522results about How to "Solve problems" patented technology

The invention discloses a power distribution network topology identification and state estimation method based on a message passing neural network and online deep learning, and the method comprises the steps: training a neural network through historical measurement data, building a power distribution network topology identification model, combining the obtained topology information with corresponding power grid measurement data, and constructing a graph data set; taking the obtained graph data set as a training set to train a message passing neural network to obtain a power distribution network state estimation model; and finally, inputting the real-time measurement data into a state estimation model of the topology identification model to obtain a real-time state estimation result under the section. According to the method, the power distribution network topology can be accurately identified under limited measurement data, accurate state estimation is provided for a power distribution system, and technical support is provided for subsequent safety assessment and optimized operation.

The invention relates to the technical field of fault detection for a wireless communication network, and discloses a fault detection method for the wireless communication network. By collecting data of base station signal strength, network delay, data packet loss rate and user equipment signal quality, the problem of insufficient fault detection caused by a single index is solved. Through multi-dimensional data acquisition, the system can comprehensively evaluate network performance, the fault detection accuracy is improved, and different fault types can be effectively identified especially in a complex fault mode. By storing the data according to the time sequence, the problem of time sequence chaos is avoided, and accurate detection of time-varying faults is ensured. The comprehensive data vector generated by each time of sampling comprises a plurality of network performance indexes, so that the fault detection and positioning capability is improved. Abnormal values are eliminated through data preprocessing, the data quality is ensured, an accurate basis is provided for subsequent analysis, and the reliability of a detection result is ensured.

The invention relates to the technical field of emergency management and big data processing, and discloses an emergency drilling system based on big data, and the system comprises a data collection and recognition module which is used for collecting on-site images and video data in real time through a camera, unmanned aerial vehicle equipment and a mobile terminal, and recognizing the key information of an accident site through an image recognition algorithm; the big data analysis and prediction module is used for processing and analyzing the real-time data acquired from the data acquisition and identification module and predicting the development of the emergency event; and the hierarchical scene analysis and simulation module is used for decomposing the emergency event into a plurality of hierarchies for independent analysis and simulation, and the hierarchies are associated through a preset rule. By means of weapon deduction, multi-level analysis, real-time data processing and intelligent scheduling modules, the problems of experience dependence, decision lag, unreasonable resource scheduling and the like in traditional emergency management are solved, and the ability of coping with complex emergencies is enhanced.

The invention relates to a cross-region computing power cooperation system and method. The method comprises the following steps: acquiring computing power resource information of computing power cluster nodes of each region in a global range; obtaining a cluster pairing strategy according to the computing power resource information of the computing power cluster nodes of each region in the global range, and obtaining a logical computing power cluster group according to the cluster pairing strategy; computing power cluster nodes in the computing power cluster group are used for mutual cooperative computing; acquiring a task processing request, wherein information in the task processing request comprises a service attribute, a computing power demand and a data volume; and according to the task processing request and the computing power resource information of the computing power cluster nodes in the computing power cluster group, determining a target computing power cluster node matched with the service attribute, the computing power demand and the data volume from the computing power cluster group, and sending the task processing request to the target computing power cluster node. The method can improve the utilization rate of global computing power resources.

The invention discloses a point cloud feature extraction method, device and system based on an inspection humanoid robot. The method comprises the following steps: acquiring point cloud data of an underground space; performing denoising, filtering and normalization processing; local features and global spatial enhancement features of the point cloud data are extracted, the local features comprise geometric structure, curvature and boundary features, curvature feature transformation is carried out, and the global spatial features comprise extracted global spatial features and global features for enhancing spatial attention obtained through convolution; and feature information is extracted and classified based on the local features and the global spatial enhanced features. According to the method, efficient extraction and enhancement of global features are realized in a sparse point cloud semantic segmentation task, and the classification precision and robustness of the model in a complex scene are remarkably improved; according to the method, the semantic segmentation precision is remarkably improved.

The invention discloses a computing power scheduling method and system. The method comprises the following steps: under a computing power scheduling task, aiming at configuration information, task resources and workflow definition selected by a user, determining a computing power business demand; target scheduling paths of the computing power service requirements under different preset dimensions are determined according to a preset scheduling mechanism on the basis of the whole network computing power resource information, the computing power scheduling task is completed on the basis of the target scheduling paths, and the preset dimensions comprise one of the cost dimension and the distance dimension. According to the scheme, the scheduling of the computing power resources can be realized.

The invention relates to the technical field of remote water meter abnormity early warning, and discloses a remote water meter abnormity early warning method and system based on historical water volume, and the method comprises the steps: dividing a user water meter into three classes A, B and C according to the installation time of the user water meter, and employing a differentiated abnormity detection strategy. For the A-type users, establishing a water consumption prediction model based on historical consumption data of the A-type users; for the B-type users, finding similar reference water meters from the A-type users by using a K-means clustering algorithm; and for the C-type users, monitoring whether the accumulated water consumption for continuous N days is completely equal or not after the C-type users are installed. After real-time usage data is obtained, usage deviations of various users are calculated respectively, if the deviations exceed a set threshold value, abnormal early warning is triggered, and the early warning level is dynamically adjusted according to the number of abnormal duration days. According to the method, technologies such as machine learning and clustering analysis are comprehensively utilized, a set of comprehensive, flexible and efficient water consumption abnormity early warning system is constructed, and the refinement and intelligence level of urban water supply management can be remarkably improved.

The invention provides a water chilling unit fault detection and classification method, system and device and a medium, and the method comprises the steps: collecting the operation data of a water chilling unit, and recording the operation data as characteristic variables; preprocessing the feature variables, extracting fault detection feature variables in the feature variables to generate time sequence data, and storing the time sequence data in a time sequence database; collecting time sequence data in the normal operation period of the water chilling unit in the time sequence database, and constructing a training sample set; carrying out algorithm modeling on the time sequence data by using a MoE algorithm to generate a MoE model, and carrying out parameter configuration; based on the training sample set, using an Adam optimizer and a CosineLR scheduler to train a MoE model; acquiring real-time time sequence data of the water chilling unit, inputting the real-time time sequence data into the MoE model, and identifying abnormal time sequence data; and performing unsupervised classification on the abnormal time sequence data by using a K-means clustering algorithm to determine the fault type of the water chilling unit.

The invention relates to the technical field of middle and long distance running evaluation, in particular to a middle and long distance running evaluation method and system based on human face body weight recognition, and the system comprises a data acquisition module, an algorithm host, a cloud server and a terminal module. A camera group and a pressure sensing pad in the data acquisition module respectively acquire images and pressure data; the algorithm host module processes data, identifies human face body features, tracks personnel, calculates scores and analyzes violation; the cloud server stores various data; and the terminal module is used for information input and result display. The evaluation method comprises the steps of data acquisition, identity recognition and tracking, score calculation, illegal behavior analysis, data integration and storage, exception handling and correction and the like. According to the invention, many problems of an existing RFID and AI face recognition evaluation mode are solved, for example, the RFID cannot perform biological feature recognition and violation judgment, and AI face recognition is interfered by the environment. Accurate identity recognition, whole-course tracking, accurate score calculation, comprehensive violation detection and efficient data management are realized.

The invention provides a building energy consumption data generation and parallel prediction method and device based on a generative adversarial network, and a medium, and belongs to the technical field of building energy consumption prediction. The method comprises the following steps: collecting actual building energy consumption data as a training sample, including time sequence data and physical attributes of a building; constructing and training a generative adversarial network model; inputting the actual building energy consumption data into the generative adversarial network model to generate enhanced building energy consumption data; the actual building energy consumption data and the enhanced building energy consumption data are mixed to obtain mixed data, the mixed data are used for training a machine learning model, a building energy consumption parallel prediction model is constructed, and a future building energy consumption value is predicted; and evaluating the performance of the prediction model on the test set, and adjusting the model structure or increasing the number of training rounds to improve the prediction precision. According to the method, the problem of insufficient building energy consumption data is solved, the precision and reliability of building energy consumption prediction are remarkably improved, and powerful support is provided for building energy consumption management.

The invention relates to the technical field of sewage treatment, in particular to a low-carbon nitrogen and phosphorus removal sewage treatment system, a sewage treatment method and application thereof. The sewage treatment system comprises a pretreatment unit, an adsorption tank, a mid-sedimentation tank, a biochemical tank, a secondary sedimentation tank and an anaerobic hydrolysis acidification tank; the biochemical tank comprises an anaerobic tank, an anoxic tank and an aerobic tank; the anaerobic hydrolysis acidification tank is provided with a VFAs-containing supernate outlet communicated with the anaerobic tank; the mid-sedimentation tank is provided with an adsorbed sludge outlet and a sewage outlet, the adsorbed sludge outlet is communicated with the adsorption tank and the anaerobic hydrolysis acidification tank, and the sewage outlet is communicated with the anaerobic tank; the secondary sedimentation tank is provided with a biological sludge outlet and a water outlet, and the biological sludge outlet is communicated with the anaerobic tank and the anaerobic hydrolysis acidification tank. According to the sewage treatment system, denitrifying phosphorus-accumulating bacteria, nitrifying bacteria and hydrolytic acidification bacteria can be cultured in different sludge systems, and the contradiction between the denitrifying phosphorus-accumulating bacteria, the nitrifying bacteria and the hydrolytic acidification bacteria on substrate competition and sludge age is solved; and the anaerobic hydrolysis acidification tank is arranged to recycle a carbon source in the sewage, so that the advantage of low carbon is achieved.

The invention belongs to but is not limited to the technical field of radio detection direction finding, and discloses a double-antenna three-dimensional rotating baseline direction finding method, device and system. Inter-channel phase deviation table compensation is carried out, equal-frequency-interval full-working-frequency-band measurement is carried out on phase deviation between two measurement channels in advance, and a channel phase deviation compensation table AB is formed; the compensation module is used for compensating inherent phase deviation between the channel A and the channel B; after the double-antenna three-dimensional rotating baseline direction finding device starts to work, a baseline formed by the antennas AB rotates anticlockwise at an angular velocity omega, two receiving channels synchronously collect data, and phase difference is calculated for the data corresponding to the baselines AB; based on a two-dimensional joint search method, through combination of coarse search and fine search, an azimuth angle and a pitch angle are estimated. According to the technical scheme provided by the invention, the direction finding range and reliability of a traditional system are greatly expanded, meanwhile, the cost is basically not increased, and the large-scale application of the technical system can be promoted.

The invention discloses an intelligent scheduling method based on port bulk cargo transfer. The method comprises the steps of S1, obtaining related information of bulk cargos to be transferred and related information of transport vehicles; s2, matching transport vehicles of corresponding types; s3, calculating a transfer train number; s4, calculating the number of transport vehicles; when the number of the transport vehicles is one, executing the step S5; when the number of the transport vehicles is two or more than two, executing the step S6; s5, generating transfer scheduling planning information according to the loading position of the bulk cargo to be transferred and the departure position of the corresponding transport vehicle; s6, determining the departure sequence of the transport vehicles: for the first departure transport vehicle, calculating corresponding transfer scheduling planning information based on the step S5; and for the second transport vehicle and the subsequent departure transport vehicle, corresponding transfer scheduling planning information is generated according to the transfer scheduling planning information of the previous transport vehicle. According to the invention, the accuracy, orderliness and collaboration of bulk cargo transfer scheduling can be improved.

The invention provides a deep learning defect identification system and method based on simulation generation of a simulation data set. The system comprises a defect model generation module, a virtual three-dimensional CT module and a defect identification deep learning module. The defect model generation module is used for generating a defect-containing CAD model; the virtual three-dimensional CT module is used for performing CT scanning and reconstruction on the CAD model; the defect recognition deep learning module is used for marking CT sectional images and using marked data for network training, the problem that an X-ray image data set is insufficient is solved, and the method is safe, rapid and independent of equipment; the problem of inaccurate marking in deep learning defect identification is also solved, and the defect identification precision of the deep learning module obtained by training is improved.

The invention discloses an agile satellite multi-satellite collaborative observation-data transmission combined planning method, which comprises the following steps: carrying out discrete sampling on a target area, calculating the visibility of each satellite to a sampling point, and obtaining the coverage characteristic of each satellite to the area based on the visibility of the sampling point; based on satellite coverage characteristics, a K-Means clustering algorithm is used to segment the target area into a plurality of sub-areas and distribute the sub-areas to corresponding satellites; boundary adaptive stripe division is carried out on the distributed sub-regions, and the number of stripes is calculated; constructing an observation-data transmission joint planning problem model, solving based on an elite genetic algorithm to obtain a planning result, integrating all satellites, and outputting an optimal scheduling scheme; according to the method, the problem that regional target observation-data transmission combined planning is difficult to solve under multi-satellite cooperation of agile satellites is solved, the coverage of the satellites on the distribution region is ensured, the overlapping rate of observation strips of different satellites is reduced, the overall observation benefit under multi-satellite cooperation can be improved, and the method can be effectively applied to a remote sensing satellite management and control technology.

The utility model relates to the technical field of stamping dies, and provides a spring structure of a die sliding block, which comprises a top die shell, a connecting core rod is fixedly connected to the bottom of the top die shell, a telescopic sliding block fixing plate is fixedly connected to the outer part of the connecting core rod, and a plurality of clamping blocks are fixedly connected to the bottom of the telescopic sliding block fixing plate. Telescopic springs are fixedly connected into the multiple clamping blocks, telescopic sliding blocks are arranged outside the multiple clamping blocks, telescopic sliding block pressing plates are arranged at the bottoms of the multiple telescopic sliding blocks, a connecting gland is fixedly connected to the bottom of the connecting core rod, and a connecting shell is fixedly connected to the bottom of the top mold shell. A supporting bottom plate is arranged at the bottom of the telescopic sliding block pressing plate, connecting outer cylinders and connecting springs are fixedly connected to the two sides of the outer portion of the supporting bottom plate correspondingly, and sliding connecting rods are arranged in the two connecting outer cylinders correspondingly. By means of the technical scheme, the problem that in the prior art, a sliding block and parts in the device are inconvenient to replace and maintain rapidly is solved.

The invention relates to a method and system for tracking an endoscopic surgical instrument and generating a motion trail of the endoscopic surgical instrument, and belongs to the technical field of image processing. The method comprises the following steps: taking a target position of a surgical instrument in a previous frame video as a center, and sampling by adopting a set distribution form to obtain a plurality of candidate samples of a current frame; respectively inputting the candidate samples into a pre-trained SVM (Support Vector Machine) detection model based on manual features and a pre-trained CNN (Convolutional Neural Network) target detection model, and respectively obtaining positive sample scores after weighted summation of scores of two models of each candidate sample; and taking the candidate sample with the maximum positive sample score as the target position of the surgical instrument of the current frame, and connecting the tracked target positions of each frame to generate the motion trail of the surgical instrument. On the basis of using the trained SVM model classification prediction model, depth features are extracted in combination with neural network features, a background and a target object are effectively distinguished, and the problems that an endoscope image is complex in structure, fuzzy in boundary and serious in shielding condition are solved.

The invention discloses a Zhang Zheng No.1 satellite multi-parameter space-time correlation analysis method based on deep learning. The method comprises the following steps: firstly, acquiring and preprocessing ionosphere multi-parameter data, including magnetic field three-component waveform data, electron density Ne and GPS TEC data; then, a time sequence relation of different parameter earthquake precursor anomalies of the ionosphere is established by utilizing a random walk and a yoke theory; and then, on the basis of a CNN-BiLSTM deep learning network, a Swarm B satellite ionized layer multi-parameter seismic anomaly intelligent identification model is constructed. And then, by using a transfer learning technology, migrating the Swarm B satellite model to a ZH-1 (01) satellite, and constructing a ZH-1 (01) multi-parameter seismic anomaly identification model. And finally, seismic anomaly identification and verification are carried out by using Zhang Heng No.1 satellite constellation data, and the model is continuously optimized. According to the invention, time-space correlation analysis is carried out on the multi-source ionosphere parameters through a deep learning method, the accuracy of seismic anomaly recognition can be effectively improved, and a scientific basis is provided for seismic early warning.

The invention provides an electrical equipment electric leakage identification method and identification device based on voiceprint signals. The method comprises the following steps: processing an acquired sound signal by using a variational mode decomposition algorithm to obtain a plurality of intrinsic mode functions, selecting three key intrinsic mode functions from the plurality of intrinsic mode functions to respectively serve as gray values of three color channels of red, green and blue of an image, and generating a voiceprint image of the sound signal; extracting texture features of the voiceprint image through a gray level co-occurrence matrix, and performing depth feature extraction and classification identification on the texture features by adopting a CNN-SVM model to obtain an electric leakage detection result; and when the electric leakage detection result shows that an electric leakage fault occurs, the position of a fault source is determined by calculating the time delay difference of the acoustic signals transmitted to the plurality of acoustic signal sensors. According to the scheme, the problem that in the prior art, accurate electric leakage fault source positioning cannot be provided, so that maintenance and fault removal time is prolonged is solved.

The invention discloses an unmanned aerial vehicle visual angle target detection method based on improved YOLO11n, and the method comprises the steps: 1, carrying out the preprocessing of an unmanned aerial vehicle aerial image data set, and dividing the preprocessed unmanned aerial vehicle aerial image data set into a training set, a verification set, and a test set; 2, constructing an improved YOLO11n model, replacing a C3k2 module in a backbone network of the original YOLO11n model with a C3PA2 module, replacing a neck network in the original YOLO11n model with an FIFFN frame, performing feature conversion on a feature map by the FIFFN through 1 * 1 convolution and the C3PA2 module, splicing deep feature maps in an up-sampling stage and a down-sampling stage with a shallow feature map from the backbone network, and constructing an improved YOLO11n model; increasing the resolution of the prediction feature map to 160 * 160; 3, firstly setting parameters, then training the improved YOLO11n model by using the training set, and adjusting and optimizing hyper-parameters of the improved YOLO11n model by using the verification set to obtain an optimal improved YOLO11n model; and 4, inputting a to-be-detected unmanned aerial vehicle aerial image into the optimal improved YOLO11n model, and outputting a detection result, thereby improving the precision of unmanned aerial vehicle visual angle target detection.

The invention discloses a trajectory prediction method and system based on a cluster spatio-temporal dynamic graph network, and belongs to the field of spatio-temporal data mining in an intelligent traffic system, and the method comprises the steps: collecting existing traffic signal data, and carrying out the preprocessing; selecting a training set from the preprocessed traffic signal data, performing clustering, and endowing the remaining traffic signal data after the training set is removed with a category index based on a clustering result; randomly sampling two groups of data of historical traffic signals and future traffic signals and corresponding category indexes from the traffic signal data, and inputting the historical traffic signals into an embedded layer for mapping to obtain high-dimensional features; inputting the high-dimensional features into a space block and a time block to generate a space aggregation feature map and a time aggregation feature map, and fusing the space aggregation feature map and the time aggregation feature map to obtain aggregation features; and inputting the aggregated features into an inverse embedding layer for mapping to obtain a future topological signal traffic signal, and completing track prediction based on the cluster spatio-temporal dynamic graph network.

A breathing type dynamic adjustable solar photovoltaic and photo-thermal integrated curtain wall comprises a glass cover plate and a glass bottom plate which are arranged on the inner side of the front end face and the inner side of the rear end face of an installation frame respectively, and a photovoltaic assembly and a shutter device are arranged on the inner side of the glass cover plate and the inner side of the glass bottom plate respectively. The photovoltaic module and the shutter device form a ventilation cavity in the mounting frame, the ventilation cavity is communicated with a ventilation hole groove structure arranged on the mounting frame, and a gas driving device is arranged on the mounting frame in the ventilation cavity. The photovoltaic assembly comprises a light-transmitting photovoltaic panel and a transparent back plate, and the light-transmitting photovoltaic panel is arranged between the glass cover plate and the transparent back plate. The breathing-type dynamically-adjustable photovoltaic and photo-thermal integrated curtain wall module has the beneficial effects that the breathing-type dynamically-adjustable photovoltaic and photo-thermal integrated curtain wall module design is adopted, the two-stage ventilation groove holes are formed in the upper side and the lower side of the mounting frame, when the temperature of the photovoltaic panel is high, the vertical axial flow fan is started by the controller, and circulating air flow is formed in the ventilation cavity to take away heat.

The invention discloses a polarization atmospheric correction method, device and equipment based on a marine atmospheric parameter collaborative inversion strategy, and a medium. The method comprises the following steps: reading polarization marine satellite data; a collaborative inversion initial state is set, polarized ocean satellite data is recorded as an observation vector y, a state vector is recorded as x, and the state vector comprises ocean parameters and atmospheric parameters; calculating a forward model, and converting the state vector x into an analog measurement value ysim at the top end of the atmosphere through iteration based on a marine atmosphere parameter collaborative inversion strategy; and comparing the atmospheric top simulation measurement value ysim with the measurement vector y to judge whether a condition is met or not, if so, outputting a state variable, and if not, entering the step of calculating the forward model to continue iteration. Therefore, high-precision sea surface out-of-water polarization state information acquisition is realized through a strategy of collaborative inversion of marine atmospheric parameters, and the difficult problem of polarization atmospheric correction in polarization marine observation information acquisition and processing links is solved.

The invention discloses a pavement adaptive to a magnetic resonance type dynamic wireless charging system as well as a construction process, a lane and application of the pavement, and belongs to the field of road engineering and road infrastructures. The pavement comprises a lower surface layer, a middle surface layer and an upper surface layer, wherein an input power line of the magnetic resonance type dynamic wireless charging system is distributed on the surface of the middle surface layer, and then a synchronous broken stone sealing layer is adopted to ensure that the input power line is completely embedded; the middle of the upper surface layer is made of pouring asphalt concrete, and two sides are made of wear-resistant asphalt concrete; a transmitting coil of the magnetic resonance type dynamic wireless charging system is embedded in the pouring type asphalt concrete, and the transmitting coil is connected with an input power line. The mechanical compatibility performance of the transmitting coil and the road surface is improved through the special road surface design, the charging efficiency is greatly improved by cooperating with the control system to optimize electric energy input, wireless charging of the vehicle in the high-speed advancing process is finally achieved based on the magnetic resonance coupling principle, and the wireless charging device has the convenient construction technology and the excellent road performance.

The invention discloses a glass transverse cutting and removing process for a single-glass assembly. The glass transverse cutting and removing process comprises the following steps: S1, preheating; s2, performing temperature control shaping; s3, transversely cutting the glass; and S4, collecting the battery pieces. On one hand, high-quality transverse cutting with stable cutting position and stable cutting amount is kept in the working condition that the battery piece is relatively attached to the upper roller during guiding discharging of the secondary stripping roller on the basis of leveling and glue layer softening temperature control, and then secondary stripping is formed by the secondary stripping roller, so that the stripping rate is greatly increased, and the production efficiency is improved; the damage rate of the battery piece is greatly reduced; and on the other hand, the formed battery piece collecting mode not only depends on the opposite movement of the upper roller and the secondary stripping roller, but also needs to use the leveled shaping die strip again for curling and collecting, so that the battery pieces can be collected under the working conditions of multiple purposes of parts and small required space, and the practicability is high.

The invention discloses a data detection method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a source program file; performing first data extraction processing on the source program file to obtain a target test function set; performing data segmentation processing on a plurality of target test functions in the target test function set to obtain word vectors corresponding to the target test functions; calculating the text similarity between the word vectors corresponding to each target test function to obtain a similarity calculation result; generating a plurality of test suites based on the similarity calculation result, and executing each test suite to obtain a running result log; and detecting unstable test data in the source program file according to the running result log. The method can reduce the repeated operation frequency of the test, improve the test efficiency of the detection system and reduce the execution time cost, and can be widely applied to the technical field of data processing.

The invention discloses an advanced persistent threat detection and response method based on improved self-supervised learning. The method comprises the following steps: S1, acquiring an unlabeled data set in a network environment; s2, embedding a behavior path learning and prediction module in the self-supervised learning model; s3, forming a reference basis for an APT threat behavior path; s4, marking the nodes as potential threat nodes; s5, if potential threat nodes are detected, a behavior path learning and prediction module predicts potential threat nodes possibly occurring in the next attack stage according to the space-time relevance in the behavior path and the preliminary labeling information, and generates corresponding threat levels according to the features of the predicted nodes and the behavior path; and S6, triggering a response decision module according to the threat level, and triggering corresponding response measures by the response decision module according to the level of the threat node and the prediction path, including sending out an alarm, isolating a threat source, limiting access authority and recording an event log. According to the method, the detection efficiency and the response capability of the APT attack are remarkably improved.

The invention provides a photoelectric sensor simulation platform and a synthetic aperture radar (SAR) imaging method in the simulation platform. The method comprises the following steps: step 1, subdividing a target three-dimensional model; step 2, acquiring a propagation path of a ray by using a ray tracing method homologous with that in an optical imaging system; and step 3, performing simulation calculation on the far-region scattered field of the target by using a bounce ray method, and performing data processing on the far-region scattered field to obtain a synthetic aperture radar image of the target. According to the technical scheme, the working frequency of the sensor simulation platform and the photoelectric sensor simulation platform is successfully expanded to the field of radar microwaves. Radar SAR and optical homologous simulation imaging lay a good foundation for subsequent research in the fields of image registration, feature fusion, target recognition and the like.

The invention relates to the technical field of agricultural fertilizers, and discloses a preparation method of an amino acid foliar fertilizer extracted based on animal hair, and the preparation method comprises the following steps: raw material pretreatment: carrying out plasma modification on animal hair to improve the keratin hydrolysis performance; a double hydrolysis process: firstly decomposing a keratin structure in the hair through sulfuric acid hydrolysis, and then generating free amino acid through enzyme hydrolysis; recycling by-products: preparing biochar or protein feed from undissolved residues generated in the hydrolysis process, and separating and recycling amino acids and salts in waste liquid; and preparing a foliar fertilizer formula: chelating the extracted amino acid concentrated solution with trace elements. The technical effects of zero emission and high-valued conversion of wastes are achieved through a resource utilization scheme that biochar is prepared from undissolved residues through a pyrolysis process or protein feed is prepared from the undissolved residues through secondary enzymolysis, and free amino acids and salts in waste liquid are separated and recycled through a membrane separation and crystallization extraction technology.