3466 results about "Spatial distribution" patented technology

The invention relates to the technical field of meteorological data analysis, and provides an aerosol prediction method and system based on artificial intelligence, which are used for accurately identifying an abnormal aerosol gathering mode in an extreme meteorological event. The method comprises the steps that an original multi-mode meteorological observation data set is acquired, and the original multi-mode meteorological observation data set comprises an earth surface aerosol concentration sequence, three-dimensional atmosphere state variables and boundary layer dynamic parameters; performing space-time heterogeneity correction on the original multi-modal meteorological observation data set to generate a space-time aligned target multi-modal meteorological observation data set; inputting the target multi-modal meteorological observation data set into a spatial-temporal feature fusion network to extract multi-scale spatial distribution features and cross-modal time sequence correlation features, and generating a spatial-temporal feature matrix based on the multi-scale spatial distribution features and the cross-modal time sequence correlation features; and performing multi-stage recursive optimization on the spatial-temporal characteristic matrix through a hierarchical time domain aggregation algorithm to generate an aerosol concentration prediction sequence of the target region.

The invention belongs to the technical field of earthquake disaster scene recognition, and discloses an earthquake disaster scene recognition method based on deep learning. The method comprises the following specific steps: S1, data acquisition and preprocessing; S1.1, multi-source heterogeneous data acquisition and establishment of a comprehensive database containing seismic waveform data, surface deformation data, building structure data, geographic information data and historical disaster record data; through fusion of a 3D convolutional network, a graph attention mechanism, a space-time LSTM and an adaptive cross-modal attention fusion technology, combined modeling of a seismic waveform space-time evolution law, an earth surface deformation space distribution characteristic, a building group topology vulnerability and disaster chain time sequence association is realized, the characterization capability of a complex nonlinear disaster mode is effectively improved, and the method has the advantages of high adaptability and high reliability. And disaster assessment response time is shortened to a sub-second level through mixed precision quantification and edge computing deployment, and high recognition accuracy is still kept in a scene with strong noise and data missing in combination with a multi-task classifier and a physical constraint verification mechanism.

The invention discloses a geological disaster networking monitoring and early warning method, and relates to the technical field of geological disaster monitoring and early warning, and the method comprises the following steps: S1, collecting the original data of multiple types of monitoring equipment in a monitoring region, extracting a high-amplitude sudden change region and a frequency drift factor according to the time and frequency distribution, constructing a high-frequency disturbance sensing matrix, and carrying out the recognition of the high-frequency disturbance sensing matrix; and generating a disturbance characteristic index map for representing the spatial distribution of the unnatural disturbance source. According to the method, active identification and modeling of non-natural interference are realized by constructing a high-frequency disturbance perception matrix and a disturbance index map, disturbance propagation analysis and residual difference are combined to strengthen precursor signal features, the risk level is accurately judged through trend identification and causal analysis, and finally, early warning model parameters are dynamically optimized based on response regulation factors, so that the early warning accuracy is improved. A closed-loop mechanism of interference identification, signal purification, trend extraction, risk judgment and strategy adjustment is formed, and the early warning stability, accuracy and practicability of the system in a high-interference environment are remarkably improved.

The invention provides an early warning analysis method based on intelligent vision and a server, and the method comprises the steps: obtaining a multi-mode video stream containing a visible light and infrared thermal imaging image sequence through a vision collection device in a monitoring region, and carrying out the spatial-temporal feature extraction of the multi-mode video stream; the method comprises the steps of generating dynamic behavior sequence characteristics including a target object moving track, posture change and thermodynamic distribution characteristics, analyzing the dynamic behavior sequence characteristics through a preset abnormal behavior judgment model in a multi-dimensional matching mode, judging whether the target object triggers abnormal behavior characteristics or not, and if yes, generating a graded early warning signal in combination with an association weight. In addition, according to the spatial distribution and the time length of the abnormal behavior characteristics, the parameters of the acquisition equipment and the coverage range of the monitoring area are dynamically adjusted, and the subsequent video stream anomaly detection precision is optimized, so that intelligent and accurate early warning analysis is realized, and the security efficiency is improved.

The invention relates to the technical field of voice semantics, can be applied to business scenes of financial science and technology, medical health, poster design and the like, and discloses a visual sense generation method and device based on semantic association modeling, equipment and a medium. Generating a demand text containing theme and style parameters; semantic features in the demand text are extracted, semantic association weights are constructed, and element layout coordinates are optimized in combination with spatial distribution constraints; and encoding the layout information into a control matrix, fusing the control matrix with the initial noise, adjusting a noise reduction process through an encoding and decoding network, and generating target visual content highly matched with the semantic meaning of the user instruction. According to the method, the layout optimization function is constructed, the diffusion model is guided to focus the semantic salient region in space, language model output and the visual generation process are closely combined, structured response and space mapping of user semantic requirements are achieved, and the expression consistency and personalized adaptation capacity of visual content generation are improved.

The invention relates to the field of defect detection, in particular to a PCB defect detection method and system based on image recognition. The method comprises the following steps: collecting a multidirectional PCB detection image, carrying out pixel-level registration correction and adaptive pixel stability compensation, and constructing a space-time stability compensation image sequence; performing reverse pyramid structure division on the space-time stability compensation image sequence, performing normalized similarity probability calculation, and constructing an initial region classification result; based on an initial region classification result, depth image visual analysis is carried out, pseudo defect comprehensive elimination optimization is carried out, and a pseudo defect purification high-confidence image is constructed; pCB connection defect identification is carried out on the pseudo defect purification high-confidence image, global defect point distribution marking is carried out, and a defect point space distribution diagram is constructed. According to the invention, high-credibility, high-precision and high-closed-loop PCB defect detection is realized.

The invention relates to the technical field of environment monitoring, in particular to an underground water pollution source traceability identification method and system, and the method comprises the following steps: S1, collecting the flow velocity, flow direction and water level change data of underground water in a polluted region in real time; s2, constructing a preliminary underground water flow model; s3, simulating a migration path of pollutants in underground water, and predicting a potential pollution source position; s4, processing the collected underground water flow velocity and pollutant diffusion path data, and removing abnormal data interference; s5, generating a spatial distribution map of the potential pollution source in combination with a geographic information system; and S6, verifying pollutant components and concentration distribution thereof, and confirming the accuracy of a traceability result. According to the invention, by combining a multi-pollutant traceability algorithm, an underground water flow model and a geographic information system technology, a pollutant migration path is accurately simulated and a pollution source spatial distribution map is generated, so that the precision and reliability of underground water pollution source traceability are greatly improved.

The invention provides an intelligent image signal processing method and system based on multi-modal fusion. According to the invention, a multi-mode input signal is received, is divided into a spatial distribution feature extraction region and a dynamic change trajectory capture region, and is decomposed into a penetrability feature layer and a substance reflection feature layer; carrying out association mapping on missing pixel information in the dynamic change trajectory capture region and energy distribution of the penetrability feature layer to generate enhanced dynamic trajectory data, identifying a determined reflection mode in the substance reflection feature layer, and carrying out frequency domain superposition on corresponding frequency band response and the spatial distribution feature extraction region; generating a composite spatial feature, then constructing a multi-modal joint optimization model, then adjusting the contribution ratio of the two data, and generating a fused image signal; the technical scheme provided by the invention not only solves the problems of detail loss, artifact generation and poor dynamic adaptability caused by single-mode limitation, but also improves the spatial resolution and tracking precision of the image signal.

The invention relates to the technical field of data processing, and discloses a hazardous chemical substance leakage and diffusion prediction method and system based on an unmanned ship, and a storage medium. The method comprises the following steps: acquiring water quality, weather and geographic data through an unmanned ship sensor, and processing to obtain an initial data set; calculating a diffusion range according to the data, optimizing a monitoring area, and generating a cooperative monitoring track; performing exception elimination and smoothing processing on the data, and constructing spatial distribution; solving a convection diffusion equation, and establishing a three-dimensional flow field model; analyzing a hazardous chemical substance diffusion path, and predicting pollutant distribution; and establishing a risk assessment system, and generating emergency disposal suggestions and decision-making schemes. According to the application, rapid and accurate monitoring of the polluted area is realized through the multi-parameter sensor array carried by the unmanned ship, and accurate prediction of the hazardous chemical substance pollutant diffusion process is realized in combination with multi-source data fusion processing and a three-dimensional flow field simulation technology.

The invention discloses a key infrastructure risk identification method and device in a flood disaster chain scene. The device comprises an acquisition module used for acquiring multi-source data and performing time-space alignment and fusion processing; the extraction module is used for extracting spatio-temporal evolution characteristics of a flood disaster chain based on the multi-source data; the construction module is used for constructing a key infrastructure coefficient according to the prior infrastructure information; the coupling module is used for coupling the spatio-temporal evolution characteristics and the key infrastructure coefficient, and calculating a regional risk index through a dynamic coupling coefficient and a space sensitivity factor; and the early warning module is used for generating a risk space distribution map and triggering dynamic early warning. According to the technical scheme, through multi-source data collection and space-time fusion, the space-time evolution characteristics of the flood disaster chain are extracted, key infrastructure coefficients based on information of buildings, roads, population and the like are constructed, the regional risk index is calculated through dynamic coupling, risk distribution is visually reflected, accurate early warning and emergency response are achieved, and the reliability of the system is improved. The urban disaster prevention and reduction level is effectively improved, and the public safety guarantee efficiency is remarkably enhanced.

The invention relates to a fermentation process abnormal state early warning method, system and device and a medium, and the early warning method comprises the steps: collecting fermentation process parameters in a fermentation tank in real time, and generating a physical parameter matrix; obtaining Raman spectrum data of the fermentation liquor and extracting metabolic feature vectors; fusing the physical parameter matrix with the metabolic feature vector to obtain a multi-modal data cube; constructing a metabolic network model, inputting the physical parameter matrix as a boundary condition, and generating calibrated metabolic flux distribution data; dynamically adjusting the monitoring weight coefficient of the fermentation process parameter, and generating a parameter sensitivity weight vector; inputting the multi-modal data cube and the parameter sensitivity weight vector into a space-time convolutional network, extracting time sequence features and spatial distribution features, and performing similarity matching with a preset abnormal mode library to obtain a matching result; and determining an abnormity early warning type according to a matching result with abnormity, and generating a corresponding compensation control instruction. The stability of the fermentation process is improved.

The invention provides an LED product surface defect detection method, which comprises the following steps: carrying out defect feature extraction on a preprocessed LED surface image by adopting a multi-scale morphological algorithm, and respectively carrying out morphological opening operation and closing operation on surface recesses and fine scratches by setting structural elements with different sizes and shapes to obtain candidate regions of the recesses and the scratches; according to the geometric dimensions, morphological characteristics and spatial distribution detail attributes of the recesses and scratches, constructing defect feature vectors, and performing classification and severity evaluation on defect candidate areas through a support vector machine algorithm to obtain the types, positions and quantitative description parameters of defects; and updating the light source parameter database and the defect feature database according to a quality detection result of the LED product, analyzing an association rule between defects and light source parameters through a data mining algorithm, and optimizing a light source parameter configuration strategy and a defect detection model.

The invention discloses a real-time surrounding rock deformation monitoring and data acquisition method and system, which is applied to long-distance weak surrounding rock tunnel construction, and comprises the following steps: determining the dynamic change trend of underground water seepage rate and ground stress distribution gradient by adopting a time sequence analysis method; based on the trend, carrying out risk partitioning on the tunnel construction section by adopting a K-means clustering algorithm, determining a deformation sensitive area, and optimizing the spatial distribution of the monitoring points according to the deformation sensitive area; monitoring data are acquired in real time, and when the data fluctuation period exceeds a threshold value, the data acquisition frequency of the corresponding monitoring point is automatically improved; processing high-frequency acquired data by adopting a long-short-term memory network to obtain a real-time surrounding rock deformation prediction result; the prediction result and the multi-source real-time geological parameters are fused, a Bayesian updating method is adopted for processing, a quantitative surrounding rock stability evaluation result is obtained, closed-loop self-adaptive optimization of a monitoring scheme and accurate risk prediction are achieved, and the safety early warning capacity of tunnel construction and the utilization efficiency of monitoring resources are remarkably improved.

The invention relates to the technical field of agricultural meteorological disaster monitoring, in particular to a construction method of a composite agricultural meteorological disaster monitoring index system. The method comprises the following steps: acquiring regional agricultural component data, carrying out spatial distribution analysis to identify an agricultural distribution boundary, delimiting an agricultural overlapping region, then collecting historical meteorological disaster data, generating a meteorological disaster time-space sequence, identifying a correlation mode of meteorological and agricultural disasters, and determining the agricultural overlapping region according to the correlation mode of the meteorological and agricultural disasters. According to the agricultural overlapping area, composite agricultural boundary development data is predicted, available resources are identified, heterogeneous component interaction simulation is performed, an agricultural interaction effect field is constructed, meteorological disaster absorption capability is deduced, finally, composite agricultural disaster prediction is performed based on a historical association mode and the absorption capability, and a monitoring index system is constructed. And systematic monitoring and management of agrometeorological disasters are realized. According to the invention, systematic monitoring and evaluation of agrometeorological disasters are realized, and intelligence and scientization of agricultural management are promoted.

The invention provides a forest area monitoring method and system based on unmanned aerial vehicle routing inspection, and the method comprises the steps: firstly obtaining a historical routing inspection data set containing a geographic position identifier and a topographic feature parameter in a target forest area, and generating an initial routing inspection route indicating a flight path and an image collection node according to the historical routing inspection data set; then, an unmanned aerial vehicle carrying a multispectral sensor is called to carry out dynamic routing inspection according to an air route, a real-time monitoring image set composed of vegetation coverage images of a plurality of monitoring areas under different timestamps is obtained, and then feature extraction and anomaly detection are carried out on the real-time monitoring image set; according to the method, an image anomaly feature set containing vegetation and surface structure anomaly indexes is determined, and finally, a forest region monitoring optimization strategy is generated based on the image anomaly feature set, so that the unmanned aerial vehicle inspection frequency and image acquisition node space distribution are adjusted, and more efficient and accurate monitoring of a forest region is realized.

The invention relates to the technical field of big data, and discloses a space-time big data abnormal event detection method and device and a storage medium. The method comprises the following steps: performing timestamp unification and space coordinate standardization processing on urban space-time big data to obtain a standardized space-time data set; feature vector cosine similarity between each data point in the standardized spatio-temporal data set and other data points is calculated to obtain a target similarity value, and an initial neighborhood data set is selected; performing space-time density distribution calculation on the initial neighborhood data set to obtain a core neighborhood data set; performing time sequence feature and spatial distribution feature extraction on the core neighborhood data set to obtain a fusion feature weight matrix; and inputting the fusion feature weight matrix into a support vector machine classifier to perform abnormal event recognition to obtain an abnormal event detection result, thereby improving the classification capability of high-dimensional nonlinear features, and simultaneously realizing all-directional feature description and detection of abnormal events.

The invention relates to the technical field of geological disaster prevention and control, in particular to a rock engineering slope stability classification method. According to the technical scheme, the method comprises the following steps: obtaining spatial distribution characteristics of a slope structural plane through combined detection of three-dimensional laser scanning and a geological radar, and establishing a quantitative index system containing occurrence, density and connectivity of the structural plane; the method comprises the following steps: acquiring a fracture signal in a slope in real time by adopting a micro-seismic monitoring system, and extracting a micro-seismic event energy release rate, a dominant frequency offset and a seismic source mechanism parameter through time-frequency analysis; and constructing a geologic structure evolution inversion model, reconstructing a geologic structure evolution process based on the regional geologic database and the field drilling data, and calculating the distribution characteristics of the residual stress field of the structure. According to the method, comprehensive, accurate and real-time monitoring and classified evaluation of the rock engineering slope stability are realized, potential risks can be effectively predicted, a scientific and accurate decision basis is provided for slope support and treatment, and the safety and stability of the rock engineering slope are greatly guaranteed.

The invention relates to the technical field of drainage basin water scheduling, and discloses a drainage basin water regulation and control optimization method based on ecological element changes. The method comprises the following steps: deploying a drainage basin monitoring system, and collecting ecological element real-time data such as a hydrological parameter sequence and a remote sensing image; after the data is cleaned and converted, hydrological trend features and spatial distribution features are extracted by adopting a feature learning model, and the hydrological trend features and the spatial distribution features are fused into unified ecological representation through a cross-modal alignment mechanism; inputting the unified ecological representation into a physically constrained neural network prediction model, and outputting a water regimen dynamic prediction value; and finally, based on the predicted value, a water resource regulation and control instruction is generated and executed by using a multi-objective decision algorithm so as to optimize the watershed water circulation process. According to the method, feature extraction comprehensiveness is improved through multi-source data fusion and cross-modal analysis, prediction reliability is enhanced in combination with physical constraints, reasonable allocation of water resources is achieved by means of multi-target decision, the ecological condition of a drainage basin can be improved, and the water utilization efficiency is improved.

The invention relates to a coastal wetland ecological restoration effect evaluation method and system, and belongs to the technical field of ecological monitoring and restoration. Aiming at the problems of static evaluation index lag, insufficient data fragmentation integration and lack of causal diagnosis mechanism in the prior art, multi-source time sequence ecological data are acquired by constructing a three-dimensional monitoring network consisting of satellite remote sensing, an unmanned aerial vehicle, an Internet of Things sensor and an underwater robot; a fusion data set is generated based on space-time alignment and tidal phase marking, dynamic evolution characteristics are extracted by using an LSTM network, and a spatial distribution difference chart of ecological parameters before and after restoration is generated in combination with a GAN; and furthermore, through hot area clustering and historical data association mining, a degradation driving factor is positioned, and an optimization suggestion is generated. The method breaks through the space-time limitation of a traditional evaluation method, remarkably improves the degradation diagnosis precision and the regulation and control efficiency of restoration engineering, and is suitable for various ecological restoration scenes such as estuary delta and coral reefs.

The invention relates to the technical field of flood early warning, and discloses an urban flood disaster early warning method and system based on artificial intelligence, and the method comprises the steps: collecting five types of information, i.e., meteorological perception, hydrological monitoring, geographic space, urban operation and social perception in real time, and obtaining multi-source data with precise space-time coordinates; through preprocessing, gridding space-time alignment and key feature screening, rainfall accumulation and confluence evolution related features are extracted; constructing a physically constrained space-time fusion deep learning model, and outputting a future ponding depth prediction result in combination with a multi-head attention mechanism; environmental changes such as urban terrains and drainage facilities are adapted through incremental updating and transfer learning; and fusing the ponding depth, the influence range and the regional vulnerability characteristics to generate multi-level early warning, and synchronously outputting a spatial distribution map, a time evolution trend and affected object evaluation information. According to the invention, urban flood control and disaster reduction decision making and public accurate risk avoiding can be effectively supported.

The invention discloses a bridge structure health monitoring data anomaly detection method based on deep learning, particularly relates to the technical field of structure health monitoring, and is used for solving the problems of high environmental interference sensitivity and insufficient cross-modal data fusion capability caused by image enhancement and feature extraction process splitting in the existing method. A cross-domain feature mapping relation is generated through combined training of dynamic image enhancement and a deep learning model, and collaborative optimization of enhancement parameters and feature space is achieved; time-frequency resonance parameters of visual images and acoustic emission signals are fused based on cross-modal convolution, and damage feature space distribution is corrected in combination with an attention mechanism; analyzing and quantifying the structural difference of the cross-domain features by using topology persistence coherence, and iteratively optimizing the feature mapping network through an optimal transmission theory; and finally, a multi-level feature template matching and self-adaptive threshold judgment mechanism is adopted to output an abnormal detection result, so that the robustness and generalization ability of bridge structure health detection in a complex environment are remarkably improved.

The invention discloses an amphibious unmanned aerial vehicle riverway sediment concentration real-time monitoring method based on underwater light field imaging and cross-modal fusion, and relates to the technical field of riverway sediment monitoring. Comprising the following steps: acquiring aerial multispectral data and preprocessing the aerial multispectral data; an integrated polarized light field camera is carried on an amphibious unmanned aerial vehicle to capture underwater light field data, and underwater three-dimensional light field information is reconstructed and sediment scattering characteristics are extracted through an optical model and a lightweight deep learning network; the air multispectral data and sediment scattering characteristics are fused through space-time alignment and an attention mechanism, a sediment concentration prediction model is generated, and then a spatial distribution diagram of the river sediment concentration is output; and carrying out real-time monitoring and early warning on the river sediment concentration according to the spatial distribution diagram of the river sediment concentration through edge calculation and cloud collaboration architecture. The method is combined with an advanced deep learning algorithm, high-resolution silt concentration inversion can be realized, and the method has real-time performance and adapts to a complex water body environment.

The invention discloses a pollution space analysis method and system based on atmospheric pollution concentration data, and particularly relates to the technical field of data analysis. A high-precision pollutant concentration spatial distribution model is constructed by combining pollutant diffusion characteristics and geographic information, and regional pollution differences are revealed; identifying a pollution source emission mode and analyzing the evolution trends of pollutants in different time and space ranges by matching spatial and temporal change analysis with a pollution source emission list; the influence of real-time meteorological data on pollution diffusion is analyzed, and a pollutant spatial distribution model is adjusted, so that the prediction precision is improved; pollution concentration distribution, pollution source emission and meteorological influence are combined, pollution hot spot areas are accurately identified, future pollution evolution conditions are analyzed and predicted based on historical trends, scientific and reasonable pollution treatment suggestions are provided, the limitation of traditional interpolation and statistical regression models is broken through, dynamic and accurate modeling of pollutant concentration is achieved, and the method is suitable for large-scale popularization and application. And the scientificity of pollution source tracing and treatment decision making is improved.

The invention discloses a geothermal field parameter inversion calculation method, device and system, and a storage medium. The method comprises the following steps: establishing a geophysical stratified model; through Monte Carlo sampling of preset parameter spaces of the crustal heat generation rate and the heat conductivity, a temperature value is calculated in combination with a heat conduction equation, and a Gaussian likelihood function evaluation model is constructed to predict the matching degree of the temperature and the actually measured temperature; obtaining the optimal estimation of the thermal parameters, a confidence interval and a correlation matrix among the parameters based on the posterior probability distribution; and according to the correlation matrix, taking a high-confidence result generated by Monte Carlo inversion as priori knowledge of a physical guidance neural network PINNs, and finally outputting a thermal parameter spatial distribution prediction result of the target area through parameter initialization constraint, output layer range limitation and a physical regularization loss item. By adopting the technical scheme of the invention, the defects that the existing actually measured geothermal field parameters are rare and the regional characteristics cannot be described and the geophysical joint inversion of the geothermal field parameters cannot be realized in the prior art are overcome.

The invention provides a power transmission line inspection data asset management method and system, and the method comprises the steps: obtaining the multi-modal data of a power transmission line through a plurality of sensors disposed on the power transmission line and the surrounding environment, and sensors carried by a helicopter and an unmanned plane; and processing the multi-modal data based on the space-time correlation model to generate a fusion data set. Analyzing the fused data set by using an adaptive feature extraction algorithm, generating structured power transmission line data assets including environmental features, defect features and hidden danger features, and evaluating the possibility of triggering defects by environmental conditions based on the association of the environmental features and the defect features; according to the hidden danger characteristics and the environmental conditions, constructing a graph network model which takes a power transmission line as a node and sensor data as an edge weight and is used for analyzing a defect propagation path and predicting time and space distribution of hidden dangers; defect risk level early warning is generated based on a prediction result, a power transmission line hidden danger distribution diagram is generated in combination with environmental meteorological data, and accurate assistance is provided for patrol and maintenance plans.

The invention discloses a soil organic matter prediction mapping method, device and equipment based on deep learning and a medium, and relates to the field of digital soil mapping, and the method comprises the steps: obtaining a remote sensing image of target region soil, and setting a preset number of soil sampling points; comprehensively acquiring environmental data indexes such as terrain factors, vegetation factors, climate factors and land utilization factors; constructing three-dimensional environment tensor data in a preset range of each sampling point, and inputting the three-dimensional environment tensor data into the soil organic matter content prediction model to obtain an organic matter content prediction value set of the soil sampling points in the target area; and generating a spatial distribution prediction map of the soil organic matter content of the target area by combining the prediction value set and the spatial coordinates of the soil sampling points, thereby improving the accuracy of soil organic matter prediction and the mapping efficiency, and providing technical support for soil resource management and agricultural sustainable development.

The invention relates to the technical field of fire early-stage recognition, in particular to a multi-source information-based conveying belt fire early-stage recognition and early-warning system, which comprises a sudden change detection module, a synchronous analysis module, a spatial trend recognition module, a coupling fluctuation screening module and a probability evaluation module. According to the invention, through multi-source information linkage acquisition, collaborative monitoring of parameters such as along-line temperature, smoke, gas, images, load and heat source temperature difference, combined characteristic analysis among parameters and synchronous response trend determination are driven, and active revelation of early risk hidden dangers and dynamic discrimination of spatial distribution consistency and fluctuation continuity are realized. Potential abnormal focusing locking under a high-interference complex working condition is promoted, collaborative fluctuation between a load and a heat source temperature difference further eliminates environmental noise influence, risk weight dynamic adjustment strengthens classification sensitivity of probability identification, fire risk clustering division promotes accurate mastering of distribution of tiny initial hidden dangers, and the probability identification accuracy is improved. And the reliability of fire early warning in a coal mine area conveying belt scene is obviously improved.

The invention provides a mechanical arm autonomous grabbing method and system based on image instance segmentation, relates to the field of computer vision and mechanical arm grabbing, and solves the limitation problems of poor generalization and stability and the like in traditional mechanical arm visual grabbing. The method comprises the following steps: performing multi-angle image acquisition on a target object, segmenting and outputting object mask information, adjusting a mechanical arm to an observation position, and obtaining depth point cloud data of the target object; performing principal component analysis on the depth point cloud data, extracting a principal direction vector representing spatial distribution of a target object, and constructing a principal direction coordinate system; generating a candidate grabbing pose set based on the depth point cloud data, and screening out an optimal grabbing pose meeting a preset direction constraint; and based on the optimal grabbing pose, the mechanical arm is driven to execute the grabbing action. The method has the advantages of being small in calculation amount and insensitive to environmental changes, the mechanical arm can autonomously move to the optimal position where the target object is observed and conduct grabbing, and the grabbing accuracy is improved.

The invention relates to the technical field of shock absorber defect detection, and discloses an electric vehicle shock absorber defect detection method and system based on machine vision, and the method comprises the steps: collecting an initial image set under the irradiation of a multi-angle light source through high-resolution imaging collection equipment; performing denoising and filtering processing according to the initial image set to obtain clear image data; performing defect classification and spatial distribution analysis according to the clear image data to obtain surface feature vectors containing defect types and defect spatial distribution; carrying out vibration amplitude acquisition and phase angle measurement operation according to the surface feature vector, and carrying out spectral analysis to construct a performance parameter vector; performing data fusion according to the performance parameter vector and the surface feature vector to obtain a fusion feature; inputting the fusion features into a pre-constructed association prediction model to obtain defect prediction data; and performing defect influence degree analysis according to the defect prediction data to obtain a defect evaluation result. The method provides a basis for quality control and performance optimization of the shock absorber.

The invention discloses a building waterproof construction quality monitoring and early warning system based on the Internet of Things, relates to the technical field of building engineering, and realizes omnibearing and real-time monitoring and intelligent early warning of a building waterproof construction area through a multi-layer architecture formed by a data acquisition layer, a data transmission layer, a data processing layer and an application layer. The data processing layer analyzes data features from a time sequence and a spatial distribution dimension through normalization, denoising processing and a multi-head attention mechanism in a Transform model, and accurately recognizes the abnormality and risk trend of a weak point in combination with dynamic threshold and trend analysis, and the application layer determines the risk trend of the weak point through multi-dimensional data visualization, historical playback and real-time interaction. And the early warning module is combined with intelligent reasoning to generate dynamic maintenance suggestions, a maintenance scheme is optimized, the construction quality and the maintenance efficiency are improved, the intelligent level of building waterproof monitoring is improved, risk prediction and accurate maintenance are realized, the management cost is reduced, and the system has wide application value.