity and a steering angular velocity control quantity of the mobile robot; the control quantity is output through a closed-loop control structure so as to synchronously realize two control targets: driving an image error signal to converge to an expected steady-state coordinate, and ensuring that a target feature point is always in an effective view range of a monocular camera in the whole surrounding control process; and outputting the forward linear velocity control quantity and the steering angular velocity control quantity obtained by calculation to a motion execution mechanism of the mobile robot, and driving the mobile robot to execute fixed-distance surrounding control on the unknown static target at an expected surrounding radius.

The invention discloses a parallel flow channel heat exchanger flow distribution simulation method based on a general flow resistance model. The method comprises the following steps: 1, a single-channel general flow resistance model construction stage: creating a general flow resistance model; carrying out single-channel fine modeling and simulation; acquiring pressure drop-flow velocity data; fitting to determine a flow resistance coefficient; 2, a whole machine flow distribution simulation stage: constructing a whole machine simulation model: establishing a three-dimensional geometric model of a macroscopic flow channel system of a target heat exchanger, performing grid division to obtain a macroscopic flow channel CFD model, abstracting all parallel micro-channels of a core heat exchange section of the target heat exchanger into a parallel flow resistance network consisting of a large number of flow resistance elements, each flow resistance element is endowed with a universal flow resistance model which is determined in the first stage and has a specific flow resistance coefficient; coupling solution is carried out, an iteration process is established, the error between the driving pressure difference of each flow resistance element and the pressure drop calculated according to the general flow resistance model is smaller than a preset threshold value, and iteration convergence is carried out; and outputting a flow distribution result of all parallel flow channels of the whole target heat exchanger.

The present application relates to the field of agricultural environmental information technology, and discloses a remote control method for nitrogen and phosphorus loss amount of paddy field facing environmental data. The method comprises the following steps: obtaining historical nitrogen and phosphorus loss data, real-time meteorological data and water and fertilizer operation information of the paddy field; constructing a mechanism constraint sub-model combining nitrogen migration and transformation dynamics and phosphorus adsorption and desorption balance; performing multi-source feature extraction on the meteorological data to generate a standardized environmental vector; inputting the light residual convolutional neural network to output an initial prediction value; combining the mechanism model to generate a corrected prediction value conforming to the conservation of mass through the Lagrange multiplier method; when the prediction value exceeds the threshold value, generating a drainage gate opening degree and a fertilizer plan adjustment instruction, and delivering the instruction to an edge terminal for execution through a narrowband Internet of Things.

The invention discloses a cable fault diagnosis method and device based on a neural network model, and relates to the field of power system equipment state monitoring and fault diagnosis, and the method comprises the steps: collecting and preprocessing time domain signals of a cable in different states, employing a PSO algorithm to improve a CAOA algorithm to obtain a CAPSO algorithm, and employing the CAPSO algorithm to optimize the parameters of a TVFEMD algorithm; decomposing the time domain signal based on a TVFEMD algorithm after parameter optimization to obtain a plurality of IMF components and determining an optimal IMF component; and extracting features of the optimal IMF component, carrying out feature dimension reduction processing, and training a neural network model based on the features after the feature dimension reduction processing so as to realize cable fault type identification. According to the invention, accurate diagnosis of cable faults can be realized.

This invention belongs to the field of mobile communication technology and discloses a RIS-assisted high-speed railway tunnel communication resource scheduling method and system. By acquiring real-time train location information, it dynamically judges and selects direct links or RIS-assisted links to adapt to changes in the tunnel propagation environment, effectively alleviating non-line-of-sight transmission problems caused by tunnel obstruction and improving the continuity and stability of communication links. When an auxiliary link is selected, the RIS target phase configuration is determined based on real-time location information, eliminating the need to frequently acquire full instantaneous channel information, reducing computational complexity and signaling overhead, and improving engineering practicality. Based on the selected link type and channel state information, the achievable rate of each service flow is determined, the resource amount required for its QoS requirements is estimated, and the scheduling priority index is calculated by combining the service flow priority weights. Transmission resources are allocated within available resources, taking into account the QoS requirements of multiple services and resource utilization efficiency, giving full play to the coverage advantages of RIS assistance, and ensuring stable transmission of multiple services in tunnel scenarios.

This invention discloses a method for predicting influent water quality of wastewater treatment plants based on impact-triggered refitting, specifically relating to the field of urban wastewater treatment technology. The prediction method includes the following steps: S1: Data acquisition and time alignment; S2: Sensor dead value removal and causal forward imputation; S3: Causal anomaly identification and peak / impact classification; S4: Impact-preserving preprocessing sequence construction; S5: Daily Fourier term construction; S6: SARIMAX model establishment; S7: H-step forward rolling prediction; S8: Impact-triggered short-window refitting; S9: Result output and impact early warning; Integrated prediction and early warning, with high engineering practicality: It realizes the integration of short-term rolling prediction of water quality indicators and impact risk early warning, outputting multi-dimensional prediction evaluation indicators and graded early warning information. The early warning information includes key engineering parameters such as impact amplitude and duration, which can directly provide accurate decision support for process control and operation scheduling of wastewater treatment plants.

The invention belongs to the technical field of radar data processing, particularly relates to a compact ground wave radar track association method and system, aims to solve the problem of fuzzy association of T / R and T-R dual radars in a multi-target dense scene, and generates time-space aligned short-time track segments by acquiring and preprocessing track data of the two radars; constructing a local topological graph with a main target as a center and a neighborhood target as a node for each fragment, and forming a graph sequence pair to be compared according to a time sequence; the graph sequence pairs are input into a twinning GAT-LSTM network, spatial features are extracted through a graph attention network (GAT), time sequence features are fused through a long short-term memory network (LSTM), double-branch high-dimensional spatial-temporal feature vectors are output, and segment-level association scores are obtained through splicing and similarity estimation; and calculating a consistency index based on the score set of all the fragments in the track overlapping region and judging track association. The accuracy and robustness of track association in a target dense environment are improved by combining time and space features of a modeling target.

The present application belongs to the technical field of power supply and distribution. A parallel three-level inverter modulation method, system, product, medium and equipment are proposed, which equivalent double parallel three-level inverters as a whole five-level inverter, inject zero sequence components to the initial three-phase reference signal to obtain the reference signal, identify the resultant vector through rounding and remainder operation, and then adjust the reference signal according to the sum of the three-phase state of the initial resultant vector to output a five-segment switching sequence, and then decompose the equivalent five-level reference signal into two sets of three-level reference signals, allocate the reference signals by dividing the half-carrier period and compare them with the triangular carrier to obtain a three-level vector sequence combination. The present application realizes the comprehensive optimization of common-mode voltage, zero-sequence circulating current and output current ripple, avoids complex sector judgment and duty cycle calculation, and reduces the implementation complexity and computational burden.

The invention provides a power system load frequency control method based on a high-order all-drive system theory, and relates to the technical field of power system load frequency control. Comprising the following steps: collecting physical parameters of a power system, and establishing a power system state space equation mathematical model according to the physical parameters of the power system; verifying the controllability of the power system according to the state-space equation model, and obtaining a system controllability matrix; based on the controllability matrix, converting a state-space equation model of the power system into a high-order all-drive system model; providing a power system state observer and a spoofing attack observer according to the high-order all-drive system model; and providing a power system load frequency controller based on a high-order all-drive system theory according to the high-order all-drive system model and the observer. According to the method, the linear dynamic model of the electric power system more conforming to actual working conditions is constructed. Based on the high-order all-drive method, the design complexity of the power system controller can be reduced, and the stability of the power system is ensured.

The invention belongs to the technical field of source network coordination control, and particularly relates to a hydroelectric generating set source network coordination control parameter dynamic optimization method fusing historical context and an improved multi-target whale algorithm, a related system, equipment and a storage medium. Aiming at the problems of poor adaptability of control parameters, conflict of multi-target optimization, lack of historical working condition reference and the like in the process that a hydroelectric generating set participates in primary frequency modulation of a power grid and AGC (Automatic Gain Control), the method comprises the following steps: acquiring and preprocessing multi-source heterogeneous data, mining context information of historical working conditions to generate initial optimization parameters, and extracting real-time working condition characteristics and constraint conditions; and constructing a multi-objective optimization model fusing assessment of two detailed rules and unit performance, solving optimal control parameters by adopting an improved multi-objective whale algorithm, executing the optimal control parameters, and dynamically adjusting the parameters in combination with an online rolling optimization mechanism. According to the method, the source-network coordination response speed, the control precision and the operation economy of the hydroelectric generating set are effectively improved, the power grid assessment risk is reduced, and the method is adaptive to a novel power system under high-proportion new energy permeation.

The application relates to a target detection simulation method based on a satellite-borne load, which comprises the following steps: satellite orbit simulation, star background simulation, visible target judgment and detection image simulation; image processing is performed on the ideal detection image so that the detection image simulation result approaches the detection image of the actual satellite-borne load. The method is realized by using C++ programming to improve the algorithm safety and flexibility.

This application discloses a block coordinate descent joint spatiotemporal calibration method and system, belonging to the field of sensor fusion and calibration technology. The method involves acquiring camera image sequences and inertial measurement unit (IMU) data; performing a first-stage coarse temporal alignment, estimating the temporal mapping parameters between the camera and IMU based on the camera image sequences and IMU data; performing a second-stage spatiotemporal joint optimization, jointly optimizing spatial extrinsic parameters, camera intrinsic parameters, and IMU data under the constraints of the temporal mapping parameters; iterating alternately between the first-stage coarse temporal alignment and the second-stage spatiotemporal joint optimization until a preset convergence condition is met; and outputting calibration results, including spatial extrinsic parameters, temporal mapping parameters, a noise model, and a calibration quality report. The method employs an alternating strategy of first performing coarse temporal alignment and then optimizing spatial extrinsic parameters, significantly improving the algorithm's convergence stability and calibration reliability.

The invention discloses an evaluation and screening method for grass seeds suitable for vegetation concrete, and belongs to the technical field of ecological restoration and slope protection. The method comprises the following steps: constructing an evaluation index system of the vegetation concrete protection plant comprising a plurality of evaluation indexes, and setting grading standards of four grades A, B, C and D for each evaluation index; determining the weight of each evaluation index, and extracting a decisive index from the evaluation indexes; the screening process comprises two stages: first-stage theoretical screening is based on grass seed data contrast grading standards, and direct elimination is carried out when decisive indexes appear in a D stage; and second-stage practice verifies that the grass seeds screened out in the first stage are sown in plant-growing concrete with the pH value larger than or equal to 10, and according to the actual measurement performance of the germination rate and the growth speed, the grass seeds with the two indexes both reaching the preset qualified standard are screened out to serve as the final applicable grass seeds. Through systematic quantification and dual verification, the grass seed screening problem in the high-alkali environment is scientifically solved.

The application discloses a high-resolution satellite image urban area shadow-removed ARD production method and device, and belongs to the technical field of remote sensing image processing. The method comprises the following steps: firstly, screening out images covering urban areas; performing radiation calibration on high-resolution satellite images covering urban areas to obtain apparent radiance images; based on the apparent radiance images, performing preliminary shadow detection by using a pre-trained deep learning model; using a near-infrared band of an atmospheric-corrected ground reflectivity image, performing false detection elimination on the preliminary detection result by adaptive threshold segmentation to obtain a final shadow mask; performing histogram matching processing on the shadow area based on the shadow mask on the atmospheric-corrected ground reflectivity image to realize shadow removal; and finally, outputting the shadow-removed ARD product. The application significantly improves the shadow detection precision and the quantitative reliability of the ARD product, and is suitable for large-scale ARD production of high-resolution satellite images in urban areas.