196 results about "Hash coding" patented technology

The invention discloses a quick retrieval method for e-commerce information, and relates to the technical field of e-commerce information.The method comprises the steps that features are extracted, multi-modal feature learning is conducted through image-text semantic matching and high-order semantic fusion, and commodity retrieval efficiency and accuracy are optimized through hash coding; analyzing and structuring user query through natural language processing and a deep semantic reasoning technology, and extracting a user intention in combination with numerical reasoning and logic processing; commodity matching is accelerated through Hash coding and approximate nearest neighbor retrieval, a retrieval result is optimized in combination with image-text semantic fusion and multi-factor sorting, and recommendation accuracy and personalization are continuously improved through a user feedback mechanism; the interpretability of a result is enhanced through reasoning chain echoing, and commodity recommendation is dynamically optimized by utilizing user feedback and reinforcement learning. According to the technical method, a more efficient, accurate and personalized commodity retrieval scheme is provided for the e-commerce platform, and the technical method has a wide application prospect.

The invention relates to a video frame data segmentation right confirmation and dynamic transaction method and system based on a block chain. The method comprises the following steps: segmenting video stream data according to a preset frame rate to generate independent frame images, and carrying out anti-collision hash calculation on the independent frame images to generate a unique identifier; generating a comprehensive value score through a multi-modal analysis model in combination with the independent frame image and the corresponding script text data; generating NFT assets anchored to the block chain according to the comprehensive value score and the unique identifier; and dynamically adjusting the income distribution proportion of the NFT assets through the smart contract based on the user film watching data. According to the method, uniqueness right confirmation of frame data is realized through precise segmentation and anti-collision hash coding, value evaluation is performed by using a multi-modal analysis model, and capitalization and on-chain right confirmation are completed by means of a block chain technology. In addition, the income distribution proportion is dynamically adjusted in combination with the smart contract, efficient transaction and right distribution of the video frame data are realized, and the commercial value and transaction efficiency of the video data are improved.

The invention provides a software demand defect detection method based on a large language model and a satisfiability problem solver, and the method comprises the steps: constructing a domain knowledge base, guiding the large language model to extract key elements in a demand text through combining with a cue word project, and recognizing imperfect and ambiguous defects; and meanwhile, the demand is converted into a logic expression, and logic inconsistency is detected by using an SAT solver. According to the method, incremental updating of the knowledge base is supported, efficient retrieval and comparison are achieved through vectorization and Hash coding technologies, experiments show that the method is remarkably superior to a traditional technology in demand detection in the fields of finance and the like, defects can be accurately positioned, correction suggestions can be provided, and the efficiency and accuracy of demand analysis are effectively improved.

The embodiment of the invention relates to the technical field of data storage, and discloses a cross-K8S cluster configuration change storage method and device based on a block chain, and the method comprises the steps: obtaining a transaction request message passing the compliance verification of an intelligent contract engine, and the transaction request message comprises a configuration change request; obtaining log information of the configuration change request in the transaction request message executed by the target K8S cluster, wherein the log information further comprises configuration change cluster difference information; the log information is stored in a fragmented mode based on the IPFS network, and content hash codes of the log information are obtained; and taking the transaction request message and the content hash code as complete transaction information, and storing the transaction request message and the content hash code in a transaction pool. Decentralized storage is achieved based on an IPFS network fragmentation storage mode, the problem that a centralized database or a log system is maliciously modified or historical records are deleted easily in centralized storage is solved, and auditing integrity and data storage safety are guaranteed.

The invention discloses an Internet of Things data storage method and device based on a block chain and a medium, and relates to the technical field of data storage, and the method comprises the steps: collecting an Internet of Things terminal data set, carrying out the noise disturbance and differential aggregation, and generating privacy protection data; performing hash coding on the privacy protection data, and outputting a tamper-proof to-be-stored data packet; performing security level encryption on the tamper-proof to-be-stored data packet by using a homomorphic encryption algorithm to form an anti-attack ciphertext vector; and carrying out data fragmentation and storage node mapping on the attack-resisting ciphertext vector to obtain a fragmentation storage index. According to the invention, through a dual privacy protection mechanism of homomorphic encryption and differential privacy, the privacy protection strength in the data storage process is improved. And meanwhile, safe and efficient multi-chain collaborative storage in a high-concurrency scene is realized through the constructed hierarchical cross-chain collaborative model.

The invention discloses an internet consumption analysis method based on data analysis, and relates to the technical field of data analysis, and the method comprises the steps: a plurality of participation platforms generate local cause-effect sub-graphs according to user behavior data, convert the local cause-effect sub-graphs into Hash codes, upload the Hash codes to a central server, and aggregate the Hash codes to generate a global cause-effect skeleton graph; injecting a predefined sequential logic rule based on the global causal skeleton graph, dynamically adjusting a causal edge weight according to a user real-time behavior event, and incrementally updating nodes and edges in the dynamic knowledge graph; pre-training the policy network parameters by using each participation platform, and uploading the pre-trained policy network parameters to a central server for aggregation; and issuing the aggregated policy network parameters to each participation platform, executing an interpretable policy according to the user real-time behavior event, and returning user feedback behavior data to each participation platform. According to the method, collaborative optimization of privacy security, real-time response and causal interpretability is realized through federal causal discovery and a dynamic knowledge graph evolution architecture.

The invention relates to an asteroid global terrain three-dimensional reconstruction method for illumination variation, and the method comprises the following steps: inputting an asteroid optical image, estimating the pose of a camera, generating a sparse point cloud, and initializing a Gaussian radiation field; using the image segmentation large model to output a segmentation mask of the asteroid optical image, and pruning the Gaussian radiation field; identifying a shadow region on the surface of the asteroid by combining a # imgabs0 # mixed mask output by a Gaussian radiation field; the camera position, the spherical harmonic coefficient and the Hash code are combined with the angle of the incident light to be input into a neural network, and color values of Gaussian primitives of illumination and shadow areas are regressed; depth normal consistency loss and rotation variance and scale constraint loss are added, optimization of a Gaussian radiation field is jointly supervised in combination with luminosity loss, a Gaussian opacity field is calculated, and a three-dimensional model of the asteroid global terrain is extracted. According to the method, the shadow region on the asteroid can be identified, and the three-dimensional reconstruction precision of the asteroid with surface illumination variation is improved.

The invention discloses a dynamic airspace gridding management method and system for low-altitude economy, and belongs to the technical field of unmanned aerial vehicle traffic management, and the method comprises the steps: collecting airspace state data in real time through a multi-source sensing device, and constructing a four-dimensional space-time grid model; generating a four-dimensional space-time grid with a block chain hash code by fusing meteorological data, an airspace control rule and a real-time flight demand; receiving a space-time grid use request submitted by the aircraft through the smart contract, and calculating an optimal grid allocation scheme based on a deep reinforcement learning model; and the edge computing node executes local track prediction, issues a navigation instruction to the aircraft through the distributed account book, monitors a grid occupation state in real time, and triggers a dynamic grid recombination mechanism when sudden conflicts are detected. According to the method, the rigid constraint of static airspace division can be broken through, the cooperative conflict of multiple aircrafts is eliminated, and the marketization configuration of airspace resources is realized.

The invention discloses a conference content intelligent generation processing method and system based on a multi-modal large model, and the method comprises the steps: collecting the original data of a conference, and completing the standardization preprocessing; inputting a multi-modal large model, extracting multi-modal features and carrying out semantic alignment; executing cross-modal hash coding, generating binary codes and establishing an index database; performing hash retrieval on the related fragments, and constructing a conference content directed graph; based on a conference content directed graph structure, searching an optimized path by adopting a Monte Carlo tree; and generating structured conference content, and outputting a summary, an abstract and an action item. According to the method, efficient extraction, accurate retrieval and structured intelligent generation of the conference content are realized by fusing a multi-modal large model, cross-modal Hash coding and Monte Carlo tree search.

The invention discloses a neural radiation field rendering method based on dynamic hash coding, and the method comprises the steps: employing the feature sequence data as the input, calculating the density value and color value of each sampling point through the forward propagation of a neural network, carrying out the volume rendering integral operation according to the ray tracing principle in the direction of a ray, and obtaining the feature sequence data; judging a final color output result of the current pixel point; according to an error value between the color output result and a real image, updating a network parameter weight through a back propagation algorithm, and if the error value is greater than a convergence threshold, continuing to iterate the training process to adjust a feature coding strategy to obtain an optimized neural radiation field model parameter; and after the rendering performance configuration parameters are obtained, optimizing a storage allocation strategy of feature data through a memory pool management mechanism, and if the current memory occupancy rate exceeds a safety threshold, starting a data compression algorithm to reduce the storage space requirement, and obtaining a real-time rendering output result. According to the invention, high-quality real-time rendering of the dynamic scene is realized.

The invention provides a deep hash image retrieval method based on hierarchical multi-scale feature fusion. The method comprises the following steps: firstly, adopting a Nested Hierarchical Transform as a backbone feature extraction network, and synchronously capturing global context information and local detail features of an image through a nested local self-attention mechanism and a hierarchical feature aggregation module; then, constructing a multi-scale feature fusion module, extracting visual features of different receptive fields in parallel by using a multi-branch unequal-ratio expansion rate dilated convolution kernel, and adaptively weighting important features in combination with attention to generate discriminative multi-scale fusion features; and finally, designing a mixed loss function, optimizing intra-class feature compactness through center similarity loss, and realizing efficient and semantic similarity keeping Hash code generation in combination with a quantization loss constraint Hash code discretization process. According to the method, full expression of image features is realized through a collaborative mechanism of Transform and multi-scale feature fusion, and the precision and efficiency of large-scale image retrieval are improved through an end-to-end deep hash learning framework.

The invention relates to the technical field of image processing and information retrieval, and discloses an image feature extraction and appearance trademark retrieval method based on deep learning, which comprises a feature extraction unit, a cross-modal fusion module, a retrieval strategy module and a dynamic weight distribution unit. The feature extraction unit extracts multi-level semantic information through a multi-branch convolutional network and multi-scale pyramid pooling; the cross-modal fusion module integrates vision, text and color information by using space attention, channel attention and cross attention mechanisms; the retrieval strategy module adopts Hash coding and global self-attention to realize rapid screening and fine matching; the dynamic weight allocation unit suppresses background interference through learnable parameters. The trademark retrieval precision and efficiency can be effectively improved, and the method is suitable for a multi-modal data processing scene.

The invention discloses a wafer defect real-time detection method and system based on multi-scale feature fusion, and belongs to the field of wafer defect detection, and the method comprises the steps: carrying out the preliminary coding of each scale feature through employing a convolutional neural network for a generated feature set, and obtaining a feature vector group containing the local and global information of a defect; if the saliency of the high-frequency component in the feature vector group exceeds a preset threshold value, performing weighted enhancement on the high-frequency feature through an attention mechanism to obtain an enhanced feature set; according to the enhanced feature set, constructing an index structure based on Hash coding, and generating a defect feature index table capable of being quickly retrieved by mapping feature vectors to a low-dimensional space; updating the index table according to the matching result, combining the newly detected defect features with the time sequence prediction result, and generating an expanded defect feature index table; and for the expanded index table, an incremental clustering algorithm is adopted to classify defect features, and defect type distribution updated in real time is obtained.

The invention provides an AI-based photovoltaic module construction quality tracing system and method, and the system comprises an image collection module which obtains visible light, infrared and depth information, and forms original image data; the preprocessing module is used for carrying out geometric distortion correction, multispectral registration and noise suppression and outputting standardized image data; the feature extraction module is used for performing multi-scale feature fusion on the standardized image data to generate a multi-dimensional feature vector; the defect detection module is used for analyzing the multi-dimensional feature vector, detecting the defect type of the photovoltaic module and outputting a defect position coordinate; the construction parameter matching module is used for retrieving a construction process database according to the defect position coordinates and outputting a construction quality evaluation result; and the quality tracing module is used for performing hash coding, generating a tracing code and storing the tracing code to the block chain. According to the invention, the AI intelligent quality inspection robot is introduced and multispectral imaging is combined, so that real-time quality inspection of the photovoltaic module in the construction process is realized.

The invention discloses an image retrieval method and a medium, and the method comprises the following steps: obtaining a to-be-retrieved image, and carrying out the feature extraction of the to-be-retrieved image, so as to obtain a depth feature map corresponding to the to-be-retrieved image; performing weighting processing on each channel of the depth feature map to generate a weighted feature map; extracting local features and global features of the weighted feature map, and performing feature fusion based on the local features and the global features to generate fused features; calculating a to-be-retrieved hash code corresponding to the fusion feature based on a hash-guided measurement loss method; and calculating the similarity between the to-be-retrieved image and the database image based on the to-be-retrieved hash code and the database image hash code, and determining a target image according to the similarity. Loss of important semantic information in the binaryzation process can be effectively prevented, and then the image retrieval precision is improved.

The invention relates to the technical field of digital health, in particular to an intelligent health data early warning method and system.The method comprises the steps that multi-channel signals such as electrocardio signals, pulse oximeter signals and continuous blood pressure signals which are synchronously filtered are injected into an erbium-doped annular cavity through light modulation to generate delay chaos, and sparse binary vectors are obtained through frequency domain hash coding; the vector predicts a future cardiovascular state through a spiking neural network and a reversible ordinary differential equation network; inputting the continuous prediction frame into a reversible tensor flow network, and obtaining a risk vector through anti-fact disturbance; splicing risks, states, codes and contexts to calculate risk energy, and outputting personalized intervention actions by means of a diffusion generation model; and synchronously updating model parameters and coupling coefficients according to the rewards. According to the invention, millisecond-level prospective early warning, low-power-consumption operation and interpretable intervention are realized, and the method is suitable for long-term wearable health monitoring.

The invention discloses an industrial smart energy multi-energy evaluation visualization system and method, and relates to the technical field of energy big data visualization crossover, and the method comprises the steps: collecting and preprocessing multi-source heterogeneous data, and generating space-time aligned high-dimensional energy data through a space-time hash coding method; based on the three-dimensional manifold coordinate model, converting equipment energy efficiency parameters into an equivalent energy mass field, and through an energy field reconstruction and geometric optimization method, generating a curved manifold space and identifying an optimal transmission path and a distortion strength cloud picture; and based on the distortion strength cloud picture, generating a thermodynamic color gradation through a thermodynamic color gradation mapping algorithm, rendering three-dimensional gradient field distribution of the equivalent energy mass field by adopting a ray tracing method, and generating a visual energy evaluation interface. According to the method, space and time hash codes are generated through a space-time hash coding method, the equipment-grid mapping relation is established in combination with GeoHash coding, and accurate space-time alignment of multi-source heterogeneous data is achieved.

The invention discloses a general model pre-training and self-adaptive optimization system and method for a remote sensing image in the technical field of remote sensing images. The method comprises the following steps: acquiring a structured data set containing multi-modal remote sensing core data and corresponding auxiliary geographic information; based on the structured data set, utilizing a two-channel feature extraction architecture to synchronously process visual spectral features and geographic spatial features, and performing model training in combination with innovative training tasks of geographic context contrast learning and multi-temporal mask reconstruction to construct a pre-training model with general characterization capability; and when the pre-training model is applied to a target domain, performing cross-domain feature distribution alignment by utilizing a dynamic gradient inversion layer, and selectively unfreezing part of model parameters by adopting a task perception unfreezing strategy to complete adaptive migration. According to the method, through a self-supervised pre-training architecture of geographic coordinate hash coding and multi-temporal mask reconstruction, the spatial-temporal feature extraction robustness is enhanced, and the problem of generalization of a small sample scene is solved at the same time.

The invention discloses a cross-modal retrieval method based on comparative learning and balanced Hash coding, which comprises the following steps: respectively extracting modal specific features and modal shared features from input multi-modal data, aligning the modal specific features of different modals through comparative learning, modal specific features and modal sharing features are optimized by using quantization loss based on optimal transmission; performing binarization operation on the modal specific feature and the modal shared feature to generate a modal specific hash code and a modal shared hash code; semantic clustering is carried out through a K-means algorithm based on the modal shared hash code to generate a semantic index; candidate samples are coarsely screened in the cross-modal retrieval stage through semantic indexes, then fine-grained comparison is conducted through generated modal specific hash codes and modal shared hash codes, and efficient cross-modal retrieval is achieved. According to the method, semantic alignment of different modes is realized by utilizing comparative learning, and the distribution characteristics of hash codes are optimized through the quantization loss based on optimal transmission, so that the retrieval performance is improved.

The invention belongs to the technical field related to volume video coding compression, and particularly relates to a multi-view video compression method and application thereof, and the method comprises the steps: initializing the information of each anchor point according to a video first frame; performing quantization and entropy coding on the current anchor point information of the partial anchor points by adopting a preset quantization offset parameter, and adding coding results to obtain Loss2; respectively adding corresponding quantitative offsets to current anchor point information of all anchor points, respectively and correspondingly inputting static and dynamic characteristics with offsets of each anchor point into mlp of neural Gaussian static and dynamic attributes to obtain static and dynamic attributes of each neural Gaussian of the anchor point, obtaining deformation attributes from the dynamic attributes, and obtaining deformation attributes from the deformation attributes; rendering based on the attribute information of each neural Gaussian to obtain a picture, and comparing the picture with a real picture to obtain Loss1; the mlp and all anchor point information are optimized through the two Loss, after optimization, the optimized anchor point information is quantized through Hash coding, entropy coding is conducted, and the coding result and the optimized mlp are compression results. The compression rate can be greatly improved.

The invention relates to a deep hash image retrieval method based on feature fusion and a dynamic converter, and relates to the technical field of artificial intelligence and computer vision, and the method comprises the steps of data preprocessing, feature fusion extraction, hash code generation and joint loss optimization. By combining the local feature extraction capability of CNN and the global modeling advantage of ViT, introducing dynamic convolution, a KAN module and a LoRA low-rank adaptation mechanism, and designing a joint optimized multi-branch loss function, the semantic consistency and retrieval performance of Hash codes are effectively improved, efficient and accurate large-scale image retrieval is realized, and the retrieval efficiency is improved. The problems that an existing image retrieval method is insufficient in feature expression, insufficient in retrieval precision and high in calculation overhead are solved.

The invention provides an immersive video coding method and system based on 3DGS, and the method comprises the steps: carrying out the sparse reconstruction of each frame of multi-view video data, and obtaining an initial point cloud; determining anchor points of three-dimensional Gaussian distribution in each frame; extracting spatial context information of the anchor points through multi-resolution hash coding, and splicing the spatial context information with the features of the anchor points to form fusion features; predicting parameters of three-dimensional Gaussian distribution corresponding to each anchor point through a neural network by using the fusion features and camera parameters, and obtaining a rendered image; calculating the color loss between the rendered image and the original video frame, and optimizing the parameters of the anchor points; performing quantization and entropy coding on the optimized parameters of the anchor points; in combination with the color loss and the coding rate, performing rate distortion optimization on the quantization parameter to generate compressed three-dimensional scene representation; and repeating the steps for each frame of the multi-view video, and finally outputting a compressed immersive video code stream. According to the invention, high-quality representation and efficient compression of the immersive video are realized, and the rate-distortion performance is improved.

The invention discloses a two-stage three-dimensional reconstruction method for a neural radiation field based on a symbolic distance function. A more accurate and real object surface grid is generated from a two-dimensional image. The method comprises the following steps of: dividing a reconstruction process into two stages, and respectively modeling the color and the volume density of an object by using two different networks; in the first stage, a zero-order set of a symbol distance field is used for representing the surface of an object, and rough grids are preliminarily extracted; in the second stage, loss is optimized continuously, and the vertex position and the surface density are adjusted step by step to achieve refinement of the grid surface; multi-resolution hash coding is utilized to accelerate training; three-dimensional position and view vector information are added into each layer of a multi-layer perceptron, supervision network training is displayed through multi-view geometric constraints, and reconstruction quality is improved. According to the method, the three-dimensional reconstruction process is divided into two stages, a good view synthesis effect is kept by using implicit representation and geometric constraint, and the precision and speed of model training and reconstruction are improved.

The invention provides a NeRF visual SLAM mapping method based on ORB tracking and three-plane hash coding. Firstly, ORB feature point extraction and feature matching are carried out, key frames are obtained through screening, and an initial sparse map is constructed: local common-view key frames are screened and optimized through a local beam adjustment method, and light sampling is carried out on the key frames in a local common-view key frame window; performing local optimization on scene features corresponding to the key frames in the NeRF map by using a NeRF optimization method based on three-plane hash coding; after loopback is detected, global light beam adjustment method optimization is carried out on the global key frame, and a NeRF optimization method based on three-plane Hash coding is used for carrying out global optimization on scene features of a NeRF map so as to guarantee the global consistency of the constructed map; and finally, extracting surface information of the scene from the NeRF map, and generating a three-dimensional reconstruction model. The combination of three-plane Hash coding and a multi-layer perceptron is used as NeRF map scene representation, and scene details can be reconstructed efficiently and delicately.

The invention discloses a Hash retrieval method for solving a concept drift phenomenon. The Hash retrieval method comprises the following steps: acquiring a target image and preprocessing the target image; performing feature extraction by using an online updated deep hash network and calculating a feature vector center of each class of training set image; using the sample set of the previous time step and the training set image of the current batch to train a Hash model; according to the designed loss function, utilizing knowledge distillation to guide deep Hash network training, and generating a Hash code; and calculating the similarity according to the Hash codes of the training set images and the test set images, and sorting. According to the method, the problem of online updating of the deep hash network and the problem of disastrous forgetting occurring along with continuous accumulation of images are solved.

The invention discloses a Hash code generation method and device, equipment, a storage medium and a product, and relates to the technical field of data processing, and the method comprises the steps: carrying out the data feature pre-extraction and discretization processing of an original data sample obtained in advance through a regular auto-encoder, and obtaining a common feature coded value; learning multi-dimensional subspace data features of the sampled data samples through a multi-head gating attention network model to obtain subspace personality feature codes of the sampled data samples; and performing linear splicing on the common feature coding value and the subspace personalized feature coding value corresponding to the sampled data sample to form a hash code and storing the hash code in the preset hash bucket, thereby obtaining the feature discrete value with high difference to form the hash code through the method, improving the discrimination of the data sample hash code, reducing the hash index conflict, and improving the accuracy of the data sample hash code. Therefore, the effects of improving data calculation efficiency and reducing resource consumption are achieved.

The invention provides a data tracing method, system and device based on big data analysis, and the method comprises the steps: analyzing original data in a data set based on big data analysis, and mapping the original data into Hash codes in a distributed manner according to an analysis result, thereby obtaining a Hash code set; clustering similar Hash codes in the Hash code set into one class by using a locality sensitive Hash algorithm to obtain a plurality of clustering Hash code sets; for each clustering Hash code set, extracting clustering features and data features of original data mapped by the clustering Hash code set; based on the clustering features and the data features, adopting a binary tree structure to generate a traceability label; and determining a traceability relationship between each piece of original data and the corresponding traceability label so as to realize data traceability. According to the method and the device, the efficiency bottleneck caused by one-by-one data processing in a traditional method is avoided, and the processing efficiency is improved.

The application provides a fishery resource dynamic monitoring method and system based on an underwater detection device, relates to the technical field of dynamic monitoring analysis, and realizes fine management of water area monitoring by collecting fish group density data in real time, constructing a fish group density matrix, and combining grid element division and geographic hash coding. The gradient value of adjacent grid elements is calculated based on the density matrix to generate a gradient matrix, which can accurately identify the fish group density mutation area, mark the abnormal area, and then dynamically adjust the sonar sampling frequency of the underwater detection device to improve the monitoring accuracy of the abnormal area. Compared with the prior art, the application has been significantly optimized in terms of monitoring accuracy, abnormal detection response capability and resource utilization efficiency, especially in terms of fish group migration trend prediction and dynamic adjustment of grid division, which improves the adaptability and prediction capability of the monitoring system.

The application belongs to the field of three-dimensional vision and digitization, and relates to a three-dimensional human body reconstruction method and system based on three-dimensional Gaussian splashing. The method steps are as follows: based on a layered hash coding parameter field, the center position of each Gaussian primitive in the constructed three-dimensional Gaussian primitive set is corrected, and the color of the Gaussian primitive under the current observation angle is predicted; based on the human body posture parameters and shape parameters corresponding to the monocular video sequence, linear mixed skin transformation is performed on the corrected Gaussian primitive to map to the posture space; based on the Gaussian primitive parameters mapped to the posture space, three-dimensional Gaussian differentiable rendering is performed on the Gaussian primitive mapped to the posture space to obtain a rendering image consistent with the corresponding view angle of the monocular video; based on the constructed joint loss function, the Gaussian primitive parameters and the layered hash coding parameter field are optimized to obtain a three-dimensional human body model. The application can quickly reconstruct an animatable three-dimensional human body model with stable contours and clear textures from a monocular video.

The invention provides a place identification method based on a background dictionary mechanism, and belongs to the technical field of image identification. According to the method, the accuracy and reliability of location identification are mainly improved, the calculation amount is reduced, the low-delay scene requirement is met, and the deployment cost and period are reduced; according to the scheme adopted by the invention, the location information and deep hash code mapping table is constructed through the feature extraction data set, and the accuracy and reliability of system location identification are improved by retrieving the location information and deep hash code mapping table. According to the location information and deep hash code mapping table, the input image can be accurately matched with the known location in the database through the pre-stored location feature information during location identification, and especially in a high-density city environment or a complex weather condition, the false detection and missed detection probability can be effectively reduced.