669 results about "Structure recognition" patented technology

The invention discloses a ring main unit monitoring method and system based on the Internet of Things, and the method comprises the steps: building a multi-source data collection network of a ring main unit, extracting the multi-dimensional time-frequency domain features of a signal through an edge computing device, generating a multi-source feature data set, and constructing a dynamic digital twinborn model, real-time mapping of the running state of the ring main unit is achieved through a physical-virtual synchronization algorithm, and twin data volumes are output; according to the node influence coefficient of the twin data volume, analyzing a topological structure of a ring network system by adopting a graph neural network, identifying a key fault propagation path and generating a priority monitoring node list; based on the priority monitoring node list and the twin data volume, dynamically generating an adaptive monitoring strategy through a reinforcement learning algorithm, executing the adaptive monitoring strategy and collecting feedback data in real time, and completing closed-loop iteration of the monitoring strategy through an online learning algorithm; through closed-loop control of multi-source perception-digital twinning-intelligent decision, high efficiency, intelligence and reliability of ring main unit monitoring are realized.

The invention relates to the technical field of inspection or monitoring, in particular to an industrial equipment anomaly detection method and system based on machine vision, and the method comprises the following steps: obtaining a multi-channel pixel matrix of an industrial equipment surface image, calculating a pixel gradient and a texture direction change rate, and building a high-dimensional feature space. According to the method, the high-dimensional feature space is constructed by calculating the pixel gradient and the texture direction change rate, so that abnormal feature points are clearly distributed, the misjudgment probability is reduced, the shrinkage factor is dynamically adjusted based on the projection offset, the detection standard is optimized along with data change, and the adaptability is improved; the local gradient change rate and the texture direction change amplitude are combined to carry out offset correction, the precision is enhanced, image block division is subdivided according to structural features, the complex structure recognition capability is improved, local abnormal feature and overall feature relevance calculation is combined with weight correction, the global accuracy is enhanced, sensitive weights are introduced in abnormal point screening, and the judgment precision is improved; and the anomaly detection credibility is enhanced.

The invention discloses a cloud information system index collection method, system and device and a medium, and relates to the technical field of cloud information system operation and maintaining.The method comprises the steps that information collection interfaces of a cloud system are integrated, data flow information entropy is quantized, and an entropy value sequence is constructed; an interface calling serial number is traced based on a historical log, a calling chain is formed, a directed graph topological structure is established, an influence propagation path of an entropy mutation node is identified, and a collection regulation priority of an interface cluster is determined; an acquisition strategy is formulated in combination with entropy time sequence data and a load level, a state-strategy mapping relation matrix is optimized, and a time period driving type strategy adjustment parameter set is generated; and issuing the strategies in sequence, dynamically regulating and controlling the acquisition frequency, and if state transition or fitting degree abnormity occurs, triggering strategy rollback and optimizing the learning weight. According to the method, on the premise of guaranteeing the integrity of the monitoring data of the power equipment, the utilization rate of edge computing resources is remarkably optimized, and high-timeliness and high-fidelity data support is provided for safe and stable operation of a power grid.

The invention discloses an AI and BIM-based project cost control method and system, and the method comprises the steps: obtaining the three-dimensional model data of a target building, analyzing the file structure of the three-dimensional model data, recognizing the type, size parameters and material attributes of a component, building a mapping relation between a component coding system and an attribute tag if the three-dimensional model data passes the integrity inspection, and carrying out the construction cost control of the target building. Generating a standardized component data set; constructing a material price fluctuation prediction model according to the historical price record, intercepting time series data by adopting a sliding window mechanism, and if the price fluctuation amplitude in the material price fluctuation prediction model exceeds a preset threshold, triggering an early warning identifier, and generating cost prediction data with a risk level; and carrying out association mapping on the standardized component data set and a quota library by adopting a coding matching mechanism, and if component attributes are successfully matched with quota entries, converting and generating engineering quantity data based on geometric parameters, and calculating the cost of a single project. The cost prediction accuracy is improved.

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 semantic enhancement adaptive partitioning method and system for natural resource large model questions and answers, and aims to solve the problems of difficulty in term boundary recognition, damage to semantic integrity and the like. According to the method, three core technologies including theme perception coarse-grained paragraph division, self-adaptive sliding window theme hierarchy division and embedded perception context self-adaptive text segmentation are fused. The system firstly analyzes a natural resource long text structure, identifies titles and theme levels and aligns associated contents; paragraphs are extracted according to a theme perception strategy and are subdivided into sentence sets according to grammar rules; an improved sliding window mechanism is adopted to divide sentences into window sentence block groups. The method is characterized in that a dynamic aggregation threshold mechanism is introduced, the semantic association degree between adjacent sentence blocks is calculated through an embedded perception context semantic segmentation technology, whether the sentence blocks are combined or not is judged by combining a similarity distribution change trend and a dynamic adjustment threshold, self-adaptive delimitation of semantic boundaries is achieved, and text blocks which are clear in structure and coherent in semantics are generated.

The invention discloses an optical performance detection system and method for a camouflage material, and relates to the technical field of camouflage material detection, and the system comprises a structure identification and illumination control module which is used for collecting a surface image of a camouflage material sample, identifying the surface structure of the sample, starting a full-spectrum light source, and simulating step-by-step illumination from weak light to strong light; carrying out RGB imaging based on the standard illumination; and the region division module is used for applying a superpixel segmentation algorithm to the image, automatically dividing sub-regions, performing hyperspectral reflectivity scanning on each region, synchronously acquiring a light intensity image in a polarization direction, calculating a Stokes vector and constructing a polarization enhanced image. According to the method, frequency domain quantitative analysis of microcosmic interference fringes and accurate mask extraction of polarization abnormal areas can be realized, optical exposure risks of camouflage materials under complex illumination and visual angle conditions are effectively quantified through multi-angle reflection sampling and scoring function modeling, and the detection precision, the area pertinence and the automation level are remarkably improved.

The invention provides a dexterous hand multi-object stable grabbing method and system based on vision and touch perception. The dexterous hand multi-object stable grabbing method comprises the steps that RGB images and depth maps of target objects are collected through a monocular RGB-D camera, and dense point cloud data are generated; geometric features are extracted by using a three-dimensional point cloud neural network, and composite object distribution features are analyzed in combination with visual information; an initial grasping strategy is generated based on the features, actions are executed, and the joint state and contact force data are synchronously obtained in real time through body sensing and a fingertip force sensor; in the carrying process, multi-dimensional force perception and the joint state are fused to analyze object stress distribution and center-of-gravity shift; vision and force sensing data are used as input. Vision, body sensing and force sensing data are fused, the problems of three-dimensional structure recognition and dynamic stress regulation and control in multi-object composite grabbing are solved, compared with a single sensing scheme, the grabbing planning precision and the self-adaptive capacity in a complex scene are remarkably improved, and the method is particularly suitable for stable carrying of combinations of loose objects such as dinner plates carrying tableware.

The invention relates to the technical field of particle analysis, in particular to a cyst particle analysis system based on an AI algorithm, and the system comprises a particle image screening module, a boundary feature module, a structure deduction module, a particle recognition module and a performance analysis module. According to the method, the gray contrast and edge change rate analysis is carried out on an encapsulated lubricant image frame sequence in a liquid or semi-solid state to realize accurate positioning of a particle target area, an edge angle continuous change slope sequence is utilized to determine a particle structure boundary sudden change position, and the particle structure edge is deduced according to a boundary trend continuous behavior. The method comprises the following steps: accurately establishing a closed structure path, identifying an adhesion region, carrying out decoupling division on particles by using included angle range and direction consistency indexes, determining the boundary contour of the particles, rapidly and accurately marking a structure dissimilatory aggregation region through the area pixel number and gray gradient characteristics of a structure unit, and improving the particle structure identification precision. And the misjudgment risk caused by particle overlapping and adhesion is reduced.

The invention provides an image acquisition method for three-dimensional point cloud reconstruction and component identification driving of a contact network, and particularly relates to the field of image acquisition of the contact network, and the method comprises the steps: constructing a three-dimensional point cloud model of a contact network structure through a two-dimensional laser radar and a high-precision odometer which are installed on a pushing device; identifying a work support, a carrier cable, a dropper, an electric connection and a positioning point through clustering and principal component analysis, and obtaining key parameters such as a guide height, a pull-out value and a support column limit; and in combination with camera view field parameters, a geometric model is established, a pitch angle and a yaw angle are solved simultaneously, and the camera is controlled to complete image acquisition at the optimal angle and position. Through the method provided by the invention, self-adaptive shooting control under structure identification driving can be realized, complete and clear imaging of key components is ensured, blind shooting and repeated shooting are avoided, and the method has the advantages of high precision, high efficiency and strong real-time performance, and is suitable for a complex overhead line system inspection scene.

The invention discloses a multi-signal quality monitoring system and method in the numerical control machining process, and belongs to the technical field of numerical control machining. The method comprises the following steps: step 1, synchronously acquiring multi-source signals in a microscopic level, a mesoscopic level and a macroscopic level in a processing process; 2, screening to form an optimal feature subset, and constructing a multi-dimensional feature representation space; 3, constructing a same-scale transverse incidence matrix, a cross-scale longitudinal incidence matrix and an interaction incidence matrix based on a bidirectional attention mechanism, and forming an incidence network structure for describing feature coupling and dependency relationships among signals of different levels; step 4, calculating deviation metric values of various signal features, identifying an abnormal diffusion path in combination with a topological structure of the association network, and analyzing a causal relationship chain between the signals; and step 5, matching the identified abnormal mode with a pre-constructed process knowledge graph, automatically generating a targeted process parameter optimization suggestion, and performing real-time evaluation to realize closed-loop adaptive control for quality abnormity.

The invention discloses a tea storage bin management system and method, and particularly relates to the field of tea intelligent storage. Comprising a storage environment modeling module, an information input classification module, a structure identification and authentication module, an environment gradient sensing module, a breathing parameter monitoring module, a tea quality sensing module, a storage environment regulation and control module, a storage opening and closing management module, a quality decay modeling module, a storage dynamic early warning module and a storage strategy generation module. According to the invention, environment blind areas can be effectively avoided, the accuracy and responsiveness of regulation and control are improved, quality deterioration signals can be extracted in real time, quality abnormity early warning can be given out in advance, manual detection errors and delay are greatly reduced, the storage safety is improved, dynamic gas interaction evaluation between tea leaves and the environment in the warehouse is realized, and the quality of the tea leaves is improved. The scientificity and effectiveness of regulation and control are improved, excessive storage or early failure is effectively avoided, differentiated and intelligent management is realized, and the automation and intelligent level is remarkably improved.

The invention discloses a box-type transformer substation monitoring method based on unsupervised learning, and the method comprises the following steps: S1, collecting multi-source sensing data in the operation process of a box-type transformer, and generating a standardized input data set; s2, selecting a data sample in a normal operation state, constructing an unsupervised learning model, and obtaining a feature representation set; s3, inputting data, extracting current operation state characteristics, and recognizing a voiceprint abnormal state by combining outlier detection; s4, performing window sliding and statistical analysis on the time sequence data, and outputting a trend abnormal interval and an abnormal index type; s5, constructing a variable association graph structure, and identifying potential abnormal variables and propagation paths; s6, integrating the various types of abnormal information and the feature representation set, and constructing a voiceprint map library; and S7, integrating and processing the abnormal result and the voiceprint map database, and executing visual display and intelligent early warning. According to the invention, box transformer substation abnormity monitoring and early warning based on unsupervised learning are realized, and the fault identification accuracy and response efficiency are improved.

The invention relates to the technical field of image analysis, in particular to a three-dimensional hepatic duct, hepatic artery, portal vein and hepatic vein image segmentation method and system. The method comprises the following steps: acquiring multi-phase medical image data of a liver region; performing region identification on the image data, and extracting hepatic duct, hepatic artery, portal vein and hepatic vein regions; executing structure tracking on the hepatic duct area to obtain a hepatic duct positioning result; carrying out improved contrast extraction on the hepatic artery region to obtain hepatic artery positioning data; performing directional segmentation analysis on the portal vein region to obtain a portal vein positioning result; performing branch angle extraction on the hepatic vein region in combination with the portal vein direction to obtain hepatic vein positioning data; taking the hepatic artery as a reference structure, performing elastic registration on each blood vessel structure, and generating registration data under a unified coordinate; and completing three-dimensional visualization based on a registration result. According to the method, multi-stage image joint segmentation and registration are realized, and the method has the technical advantage of high structural recognition integrity.

The invention relates to the technical field of industrial information and data processing, in particular to a cutting fluid multi-parameter self-adaptive monitoring and early warning method and system based on a dynamic threshold value, and aims to obtain and standardize multi-modal data of a cutting fluid in real time, construct a coupling attenuation prediction model in combination with processing working condition parameters, and predict a joint attenuation trend under a specific working condition. A cutting fluid performance time sequence is modeled through a Gaussian process, a confidence interval boundary is dynamically adjusted, and a self-adaptive single parameter dynamic threshold value is calculated. And a Copula function is used to learn a non-linear dependence structure among multiple parameters, a combined overrun risk is identified, and a multi-parameter combination threshold value is dynamically adjusted according to a preset rule. The system monitors data in real time and triggers early warning when abnormity is found, a digital computer is used for intelligent decision support processing, a maintenance knowledge base and a process optimization rule are combined, and accurate maintenance suggestions are generated. In addition, the edge intelligent unit ensures the independent operation capability of the system when the communication is interrupted.

The invention relates to the technical field of artificial intelligence, in particular to a large model and multi-agent collaborative decision-making method based on dynamic knowledge flow, which comprises the following steps of: analyzing a static knowledge and dynamic information fusion relationship through joint modeling, extracting a hierarchical structure of equipment constraints and environment variables, identifying constraint conflicts and deviations in task decomposition, and obtaining a multi-agent collaborative decision-making result; screening a consistency decomposition direction, extracting a task constraint parallel optimization theory, correcting constraint conflicts, evaluating consistency changes, and outputting a collaborative task convergence robust state identifier. According to the method, by integrating static knowledge and dynamic information and optimizing understanding of task decomposition and equipment constraints, the collaborative decision-making capacity of multiple agents in a complex environment is enhanced, the conflict and deviation processing capacity in the task execution process is improved, the accuracy and consistency of tasks are enhanced, and the convergence and stability of task targets are improved; the robustness of multi-agent cooperative work is promoted, and finally more efficient resource utilization and task completion effects are achieved.

The invention provides an engineering drawing cross-format intelligent conversion method and system, and relates to the technical field of drawing format intelligent conversion, the method comprises the following steps: loading a first format engineering drawing, activating a multi-modal fusion structure identification engine, and obtaining auxiliary input information of a user; performing feature extraction according to the auxiliary input information, and establishing an identification attention constraint; after a multi-modal fusion structure recognition engine is initialized through recognition attention constraints, multi-modal content extraction of the first-format engineering drawing is executed, and a primitive extraction result is established; analyzing the auxiliary input information to obtain a second format; performing multi-dimensional format difference modeling on the first format and the second format; and performing format adaptation optimization on the primitive extraction result by using a difference compensation network, performing optimization verification by using the bound semantic identifier, and establishing a second format engineering drawing. Through the method and the device, the technical effect of improving the drawing structure reduction degree, the semantic recognition accuracy and the visual style matching degree can be achieved.

The invention relates to the technical field of automobile safety, in particular to a human body finite element model soft tissue motion deformation non-simulation rapid calculation method. Comprising the steps of initializing a calculation structure, recognizing a skeleton-soft tissue common node NBS, and extracting a soft tissue inner surface node NSS, a near-end end face node NSP and a far-end end face node NSD. And extracting skeleton motion information, calculating node coordinate sets BS0 and BS1 based on the initial configuration B0 and the target configuration B1, and determining end face boundaries SP0 and SP1 and SD0 and SD1 by utilizing rigid body transformation. Calculating an initial displacement field D0, acting on the NSS to obtain an initial deformation inner surface SS1, and processing a penetration site to obtain SS2; and fixing the BS1, and optimizing the coordinates of the SS2 node to obtain SS3. And integrating the initial mark point sets SS0, SP0 and SD0 and the post-motion mark point sets SS3, SP1 and SD1, and calculating a final displacement field D1 through spatial interpolation. The technical scheme can quickly and accurately simulate the large deformation of the soft tissue involved in human motion.

The invention discloses a self-adaptive budget report generation method based on multiple agents, and belongs to the technical field of report generation, and the method comprises the steps: according to a collected budget table sample, analyzing the agents through the table sample, obtaining a table sample structure based on image recognition, recognizing a basic index field, and carrying out semantic matching according to the basic index field to obtain analysis logic; according to the analysis logic, basic data are collected through a data collection agent, and analysis tasks are allocated to all professional agents through a task allocation agent, so that the basic data are analyzed; and generating a budget report through the large model according to the basic data and the analysis result. Through the image recognition technology and semantic matching of the table sample analysis agent, template-free dynamic table sample analysis is realized, the structure and the field are automatically extracted, and the manual configuration cost is reduced. The data collection agent integrates multi-source heterogeneous data, and the task distribution agent dynamically schedules professional agents for division and cooperation, so that the analysis accuracy and efficiency are improved.

The invention discloses a building roof elevation extraction method and system based on city modeling, and the method comprises the steps: S1, point cloud data and image data fusion registration: extracting data features, and carrying out alignment registration; s2, roof denoising and boundary optimization: identifying interference information, extracting a roof boundary, and generating a roof digital surface model through interpolation; s3, roof elevation extraction: for a flat roof, extracting the top elevation of the flat roof through a plane fitting method; for the slope top roof, extracting the top elevation of the slope top roof through an extreme point detection method; and extracting the top elevation of the roofs except the flat roofs and the slope roofs through a point cloud clustering method. According to the scheme, the roof elevation can be rapidly and accurately extracted, and high-precision three-dimensional data support is provided for urban planning, energy assessment and disaster emergency; the problems that in the prior art, roof elevation extraction efficiency is low, complex roof structure recognition precision is insufficient, and multi-source data fusion is difficult are solved.

The invention relates to the technical field of English language ability assessment, in particular to an English language ability assessment method and system. The method comprises the following steps: collecting a writing text uploaded by a student, and analyzing the literature and style category and sentence pattern structure of the writing text to obtain a content literature and style category sentence pattern structure; thirdly, identifying logic relation deviation in the text based on the structures, performing clustering, further quantizing the context fuzzy degree, and generating context fuzzy degree quantized data; and finally, designing an English language ability evaluation architecture by adopting a Q-learning algorithm. According to the invention, the English language ability evaluation technology is optimized, so that the English language ability evaluation technology is more accurate.

The invention relates to the technical field of radio frequency transmit-receive control, in particular to a radio frequency transmit-receive control system for radar height measurement, which comprises a pulse adaptation control module, a time resolution partition module, an echo structure identification module, a multi-frequency channel screening module and a transmit-receive control decision module. According to the method, by dynamically extracting the echo amplitude jump point and compressing the pulse width, the time domain separation capability can be improved, the recognition precision of an adjacent target can be enhanced, the interval distribution characteristics are constructed based on the jump time difference, the sparse time period is screened for evaluating the resolution feasibility, and the adaptability to different echo densities is enhanced; a structure identification window is established around the maximum value of a main lobe, the overrun number of side lobe samples is counted, the accuracy of signal morphological structure identification is improved, the ratio of the main lobe samples to the side lobe samples is combined with a signal-to-noise ratio threshold to perform frequency band screening, a channel priority sequence is formed, and the flexibility and accuracy of signal processing and the spectrum utilization efficiency in the radar height measurement process 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 a method, a system, equipment and a medium for analyzing project progress based on a BIM model and an AI video, and relates to the technical field of constructional engineering management, and the method comprises the steps of collecting regional image data, and executing semantic segmentation and environment modeling. And constructing a multi-scale spatial feature network and a dynamic skeleton modeling engine, inputting the three-dimensional environment diagram, the structure recognizable region diagram and the obstacle probability distribution diagram into the multi-scale spatial feature network and the dynamic skeleton modeling engine, and outputting execution structure recognition and defect anomaly labeling. And triggering an engineering response mechanism based on the risk event scoring function and remotely and dynamically adjusting the construction path. According to the method, through a mode of combining semantic segmentation and three-dimensional environment modeling, high-precision structure identification of the construction site image is realized, and the problems of unclear structure identification and fuzzy component boundary in traditional video monitoring are effectively solved.

The invention discloses an intelligent water conservancy integrated big data analysis method based on a clustering algorithm, and the method comprises the following steps: S1, collecting hydrological data in an intelligent water conservancy system, and carrying out the preprocessing of the hydrological data; s2, performing data enhancement on the preprocessed hydrological data; s3, constructing and training a SimCLR self-supervised contrast learning model, and outputting embedded feature representation; s4, taking the embedded feature representation as the input of an improved fuzzy clustering model, and outputting a membership matrix and a clustering center set; s5, according to the distribution of the membership matrix and the structure of the clustering center set, identifying a hydrological characteristic region, a pollution diffusion region and an abnormal hydrological mode; and S6, inputting an identification result into the intelligent water conservancy scheduling system. According to the method, self-supervised contrast learning and an improved fuzzy clustering model are fused, complex hydrological data recognition is achieved, and the method has the advantages of being excellent in feature expression, high in clustering precision and high in application adaptability.

The invention provides an OCR character recognition method and system based on an end-to-end network, and relates to the technical field of image recognition, and the method comprises the steps: inputting a to-be-recognized image into a preset neural network for degeneration type recognition and corresponding processing, and obtaining a preprocessed image; performing character recognition and structure recognition on the preprocessed image by using a pre-trained end-to-end comprehensive model to obtain character text information and character structure information; recognizing a character image according to the character text information, and generating a character structure constraint graph based on the character image; and constructing a feedback result based on the character structure constraint graph and the character structure information, and optimizing the end-to-end comprehensive model by using the feedback result. According to the method, the identification precision can be improved, the robustness and the adaptive capability are improved, and the problems that in the prior art, in the face of an actual complex degraded scene, the adaptive capability is lacked, errors cannot be effectively fed back and optimized, and the identification accuracy and robustness are reduced are solved.

Disclosed herein are methods and systems to generate code for a workflow. A non-limiting example of a method comprises executing, by a processor, a large language model, which receives an input of an intent associated with a workflow and provides a machine-readable description by: identifying, by searching a hierarchical data structure using a vector embedding associated with the intent, an object corresponding to the intent; identifying a set of data paths within the hierarchical data structure to retrieve the identified object; and generating the machine-readable description, the machine-readable description describing the object and at least one data path within the set of data paths; and generating, by the processor, code for the workflow using the machine-readable description.

The invention discloses a drilling rock core image automatic identification and recording method and system based on deep learning. The method comprises the following steps: preprocessing a collected to-be-detected drilling rock core image; the preprocessed image is input into a trained Transform semantic segmentation model, a pixel-level probability graph is output, and a binary mask image is generated through binarization processing; performing morphological processing, connected domain analysis and contour detection on the binary mask image to obtain edge coordinate information of the rock core frame, extracting a standardized rock core image from the original image according to the edge coordinate information, and performing result storage and visualization; inputting the standardized rock core image into a trained Transform multi-category rock core structure recognition model, outputting a pixel-level probability graph of multiple channels, and generating a binary mask image of each channel through binarization processing; and three-rate calculation and multi-structure statistical analysis of the drilled rock core are carried out, and data output and visualization are carried out.

The invention relates to the technical field of reflected waves, in particular to a coal mine disaster comprehensive evaluation system based on three-component micro-motion detection, which comprises a frequency direction extraction module, a propagation included angle calculation module, a sensitive path identification module, a critical turning identification module and a structural disturbance mapping module. According to the method, the continuous sequence is formed in the space through the direction vector constructed through the main frequency value of the measuring point, an included angle screening strategy is used for eliminating incoherent direction sections, it is ensured that the path has trend consistency, the frequency amplitude difference value and symbol inversion are used for detecting a mutation point, and the response recognition capacity for directional changes is enhanced; the normalized frequency ratio is combined with a standard deviation and a difference value to carry out joint calculation so as to quantify a structure jump degree and improve the accuracy of reflex position expression, disaster boundary points form an image set, and a structure identification channel is established by taking a frequency direction as a principal line; and the spatial expression definition and precision are improved in the aspects of path aggregation, boundary precision, disaster recognition and partition and continuous structure drawing.

The invention relates to the technical field of medical image recognition, in particular to a fat content analysis method and system based on a liver pathological section image, and the method comprises the following steps: extracting a liver fat vacuole gravity center sequence, recognizing and arranging mutation nodes, removing abnormal vacuoles, analyzing a boundary structure, extracting a closed boundary, and generating a fat content analysis result. According to the method, through selection of the adjacent pixel group of the structure edge in the fat cavitation region and construction of the gravity center sequence, deep characterization of the fat cavitation arrangement characteristics is realized, and stable reference is provided for subsequent structure identification; abnormal change nodes are identified based on an evolution trend of an included angle of adjacent gravity center vectors in the gravity center sequence, arrangement direction mutation points are effectively positioned, impurity interference is screened out, and misjudgment of fat assessment caused by irregular accumulation is avoided; by means of calculation of a gravity center drift curve and a compactness variable coefficient, a form fluctuation area is recognized, abnormal cavitation bubbles are removed, and the consistency and stability of structure extraction are enhanced.