105results about How to "Precision Fusion" patented technology

The invention relates to the technical field of security knowledge maps, and discloses a city security knowledge map construction method based on an AI large model and a related device, and the method comprises the steps: carrying out the vectorization feature extraction of multi-source city security event data, obtaining a multi-dimensional feature matrix, carrying out the feature analysis through a BERT encoder, and obtaining a multi-dimensional feature matrix; performing key value pair generation and feature processing on each group of query vectors in the obtained grouped query vector set; performing spatial-temporal correlation calculation on the event association feature matrix to obtain an initial security knowledge graph, and performing graph neural network learning and event classification on the initial security knowledge graph to obtain an event type division result; performing situation assessment index calculation on the event type division result to obtain security situation early warning data; and performing knowledge graph updating on the security situation early warning data to obtain an urban security knowledge graph, thereby realizing accurate classification and situation evaluation of security events and dynamic updating of the urban security knowledge graph.

The invention belongs to the technical field of data processing, and discloses a data analysis method and system based on three-dimensional map display, and the method comprises the steps: collecting real-time data from a plurality of data sources, adding geographic position information to each piece of data after preprocessing, marking the spatial position of the data in a three-dimensional map through geographic coordinates, and obtaining a data analysis result; a standardized three-dimensional map basic model is loaded and constructed, a three-dimensional space environment is constructed through a GIS standard, different marks are visually marked on a map according to attributes and features of data, and a convolutional neural network is used for automatically performing anomaly detection and classification on a time sequence of the data. And generating and displaying a dynamic animation according to the time dimension of the data. According to the invention, the three-dimensional map technology is utilized to enhance the intuition, interactivity and flexibility of data analysis, and meanwhile, the efficiency of data monitoring and exception handling is improved through intelligent data early warning and dispatching command functions.

The invention relates to an unmanned aerial vehicle multi-modal visual positioning method and system in a GNSS denial environment, and the method comprises the steps: constructing a visual inertial mileage calculation method based on deep learning feature extraction and matching, calculating the relative positioning of an unmanned aerial vehicle, and outputting to obtain high-frequency 6-DoF pose information; performing scene matching of infrared RGB image multi-modal fusion based on the Officient-LoFTR, and obtaining absolute positioning in a world system by solving a relative pose between a current key frame picture and a matched satellite image; a factor graph optimization mode is adopted to carry out self-adaptive weight information fusion according to the confidence coefficient of absolute positioning and relative positioning under a world system, and 6-DoF pose information with high frequency and higher precision is output; based on high-frequency and higher-precision 6-DoF pose information, after the unmanned aerial vehicle is recovered, all aerial images and satellite images are fused and updated. The system comprises a calculation module and a fusion updating module. According to the invention, more accurate and high-frequency pose output can be provided.

The invention discloses a tunnel crack detection method and a tunnel crack detection system, and belongs to the field of tunnel safety detection.The tunnel crack detection method comprises the steps that a tunnel inspection robot is utilized to conduct inspection in a tunnel to obtain complete three-dimensional data in the tunnel so as to construct a three-dimensional map in the tunnel, and the position of each crack area in the three-dimensional map is determined; determining an inspection tour area corresponding to each crack area and executing path planning, and generating an optimal inspection tour path of the unmanned aerial vehicle; the optimal inspection path enables the unmanned aerial vehicle to sequentially pass through each inspection area and then leave the tunnel after entering the tunnel under the condition that obstacle avoidance conditions are met, and the total time is shortest; enabling the unmanned aerial vehicle to move according to the optimal inspection path, and scanning the crack region by using the depth camera when the unmanned aerial vehicle moves to one inspection region so as to obtain fine three-dimensional data of the crack region; and performing analysis processing on the fine three-dimensional data of the crack area to complete tunnel crack detection. According to the invention, the efficiency and precision of long tunnel crack detection can be improved.

The invention discloses a multi-modal emotion analysis method and device, and relates to the technical field of emotion analysis. The method comprises the following steps: preprocessing each modal emotion of multi-modal emotion data, performing dimension alignment, and determining a target text feature, an aligned video feature and an aligned audio feature; a single-mode encoder is adopted to encode the aligned video features and the aligned audio features, and target video features and target audio features are correspondingly obtained; performing cross-membrane attention mechanism operation on the basis of the target text features, the target audio features and the target video features through a cross-modal fusion network and a cross-membrane conversion network, inputting the target text features, the target audio features and the target video features into a Transform network, performing self-attention operation, performing feature fusion, and determining emotion fusion features; and performing sentiment classification on the sentiment fusion features by adopting a sentiment classification layer, and outputting a sentiment analysis result. Based on the scheme, information loss during modal interaction is reduced, and the reliability of multi-modal emotion analysis is improved.

The invention discloses a radar and video information fusion coding method for vehicle over-limit early warning. The method comprises the following steps: acquiring three-dimensional point cloud data through a radar module; measuring three-dimensional sizes of length, width and height of the target vehicle in real time; a video information acquisition module is used for acquiring vehicle image data, and the video data identifies license plate numbers and vehicle appearance features through an improved YOLO deep learning algorithm to assist in judging whether a vehicle exceeds a limit or not; the vehicle three-dimensional size measured by the radar is compared with the standard size in the database through the data processing module, whether the vehicle exceeds the limit or not is judged through a threshold value method, radar data and video data are effectively fused through an algorithm, and the fused data are compressed. The radar and video information fusion technology is utilized, an efficient coding scheme is combined, the high-precision and high-efficiency vehicle over-limit early warning function can be achieved, and the method is suitable for complex road environments and variable traffic scenes.

According to the panorama camera and 3D laser scanner mobile measurement matching method, distortion correction, camera calibration and multi-lens relative orientation matching of the panorama camera are carried out based on the characteristics of the panorama camera for a monocular camera and the panorama camera; establishing collaborative matching between the laser data and the image data and a matching and fusion basis between the laser data and the image data; based on a pinhole imaging model, all parameters of camera calibration are introduced to complete camera calibration, and the relative orientation relation between the laser scanner and the panoramic camera is deduced according to a cylindrical or spherical projection method; a cooperative calibration method based on plane constraint is adopted, initial values of rotation and translation vectors between a checkerboard plane and a distance are solved by utilizing a constraint relation between the normal vector and the distance of the checkerboard plane in a laser and camera coordinate system, and finally a global optimal solution is obtained by iteratively optimizing a target equation. The method is simpler to operate and does not need too much manual intervention. And the matching precision is high, the speed is high, and the robustness is good.

The invention discloses a cauliflower phenotype analysis and prediction method based on a field high-throughput phenotype system. According to the method, on the basis of RGB images and hyperspectral images of field cauliflower, through a cauliflower segmentation model, cauliflower, flower ball and leaf areas are automatically extracted, phenotype data and chlorophyll content are extracted, data of different growth periods are sorted according to a time sequence, a growth curve is drawn, and a regression model is constructed, so that the growth states of the cauliflower in different stages are predicted. Compared with the prior art, an effective non-contact scheme is provided for cauliflower phenotype analysis, phenotype parameters of cauliflower can be obtained without manual field on-site measurement, and corresponding culture measurement is combined, so that the cauliflower culture efficiency can be effectively improved, the culture cost can be reduced, and double friendliness of human resources and biological safety is realized.

The invention relates to the technical field of environmental science and information, in particular to a rural land space carbon emission assessment method, which collects real-time data through a distributed sensor network and an unmanned aerial vehicle remote sensing technology, analyzes and extracts key features by using an adaptive variational auto-encoder and topological data, and effectively identifies a land utilization mode. The graph neural network and the Bayesian optimization generative adversarial network are combined to dynamically evaluate and predict carbon emission, and uncertainty quantitative data are generated; a multi-objective genetic algorithm and a game theory model are used for optimizing land utilization layout, an optimization scheme is executed on a block chain through an intelligent contract and a multi-agent system, and automatic management and dynamic adjustment are achieved; according to the method, the accuracy and the real-time performance of rural land carbon emission evaluation are remarkably improved, and a scientific basis is provided for low-carbon sustainable development.

The invention discloses an unmanned aerial vehicle target detection method based on an improved YOLOv5 algorithm, and belongs to the technical field of unmanned aerial vehicle target detection. According to the unmanned aerial vehicle target detection method, through the improvement of respectively calculating anchor frame parameters on feature maps with different scales, the detection capability of a model on targets with different scales is improved, and the detection efficiency is improved. And especially for a small target and a large target, more accurate positioning can be realized. And meanwhile, the generalization ability of the model in a complex scene is enhanced, and diversified target distribution can be better adapted. The calculation process of the anchor frame parameters is optimized, the workload of manually adjusting the anchor frame parameters is reduced, and the automation degree of the model is improved. Through multi-scale anchor frame parameter fusion, the precision of the detection frame is improved, and the overall performance of unmanned aerial vehicle target detection is further improved; and meanwhile, the FPN and PAN structures of the Neck part effectively fuse the characteristics of different scales, so that the target positioning and identification capability of the network is improved, and the detection precision is improved.

The invention relates to a polarization image fusion method and system based on multi-scale feature fusion. The method comprises the following steps: acquiring an original polarization image of a polarization camera; according to the original polarization image, a light intensity image, a linear polarization degree image and a linear polarization angle image are obtained through Stokes vector calculation; performing feature extraction and fusion on the light intensity image, the linear polarization degree image and the linear polarization angle image in a neural network to form a fused image, and training the neural network based on a polarization characteristic retention loss function and a multi-scale feature loss function; and inputting the original polarization image into the trained neural network to obtain a polarization image with polarization information reserved. According to the invention, image fusion can be carried out by adding one piece of polarization information, so that the image quality is improved, and the polarization information contained in the image is effectively reserved.

The invention relates to the technical field of multi-modal data fusion, and discloses a geochemical data prediction method and device based on multi-modal data fusion. The method comprises the following steps: inputting multi-modal geoscience data into a geochemical data prediction model to obtain geochemical prediction data; the multi-modal earth science data comprises vegetation information, a digital elevation model, aeromagnetic data and a remote sensing image; the spatial multi-modal extractor of the geochemical data prediction model comprises a plurality of feature extraction modules, and each feature extraction module is used for extracting spatial features of earth science data of one modal; the multi-head convolution attention layer is used for fusing the spatial features extracted by each feature extraction module; the spectral convolution module is used for extracting spectral features of the remote sensing image; and the feature fusion layer is used for fusing the multi-space features and the spectral features. According to the invention, accurate fusion of the multi-modal earth science data is realized, and the precision of data prediction is improved.

The invention relates to the field of data fusion, in particular to a data fusion method and system based on reservoir multi-source data. The method comprises the following steps: performing coarse alignment on sensor data with different acquisition frequencies to obtain coarse alignment data; training a deep learning model according to the coarse alignment data to obtain a pre-trained fine alignment time model; setting an initial offset, inputting the coarse alignment data superposed with the initial offset into the branch network to obtain feature vectors, and stacking the feature vectors to obtain a two-dimensional matrix; inputting the two-dimensional matrix into a pre-trained fine alignment time model, carrying out secondary model training, and taking an offset parameter value in a last training result as an optimal offset; and superposing the optimal offset to the coarse alignment data to complete data correction, so that the timestamps of the sensor data are aligned, and performing data fusion on the corrected data through a Kalman filter to obtain fused multi-source data. The method has the effect of improving the precision of fused data.

The invention discloses an intelligent hospital guide method and system based on symptom matching and department recommendation, and the method comprises the following steps: receiving a multi-modal symptom description inputted by a user, and carrying out the combined processing, thereby obtaining multi-modal symptom information; inputting the multi-modal symptom information into a multi-modal symptom deep learning model to obtain text key symptom features and voice key symptom features, and performing feature fusion to obtain comprehensive symptom feature representation; performing multi-dimensional matching according to the comprehensive symptom feature representation to generate a candidate department list; acquiring the real-time position of the user, inputting the real-time position of the user, the candidate department list and the voice key symptom features into a department recommendation model together, outputting a recommended department by calling a # imgabs0 # minimum spanning tree path algorithm, and generating a department information graph for the recommended department; and displaying department information, a doctor-seeing path and an appointment mode for the user through the department information atlas, thereby realizing intelligent hospital guide. According to the invention, the symptom information of the user is accurately identified and efficient matching with related departments is realized.

The invention discloses an SPECT / CT image fusion method and system based on deep learning, and the method comprises the steps: obtaining an image through an SPECT technology and a CT technology; constructing a multi-modal deep learning feature extraction network, and respectively extracting the features of the SPECT and CT images; generating a fusion strategy for the extracted features; and according to the fusion strategy, carrying out fusion on the SPECT and the CT. The multi-modal feature extraction network is constructed through deep learning, precise fusion of SPECT and CT images is achieved, functional information of SPECT and anatomical details of CT are fully reserved, the definition and contrast ratio of the images are improved, the detection capacity of abnormal areas is enhanced, the diagnosis accuracy is remarkably improved, and more comprehensive support is provided for clinical decision making.

The invention provides a live-action three-dimensional model construction method and system of a building, and relates to the technical field of building modeling. According to the method, feature extraction and semantic segmentation are carried out on the two types of point clouds through the deep learning model, so that point cloud registration is completed more quickly and accurately, and the error problem caused by manual marking is avoided; and meanwhile, intelligent registration and fusion are performed on two types of point clouds obtained by oblique photogrammetry and laser scanning by using a deep learning model, manual participation is not needed, and automatic, accurate and rapid fusion of the point clouds inside and outside the building is realized.

The invention discloses a multispectral remote sensing image panchromatic sharpening method based on a frequency domain decomposition network, the method constructs a frequency domain decomposition and domain generalization network, and the network comprises three core modules: a dynamic convolution low-frequency extraction module, a high-frequency holding module and a spectrum enhancement module; a domain generalization training strategy is provided, a class generative adversarial network structure is adopted, and an adversarial loss function is introduced; and the generator is guided to realize better cross-domain generalization ability under the supervision of the discriminator by intentionally distributing the domain labels by mistake. According to the method, information of different scales is effectively captured, the quality of the fused image is improved, and accurate fusion of invisible satellite data is realized.

The invention discloses a territorial space planning and splicing system for multi-zone information processing, and relates to the technical field of territorial space planning, which comprises a data acquisition and preprocessing module, a zone analysis module, a planning classification module and a territorial space planning and splicing module, through the regional analysis module and the planning classification module, the acquired multi-source data information can be converted into the same format and substituted into the same regional coordinate, so that the landmark points of each region can be marked and inquired, and the accuracy of the data processing is improved. Meanwhile, database information is subjected to unified classification and feature extraction through automatic classification and extraction, the purpose of classifying territorial planning is achieved, collected space images are fused more accurately through a territorial space planning and splicing module, in the image splicing and fusion process, fusion gap traces between the images are reduced, and the fusion efficiency is improved. The images are more natural and smooth when being spliced, so that the quality and the stability of the fused images are improved.

The invention discloses a three-dimensional image synthesis method based on two-way vision, and relates to the technical field of vision and image processing, and the method comprises the following components: a two-way vision image collection module which is responsible for collecting image data of a left view angle and a right view angle, and carrying out the preprocessing of the collected image, the preprocessing comprises the steps of denoising, contrast enhancement, size adjustment and image alignment, and aims to improve the image quality and provide more accurate original data for subsequent matching and synthesis, a matching algorithm based on a deep neural network is adopted, and a large amount of image data is trained, so that the algorithm can learn a deep corresponding relationship between the images, and the image quality is improved. Therefore, higher-precision matching is realized, the problems of image distortion and fuzziness caused by matching errors are effectively reduced, a more reliable data basis is provided for subsequent three-dimensional image synthesis by the high-precision matching, and the definition and fidelity of the generated three-dimensional image are ensured.

The invention provides a wind power output prediction method based on a large meteorological model and a multi-head self-attention mechanism, and the method comprises the steps: S1, obtaining weather forecast initial field data as the input of the large meteorological model, and generating coarse-resolution weather forecast data based on the large meteorological model; s2, downscaling diffusion is carried out on the coarse-resolution weather forecast data to generate high-resolution weather forecast data; s3, introducing different conditions into the high-resolution weather forecast data, and generating multiple groups of high-resolution ensemble forecast data simulating different forecast scenes; and S4, adopting a multi-head attention mechanism to adaptively aggregate the multiple groups of high-resolution ensemble forecast data, and generating a wind power output prediction result. According to the method, the robustness and generalization ability of the prediction model are enhanced, the adaptability and the calculation efficiency are improved, and a high-quality and high-precision wind power prediction result with higher comprehensiveness is obtained.

The invention relates to a multi-focus image fusion method and device, computer equipment and a medium. A natural image, a multi-focus image and a pseudo label image corresponding to the multi-focus image are acquired; inputting the multi-focus images into a generator, and training and fusing the multi-focus images by the generator by taking the pseudo-label image as a learning target, and then outputting a full-focus fused image; inputting the natural image and the full-focus fusion image into a discriminator to train and distinguish the difference between the full-focus fusion image and the natural image; defining a first training loss function of the discriminator, and defining a second training loss function of the generator according to the first training loss function; and iteratively training the multi-focus image fusion network model according to the first training loss function and the second training loss function until the minimum values of the first training loss function and the second training loss function are obtained, and constructing a trained multi-focus image fusion network model. The whole scheme can realize accurate multi-focus image fusion.

The invention relates to the artificial intelligence technology, and discloses a text error correction method and device based on a post-processing discriminator, equipment and a medium, and the method comprises the steps: obtaining a to-be-analyzed text, carrying out the sentence division of the to-be-analyzed text, and obtaining original text sentences; performing sentence error checking on the original text sentence by using a sentence-level error checker and a confidence similarity decoder in the Chinese spelling correction model to obtain a first checking result; performing character error checking on the original text sentence by using a character-level error checker and a confidence similarity decoder to obtain a second checking result; performing result fusion on the first check result and the second check result by using a fusion strategy to obtain a target result; and correcting the original text sentences according to the target result to obtain corrected text sentences, and integrating a plurality of corrected text sentences to obtain a corrected text. The method is suitable for the field of medical health, and can improve the accuracy of medical and other related text error correction.

The invention discloses a method for monitoring a blood vessel and a stent in the blood vessel. The method comprises the following steps: acquiring a target blood vessel fusion image of a target blood vessel at a preset angle; wherein the target blood vessel fusion image is an image obtained after a CT angiography image and an X-ray coronary angiography image of a target blood vessel are fused, and the target blood vessel fusion image comprises the intima features of the blood vessel; acquiring stent transmission images of the stent at different positions in the moving process in the target blood vessel at a preset angle; segmenting the support transmission image to obtain a support segmented image; and performing fusion display on the target blood vessel fusion image and the stent segmentation image to obtain a multi-modal fusion image of the target blood vessel and the stent. By means of the method, display of the intima characteristics in the target blood vessel on the image is fully considered, and the condition of the stent in the blood vessel can be observed more clearly from the image. The invention further discloses a monitoring device for the blood vessel and the stent in the blood vessel, electronic equipment and a computer readable storage medium.

The invention provides a medical image fusion method and system based on a big kernel attention mechanism, and belongs to the technical field of medical image fusion. A big kernel attention mechanism and frequency domain transformation processing are introduced, and a multi-scale convolution operation, channel attention and a space attention mechanism are combined, so that the fusion image achieves a higher balance in the retention of global information and local details. Meanwhile, through separation processing of amplitude and phase information in a frequency domain, accurate fusion of image details and structures is realized, and common artifact and fuzzy problems in a traditional method are avoided. Besides, a densely connected decoder module is designed, the detail retention capability during image reconstruction is enhanced, and processing of different modal image features is more intelligent and accurate through a self-adaptive feature fusion mechanism.

The invention discloses a data-driven heavy-load locomotive primary steel spring damage detection method, and belongs to the technical field of heavy-load locomotive primary spring damage detection. The detection method comprises the following steps: S1, obtaining a vertical vibration response signal of a framework of the heavy-load locomotive, and preprocessing the vertical vibration response signal to obtain a sample set; s2, dividing the sample set into a training set, a test set and a verification set, and performing standardization processing; s3, constructing a primary spring damage detection model; s4, training and adjusting the primary spring damage detection model by adopting the training set and the verification set to obtain a final primary spring damage detection model; and S5, performing evaluation verification on the final primary spring damage detection model by adopting the test set to realize primary steel spring damage detection of the target heavy-load locomotive. The method can realize intelligent grading detection of the damage of the primary spring of the heavy-duty locomotive, has the characteristics of accuracy, high efficiency, strong generalization ability and the like, and is expected to be developed into a long-term monitoring method for the damage of the primary spring.

The invention discloses a new energy resource reconnaissance method and system based on an unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle resource reconnaissance, and the method comprises the steps: collecting virtual terrain resource data, constructing an optimal flight path model, training the optimal flight path model, and obtaining a current optimal flight path; the unmanned aerial vehicle platform starts the unmanned aerial vehicle and executes the current optimal flight path; the RTK carrier phase difference positioning technology is adopted to carry out field positioning and investigation, and positioning data and real-time terrain resource data are transmitted to the cloud computing platform; fusing the real-time topographic resource data with the virtual topographic data to obtain fused topographic data, and adjusting the flight path of the unmanned aerial vehicle according to the fused topographic data; potential distribution rules of new energy resources are mined through big data analysis, a visual exploration report is generated, and the whole exploration process of the unmanned aerial vehicle is monitored. According to the invention, the efficient and safe completion of the flight task is ensured, and the efficiency and accuracy of investigation are improved.

This invention discloses a data synchronization method and system based on front-end orchestration management, belonging to the field of data synchronization. The method includes the following steps: selecting the data table and field information to be monitored through the front-end interface, generating configuration information, and orchestrating the synchronization process logic according to business needs; storing the configuration information in real time to the configuration center to generate real-time monitoring rules; capturing changed data of the data table and field information, encapsulating it into standard data units and sending it to a message queue; preprocessing the standard data units in the message queue; the synchronization adaptation module matching the corresponding driver type according to the synchronization process logic to generate a synchronization instruction set, and executing data synchronization operations based on the preprocessing results; performing full-link monitoring of the synchronization process, and performing task lifecycle management based on the lifecycle rules configured on the front end. This invention improves synchronization efficiency through dynamic threshold verification, multi-source field fusion weighting, and dynamic encryption strategies, and the full-link monitoring further enhances front-end manageability.

The application discloses a liquid droplet fusion microfluidic method based on a liquid droplet fusion microfluidic device regulated by focused surface acoustic waves, and the device comprises an interdigital transducer, two focusing arc electrodes arranged on the interdigital transducer, and a microfluid channel system bonded on the upper part of the interdigital transducer. The focusing arc electrodes are matched with the microfluid channel system, and the microfluid channel system is provided with a first collection outlet joint, a first dispersed phase inlet joint, a continuous phase inlet joint, a second dispersed phase inlet joint and a second collection outlet joint. The focusing surface acoustic waves generated by the interdigital transducer and the liquid droplet microfluid channel structure are used to flexibly realize the fusion control of liquid droplets with different sizes in the microfluid channel. The symmetrical structure is adopted to enhance the durability and repeatability of the microfluid device. The method is not only suitable for the fusion control between microdroplets, but also suitable for the fusion control between microbubbles and between microdroplets and microbubbles. The surface acoustic waves are non-contact and have good biological compatibility, and thus meet the needs of the liquid droplet fusion technology in the fields of biochemistry and medical treatment.

The invention relates to an edge calculation method and system of an edge server. The method comprises the following steps: sending to-be-processed data generated by terminal equipment to a nearest edge server for data complexity calculation; when the complexity is not greater than a preset complexity threshold, sending the to-be-processed data to a matched edge server for processing, otherwise, performing data decomposition to obtain data blocks with different complexities; according to the data type and the corresponding complexity, different data blocks are transmitted to a matched edge server for processing and sent to the nearest edge server for fusion processing, and the fusion processing is returned to the terminal equipment; data prediction is carried out through the data prediction model, a prediction result is transmitted to the data decomposition module for pre-decomposition, and after decomposition is carried out according to a pre-decomposition scheme, the prediction result is sent to a matched edge server for data processing; and the matched edge server transmits the data processing result to the nearest edge server for data fusion and then returns the data processing result to the terminal equipment, so that processing of large-scale data and complex calculation tasks is realized.

This application provides a map generation method and apparatus based on high-resolution remote sensing imagery. Its purpose is to achieve accurate matching and fusion of small patches and semantically compatible large patches through comprehensive similarity calculation of spectral, texture, and shape three-dimensional features, ensuring the accuracy of the generated map of the target area. Specifically, the map generation method based on high-resolution remote sensing imagery includes: acquiring a plot raster map based on remote sensing imagery of the target area; converting the plot raster map into an initial vector polygon map to obtain vector patches; calculating the comprehensive similarity between small patches and any large patch based on spectral, texture, and shape features in the feature data of the vector patches, wherein the large patch is a patch whose minimum distance to the small patch is within a preset distance threshold; fusing the large patch corresponding to the maximum comprehensive similarity with the small patch to obtain a plot vector file of the target area, and using the plot vector file to generate a map of the target area.