345 results about "Structure recognition" patented technology

The invention relates to the technical field of intelligent manufacturing, and discloses a target structure automatic detection method, device and equipment and a medium, and the method comprises the steps: obtaining scanning path planning data of a target detection structure, driving an ultrasonic probe to execute surrounding scanning motion, and collecting an ultrasonic image sequence and spatial pose data; dynamically adjusting a pressure application angle and a scanning speed based on force feedback information, fusing spatial pose data and an image sequence to perform three-dimensional reconstruction, constructing a three-dimensional geometric model of a target detection structure, extracting feature distribution data by applying an intelligent analysis model, generating a feature decision set, and performing feature extraction; and mapping the feature decision set to a three-dimensional coordinate system to construct an analysis report containing the feature type marks and the topological relation. Through fusion of force control scanning, image reconstruction and intelligent analysis, standardization and intelligence of a detection process are realized, image consistency and structure identification precision are improved, manual dependence is reduced, and comprehensiveness and reliability of lesion identification are enhanced.

The invention provides a full-text retrieval method and system fusing various types of documents, and relates to the technical field of information retrieval, and the method comprises the following steps: obtaining document representation through document content extraction and structure recognition, generating a cross-modal semantic vector by using word embedding and nonlinear transformation, constructing a hierarchical index and a cross-document association graph, and obtaining a full-text retrieval result; the basic correlation score is calculated after the query request is received, and the comprehensive score of the candidate content segments is calculated based on the association graph to determine the optimal retrieval result, so that unified representation and retrieval of heterogeneous documents are realized, the cross-document retrieval precision and relevance are improved, and the processing capability of a retrieval system on complex queries is enhanced.

The invention discloses an AI-based archive digitization full-process management and control method, which comprises the following steps: acquiring archive image data and text data, constructing an original archive data set, and generating an initial directory set; constructing an archive directory template set, executing template matching and structure adaptation, and generating an initial structure recognition result; setting a classification rule of directory deviation and structural contradiction, constructing a deviation rule base, executing rule scanning and node comparison, and generating a deviation contradiction label sequence; constructing an evolution state vector, inputting the evolution state vector into an improved GMN model, and generating a structure repair state vector through a structure guiding unit, a deviation sensing unit, a path fusion unit and a node evolution unit in sequence; and executing result updating and conflict rearrangement, completing standard template alignment and semantic coverage verification, and generating a final structure recognition result. According to the method, the structural integrity, the semantic consistency and the automatic repair capability of directory recognition are improved, and the method has relatively high adaptability and intelligent level.

The invention discloses an aeronautical ice accretion identification method based on multi-source data, which solves the coverage blind area of a single data source (mode / satellite) through multi-source data fusion processing, improves the integrity of a three-dimensional space, and further establishes a scene classification foundation through cloud layer structure identification and accurate division of a cloud area / a precipitation area. Through membership function construction processing, a nonlinear relation between meteorological elements and icing is quantified, and a traditional threshold value method is replaced; furthermore, through physical scene classification processing, single-layer cloud / multi-layer cloud / freezing water freezing mechanisms are distinguished, and misjudgment is avoided; through initial index calculation, CTTmap is dynamically adjusted according to different scenes, and satellite detection deviation is corrected; finally, through dynamic index correction processing, vertical motion, supercooled water and a real-time report are introduced, and result self-adaptive optimization is achieved.

The invention discloses a sewage high-risk pollutant screening and identification method based on fragmentation tree pre-training, and the method comprises the steps: constructing a fragmentation tree pre-training data set according to the second-level mass spectrum data of known high-risk pollutants, so as to carry out the self-supervision pre-training of a graph neural network encoder, and obtaining a fragmentation tree editor; the method comprises the following steps: acquiring secondary mass spectrum data of suspected high-risk pollutant related compounds in a to-be-detected sewage sample, constructing a fragmentation tree set, constructing a high-risk pollutant screening model based on a fragmentation tree encoder, performing high-risk pollutant signal screening on the fragmentation tree set to obtain a high-risk candidate fragmentation tree set, and performing high-risk pollutant signal screening on the high-risk candidate fragmentation tree set. And generating a candidate molecule set of each high-risk candidate fragmentation tree, and identifying a target molecular structure and a matching score of the high-risk candidate fragmentation tree from the candidate molecule set. According to the invention, rapid screening and priority ranking of high-risk pollutants in sewage are realized, and candidate output of structure identification is further provided on the basis of screening.

The invention relates to the technical field of intelligent medical treatment, and discloses a cataract surgery navigation control method based on multi-modal fusion, which comprises the following steps: carrying out lightweight preprocessing on an eye fundus image, ultrasonic probe data, a corneal topographic map and intraocular pressure parameters through an edge computing architecture, and constructing an efficient feature extraction network by adopting a knowledge distillation technology; self-adaptive feature fusion is realized based on an uncertainty quantification mechanism, real-time structure recognition is provided by utilizing an augmented reality technology, an intelligent navigation strategy is generated by pre-training a reinforcement learning model, an instrument control system with multilayer safety guarantee is established, and continuous optimization of the system is realized by adopting an online learning mechanism. According to the invention, the precision and safety of the operation are effectively improved, and an effective technical scheme is provided for intelligent navigation control of the cataract operation.

The invention relates to the technical field of computer vision and document analysis, and discloses a table structure identification method and system based on a self-adaptive anchor frame, a terminal and a medium. The method comprises the following steps: acquiring multiple types of typical table image samples, labeling structured data and merging types of table cells, and constructing a travel channel data set and a column channel data set through multi-modal processing; constructing a table structure recognition model, wherein the model comprises a trunk feature extraction network, a double-branch detection head and a merging cell classification module; training a table structure recognition model based on the row channel data set and the column channel data set; and executing a structure identification task on an input table image by utilizing the trained model, and generating structured data containing physical coordinates and logic position relationships of the cells according to an identification result. According to the method, the problem of accumulative errors caused by asymmetry of row and column detection logic in a traditional method can be effectively solved, and meanwhile the problem that the row width and the column height are inconsistent due to complete dynamic adjustment is avoided.

The invention discloses a dynamic core binding and energy efficiency optimization method and device based on a scheduling domain. The method comprises the following steps: analyzing a CPU topological structure when a kernel is started, and identifying and registering a Cluster scheduling domain; constructing a multi-level scheduling domain hierarchy comprising a Cluster layer, an MC layer and an NUMA layer; regularly detecting the utilization rate of the current scheduling domain, and dynamically switching the levels of the scheduling domain according to a comparison result of the utilization rate and a preset threshold value; and when it is detected that the scheduling domain level switching frequency exceeds a preset frequency threshold, starting an oscillation suppression mechanism. Through the three-layer elastic scheduling domain architecture comprising the Cluster layer, the MC layer and the NUMA layer and the dynamic oscillation suppression mechanism, the task scheduling efficiency and the system energy efficiency are optimized, fine-grained load balancing can be achieved, memory access delay can be reduced, scheduling domain level oscillation caused by short-term fluctuation and floating can be avoided, and the system stability can be enhanced.

The invention relates to the technical field of software engineering and information processing, in particular to a semantic verification method and device based on a large model.The method comprises the steps that a source code file and a corresponding software requirement and design document are obtained, code semantic representation is generated based on grammar analysis and static program analysis, and a semantic verification result is obtained; based on text analysis and business flow chart structure identification, generating a design demand semantic representation; respectively inputting the code semantic representation and the design demand semantic representation into a pre-training language large model to generate a code implementation logic description and a design demand logic description; and performing semantic consistency verification on the two types of logic descriptions to obtain a semantic inconsistency result, determining a code position in the source code file based on the semantic inconsistency result, and outputting a scanning report. According to the invention, the automation degree and the rechecking performance of consistency checking of requirements can be improved, and the leak detection and reworking cost can be reduced.

The invention discloses a handwritten table structure recognition and Excel cooperative processing method and system, relates to the technical field of computer vision, and comprises the steps of image preprocessing, table structure recognition, cell positioning, content recognition, Excel template analysis and text mapping. The system preprocesses the input image; respectively extracting horizontal and vertical lines based on morphological operation, and identifying a logic rank range of the merged cells; calculating a cell pixel bounding box through the cross points, and carrying out logic sorting and interference filtering; independently cutting each cell and calling a handwriting OCR (Optical Character Recognition) engine to extract text content; matching a preset Excel template according to the header or the code, and analyzing the coordinates of the combined and non-combined cells of the Excel template; and accurately mapping the identification content to the corresponding position of the Excel based on the row and column logic sequence numbers by taking the anchor point cell as a reference, and generating a spreadsheet. According to the method, the problems of inaccurate merging cell recognition, content dislocation and the like are effectively solved, and the electronization precision and efficiency of the handwritten form are remarkably improved.

The invention relates to a self-supervised cerebral apoplexy focus segmentation method based on sparse stripe sampling and a lightweight encoder. The invention relates to the technical field of cerebral apoplexy image segmentation, and the method comprises the steps: carrying out the preprocessing of input MRI image data, and carrying out the sparse fringe patch sampling of the preprocessed data; establishing a lightweight encoder, and carrying out self-supervision pre-training; based on the trained lightweight encoder, performance optimization and task adaptation are realized through a lightweight fine tuning mode, and optimization of the encoder is completed; and according to the optimized lightweight encoder, encoding and decoding the area after sparse fringe patch sampling, and outputting a segmentation result. According to the method, local convolution and global attention modeling are comprehensively considered, and the structural recognition capability of the focus with the complex form and the fuzzy boundary is effectively improved.

The invention relates to the technical field of natural language processing, in particular to a semantic understanding-based completion environmental protection acceptance survey report auxiliary auditing system, which comprises a structure recognition module, a semantic chain construction module, a semantic reconstruction module, an image-text coordinate fusion module and a spatial offset judgment module. In the method, through extracting subjects, terms and facility words in facility paragraphs and establishing a combination relationship, ordered labeling of semantic elements is realized, statement structure definition is enhanced, a semantic chain group is constructed and facility classification is associated, semantic consistency comparison is supported, semantic sequences and word frequency distribution in design and reply contents are compared, and description consistency is verified; facility coordinates in an image are extracted, character paragraphs are matched, image-text content anchoring, orientation, terrain and distance word extraction and conversion into space parameters to be compared with an image path are achieved, space consistency verification is completed, and the intelligent level of auditing on semantic comprehension, image-text fusion and space logic is overall improved.

The invention discloses a photovoltaic module array region boundary generation method, which comprises the following steps of: obtaining the size of a block reference object screened in a target CAD drawing, and identifying the size with the maximum clustering number by using a clustering algorithm; determining a feature threshold value of the size with the maximum clustering number, and according to the feature threshold value and a preset triple discrimination mechanism, identifying a block reference object meeting conditions as a photovoltaic module; performing clustering analysis on the geometric center points of all the photovoltaic modules by using a clustering algorithm; the arrangement regularity of the photovoltaic modules in each photovoltaic module array is judged, and boundary construction is conducted on the arrays with the arrangement regularity through a row structure recognition boundary generation algorithm; and carrying out boundary construction on the array which is irregularly arranged by adopting a concave envelope boundary generation algorithm. The method solves the problems that the drawing of the current photovoltaic module array area boundary mainly depends on manual operation of designers, time and labor are wasted when the area boundary of a large photovoltaic power station is drawn, and personal errors are prone to occurring.

The invention discloses a method for automatically correcting radial distortion of a wide-angle lens, and relates to the technical field of computer vision and image processing, and the method comprises the steps: collecting an original image, calculating the distance from a pixel to an imaging principal point, and extracting a multi-scale image and edge features; analyzing the edge direction and radial change and linear extension of the structure, extracting a linear structure candidate region, obtaining a distortion compensation parameter, generating a multi-scale candidate linear parameter set, and calculating a multi-scale linear consistency index for adaptive adjustment; extracting a point set from the candidate straight line parameter set, executing forward and reverse mapping to calculate a dual-space consistency index, and optimizing local parameters and the point set; calculating a minimum linear complexity index based on the line segment curvature, the curvature change rate and the length, and implementing local optimization; and performing weighted fusion on each index to construct a comprehensive objective function, performing step-by-step shrinkage optimization to obtain an optimal compensation parameter, performing global geometric correction on an original image, generating a distortion correction image, and realizing improvement of the structure recognition precision and the geometric correction effect.

The invention discloses a multi-modal large model power grid industry PDF file review method based on reinforcement learning optimization, and aims to solve the problems that power grid industry PDF table review is low in efficiency, insufficient in accuracy and dependent on manpower. The method is implemented in two stages: in the first stage, large model table structure understanding pre-training is completed through multi-modal input construction, cell state modeling, inference chain execution and a reinforcement learning reward mechanism, and the complex table structure recognition capability is improved; and in the second stage, multi-modal embedded representation of the to-be-compared table is constructed, an explicit reward mechanism and GRPO unsupervised strategy optimization are combined, semantic comparison and difference recognition of table fields are realized, and a multi-dimensional review result containing a difference list, an interpretation text, a comparison track and a visual report is output. The power grid industry PDF table review efficiency and precision are remarkably improved, auditing traceability is supported, and the method is suitable for professional table review scenes of electric power engineering budget, equipment parameters and the like.

The application provides a large language model power grid regulation system semantic retrieval method based on a knowledge graph, and the method comprises the following steps: obtaining a regulation document through a company regulation document storage system API call, inputting the regulation document into a semantic recognition model after structure recognition processing, outputting a provision semantic unit set, constructing a double-structure knowledge graph suitable for a power grid regulation system retrieval scene based on the provision semantic unit set, mapping a natural language question input by a user into a structured semantic representation, completing multi-hop retrieval of a provision set related to the semantic of the question of the user and a path structure thereof, matching all path structure representations with the structured semantic, and taking the first several paths with the highest cosine similarity scores as retrieval results. A structure-driven retrieval system capable of realizing responsibility chain reconstruction and path closed-loop semantic output is constructed, and a reliable semantic retrieval scheme is provided for key businesses such as power system compliance query, fault review and responsibility positioning.

The invention discloses a traffic anomaly detection method based on a space-time double-flow network and multi-modal feature fusion, relates to the technical field of intelligent video analysis and traffic behavior recognition, and is used for solving the problem of high false detection rate of existing traffic anomaly detection in a complex environment. A video image sequence is extracted through a fixed and dynamic combined frame sampling strategy, and motion features are constructed. And in combination with the direction consistency coefficient and the perturbation evolution trend, repairing direction perception distortion by using a mirror image mapping mechanism. Target appearance features are extracted through spatial branches, behavior evolution trajectories are extracted through time branches, and meanwhile, a modal weight dynamic adjustment mechanism is introduced to enhance the adaptability to complex scenes. The multi-target interaction relation is quantified based on a graph structure modeling mode, an adjacent structure is reconstructed through a graph self-encoder, and local behavior mutation is recognized. And finally realizing high-confidence anomaly marking by combining state transition analysis and spatial aggregation characteristics. The method has the advantages of high real-time performance, high robustness and wide scene adaptability.

The invention relates to the technical field of image analysis, in particular to a traditional Chinese medicine acupuncture positioning method and system based on image processing, and the method comprises the following steps: collecting a posture image, analyzing the deviation and motion state of an included angle, screening a bone segment with a stable structure, mapping acupuncture point anchoring input, recognizing the sudden change of a proportion, and adjusting the coordinates of the bone segment. And after affine errors are compared, graph mapping information is obtained, and acupoint path output annotation records are segmented. According to the method, by analyzing the posture deviation and the bone chain dynamic characteristics, the judgment capacity for the motion state is enhanced, the stable structure is screened through the bone segment included angle change trend, the acupoint anchoring precision is improved, the accuracy of bone segment structure fitting is optimized by combining the proportion trend and mutation point recognition, and the accuracy of bone segment structure fitting is improved by adopting bidirectional mapping error comparison and type classification. According to the method, the adaptability of graph coordinates and template matching is improved, continuous segmentation and structure recognition of path paragraphs are achieved through sliding window merging and label labeling, acupuncture point positioning is more coherent, and the structure is clear.

The invention relates to the technical field of document duplicate checking. The document duplicate checking method comprises the steps that according to syntactic dependency structures in text information of a document to be subjected to duplicate checking and text information of a plurality of comparison documents, keywords and context dependency relationships of each sentence are recognized, a semantic map is constructed, and the semantic map is subjected to duplicate checking; mapping the keywords and the context dependency relationship to corresponding concept nodes in the semantic graph, constructing a semantic sub-graph of the document to be subjected to duplicate checking and a plurality of semantic sub-graphs of the comparison document, and comparing the structural similarity between the semantic sub-graph of the document to be subjected to duplicate checking and the semantic sub-graphs of the comparison document to obtain a structural similarity score; and based on a node sequence in the semantic subgraph of the document to be subjected to duplicate checking, constructing a disturbance semantic subgraph, calculating a disturbance score fluctuation range, screening a comparison document of which the structural similarity score is higher than a preset threshold value and the disturbance score fluctuation range is lower than a disturbance tolerance threshold value, and outputting duplicate checking difference information. The method and the device have the effect of improving the document duplicate checking accuracy.

The invention provides a real-time monitoring and root cause positioning method and system for software defects. The method comprises the following steps: collecting real-time monitoring data of a software processing thread, and generating a thread competition thermodynamic diagram for identifying a shared resource contention point through time sequence association processing; triggering a server memory bus embedded hardware detection device based on a contention point to dynamically capture a locking state transition signal, and converting the locking state transition signal into a locking event sequence; constructing a locking contention track diagram according to timestamp sorting, and completely displaying a process path of a locking mechanism from obtaining to releasing; constructing a dependency topological structure of the thread and the lock based on the trajectory graph, and identifying a resource contention chain; and mapping the contention chain into an unsynchronized shared variable access path in the source code, and triggering a real-time alarm signal. According to the method, the access path of the unsynchronized shared variable is accurately positioned, the lock competition diagnosis efficiency is improved, and the system deadlock risk is zeroed.

The invention relates to the technical field of behavior modeling, in particular to a multi-scene market declaration and execution coupling method based on energy storage behavior response modeling, which comprises the following steps of: establishing an index, confirming continuity and boundary connection of a declaration direction, and introducing structured jump pairing logic on the basis of constructing a cross-market behavior sequence; a high-frequency behavior chain in a time sequence is accurately captured, the network attribute of a jump structure is remodeled, the structure recognition capability between behavior segments is enhanced, a dynamic transition rule of the behavior sequence is established by introducing time window recognition and tail segment anomaly positioning operation between the jump segments, continuous interference caused by data abrupt change or delay jump segments is effectively eliminated, and the stability of the behavior sequence is improved. Through multiple innovative actions of sequence construction, behavior clustering, structure learning, time sequence comparison and price intersection pairing, a full-chain closed loop from response to declaration behavior correction is formed, and multiple improvements of market behavior understanding ability, data jump expression precision and real-time correction response ability are realized.

The invention discloses a laparoscopic surgery image enhancement and real-time auxiliary system, and relates to the technical field of medical image processing and intelligent auxiliary surgery. According to the laparoscopic surgery image enhancement and real-time auxiliary system, a laparoscopic image flow is obtained and cleaned through the video input and preprocessing module, image enhancement processing is carried out through a deep learning model, then spatial position features of a tissue structure and an instrument are extracted, and a structure recognition index and a spatial safety index are analyzed; according to the invention, through cooperative work of the image preprocessing module and the depth image enhancement model, brightness compensation, edge enhancement, denoising, smoke suppression and other operations are performed on the laparoscope image stream, the depth model is utilized to extract multi-scale image features and reconstruct details, and an enhanced video image stream is generated; therefore, the intraoperative abdominal cavity structure and instrument boundary is clearer, the positioning judgment precision of a doctor is remarkably improved, and the misjudgment risk caused by image blurring is reduced.

The invention discloses a code processing method and device fusing grammar structure and graph structure learning, and the method comprises the steps: collecting high-performance project codes, extracting hot code segments, and analyzing the structural features of the hot code segments; converting the hot code segment into an abstract syntax tree AST file, performing structured analysis, converting the file into Python tree structure data, and expanding node attributes; converting the tree structure data into graph structure data, generating edge, graph and node index files, and constructing the graph structure data and corresponding code structure labels; constructing a graph attention network model for different code structures, and training a plurality of GAT models based on graph structure data and labels; and converting the user input code, inputting the converted code into each GAT model for prediction, and integrating and outputting a feature vector. According to the method, grammar logic is accurately captured through AST conversion and node expansion, multi-language AST is unified into a graph structure, the recognition accuracy of a complex code structure is improved through a GAT multi-model architecture, full-process automation is achieved, and the labor cost is reduced.

The invention relates to the technical field of material nondestructive testing and image analysis, in particular to a topology preserving-based accurate segmentation method and system for a material pore network, and the method comprises the steps: introducing topological structure priori and connectivity constraints into a plurality of links, such as feature extraction, depth model design, training constraint and post-processing structure repair; the real physical morphological characteristics of the material can be fully utilized in the segmentation process, and the accuracy, connectivity and stability of pore structure recognition are fundamentally improved. According to the overall idea of the method, multi-scale texture information, main channel skeleton information and topological feature information of a material image are used as additional priori to be fused into a segmentation model, so that the model does not only depend on pixel-level classification any more, but understands the morphological law of a pore network from the structural level, and thus real recovery of a complex pore structure is achieved.

The invention is applicable to the technical field of low-voltage power distribution networks, and relates to a transformer area topology identification method based on multi-source time sequence fusion and sparse optimization, which comprises the following steps: S10, acquiring and processing node data in a low-voltage power distribution network, finding out a common time point set of all nodes, and eliminating initial state differences; s20, based on the energy and voltage relationship between the nodes, creating an initial network topology structure, generating an initial adjacency matrix, screening out a reasonable node relationship, and reordering the adjacency matrix; s30, constructing an optimization problem P4, setting an optimization objective function containing a regularization item, iteratively solving the optimization problem P4, and updating an adjacent matrix; s40, initializing optimization parameters, iteratively executing the step S30 to optimize the topological structure, and updating the adjacent matrix; and S50, outputting a final optimized topological structure. The method is simple in process, convenient to operate and high in topological structure recognition accuracy and stability.

The invention relates to the technical field of campus information management, in particular to a multi-device synchronous campus information management platform, which comprises the steps of identifying terminal roles and field permissions, judging field consistency and source differences, analyzing field dependency and upstream and downstream relationships, labeling field sensitivity levels and transmission paths, classifying multi-terminal data and generating a synchronous content set. The method comprises the following steps: dynamically identifying a corresponding relation between field sources and role permissions, determining a writable boundary of a field, positioning conflicts in combination with field coverage frequency and a submission sequence, extracting a field dependency structure to identify upstream and downstream inconsistent items, marking a transmission path and a priority according to field sensitivity and an equipment state, and constructing a field transmission map; ordered transmission and reasonable distribution of fields among multiple devices are achieved, terminal data fragments are merged based on the field affiliation relation, service scene identifiers are unified, and conflict elimination and synchronous release of multi-source data are achieved.

The invention relates to the technical field of unified management, in particular to a localized key component adaptation and unified management platform construction method, which comprises the following steps: extracting and merging path information, comparing an affiliation relationship to execute binding, extracting a migration content annotation jump structure, and summarizing and registering combined path data in sequence. And generating a key component adaptation and unified management platform construction scheme. According to the method, merging processing is carried out through the triggering relation of the action field and the scheduling instruction, unified extraction and non-repeated integration of path behavior information are achieved, the attribution path relation is compared according to the action frequency and the field span, the structure recognition accuracy is improved, channel binding actions without name conflicts are screened, and clear path matching is ensured; the method comprises the following steps of: marking a jump source and a pointing position in a path migration process, realizing the traceability of structure change, integrating an affiliation label and an action field according to an original sequence, registering and archiving, and enhancing the orderliness and calling consistency of a data structure.

The invention relates to the technical field of optimization of pneumatic clamps, in particular to a simulation optimization design method for structural size parameters of a pneumatic clamp. The method comprises the following steps: acquiring standard cylinder data and target fixture workpiece data, extracting geometric boundary data to reconstruct a three-dimensional cylinder structure, identifying fixture workpiece interface data and analyzing installation constraints, thereby projecting the workpiece data into a three-dimensional cylinder to form a projection combination structure, and simulating the working process of a pneumatic fixture. A guide limiting structure in the clamp is determined based on the simulation result, a clamp guide rail is adjusted to generate an optimized structure, a clamping force path is captured, workpiece deviation is predicted, clamping stability adjustment is conducted, and finally the optimized pneumatic clamp structure is obtained by combining the optimized guide rail and a clamping structure. High-precision and high-efficiency pneumatic clamp design optimization is achieved, and the clamping speed and the machining efficiency are improved.

The invention discloses an in-situ detection device and method integrating an electrochemical impedance spectroscopy technology and static sounding, and aims to realize synchronous in-situ detection of macromechanical parameters and microcosmic electrochemical properties of a soil body so as to improve the comprehensiveness and accuracy of geotechnical engineering field test. The device comprises an electrochemical test probe rod, a static sounding annular probe and a test analyzer, and the electrochemical test probe rod is provided with a linearly arranged three-electrode system and is integrally installed through an insulation module; the static sounding annular probe is provided with a plurality of sensors; the test method comprises the steps of connection of the test analyzer and the probe rod, penetration of the probe into the soil body, electrochemical test parameter setting, impedance spectrum acquisition, data fitting analysis and the like. Compared with a traditional drilling collection method, the method has the advantages that the original state of the soil body can be effectively kept, and meanwhile, the stratum structure identification, the moisture distribution prediction and the unfrozen water content analysis are realized in combination with the microscopic information obtained by the electrochemical impedance spectroscopy and the static sounding mechanical indexes, so that the macroscopic and microscopic properties of the soil layer are comprehensively reflected.

The application provides a building group structure type identification method and device based on a multi-modal large model, and relates to the technical field of multi-modal large models.The method comprises the following steps: extracting observable building features through a multi-modal large model; establishing a graph-enhanced multi-modal large model to match and reason the features and a knowledge graph; introducing an expert review mechanism to correct the identification results, dynamically update the knowledge graph, and fine-tune the graph-enhanced multi-modal large model; establishing a city building group rapid identification system, using the knowledge graph to establish a rapid identification model based on deep learning and an interpretable identification system based on the graph-enhanced multi-modal large model.The application combines the rapid classification of deep learning and the interpretable reasoning ability of graph enhancement, and constructs a structure identification closed-loop system with self-learning and self-updating capabilities.The application is suitable for city building census, seismic evaluation, BIM-assisted modeling and other scenes, and has high accuracy, strong generalization and continuous evolution capabilities.