1846 results about "Identification technology" patented technology

The invention discloses a robot obstacle avoidance method and system based on millimeter wave radar sparse point cloud, and relates to the technical field of obstacle avoidance recognition. A robot obstacle avoidance system based on millimeter wave radar sparse point cloud comprises a point cloud acquisition module, a negative obstacle identification module, a weak obstacle identification module, a point cluster identification module, a risk map module, a tentative verification module and an obstacle avoidance decision module. According to the invention, suspected obstacle point clusters are extracted based on a reflection intensity threshold and a spatial proximity relation in an enhanced point cloud, a theoretical parallax model of a real static obstacle is constructed under the constraint of a robot motion trajectory, and Doppler velocity distribution of each frame is combined with a static obstacle Doppler physical law for comparison. And classifying the point clusters which do not meet the multi-view geometric consistency or Doppler physical law, and distinguishing multipath false point clusters from dynamic point clusters.

The invention relates to the technical field of rural image inspection and recognition, and discloses a multi-source image collaborative inspection and recognition analysis system and method for a digital rural area, and the method comprises the steps: collecting multi-source image data in real time; obtaining a plurality of characteristic parameters corresponding to each image data item in the image data set, and carrying out space-time registration and multi-scale fusion processing on the plurality of characteristic parameters of each image data item; performing target detection and identification analysis on the plurality of feature parameters in the fusion feature parameter set, and constructing an abnormal point identification model; setting a plurality of abnormal point change thresholds according to the inspection coordinate data set for classification processing to obtain a plurality of abnormal point categories; and setting a corresponding co-processing scheme according to the plurality of abnormal point categories, and setting early warning information corresponding to the change trends of the plurality of abnormal point categories based on the co-processing scheme. According to the invention, the intelligent degree and response efficiency of rural inspection are improved, and the safety and sustainable development of digital rural construction are effectively guaranteed.

The invention discloses an optical cable micro-disturbance identification method based on physical simulation and self-supervised time sequence decoupling, and relates to the technical field of optical cable identification, and the method comprises the steps: constructing a physical digital twin simulator, and generating a high-fidelity training set; constructing a deep learning model, wherein the deep learning model adopts a lightweight time sequence decoupling network; training the model by adopting a staged training strategy, and sequentially carrying out self-supervised noise distribution pre-training, simulation supervised training and spectral domain physical consistency fine tuning operation; inputting DAS time sequence data collected in real time into the trained model, and outputting the data as an optical cable identity ID and a physical position; the lightweight time sequence decoupling network comprises a physical guide preprocessing module, a lightweight U-Net separation module, a sparse gating module and an intelligent parallel decoding module. Through the technical means of simulation-driven data generation, staged training strategies and the like, the defects of the prior art in the aspects of reducing the data cost, improving the detection capability in a low SNR environment, realizing multi-source blind source separation and the like are overcome.

The invention provides a multi-task identification method for strawberry diseases and insect pests, and belongs to the technical field of strawberry leaf disease identification. The method comprises the following steps: marking an enhanced original strawberry leaf image, and constructing a multi-task training data set; inserting a double attention unit on a semantic segmentation model decoder as a segmentation network, constructing a detection network based on a lightweight detection model, inputting a segmentation mask output by the segmentation network into the detection network to construct a cascade model, and performing preliminary training; performing multi-task joint learning on the cascade model; based on the multi-task data set, a progressive training strategy is combined with a cosine annealing learning rate adjustment strategy, and final training is carried out on the cascade model after joint learning; and inputting a to-be-detected strawberry leaf image into the finally trained cascade model, and outputting a disease type and a severity level. According to the method, the recognition sensitivity of tiny disease spots can be effectively improved, and the generalization ability of the model to complex illumination and shielding scenes is enhanced.

The invention discloses a technology development analysis method and system based on a patent big language model, and relates to the field of big data analysis. The problems that an existing artificial intelligence patent analysis method is difficult to accurately analyze a technical structure in a patent text, cannot describe a technical hierarchical relationship, is difficult to eliminate technical semantic diversity, cannot systematically analyze a technical evolution process and the like are solved. And the network structure characteristics and the technology node importance are analyzed, and the evolution characteristics of the technology system in different stages, the key technology nodes and the change trend of the technology community structure can be identified, so that technical support is provided for artificial intelligence technology development situation research and judgment and technology layout decision. The method is suitable for a technical analysis scene of million-level patent data.

The invention discloses a medical image automatic identification system based on a neural network, and relates to the technical field of medical image identification. The method is used for solving the problem that early recognition of neurodegenerative diseases is difficult due to medical image and genome data splitting and poor model interpretability in the prior art. The method comprises the following steps: firstly, extracting multi-scale features of a brain structure through a three-dimensional convolutional neural network and a self-attention mechanism, calculating a multi-gene risk score based on a risk site, and encoding the score into a feature vector; secondly, using a cross attention mechanism to take gene features as query vectors, fusing the gene features with image features, and generating brain structure anomaly features under gene regulation; then, gradient weighting class activation mapping is applied to generate a visual thermodynamic diagram, and gene-image association weight weighting is combined to construct a brain region risk distribution diagram; and finally, a high-risk brain region space coordinate set is extracted through threshold segmentation, and an accurate quantification basis is provided for early recognition.

The invention discloses a personnel information security screening method and system based on big data, and relates to the technical field of image processing, and the method comprises the steps: obtaining to-be-processed face data in a region, screening out unregistered personnel, carrying out the matching and correlation of the images of the same unregistered personnel at different image collection points through calculating the feature similarity, and carrying out the recognition of the unregistered personnel. And connecting time points and spatial positions corresponding to the images of the same unregistered person at different image acquisition points into a spatial-temporal trajectory through a trajectory reconstruction algorithm, and pushing multi-modal feature information of the person to an adjacent preset area for the identified abnormal behavior. According to the invention, through a density clustering algorithm based on time and space constraints, feature association is carried out on personnel images collected by different cameras, a cross-region behavior track of the unregistered personnel is constructed, high-risk behaviors such as wandering are detected by using track analysis and an abnormal behavior identification technology, and early warning information is generated and sent to other regions. And the initiative and the coverage range of safety management are improved.

The invention provides a box girder defect recognition method and system based on structured light and deep reinforcement learning, and relates to the technical field of box girder defect recognizing.The method comprises the steps that equipment integration and joint calibration are conducted on a to-be-detected box girder component, structured light projection is conducted according to a preset track, and a to-be-detected box girder component is obtained; the method comprises the following steps: triggering an event camera to image by utilizing controlled stroboscopic scanning of structured light, obtaining an event stream and a structured light depth map, carrying out representation conversion and multi-modal alignment, obtaining multi-modal mapping alignment data to carry out primary defect identification, obtaining an initial defect identification result, and triggering a deep reinforcement learning active perceptron to carry out perception strategy analysis. And obtaining a perception strategy analysis result, and carrying out deepening defect identification on the to-be-detected box girder component to obtain a defect identification result. The technical problems that in the prior art, box girder defect detection is low in detection efficiency, not high in accuracy and poor in detection comprehensiveness are solved. The technical effect of improving the efficiency, accuracy and comprehensiveness of box girder defect detection is achieved.

The invention provides a photovoltaic module defect identification method based on an infrared image, and belongs to the technical field of photovoltaic module defect identification, and the method comprises the steps: collecting the infrared image of a photovoltaic module through an unmanned plane, extracting the temperature distribution, texture, shape and gradient features, building a secondary thread grid in a CUDA flow of a GPU, and carrying out the recognition of the defect of the photovoltaic module through the secondary thread grid; a primary screening thread grid is used for operating a lightweight defect primary judgment model to quickly filter normal components, a fine screening thread grid is used for operating a thermal image defect recognition model to carry out multi-modal feature fusion and deep recognition on suspected defect components, a differentiation processing strategy is adopted according to a judgment confidence coefficient value, and defect types, positions and severity are output; the technical problem that the calculation efficiency is low due to the fact that unified deep processing is carried out on mass image data in the photovoltaic module defect recognition process is solved.

The invention provides a multi-source remote sensing collaborative recognition method and system for road passing height-limiting obstacles, and relates to the technical field of road height-limiting recognition. Pixel-level elevation statistics is carried out by moving a sliding window in an elevation information block to position M height-limiting obstacles, and then clearance height calculation is carried out to correct and output M reference height-limiting heights to be projected to a passing road network; and according to the vehicle type of the freight vehicle and the real-time load information, height-limited negative compensation is carried out to output a dynamic passing height so as to traverse a passing road network to locate R feasible routes and display the R feasible routes to a vehicle-mounted display screen of the freight vehicle. The technical problems that in the prior art, due to the fact that manual inspection is relied on for height limiting obstacle state monitoring, the height limiting state is not updated in time, and due to the fact that a static height threshold value is adopted for vehicle height limiting passing judgment, large vehicle passing path planning is affected are solved. The technical effects of dynamically tracking the state change of the height-limiting obstacle and the passing height of the vehicle and carrying out precise path planning and visual navigation of the large vehicle are achieved.

The invention relates to a knowledge base construction method and system for multi-source heterogeneous files, and the method comprises the steps: receiving Word, PDF, Excel and other multi-source heterogeneous files, and converting the multi-source heterogeneous files into standardized representation through a file analysis function; an event boundary recognition technology fusing rule matching, entity recognition, semantic similarity and large language model verification is adopted, and a cross-paragraph complete business event is accurately extracted; generating structured information through an information extraction function; the information is converted into vectors, full-text indexes and a graph database to be stored by means of a multi-dimensional storage conversion function; and verifying and correcting an extraction result through a quality auditing function, and finally outputting a knowledge base containing an event set, an entity relationship and vector representation. According to the method, the problem of knowledge fragmentation in traditional document processing is effectively solved, and the consistency, the searchability and the reasonability of knowledge are remarkably improved.

The invention discloses a livestock identity recognition method and system based on an unmanned aerial vehicle, and relates to the technical field of livestock identity recognition, and the method comprises the steps: constructing an identity information database which comprises the electronic ear tag IDs of all target livestock individuals in a pasture, a visible light feature image, an infrared thermal imaging contour template and a historical motion behavior sequence in a cloud server; initialization of an unmanned aerial vehicle system carrying a visible light camera, an infrared thermal imager, an RFID reader and an edge calculation module is completed, and communication connection between the unmanned aerial vehicle system and a ground control station and between the unmanned aerial vehicle system and a cloud server is established; controlling the unmanned aerial vehicle to enter an inspection area, monitoring a ground target through a visible light camera, synchronously acquiring a visible light image, an infrared thermal image and a radio frequency response signal of a target individual when the livestock is detected to enter an effective identification range, and performing timestamp alignment on the three types of data; a first visual feature vector is extracted from the visible light image and a visual confidence score is generated.

The invention discloses a multi-modal depth perception and adaptive segmentation full-spectrum primitive intelligent identification method, system and device for a primary main wiring diagram of a plant station, and a medium, and belongs to the technical field of intelligent identification of engineering drawings of power system plant stations, and the method comprises the steps: fusing the dual-source data of a vector diagram and a grating diagram, and constructing a standard primitive library; secondly, primitive detection is realized by adopting a recognition and segmentation collaborative multi-stage network, and the contour precision is improved through model distillation; secondly, iteratively optimizing the recognition capability of the model on the difficult sample by using an active learning mechanism; and finally, finishing primitive classification and result optimization in combination with deep metric learning and electrical topology constraints. According to the invention, through multi-modal data fusion and multi-scale feature adaptive fusion, the recognition capability of a small target and a fine primitive is improved; by means of a double-branch heterogeneous architecture and a structural distillation mechanism, pixel-level high-precision edge segmentation is realized, and the identification problem of dense adhesion primitives is effectively solved.

The invention discloses a fan abnormity monitoring method, device and equipment based on image recognition and a medium, relates to the technical field of image recognition, is applied to a wind turbine generator cabin, and comprises the following steps: acquiring an original image sequence in the wind turbine generator cabin in a first candidate image area in real time, eliminating environmental interference factors in the original image sequence of the first candidate image region to obtain a target image sequence; extracting visual features in the target image sequence of the first candidate image area, and identifying visual anomalies in the target image sequence of the first candidate image area based on the visual features of the first candidate image area; and when the visual anomaly is identified in the target image sequence of the first candidate image area, outputting alarm information containing the visual anomaly of the first candidate image area. The technical problem that a current fan monitoring scheme is poor in monitoring effect is solved.

The invention discloses a network security alarm intelligent identification method and device, and relates to the technical field of network security alarm identification, and the method comprises the steps: carrying out the preprocessing of original data from network traffic, log files, user behavior records, a firewall and an intrusion detection system, and obtaining a multi-dimensional security event data set; node modeling and edge relation modeling are carried out based on a GNN and the multi-dimensional security event data set, and cross-system and cross-time-dimension security event association features are extracted to obtain a high-dimensional context sensing feature vector set; a time sequence anomaly detection model is constructed based on a high-dimensional context sensing feature vector set and a time sequence analysis technology, the high-dimensional context sensing feature vector set is input into the time sequence anomaly detection model, and weighting calculation is performed on each time step by extracting time dependent features and combining an Attention mechanism. Outputting an abnormal score vector corresponding to each time point; and constructing a behavior deviation function based on the multi-dimensional security event data in combination with deep learning data.

The invention discloses a bridge structure damage intelligent identification method and system, and relates to the technical field of bridge health identification, and the method comprises the steps: carrying out the blind source separation of a heterogeneous data tensor, and obtaining a structural damage sensitive residual error response component; extracting nonlinear dynamic characteristics of the residual response component, and generating a primary abnormal clue; dynamically constructing a kernel mapping relation based on the primary abnormal clues, mapping heterogeneous data tensors to a high-dimensional feature space, and extracting damage-sensitive dynamic derivative features; inputting the dynamic derivative features and the structural topology priori into a graph neural network, and generating a group of competitive damage hypotheses through an attention mechanism; according to the method, a competitive damage hypothesis is generated through fusion of the graph neural network and structural topology priori, and a stable mapping relation between the features and the damage state is established through local stiffness reduction field inversion, numerical forward modeling and iterative confidence updating, so that the accuracy of damage identification is greatly improved, and the misjudgment rate and the missed judgment rate are effectively reduced.

The invention relates to the technical field of risk identification, and provides a risk identification system and a risk identification method. According to the risk identification system, by introducing the scene perception module, the data alignment module and the multi-modal analysis module, automatic acquisition and fusion of multi-source heterogeneous data, including images, videos, voices, sensor data, operation information and the like, of a construction site are realized, and the comprehensive perception ability of the system to the site state is effectively improved. Through a data alignment mechanism guided by structured data, multi-dimensional alignment of time, space and task semantics can be realized based on a historical schedule, B I M parameters, a construction plan and the like, the problem that multi-source data is difficult to fuse in a traditional method is solved, and the accuracy and consistency of data modeling are improved. A pre-trained multi-modal risk identification model is adopted, the cross-modal analysis capability is achieved, images, texts and behavior data of a construction site can be comprehensively understood, and then risk identification is accurately carried out.

The invention provides an Internet of Vehicles distributed aggregation malicious node identification method based on a block chain. The block chain, a vehicle-mounted ad hoc network and a malicious node identification technology are fused. In the aspect of architecture design, a vehicle-mounted ad hoc network is abstracted into a directed acyclic graph, each node of a block chain serves as a consensus participant, and time sequence and topological data aggregated and uploaded by cluster head nodes of each group of network are collected and synchronized. The node topology influence is quantified through shortest path intermediary centrality, a time weighted local outlier detection mechanism is introduced, and the data anomaly probability is deduced in combination with an exponential time weight, local density and basis density indexes. And finally, aggregating multi-source data through a block chain smart contract, automatically updating a vehicle node reputation score, and triggering an authority revocation mechanism when the score is lower than a threshold value. The problem that a traditional centralized reputation system is prone to single-point failure is solved, the limitation that the traditional centralized reputation system only pays attention to data features and ignores topological roles is broken through, and the abnormal node recognition accuracy is improved in combination with dynamic multi-source data detection.

The invention provides a roadbed slope collapse early warning method and system based on linear structured light scanning, and is suitable for the technical field of slope safety monitoring. The method comprises the steps of unmanned aerial vehicle-scanning equipment cooperative deployment, fixed path automatic scanning, original point cloud data space registration, data feature matching and early warning response. The unmanned aerial vehicle carries line structure light scanning equipment, fixed path automatic scanning and real-time single-frame image three-dimensional reconstruction are combined, space-time continuous point cloud data of the roadbed slope are obtained, efficient dynamic monitoring of large-range slope surface deformation is achieved, and the problems that traditional manual inspection is low in efficiency and insufficient in coverage are solved; through the space-time synchronization registration and abnormal point intelligent identification technology, real slump displacement and environment interference are effectively distinguished by utilizing displacement direction analysis and threshold screening, the false alarm rate is greatly reduced, and the anti-interference capability in a complex scene is enhanced; the slope sudden change characteristics of slope crest displacement are matched through regularized piecewise linear modeling, so that the early warning response time is advanced.

The invention discloses a knowledge graph construction method and device, equipment and a storage medium. Wherein. The method comprises the following steps: acquiring classroom teaching materials; extracting voice content in the teaching video by using a voice recognition technology, and performing textualization processing on the voice content to obtain a voice time sequence text of the teaching content; analyzing the voice time sequence text of the teaching content through a natural language processing technology to obtain an analyzed time sequence text; obtaining a user prompt (user prompt); using the user prompt and the analyzed time sequence text as input parameters of a large model application, inputting the input parameters into a large model for analysis, and extracting knowledge points and timestamps corresponding to the knowledge points; and generating a knowledge graph according to the extracted knowledge points, and associating the knowledge points to corresponding video clips according to timestamps corresponding to the knowledge points. The knowledge nodes of the knowledge graph can be accurately positioned to the corresponding video clips, the user can quickly find the teaching content related to the knowledge points, and the learning efficiency is improved.

The invention relates to the technical field of sample recognition, in particular to a severe convection weather sample reconstruction super-sampling and enhanced recognition method and system. The method comprises the following steps: acquiring weather sample data of a target area; performing data preprocessing based on the acquired weather sample data of the target area; performing adaptive sampling on the preprocessed data by using a multi-granularity manifold structure regularization strategy; carrying out feature optimization on the sampled data by utilizing a progressive structured manifold embedding loss function; according to the optimized data, a non-Euclidean all-pure geodesic embedder is used to carry out discriminative manifold reconstruction in a bending measurement field; and the topology sensing type heterogeneous feature aggregation network architecture identifies the reconstructed data.

The invention discloses an AI model manual annotation method with associated logic, relates to the technical field of artificial intelligence and optical character recognition (OCR), and aims to solve the problems that the existing OCR recognition technology is low in accuracy and lacks an effective content verification and correction mechanism when processing complex certificates. The method comprises the following steps of: firstly, extracting a driving license, a driving license, an electronic license and text contents of various certificates through OCR (Optical Character Recognition) scanning equipment; constructing an association logic AI model for storing certificate information association logic rules; performing structured labeling processing on an OCR recognition result to realize document information classification and regularization; performing logic verification on the annotated content by utilizing an association logic AI model, marking problems and proposing a modification scheme; and finally, corrected data is fed back to the model to complete optimization iteration. According to the method, the logic contradiction can be accurately found and corrected, the identification accuracy of the AI model on multiple types of certificates is remarkably improved, the manual labeling error is reduced, and the certificate information extraction efficiency and reliability are improved.

The invention discloses an intelligent sensing socket based on equipment feature recognition and an energy consumption management method thereof. The method comprises the steps that a sensing layer collects various feature parameters and environment data of connecting equipment in an omnibearing mode; the control layer is used for deeply processing and analyzing data acquired by the sensing layer and executing equipment identification, strategy management and communication coordination; the platform layer provides far-end service support and comprises an equipment management module, a data analysis module, a user interaction module, an energy consumption management module and a core algorithm module; the original data collected by the sensing layer is subjected to feature extraction and identification analysis of the control layer, and the result is uploaded to the platform layer. According to the invention, through a multi-modal feature fusion identification technology, extremely high identification accuracy can be maintained in various use scenes; the potential risk can be identified before the fault occurs through the trend analysis of the electrical parameters and the temperature change.

The invention provides a landslide hidden danger identification method and device based on three-dimensional geometrical morphological characteristics of a slope flat section and a machine learning model, and a storage medium, and relates to the technical field of landslide hidden danger identification. Comprising the following steps: acquiring plane geometric characteristics and section geometric characteristics of a slope unit based on a slope unit main sliding surface and a main section of a historical landslide disaster or a known landslide hidden danger point in a target working area; standardizing the high-dimensional slope geometric feature data to obtain a corresponding high-dimensional geometric feature vector; inputting the sample set into the trained auto-encoder model, outputting a corresponding reconstructed feature vector, and determining a discrimination threshold; inputting the high-dimensional geometric feature vectors corresponding to all to-be-identified target slope units extracted in the target working area into the auto-encoder model, calculating through the auto-encoder model to obtain corresponding reconstruction errors, and determining potential landslide hidden dangers; and isolated misjudgment units in potential landslide hidden dangers are eliminated. By adopting the identification method provided by the invention, the landslide hidden danger can be rapidly and accurately identified.

The invention discloses a distributed sensor abnormal event identification method for intelligent traffic, and particularly relates to the technical field of traffic information perception and identification, and the method comprises the following steps: generating an abnormal information initial confidence value through deploying a sensor node with a confidence value dynamic adjustment function; during fusion processing, independent identification priorities of single node anomalies are reserved; after sequence reconstruction of a unified time reference, judging whether a response condition is met or not in combination with trajectory evolution; if yes, an abnormal response process is triggered, a space-time compensation set is constructed based on historical data of the sensing blind area for verification, and finally an abnormal intervention instruction is output and a traffic scheduling system is linked; according to the method, the sensing sensitivity, the fusion accuracy and the response timeliness of the abnormal information in a complex traffic environment are improved, the problem of an identification blind area that single-point abnormity is covered is avoided, state reconstruction and supplementary verification of the sensing blind area are realized, and the rapid intervention capability of an intelligent traffic system on emergencies is enhanced.

The invention discloses a fine-grained unsupervised cross-modal pedestrian re-identification method based on large model semantic driving, and relates to a computer vision and mode identification technology. The method comprises the following steps: inputting an unlabeled visible light-infrared pedestrian data set, generating an image text description by using a vision-language model, and analyzing the image text description into a structured semantic attribute vector through a large language model; extracting visual features of the image and semantic query embedding corresponding to attributes, and generating fine-grained features of semantic alignment through a multi-head cross attention mechanism; fusing the visual similarity and the attribute similarity to generate a cross-modal pseudo tag; combining attribute-visual alignment loss and inter-attribute decoupling loss to optimize features; in the test stage, cross-modal matching retrieval is completed based on the optimized features. Through semantic analysis and enhancement, the problem that cross-modal feature alignment is insufficient in an unsupervised scene is solved, and experiments show that compared with a mainstream method, the model performance is improved, and the method can be applied to the fields of intelligent monitoring, cross-modal retrieval and the like.

The invention discloses a multi-mode dam crack detection method based on acoustics-optics, and belongs to the technical field of underwater target recognition. According to the method, an underwater camera and an imaging sonar sensor are utilized to perform joint detection on cracks possibly existing on the surface of a dam: firstly, an optical image and a sonar image are respectively acquired, and crack feature extraction and preliminary recognition are performed in respective modes; and then, mapping detection results of the two modes to the same coordinate system through coordinate transformation, fusing optical detection information and acoustic detection information based on spatial consistency, and confirming the position and range of a crack target. According to the method, the complementary advantages that optical imaging definition is high and sonar penetrates through a turbid water body are brought into full play, the high crack detection rate and precision can still be kept in the underwater environment with extremely low visibility, compared with a single sensor, missing detection and false detection can be remarkably reduced, and reliable and automatic recognition of underwater dam structure cracks is achieved.

The invention discloses a document data entry method, electronic equipment, a storage medium and a program product, and relates to the technical field of document recognition, and the document data entry method comprises the following steps: obtaining a to-be-recognized document containing at least one to-be-recognized page; the document to be recognized is recognized through the optical character recognition technology, a first recognition result and a target page are obtained, and the target page is a page to be recognized containing non-text elements; identifying the target page through the target large language model to obtain a second identification result; and inputting the first recognition result and the second recognition result into the target form according to the similarity between the form field of the preset target form and the first recognition result and the second recognition result, and obtaining the input target form. Through cooperative work of the OCR and the large language model, synchronous and efficient recognition of text and non-text information is achieved, and the accuracy and the automation level of document recognition and document data entry are improved in combination with an intelligent matching entry mechanism.

A system comprises a video capture module to acquire a video, a preprocessing module to enhance video quality and isolate regions of interest within the video, and a biometric data extraction module using remote photoplethysmography to extract heartbeat and SpO2 levels from the video. A machine learning module analyzes the extracted biometric data for liveness and deepfake detection, a verification module to compare the analyzed data against known biometric signatures, and a user interface to display analysis results and alerts.

The invention relates to the technical field of intelligent data identification, in particular to a wetland ecological drought condition identification method based on multi-source data. According to the technical scheme, the method comprises the following steps: data acquisition: acquiring a multi-source data set of a target wetland region in a preset time sequence; the multi-source data set at least comprises a remote sensing reflectivity data set, a surface temperature data set, a meteorological reanalysis data set and an on-site monitoring hydrological data set; and data processing: sequentially carrying out space-time registration, denoising and missing value interpolation processing on the multi-source data set to generate a standardized space-time data cube. According to the method, multi-source data can be integrated to generate a standardized spatio-temporal data cube, each ecological drought index threshold value is dynamically determined based on the background of the historical wet season of the wetland, the comprehensive drought intensity is calculated in a weighted mode according to the wetland type, and accurate wetland ecological drought spatio-temporal distribution, grade sequence and early warning information are output after multi-scale verification. And a basis is provided for wetland ecological protection and water resource regulation and control.