1771 results about "Structure analysis" patented technology

The invention relates to the technical field of network security, in particular to a network security dynamic early warning method and system based on a knowledge graph, and the method comprises the following steps: collecting real-time traffic data of a network, analyzing a data sequence by recognizing the characteristics of a state sequence and using a long and short-term memory network, and recognizing a behavior mode; and constructing a state time sequence model. According to the method, structured analysis of network behaviors is realized through time dependence analysis, refined mining of incidence relations between events, determination of a trigger chain and time relation of potential threats and reasoning process of a knowledge graph, abnormal behaviors and attack behaviors can be identified more accurately, global analysis capability of network threats is enhanced, and the network threats can be analyzed more accurately. The network environment change can be adapted in real time by dynamically adjusting the early warning threshold value and the early warning parameter, a more flexible and efficient threat response mechanism is realized, the combination of the early warning signal and the security event response is updated in real time, and the response speed and the processing efficiency of the security problem are optimized.

The invention relates to the technical field of data exchange, in particular to a data exchange system of a nested tag structure, which comprises a data carrier adaptation module, a structure analysis module, a field mapping module, a logic driving module and a script generation module. According to the method, carrier identifiers are generated through comparison of MIME types, file header features are positioned through pattern matching, the recognition accuracy and adaptability are improved, a nesting relation is calculated through depth-first traversal of file paths, node sequences are generated through analysis in combination with a DOM tree, the label analysis efficiency is optimized, and a mapping table is generated through algorithm matching of field identifiers and database codes; the method comprises the following steps: dynamically constructing a field mapping relation, reducing manual configuration, constructing a decision matrix based on a mapping table, executing logical operation to generate a status bit and triggering a threshold write-in instruction, enhancing strategy dynamic adjustment capability, reducing semantic ambiguity through multi-algorithm collaboration and a dynamic decision mechanism, improving data exchange robustness, and supporting real-time state response. And the transmission timeliness of the industrial Internet of Things is ensured.

The invention provides a real-time data analysis and visualization method and system in a big data environment, and relates to the technical field of data processing, and the method comprises the steps: obtaining and preprocessing a to-be-processed document; carrying out hierarchical structure analysis and semantic understanding analysis; semantic index mapping is established; receiving a natural language processing instruction input by a user; performing intention understanding and semantic analysis; matching a semantic understanding task; processing the corresponding semantic fragment units; and obtaining the target document and visually displaying the target document. According to the method, intelligent processing and analysis of different types of documents can be realized, and the data processing efficiency is improved.

The invention relates to the technical field of medical data management, in particular to a medical data document classification and marking system which comprises a document structure analysis module, a semantic information identification module, a classification and marking module and a document intelligent label module. According to the method, document visualization and retrieval efficiency is improved by analyzing document content and extracting titles, texts and appendixes, analytic performance and operability of data are enhanced by identifying key information and semantic relations and accurately extracting and labeling medical terms, medicine research data and medical record records, storage is optimized according to priorities in combination with classification and marking, and the method is high in practicability and easy to popularize and use. Clinical values and application scenes of documents are clarified, document management processes are refined, management compatible with clinical data formats is formed through structured storage of marked data and classification label generation, storage normalization and retrieval efficiency are improved, the requirement for accurate information calling in clinical tests is met, document management capacity is improved, and the document management efficiency is improved. And support is provided for improvement of clinical decision-making and research efficiency.

The invention relates to the technical field of intrusion detection, in particular to a network threat detection method and system, and the method comprises the following steps: building a threat path logic diagram through collecting field dependency items, action trigger timestamp items and action propagation hop count items of an attack behavior chain, and matching field dependency items among nodes based on a graph theory algorithm to obtain a threat path logic diagram; and detecting a mutual exclusion logic field combination, and generating a logic diagram structure with a connecting edge and a mutual exclusion mark. In the method, a threat path logic diagram is constructed by fusing field dependence, action timestamps and propagation hops, graph theory identification field mutual exclusion combination enhances cross-protocol attack chain analysis, and hidden Markov modeling state transition probability verifies time sequence continuity and path length. And performing dynamic time warping alignment on forward and reverse instruction sequences to extract semantic offset, overlapping rate and time sequence entropy, and performing non-linear score classification based on an isolated forest to detect an adversarial sample, topological structure analysis, time sequence verification, instruction alignment and non-linear classification to cooperatively identify a composite attack with field mutual exclusion and time sequence confusion.

The invention provides a potential relation reasoning-based medical knowledge graph retrieval system and a potential relation reasoning-based medical knowledge graph retrieval method. The system comprises a medical data preprocessing module, a knowledge extraction and medical graph generation module, a medical knowledge graph potential relation reasoning complementation module, a keyword network and semantic analysis retrieval module and a semantic mapping and query response module. The system can pre-process and standardize the medical data to generate the knowledge graph containing abundant medical information, not only can complement the knowledge graph through graph structure analysis and semantic reasoning technologies to ensure the integrity and accuracy of the knowledge graph, but also can carry out efficient retrieval based on user query to improve the user experience. And the user query intention and the related entity relationship are accurately matched, and finally an accurate and comprehensive medical information query result is provided.

The invention relates to the field of image recognition, in particular to an automatic solid waste sorting method and device applying machine vision. The method comprises the following steps that monitoring images of to-be-sorted areas in multiple directions are obtained; performing spatial geometric view angle deviation correction on the monitoring images of the to-be-sorted areas in the multiple directions, performing spatial panoramic image optimization, and constructing a panoramic image of the sorting area; performing frame-by-frame solid waste visual positioning scanning on the panoramic image of the sorting area, and performing three-dimensional morphological structure analysis so as to generate a three-dimensional morphological structure representation of each solid waste; performing solid type classification decision according to the three-dimensional morphological structure representation of each solid waste, and performing bounding box coding mapping to obtain a coding mapping bounding box of each solid waste; and performing equipment action sequence simulation on the coding mapping bounding box of each solid waste, and constructing a fine tuning action sequence. Efficient solid waste recognition is achieved, and the sorting efficiency is improved.

The invention provides a computer document intelligent compliance detection system based on deep learning, and relates to the technical field of data processing, and the system comprises the steps: carrying out the topological structure analysis of an adjacent matrix through a graph neural network, so as to detect an abnormality, obtaining a structure deviation degree, and analyzing a decision path node sequence through a pre-training language model to generate semantic conflict features; fusing the structure deviation degree with the semantic conflict feature to generate a risk feature vector; generating an augmented rule set according to the risk feature vector, and updating the formalized rule base; updating parameters of the feature coding component through gradient back propagation; optimizing a differentiable logic layer judgment threshold value and the weight of an analysis module; and obtaining the updated feature coding component, the rule base version and the risk quantification parameter. According to the invention, the efficiency of document compliance detection is effectively improved.

The invention provides a semi-structured file processing method based on an LLMs (Language Language Model), which is used for efficiently analyzing a semi-structured file and converting the semi-structured file, and belongs to the technical field of intelligent document processing and multi-modal learning. The method is characterized by comprising the following steps: multi-modal fusion analysis: adopting a hierarchical cross-modal attention mechanism HCMA, and combining a multi-head attention mechanism with a graph neural network GNN to realize multi-level feature fusion of a text and an image; adaptive structured conversion: adopting a dynamic structure analysis network DPN, and combining reinforcement learning and a generation model to realize adaptive analysis of a document structure; and model generation and multi-modal reasoning: adopting a multi-modal generation reasoning network MGRN, and combining a diffusion model and neural symbol reasoning to realize joint generation and reasoning of the text and the image. According to the method, a remarkable technical breakthrough is brought to the aspects of document analysis, information extraction and intelligent reasoning, and powerful support is provided for data-driven innovation of various industries.

A system for adaptively caching network communication protocols enhances efficiency across heterogeneous device environments through a multi-level cache architecture with device-capability-based tiers. The system collects endpoint telemetry data including device capabilities and operational constraints to classify endpoints and generate context-aware protocol variants optimized for specific device types. Protocol optimization opportunities are determined through structural analysis of message patterns and state transitions. The system performs protocol deduplication by identifying functionally equivalent variants and maintaining canonical representations to reduce cache redundancy. Cache synchronization across distributed nodes uses enhanced Merkle tree structures with protocol normalization processing. The system predicts communication needs based on historical patterns, network context, and endpoint constraints, enabling proactive cache management tailored to device capabilities. Integration with event-driven data communication systems enables seamless protocol selection and translation while maintaining compatibility between diverse endpoint types, from high-performance servers to resource-constrained IoT devices.

The invention relates to the field of earth wall defect recognition, and discloses an assembled retaining wall defect recognition method and system based on image recognition, and the method comprises the steps: obtaining multi-view image data of an assembled retaining wall, and constructing a defect recognition image data set in combination with a boundary detail enhancement mechanism and a region illumination compensation strategy; performing edge guide feature extraction on the defect identification image data set, and constructing an image deep feature model based on a boundary context fusion network; judging whether the response intensity change of the image deep feature model in the crack region reaches a preset threshold value or not through an edge response enhancement mechanism, and if yes, marking that the crack region has potential defects; utilizing a multi-scale morphological structure analysis method to carry out contrast perception optimization on the corrected crack area; and based on a defect identification result, combining a component positioning mechanism and component historical operation and maintenance data to perform severity grading evaluation on the defect. The method has the advantage of improving the sensitivity to the marginal area.

The invention relates to the technical field of Otwei interactive maps, and discloses a geological data interaction method and system based on an Otwei interactive map. The method comprises the following steps: carrying out feature recognition and structural analysis on original geological data to obtain a standardized data packet, and establishing a geographic space reference conversion index table; performing feature extraction and classification on geological elements in the standardized data packet to obtain structured geological data; importing the structured geological data into an Ovoucher interactive map, and carrying out local registration through a mesh generation technology to obtain a visual geological element map layer; geological element drawing and attribute input are carried out, and an edited geological data set is obtained; and carrying out structure recombination and reverse coordinate conversion to obtain a standard format data file adaptive to the target geological information system. According to the method, accurate conversion of multi-source geological data between different coordinate systems and measuring scales is realized, and the problem of spatial dislocation during integration of different-source geological data in a traditional method is effectively solved.

The invention relates to the technical field of fault diagnosis, in particular to an intelligent monitoring system for a sewage treatment pipeline, which comprises a topological structure analysis module for collecting pipeline network nodes and communication information, calculating the connection number and geometric attributes of multiple nodes, identifying the pipeline length and the connection direction between adjacent nodes, and analyzing the pipeline network nodes; and calculating the length of a loop formed among multiple nodes and a topological structure, analyzing structural stability features, and obtaining topological geometric feature data. According to the invention, by analyzing the geometric and topological data of the sewage treatment pipeline system, the understanding of the stability of the pipeline structure is optimized, so that the accuracy of fault prediction is improved, the flow and pressure changes are monitored in real time, and the response to dynamic adjustment and abnormal phenomena in the system is more sensitive and timely; the flow gradient and the pressure difference are analyzed, so that the recognition capability of an abnormal mode is enhanced, a problem area can be quickly recognized and processed, the operation efficiency is improved, and the environmental risk and the maintenance cost are reduced.

The invention provides an additive manufacturing residual stress optimization method and system, and the method comprises the steps: searching a global optimal solution enabling the residual stress of an additive manufacturing part to be minimum from a technological parameter space through the combination of multi-physics field numerical simulation and an intelligent optimization algorithm; the multi-physics field numerical simulation comprises the steps of sampling from a process parameter space by using an optimization algorithm to obtain process parameters, inputting the sampled process parameters into a finite element model, performing thermal analysis on the process parameters based on the finite element model, and obtaining transient thermal distribution through thermal analysis. And performing structural analysis by using transient heat distribution to obtain residual stress under the current process parameters, and transmitting the obtained residual stress to an intelligent optimization algorithm for realizing process parameter optimization. The method has the advantages of high precision, low cost and the like, and high-precision collaborative optimization of multiple types of process parameters can be realized.

The invention discloses an online health monitoring system suitable for a large-span roof truss steel structure, and the system comprises a structure analysis module which is used for building a three-dimensional finite element model of the roof truss steel structure, carrying out the application of load and boundary conditions, stress-strain analysis and key position recognition, and providing a monitoring point arrangement suggestion; the real-time measurement module is used for selecting and arranging sensors, collecting and transmitting real-time data, preprocessing and storing the data and providing real-time monitoring and alarming functions; and the intelligent analysis module is used for processing and cleaning the collected data, executing anomaly detection and fault diagnosis, carrying out trend analysis and prediction, optimizing the monitoring and analysis process by utilizing machine learning and artificial intelligence technologies, and generating a visual report. According to the invention, comprehensive, online, real-time and accurate health state monitoring of the roof truss steel structure is realized, the safety and stability of the large-span roof truss steel structure are ensured, the service life is prolonged, the maintenance cost is reduced, and the method has important practical application value.

The invention discloses a prefabricated building integrated safety monitoring method based on BIM, and relates to the technical field of BIM, and the method comprises the steps: S1, building a BIM model and managing information of prefabricated parts; s2, safety assessment and simulation before construction; s3, real-time monitoring in the construction process; s4, performing quality safety evaluation after construction; the technical key points are as follows: in the construction hoisting process of the prefabricated part, the accurate control on the hoisting speed can be realized by monitoring the deviation angle of the prefabricated part in real time and dynamically adjusting the proportionality coefficient according to the deviation value by adopting a feedback control principle, so that the stability of the prefabricated part in the hoisting process is ensured; dynamic adaptability is achieved through proportion adjustment, the method is an effective means for guaranteeing hoisting safety, meanwhile, the problem that errors exist in the operation result of the structural analysis construction sub-model is solved, and the method can adapt to prefabricated parts in different environments.

The invention discloses an online public opinion research, judgment, analysis, classification and grading early warning method and system, and the method comprises the steps: constructing a malicious public opinion data detection and judgment model, and carrying out the iterative training of a pre-training neural network through large-scale online public opinion historical data, so as to achieve the fine adjustment and optimization of the model; after malicious public opinion data are detected, fine-grained classification of malicious public opinions is realized through deep semantic comprehension and multi-dimensional text structure analysis by adopting BERT and ERNIE deep learning models and combining semantic role labeling and dependency syntactic analysis technologies; a malicious public opinion data grading and distinguishing model is constructed, deep correlation between public opinion events is captured through multi-layer convolution operation, and the recognition capacity for public opinion sensitivity and urgency degree is improved. According to the invention, malicious public opinion data related to a specific industry field can be efficiently and accurately identified and classified, and the malicious public opinion data is subjected to severity grading, so that the accuracy and the real-time performance of network public opinion management are improved.

The invention relates to the technical field of image analysis, in particular to a calcium carbonate master batch wind shear forming detection system based on machine vision. A calcium carbonate master batch wind shear forming detection system based on machine vision comprises an edge pixel collection module, a boundary trend recognition module, a distortion track repair module, an internal texture analysis module and a forming anomaly detection module. According to the method, the gray difference and gradient angle of continuous pixels are calculated, an edge distribution diagram is constructed, boundary detail expression is enhanced, the pixel arrangement direction and gray change analysis are combined, continuous edge line segments are extracted, the boundary recognition precision is improved, a closed contour is repaired according to line segment connection offset, and structural integrity is ensured; the method comprises the following steps: calculating a gray change rate in an internal region block, drawing a texture fluctuation map, accurately marking a sudden change region, realizing abnormal particle identification, fusing pixel-level gray features and region structure analysis, and improving the precision and integrity of form perception and defect identification.

The invention relates to the technical field of fault prediction, in particular to an intelligent fault prediction method and system for a power adapter. The method comprises the following steps: acquiring a circuit board structure diagram in the power adapter; marking circuit component nodes based on the circuit board structure chart; performing spatial topological structure analysis on the circuit component nodes to obtain a component spatial topological structure diagram; carrying out three-dimensional topology reconstruction on the circuit component nodes, and carrying out local detail iterative optimization so as to construct a local optimization three-dimensional topology structure model; acquiring multi-dimensional operation state data of different parts in the power adapter based on multiple sensors; and performing state feature analysis on the multi-dimensional operation state data of the different parts, and performing state trend evolution so as to obtain state trend evolution data of the different parts. According to the invention, efficient and accurate power adapter fault prediction is realized.

The invention discloses an unmanned aerial vehicle intelligent obstacle avoidance system based on a laser radar, and belongs to the technical field of path planning, and the system specifically comprises a parameter collection module which is used for collecting original point cloud data of a flight area; carrying out obstacle geometric structure analysis to generate desensitization characteristic parameters including surface curvature and spatial density distribution; the feature analysis module is used for sending the desensitization feature parameters to an edge computing node through a hierarchical encryption transmission link, and performing primary aggregation on the feature parameters of a plurality of unmanned aerial vehicles in the same region to form a region feature template; the cloud server is used for integrating obstacle features of different areas through a security fusion protocol and constructing a global obstacle feature library; the path planning module is used for acquiring update data of the global obstacle feature library through the safe synchronization channel, and generating a three-dimensional obstacle avoidance path by combining the real-time scanning features with a feature library matching result; according to the invention, multi-machine collaborative obstacle avoidance path optimization based on obstacle avoidance information sharing is realized.

The invention relates to the technical field of BOM data analysis, and discloses a collaborative BOM data analysis and verification system and method based on an AI model.The collaborative BOM data analysis and verification system based on the AI model comprises an intelligent document sensing and structure analysis module, the intelligent document sensing and structure analysis module monitors and identifies a list file, and sends the list file to a server; and the AI assistant dialogue engine module adopts a language model to interact with an engineer to analyze the document in the unified format field by field. According to the method, a field-level approval mechanism is introduced, each suggestion can be modified after being confirmed by a user, and the user can execute operations such as rejection, remark and pending instead of rechecking only in the final stage, so that the sovereignty, the safety and the traceability of the user are improved, the liability boundary of AI aided design is cleared, the AI is evolved from a tool to an engineering partner with learning ability, and the work efficiency is improved. Interaction intelligent experience is obviously enhanced, and adaptability is high.

The invention discloses an intelligent pipe network topological structure restoration analysis system, and relates to the technical field of intelligent pipe network management, the intelligent pipe network topological structure restoration analysis system comprises a design management platform, and the design management platform is in communication connection with a topological structure data acquisition module, a topological structure analysis module, a pipe network fault diagnosis module, a restoration scheme generation module and a pipe network operation monitoring module. According to the method, the pipe network topological structure model is constructed by integrating various sensors and external data sources, branches, loops or special-shaped connection structures in a complex pipe network are accurately recognized, the judgment capacity of the pipe network connection relation and the flow direction is improved, and the fault diagnosis efficiency is improved by combining the pipe network topological structure model and the flow direction analysis result and utilizing the pre-constructed fault diagnosis model. According to the method, the pipe network fault can be quickly and accurately diagnosed, the fault type and position can be quickly identified by integrating the multi-source data, extracting the fault features and matching the fault features with the preset fault mode, the misdiagnosis rate is reduced, and the fault diagnosis efficiency and accuracy are greatly improved.

The invention relates to the technical field of pneumoconiosis image detection, and discloses a pneumoconiosis-oriented chest X-ray image processing method based on an AI small shadow detection rate. According to the method, a basic mask is generated through an initial segmentation network, and then an accurate lung contour is obtained through correction of a hierarchical optimization unit. The multi-scale feature extraction module analyzes gray features in the contour and constructs an initial small shadow probability distribution map. A morphological structure analyzer identifies discrete shadow regions and marks suspicious shadow clusters, and a dynamic confidence feedback mechanism evaluates its credibility to generate an optimized feature map. A shadow cluster topological incidence matrix is established by the spatial relation modeling network and matched with a pneumoconiosis pathological feature library to screen a target shadow area. The three-dimensional reconstruction engine generates a small shadow volume density thermodynamic diagram, the hierarchical fusion module integrates the thermodynamic diagram and original image space coordinates, an enhanced distribution map is output, and finally structured diagnosis report data is generated.

The invention belongs to the technical field of data processing, and particularly relates to a method and system for document content recognition and intelligent paragraph and chapter disassembly. The method comprises the following steps: S1, splitting a PDF document into pages, and converting the pages into pictures; s2, performing DOM (Document Object Model) structure analysis on the split PDF page to obtain a text character set; s3, performing layout analysis on the PDF page picture to obtain a layout region set; s4, aggregating the obtained text character set and the layout area set into character blocks; s5, performing reading sequence recovery operation on the character blocks; s6, defining a title matching mode set; s7, traversing the row sequence of the block sequence, and obtaining a hierarchical relationship between directories through a comparison algorithm; s8, outputting a structured content block result; and S9, carrying out visual display on the PDF page picture and the content block coordinates, drawing a new coordinate area by a user, and carrying out secondary disassembly according to the provided coordinate area information.

The invention relates to the technical field of mine visualization, in particular to a three-dimensional visualization method and system for a mine. The method comprises the following steps: establishing a three-dimensional terrain model of a mine; designing exploration planning space distribution parameters based on the three-dimensional terrain model of the mine, and executing exploration drilling operation through the exploration planning space distribution parameters; mine point rock stratum lithology data are collected according to exploration drilling operation; mapping the mine point location rock stratum lithology data to the mine three-dimensional terrain model to obtain optimized mine point location rock stratum lithology mapping data; analyzing mine rock stratum boundary structure data and rock stratum lithology fusion characteristic data according to the optimized mine point location rock stratum lithology mapping data, and performing mine rock stratum lithology structure analysis to generate mine rock stratum lithology structure data; and based on the lithologic structure data of the mine rock stratum, lithologic fusion differentiation rendering three-dimensional modeling of the mine rock stratum is carried out, and a three-dimensional visual rendering model of the mine rock stratum is generated. According to the invention, accurate visual analysis of the internal structure of the mine is realized.

The invention discloses a medical inquiry logic chain generation and verification method and system based on a large language model, and relates to the technical field of artificial intelligence, the method comprises the following steps: S1, generating a logic chain framework, constructing a medical inquiry logic chain generation model, and embedding a structured logic template and professional knowledge constraints by taking the large language model as a core engine; s2, designing a two-dimensional multi-level verification framework, and performing automatic verification on the consistency and medical rationality of the generated corpus logic chain; and S3, designing an evaluation index system and a dynamic optimization strategy, carrying out grading marking on the problem corpus identified in verification, carrying out corresponding correction, feeding back a correction result to a model training link, and optimizing a logic chain generation rule. Through an NLP technology, a language model generation mechanism, medical knowledge structure analysis and an intelligent verification algorithm, the quality and the structuring degree of a medical inquiry data set and the application value of the medical inquiry data set in an intelligent inquiry system are improved, and high-quality and controllable professional corpus support is provided.

The invention discloses a building facade structure line accurate extraction method based on laser point cloud feature depth, and is applied to the technical field of point cloud feature extraction. The method comprises the following steps: acquiring building point cloud data, preprocessing the acquired building point cloud data, and dividing the preprocessed building point cloud data into a training set and a test set; inputting the training set into a PCPNET network for training to obtain a PCPNET network training model, and extracting normal vector features of the test set through the PCPNET network training model; adopting a Gaussian weighting function to realize quadratic optimization of the normal vector features, and identifying edge feature points according to the difference of the normal vector features after quadratic optimization; and optimizing the edge feature points through an improved filtering algorithm, calculating normal vectors of the optimized edge feature points, and connecting the normal vectors into feature lines based on preset angle and distance thresholds. According to the method, the extraction precision and robustness of the normal vector features are remarkably improved, and a reliable data basis is provided for building facade structure analysis.

The invention discloses a method for carrying out data cleaning decision making and automatic tuning by using AI workflow, and relates to the technical field of data processing, the method comprises the following steps: obtaining original data from various heterogeneous data sources, and generating a correlation table and a result through field extraction, structure analysis and language attribute recognition; based on this, storage and task generation are performed, and standardized data and task queue information are output; identifying a quality problem through feature analysis, defining a target and constructing a cleaning task atlas, and generating cleaning metadata through preliminary screening and marking; and an AI cleaning workflow containing multiple strategy paths is constructed and optimized, after execution, a downstream feedback evaluation effect is combined and adjustment is carried out, and finally a multi-version cleaning result is integrated and output. According to the method, the adaptation capability to different types of data sources is effectively improved, the dependence on a static preset strategy is reduced, the manual intervention requirement is reduced, the tuning efficiency is improved, and the systematicness and normalization of data cleaning are enhanced.

The invention relates to the technical field of elevator fault prediction, in particular to an elevator fault prediction method and system. The method comprises the following steps: collecting an operation vibration signal of an elevator brake through a vibration sensor, performing frequency domain conversion, performing vibration frequency disorder structure analysis, analyzing abnormal vibration frequency intensity, analyzing elevator braking force increment loss, and obtaining time sequence increment data; thirdly, incremental gradient nonlinear induction is carried out on the time sequence incremental data, and an elevator fault prediction model is constructed based on a K-nearest neighbor algorithm; the elevator fault prediction technology is optimized, so that the elevator fault prediction technology is more accurate.

The invention relates to the technical field of image recognition, in particular to an unattended vision detection method and system based on image recognition, and the method comprises the following steps: collecting vision detection field image data, carrying out scene structure line detection and plane detection to obtain a line segment set and plane parameters, and establishing a basic geometric description element; and based on the basic geometric description element, combining the detected user sight line area coordinate and the vision detection screen boundary coordinate to carry out spatial integration, and obtaining the geometric structure information of the initial scene. According to the method, scene structure line detection and plane detection are carried out by collecting image data of a vision detection site, a line segment set and plane parameters are obtained, a basic geometric description element is established, and construction of an initial scene geometric structure is realized by combining spatial integration of a user sight area and a vision detection screen boundary; and the stability of on-site space structure analysis is improved.