2580 results about "Distribution diagram" patented technology

The invention provides an enterprise employee behavior analysis and safety risk early warning monitoring method and system, and relates to the technical field of enterprise risk management, and the method comprises the steps: collecting employee terminal operation behavior data, building an activity thermal distribution diagram based on office area grid behavior association intensity, and training a behavior evaluation model. And inputting the behavior scoring matrix into a deep neural network to extract target behavior characteristics, calculating an abnormal behavior risk weight coefficient in combination with a department security policy, performing classification and analyzing a risk diffusion probability, and generating a risk situation index to determine an early warning level. Starting a response strategy according to the early warning level, blocking high-risk early warning in real time, tracking associated accounts, establishing a risk association map, identifying potential risk propagation nodes and performing active protection, finally generating an early warning report and feeding back effective protection rules to a behavior baseline model, and realizing continuous optimization of a risk early warning mechanism. And the accuracy and effectiveness of safety risk early warning in the enterprise are improved.

The invention provides an external wall visual detection method and system based on deep learning, and the method comprises the steps: firstly obtaining an initial visual image set of a plurality of external wall image units which are collected at different shooting angles and under different illumination conditions and are provided with pixel coordinate marks, and then carrying out the feature preprocessing of the initial visual image set, and generating a standardized feature image set; then, a pre-trained outer wall defect detection model is called to carry out defect feature recognition on the standardized feature image set, a preliminary detection result is obtained, and a final detection result is determined according to confidence distribution of a defect type candidate set in the preliminary detection result and spatial position relevance of candidate areas. And finally, generating an outer wall defect detection report containing defect type statistical information and a position distribution diagram based on a final detection result, and sending the outer wall defect detection report to the target terminal equipment to trigger subsequent maintenance operation. Therefore, the accuracy and efficiency of outer wall detection are improved.

The invention discloses a geological disaster early warning method and a precise early warning system based on multi-source data fusion, and relates to the technical field of geological disaster early warning. According to the method, multi-source heterogeneous data such as remote sensing, meteorological and geological monitoring are fused, a standardized protocol is utilized to unify a data format and temporal-spatial resolution, a standardized data set is formed, key features are extracted by adopting principal component analysis and a recursive feature elimination algorithm, and a long-short-term memory network and a convolutional neural network model are combined, so that the real-time performance of the system is improved. According to the method, the disaster risk is accurately predicted, the space risk distribution diagram is generated, in addition, through application of the real-time stream processing framework and the self-adaptive learning algorithm, rapid distribution of early warning signals and dynamic optimization of model parameters are achieved, the accuracy and timeliness of an early warning system are remarkably improved, and the geological disaster risk is effectively reduced.

The invention relates to the technical field of data security management, and discloses a blockchain-based forestry data security management system and method, and the system comprises a data collection unit which is used for comprehensively obtaining a multi-dimensional information flow of forest ecology and resource states; the data processing unit is used for carrying out deep cleaning, standardized regulation and feature value extraction on the originally collected mass heterogeneous data; the block chain storage unit is used for constructing a permanent, tamper-proof and distributed secure storage infrastructure platform of the forestry core data; a verification unit; the safety control unit is used for constructing a covering data transmission, storage and access full-chain depth defense system structure model; a user interface unit; an auditing unit; and a network communication unit. The method is reasonable in design, and the intelligent analysis module is used for processing to form a forest interannual growth trend prediction function distribution map state visual output result set.

The invention discloses an intelligent prediction method for fatigue damage evolution of an aero-elastic sheet based on digital twinning, and the method comprises the following steps: S1, constructing a finite element model, carrying out the modal calibration, and building a digital twinning model; s2, acquiring real-time monitoring data; s3, constructing a crack-induced potential energy tensor field to obtain a crack development potential energy probability distribution diagram; s4, generating a probability prediction map by adopting a potential energy driven map evolution coupling algorithm; s5, constructing a topological damage mapping function, and predicting a dynamic evolution process of topological damage along with load time; s6, applying a time folding algorithm in the dynamic evolution process to obtain a fatigue state submerged space representation; and S7, inverting the topological damage state of the current structure based on the contrast twinborn variational self-encoding network, updating the digital twinborn model, and finally outputting the structural damage evolution trend. According to the method, digital twinning and graph evolution algorithms are fused, and intelligent prediction of the fatigue damage of the aero-elastic sheet is realized.

The invention relates to the technical field of new energy automobile electric control, and discloses a new energy automobile electric control fault prediction system. The system comprises a real-time data acquisition module, a dynamic fault prediction model construction module, a fault difference calculation module, a multi-dimensional anomaly analysis module, a fault probability positioning module and a self-adaptive maintenance strategy module. The real-time data acquisition module acquires sensor data of the electric control system in real time; the dynamic fault prediction model construction module constructs a dynamic fault prediction model based on historical fault data; the fault difference calculation module inputs real-time data into the model and outputs theoretical fault indexes; the multi-dimensional anomaly analysis module compares the theory with the actually measured fault indexes to generate an anomaly difference matrix; the fault probability positioning module inputs the matrix into a space correlation network to generate a fault probability distribution diagram; and the adaptive maintenance strategy module configures maintenance parameters according to the distribution diagram. According to the system, the fault prediction accuracy and real-time performance can be improved, and stable operation of the new energy automobile electric control system is guaranteed.

The invention provides a multi-mode tailing pond dam break digital twinning emergency deduction and decision optimization method, which comprises the following steps of: firstly, constructing a high-precision three-dimensional scene model, performing simulation calculation on the high-precision three-dimensional tailing pond model based on a finite element model to obtain a dam body stress-strain field, and performing simulation calculation on the dam body stress-strain field based on ANSYS + reinforcement learning. And predicting a dam break probability distribution diagram by using the dam body stress-strain field and the current environment state, generating a rescue scheduling scheme based on the dam break probability distribution diagram and the resource distribution data, building a dynamic virtual deduction scene for exercise, and verifying and optimizing the rescue scheduling scheme according to exercise record data. And a final rescue scheduling scheme is obtained. According to the method, the ANSYS + reinforcement learning hybrid engine is utilized to reinforce the deduction precision, dam break path prediction, optimal resource scheduling and plan self-optimization closed loop are realized, the problems of data fusion distortion, deduction stiffness, decision lag and the like in the traditional technology are solved, and the method is particularly suitable for variable working conditions such as tailing pond seepage field sudden change and extreme weather.

The invention relates to the technical field of building design and digitization, and discloses an automatic design method for a special-shaped facing structure based on parametric modeling. The method comprises the following steps: firstly, acquiring a geometrical characteristic parameter set of the special-shaped facing structure, wherein the geometrical characteristic parameter set comprises curved surface curvature distribution, boundary constraint conditions and a material attribute threshold value; thirdly, according to the curved surface curvature distribution, generating an initial geometric topological grid by using a dynamic subdivision algorithm, adjusting node positions according to boundary constraint conditions to obtain an optimized structural framework, and performing layered mapping on material attribute thresholds to generate a material distribution map; then, the input parametric modeling engine generates a three-dimensional parametric model, finally, a self-adaptive adjustment strategy graph is constructed through a multi-objective optimization algorithm, and a final design scheme is output. By means of the method, automation of design of the special-shaped facing structure is achieved, design efficiency and accuracy are improved, material utilization is optimized, scheme adaptability is enhanced, and design process collaboration is improved.

The invention provides a water conservancy data acquisition supervision method and system based on big data analysis. The method comprises the following steps: firstly, obtaining dam body surface temperature field data, reservoir water level time sequence monitoring data and a dam body three-dimensional structure, and carrying out environmental radiation interference elimination processing on the temperature field data; secondly, extracting temperature fluctuation amplitude time sequence characteristics and water level change rate in the corrected temperature field data, and constructing a matrix reflecting correlation strength of the temperature fluctuation amplitude time sequence characteristics and the water level change rate through dynamic correlation analysis; and identifying temperature fluctuation abnormal points by using the matrix, and generating a seepage correlation map in combination with the water level change rate. And dividing seepage state categories according to the water level change rate, and generating a leakage risk probability distribution diagram by combining periodic trend prediction. And finally, fusing the graph with a dam body three-dimensional structure to generate positioning supervision information with a seepage abnormal identifier. The technical scheme provided by the invention can improve the efficiency and accuracy of water conservancy data acquisition supervision.

The invention provides a machine vision defect real-time detection and classification method and system based on deep learning, and relates to the field of machine vision detection.The method comprises the steps that regional enhancement weights are determined by calculating local entropy and gradient direction consistency, and regional self-adaptive enhancement is carried out; establishing a feature transfer sequence and progressively fusing features; generating and correcting a defect area probability distribution diagram; and constructing a dynamic decision matrix to calculate a comprehensive score for defect grading. According to the method, the defect detection accuracy under a complex background can be improved, false detection and missing detection are reduced, and real-time defect positioning and accurate classification are realized.

The invention discloses a long downhill section truck brake pad high temperature detection and cooling method and system, and the method comprises the steps: collecting the surface temperature data of a truck brake pad in real time according to high-precision infrared temperature sensor arrays and thermal imagers disposed at the two sides of a long downhill section, and generating a time-space correlation temperature field distribution diagram; based on the temperature field distribution diagram, dynamically adjusting a temperature safety threshold curve through reinforcement learning, and outputting a dynamic early warning trigger signal; according to the dynamic early warning trigger signal, a corresponding spraying instruction is generated so as to drive a piezoelectric ceramic microvalve array of a roadside spraying device to conduct spraying cooling on the brake pad; and according to the temperature attenuation rate and infrared thermal image feedback data of the brake pad after spraying, the temperature safety threshold curve and the spraying instruction are corrected through a Bayesian optimization algorithm, and closed-loop safety regulation and control are formed. According to the embodiment of the invention, the temperature of the brake pad can be monitored in real time, accurate spraying cooling control is realized, and safe and stable braking performance of a truck is ensured.

The invention provides a water body monitoring method and equipment based on satellite remote sensing data and a storage medium, and relates to the technical field of water body monitoring, the method comprises the following steps: firstly, acquiring remote sensing image data and water quality measured data (including water quality parameters and geographic positions) of a target area at least covering two spectral bands; performing radiation correction on the remote sensing image data, and extracting target image data corresponding to the water body by using the image segmentation model; matching the water quality parameters with the target remote sensing image data according to the geographic position information, and constructing a training set of time-space alignment; using a multilayer model fusion strategy to train a preset water quality inversion model based on the training set to obtain a target model; and finally, inputting an image to be detected to the target model to obtain water quality parameters, and mapping the water quality parameters to a geographic space to generate a water quality two-dimensional distribution diagram. According to the invention, the water body monitoring precision and monitoring details based on satellite remote sensing data can be effectively improved.

The invention relates to the technical field of airport safety monitoring, and discloses an airport video data real-time analysis system. The system comprises a video stream spatial-temporal feature modeling module, a behavior trajectory map construction module, an abnormal region association analysis module, a risk level semantic judgment module and a situation structure visualization module. According to the method, multi-scale spatial-temporal feature analysis is carried out on an airport monitoring video stream, a multi-dimensional behavior trajectory map is established, abnormal behavior region association is analyzed, risk level semantics are judged, and finally an airport global risk situation thermodynamic distribution map is generated. According to the system, the whole process processing from video data acquisition to risk situation visualization is realized, the abnormal behavior area can be accurately identified, the risk level and category are clear, comprehensive and visual situation information is provided for airport safety management, and the intelligent level of airport safety management is improved.

The invention discloses a TSV technology-based silicon wafer image defect identification method and system, and the method comprises the steps: synchronously collecting three-dimensional images according to the depth structure characteristics of a TSV silicon wafer, mapping the three-dimensional images of different modes to a unified space coordinate system, and obtaining a three-dimensional fusion image; according to the three-dimensional fusion image, a feature extraction model based on a three-dimensional convolutional neural network is adopted, TSV hole wall morphology, depth deviation and material uniformity features are extracted, and an enhanced multi-dimensional feature tensor is obtained; according to the multi-dimensional feature tensor, modeling in combination with the spatial topological relation of the TSV structure to obtain a spatial topological distribution diagram of the defects; according to the space topology distribution diagram of the defects, the relevance between defect causes and process links is analyzed, a defect cause report and process optimization suggestions are generated, and an interpretable defect diagnosis result is obtained. According to the embodiment of the invention, a comprehensive and accurate silicon wafer image defect identification and process optimization scheme can be provided, and the reliability of a TSV manufacturing process is improved.

The invention relates to the technical field of industrial control, in particular to a robot scheduling method and system based on cooperative control, and the method comprises the steps: obtaining a task proportion and target node information in a task adjustment scheme, judging an optimal task distribution path and an optimal execution sequence according to a task priority order and a node adaptation rule, and carrying out the task scheduling according to the optimal task distribution path and the optimal execution sequence; determining an inspection task distribution blueprint; according to the routing inspection task distribution blueprint, the tasks are distributed in real time by adopting a low-delay scheduling framework, and a dynamic distribution map and real-time distribution update data of the tasks among the nodes are obtained according to node response delay and communication overhead among the nodes; the latest load state of each node is obtained through a dynamic distribution map and real-time distribution update data, and whether a potential calculation pressure overload risk exists or not is judged according to a distribution uniformity index and distribution anomaly detection. The invention aims to solve the problems of large performance difference, unbalanced load and low task scheduling efficiency in a multi-robot collaborative inspection process in a narrow space.

The invention discloses a photovoltaic glass defect detection method and system based on image recognition, particularly relates to the technical field of visual inspection, and is used for solving the problem that defect recognition is interfered by optical artifacts caused by a film layer interference effect on the surface of coated glass. The method comprises the following steps: extracting interference characteristics through a reflection image to generate an optical artifact distribution diagram, positioning an interference region in a transmission image frequency domain space, constructing a process response template in combination with a sedimentary pulse frequency, and executing polarization coherence screening and time-varying deconvolution collaborative filtering to eliminate artifacts; based on energy ratio dynamic decision reinspection or defect classification process implementation, inherent optical noise and real physical defects of a material are effectively separated, and the accuracy and reliability of coated glass defect detection are remarkably improved.

The invention relates to the technical field of control systems, in particular to a control system and a control method of an intelligent permanent magnet vacuum circuit breaker, and the system comprises a multi-source signal synchronous acquisition module, an operation mechanism box body of the permanent magnet vacuum circuit breaker and acceleration sensors arranged at preset positions around a permanent magnet operation mechanism, a voltage transformer and a current transformer are connected to a line interface, and a unified sampling frequency and a trigger condition are set. According to the method, vibration, voltage and current signals are synchronously collected at key parts of the permanent magnet vacuum circuit breaker, timestamps are applied, a data basis of multi-source information strong association is constructed, and the direct time sequence relation between the electrical transient state and the mechanical response can be revealed. Segmented windowing and frequency spectrum transformation are carried out on collected vibration signals, a time frequency energy distribution diagram is generated, and instantaneous frequency components and energy evolution details generated by non-stationary events such as impact and friction in the opening and closing process are captured.

The invention discloses a method and system for estimating irrigation water consumption and water utilization efficiency, and relates to the technical field of water resource management, and the method comprises the steps: collecting the plant stem flow rate, canopy temperature and soil profile water content data of a target area through a multi-source sensing device, and obtaining a gradient and permeability distribution diagram of a geographic information system; dividing the farmland of the target area into sub-areas with different irrigation priorities based on a soil conductivity mutation point detection algorithm; constructing a dynamic water demand model fused with the plant transpiration hysteresis effect, performing space-time correlation analysis on the stem flow rate data and the soil moisture content gradient, and calculating the real-time water demand intensity of each sub-region; the method comprises the following steps: accessing short-time rainfall prediction data of a meteorological radar, dynamically correcting a future irrigation decision by adopting a sliding time window mechanism, and triggering a gradient delay irrigation instruction when the predicted rainfall amount exceeds an irrigation threshold value; and calculating the actual moisture utilization efficiency based on the inversion data of the moisture content of the irrigated soil and the unmanned aerial vehicle multispectral image.

The invention discloses an intelligent measurement and calculation method and system for forest carbon reserves, and relates to the technical field of carbon reserve measurement and calculation, and the system obtains a forest electronic distribution diagram through a GIS technology and surveying and mapping data, collects various carbon reserve related data, and then carries out the correction and denoising of the collected data through a FLAASH algorithm and other preprocessing technologies; the AI sampling optimization model extracts forest features based on a convolutional neural network CNN, and dynamically adjusts a sampling area; secondary acquisition is carried out, data preprocessing is carried out, the total carbon reserve of the forest is estimated by calculating the carbon reserve of each grid, and the forest succession stage, the carbon sink change and the ecological carbon cycle are further analyzed; and finally, in combination with the dynamic influence coefficient of the carbon reserves and a preset threshold value Q, judging the change trend of the carbon reserves, triggering early warning and giving a coping strategy.

The invention provides an on-line monitoring method and system for crack propagation of silicon-based new material equipment in a high-temperature environment, and relates to the technical field of crack detection, and the method comprises the steps: collecting stress distribution data through arranging a stress sensor, and generating a stress field distribution diagram by using an attention mechanism deep neural network model; identifying a stress concentration region based on a region growing algorithm, when a stress value exceeds a preset threshold value, acquiring temperature distribution data, extracting temperature distribution characteristics through a deep mixed probability model, performing multi-modal data fusion in combination with a stress field distribution diagram, and establishing a crack propagation prediction model by using a graph structure neural network. The crack propagation rate and direction are solved through a swarm intelligence optimization algorithm, when the crack propagation rate exceeds a preset threshold value, a control system adjusts equipment operation parameters, the sampling frequency of a sensor is adjusted according to the crack propagation direction, and model parameters are updated through an incremental learning method to achieve real-time monitoring.

The invention relates to the technical field of ophthalmology medical diagnosis, and discloses a three-dimensional reconstruction-based fundus focus intelligent labeling method and diagnosis system. The method comprises the following steps: receiving multi-modal image data streams such as fundus color photos, OCT images and FFA images of an ophthalmological patient; performing spatial registration and feature fusion by using a pre-trained lesion feature fusion model to generate a three-dimensional lesion probability distribution diagram and a lesion category confidence matrix; constructing an adaptive annotation threshold model to generate a multi-modal annotation instruction set; based on the focus development chain model, focus development is simulated, and instruction set parameters are optimized and labeled; and iteratively optimizing through a distributed reinforcement learning framework, and outputting the focus labeling action sequence to an ophthalmology diagnosis platform. According to the method, multi-modal image information can be integrated, the diagnosis accuracy and efficiency are improved, personalized diagnosis is realized, resources are reasonably utilized, and powerful support is provided for ophthalmic disease diagnosis.

The invention discloses a mine reclamation effect evaluation method based on a multi-temporal remote sensing image, and belongs to the field of mine reclamation effect evaluation, and the method comprises the steps: obtaining and preprocessing multi-source remote sensing data, and obtaining a standardized multi-temporal data set; performing image registration and radiation correction on the standardized multi-temporal data set to obtain a correction data set with consistent space and consistent radiation; constructing a multi-dimensional feature data set based on the correction data set, wherein the multi-dimensional feature data set comprises surface morphological features, vegetation health features, soil temperature features and underground water level change features; extracting a surface form feature vector according to the multi-dimensional feature data set, and generating a vegetation health distribution map, a soil temperature feature map and an underground water level change distribution map; and fusing the surface form feature vector, the vegetation health distribution map, the soil temperature feature map and the underground water level change distribution map, constructing a comprehensive index system, and outputting a mine reclamation effect evaluation result. According to the method, the accuracy and comprehensiveness of reclamation effect evaluation are remarkably improved.

The invention relates to the technical field of semiconductor device testing, in particular to a SiC MOSFET power cycle testing method which comprises the following steps: S1, building a composite environment testing platform, configuring dynamic testing parameters, automatically calculating a physical boundary and collecting sensor data in real time; s2, establishing a finite element simulation model based on the physical boundary and sensor data, and outputting optimized dynamic test parameters and a simulated stress distribution diagram; s3, synchronously applying composite stress according to the dynamic test parameters and the stress distribution diagram, and collecting multi-dimensional test data in real time; when the system is used, through dynamic boundary calculation and real-time data acquisition, the intelligent degree and reliability of the test are improved, the test period is shortened, the failure prediction accuracy is improved, the system is suitable for reliability evaluation of SiC MOSFET devices in the fields of new energy, aerospace and the like, a large number of physical tests are avoided through virtual simulation, and the reliability of the SiC MOSFET devices is improved. And reduction of device loss and resource waste is facilitated.

The invention provides a mutual inductor test data cloud edge cooperative processing method and device, and relates to the technical field of data processing, and the method comprises the steps: carrying out the time domain and frequency domain combined feature extraction of the original measurement data of a mutual inductor test through a multi-mode decoupling preprocessing model, and forming a data feature vector set, further generating a confidence label flow through state recognition and Bayesian inference, evaluating a prediction error, and realizing data classification screening and priority queue construction; the data are uploaded to a cloud end in a semantic compression and multi-resolution representation vector mode, so that the transmission efficiency is improved; carrying out deep modeling and model performance monitoring at the cloud, and if degradation is detected, returning edge original data to update the model; and finally, generating a scheduling weight factor according to the prediction error distribution graph, and dynamically optimizing an edge cloud task allocation proportion. According to the invention, the problem of low resource allocation efficiency caused by lack of a dynamic scheduling mechanism based on prediction errors and confidence driving in existing mutual inductor test data edge cloud cooperative processing can be solved.

The invention relates to the technical field of network communication, and discloses an intelligent control method and system of a switch. The method comprises the following steps: receiving a heterogeneous data stream in a target network domain; performing data processing by using a preset flow feature aggregation model, and generating a flow dynamic pressure field distribution diagram and a protocol priority mapping matrix; constructing an adaptive scheduling threshold model, and generating a multi-target flow control instruction set; a cascade effect is simulated based on a network congestion chained conduction model, and collaborative routing parameters are optimized; and iteratively optimizing through a mixed integer programming framework, and outputting a network scheduling action sequence to a switch control engine. The system correspondingly comprises a data receiving module, a data processing module, an instruction generation module, a simulation optimization module and an instruction output module. According to the method, heterogeneous data can be effectively processed, congestion can be accurately predicted, flow control can be optimized, the overall performance of the network can be improved, and communication requirements in a complex network environment can be met.

The invention relates to the technical field of precise geometric quantity measurement, and discloses an electronic component size and contour automatic metering method, which comprises the following steps: calculating a nominal truncation phase distribution diagram of a to-be-measured electronic component by using a virtual projection imaging model, controlling a projection unit to project a single-frequency sine stripe grating to the electronic component and collecting a deformed stripe image, calculating the gradient of the modulation degree distribution map and extracting edge contour features to solve the actual pose of the assembly; performing modulo 2pi differential operation on the nominal and actually measured truncated phase distribution maps, and eliminating high-gradient artifacts by using an edge mask to generate a phase residual map; and converting the phase residual image into a height deviation distribution diagram according to the phase height mapping relation, and by constructing a phase domain differential metering channel, the phase unwrapping ambiguity in discontinuous surface measurement is avoided.

The invention provides a body armor composite interlayer defect positioning method and system based on multi-modal nondestructive testing, electronic equipment and a storage medium, and relates to the technical field of nondestructive testing. Ultrasonic guided wave, pulsed eddy current and infrared thermal wave excitation signals are synchronously applied to a body armor composite interlayer, and multi-modal response signals of all positions are collected; and generating an original multi-modal response signal set. And inputting the signal into a pre-trained deep convolutional neural network, determining a target frequency band parameter and a target noise suppression parameter of each modal response signal, and outputting an adaptive modulation parameter group. Performing frequency domain filtering and noise suppression processing on an original signal set to generate an optimized signal set, inputting spatial frequency features into a cross-modal feature fusion model, adaptively associating defect sensitive features of different modals through an attention mechanism in the model, and generating a three-dimensional defect distribution diagram of the composite interlayer, thereby realizing millimeter-level precision defect positioning. The precision and efficiency of body armor composite interlayer defect positioning can be improved.

The invention discloses a weld quality intelligent diagnosis system based on deep learning, and relates to the technical field of welding quality detection, and the weld quality intelligent diagnosis system comprises an image quality evaluation module, a feature alignment module, a deviation detection module, a path reconstruction module, a prior enhancement module and a defect identification module, identifying an area of which the signal-to-noise ratio is lower than a preset threshold value, and constructing a noise interference distribution diagram; and the feature alignment module executes a deformable convolution feature alignment operation with a confidence factor adjustment mechanism based on the noise interference distribution diagram to generate an initial space mapping result. Through mechanisms such as image quality perception, robust alignment, deviation detection, self-adaptive reconstruction and prior enhancement, a closed-loop weld joint intelligent diagnosis process is constructed, false alignment errors are effectively inhibited, the multi-modal fusion stability and the defect recognition precision are improved, and the reliability and the intelligent level of the system under complex working conditions are enhanced.

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 relates to the technical field of remote sensing image processing, and discloses an agricultural condition remote sensing image change detection method based on time sequence analysis, which comprises the following steps: acquiring an agricultural condition remote sensing image sequence through a multi-temporal remote sensing sensor, and extracting multi-scale fusion features by using a multi-modal data fusion network after registration and radiation correction of a space-time alignment algorithm. The time sequence decomposition model decomposes features, extracts time dynamic change features, and detects an abnormal change area based on an abnormal detection model of a variational auto-encoder. And optimizing the change area by a multi-objective optimization algorithm, and constructing a visual model to generate a high-resolution agricultural condition change distribution diagram. According to the method, multi-sensor data are fused, various algorithms and models are integrated, the problems of data processing, feature analysis, anomaly detection, result presentation and the like in agricultural condition remote sensing image change detection are effectively solved, the detection accuracy and efficiency are improved, and a precise basis is provided for agricultural production management.