17283 results about "Calculation methods" patented technology

The invention provides a pre-control and monitoring method for a whole process of actual grouting engineering based on digital geological model, comprising: extracting discontinuous fracture surfaces in a geologic body structure, to build a underground geologic body structure model; build a multi-source geologic body attribute model, optimizing and solving grouting simulations of a disaster high-risk region by using multiphase flow calculation method, and controlling grouting equipment by using optimized grouting parameters to carry out an actual grouting engineering; obtaining a multi-factor diffusion range of variable coefficients by adjusting physical values of the parameters in the grouting simulation and attribute data structure model by using a control variable method, learning and capturing complex mapping relationship between different parameters by using a neural network, optimizing the grouting parameters in the actual engineering in real-time, so as to realize the pre-controlling, monitoring and optimization of the whole process of the actual grouting engineering.

The invention relates to the technical field of navigation, and particularly provides a quadruped robot autonomous navigation method and system for a special environment, and the method comprises the steps: generating a semantic tag for depth point cloud data corresponding to a depth image through employing a recognition result of a target detection network for the depth image; calculating local odometer information according to the laser point cloud data and the inertial sensor data by using a laser radar mileage calculation method; fusing the depth point cloud data with the semantic label, the laser point cloud data with the semantic label and the local speedometer information to obtain positioning data in a pre-constructed point cloud map; constructing a multi-layer cost map according to the pre-constructed point cloud map, the newly acquired depth image, the laser point cloud data and the inertial sensor data by using a sensing method of a region planner; and generating navigation path information according to the positioning data and the multi-layer cost map by using a global path planning algorithm and a local path planning algorithm. The method improves the navigation precision.

The invention discloses a Text2SQL (Structured Query Language) implementation method and system, and relates to the field of data processing, and the method comprises the following steps: firstly, receiving a natural language query, and analyzing a query intention, field classification and a key entity through a planner; the searcher obtains domain knowledge, entity information, a database table structure and a historical query mode in a multi-path parallel mode based on the planning result; the generator constructs an SQL framework according to the retrieval result and generates an initial statement; the verifier carries out grammar, table field, authority and logic multi-dimensional verification on the SQL, and if the verification fails, iteration adjustment is carried out to generate logic; when the SQL is executed, the result is formatted and a natural language explanation containing query logic, a data source and a calculation method is generated if the SQL is executed successfully, and a diagnosis and error correction mechanism is started for correction and then rechecking is performed if the SQL is executed unsuccessfully. According to the method, through deep fusion of domain knowledge, whole-process verification error correction and interpretability enhancement, the accuracy, robustness and user interaction experience of SQL conversion in a professional scene are improved.

The invention provides an unstructured text data calculation method and system, and relates to the technical field of natural language processing, and the method comprises the steps: carrying out the multi-level processing of input unstructured text data, dynamically adjusting the word segmentation granularity according to the word frequency distribution, constructing a part-of-speech co-occurrence matrix in combination with context semantic information, and extracting entity information. And fusing the part-of-speech co-occurrence matrix and the entity information to generate a hierarchical semantic tag sequence. Feature extraction units with different convolution kernel sizes are adopted to extract feature representation, and cosine similarity between different semantic hierarchies is calculated to establish an association weight matrix. A semantic enhancement vector is constructed based on entity information, and adversarial training is carried out to obtain a multi-modal semantic feature matrix. And calculating the semantic similarity between the fusion feature vectors for clustering, setting difficulty weight sorting according to the complexity, consistency and ambiguity of class clusters, then inputting the class clusters into a classifier, and iteratively optimizing a classification result to obtain a calculation result.

The invention discloses an industrial equipment real-time monitoring system based on edge computing, and the system comprises the following steps: collecting, processing and analyzing the operation data of industrial equipment through distributed edge computing nodes, constructing a node cooperation mechanism, and completing the processing of the data; the dynamic data scheduling module is used for dynamically optimizing a data transmission path and task allocation according to node performance; the intelligent model dynamic migration module is used for adjusting lightweight model deployment in combination with node resource conditions; the multi-modal fault sensing module is used for extracting and analyzing multi-modal features by using a cross-node collaborative algorithm; the edge node self-adaptive management module is used for adjusting node operation parameters in real time and repairing an abnormal state; and the equipment relevance reasoning module is used for constructing an equipment relevance model through a graph calculation method and generating a real-time relevance graph. According to the method, efficient monitoring and anomaly detection of industrial equipment are realized through resource optimization and intelligent analysis of collaborative edge computing nodes.

The present disclosure relates to the technical field of artificial intelligence, and provides a multimodal large language model training method, a correlation calculation method, and a label generation method. The multimodal large language model training method comprises: on the basis of a sample text feature vector, a sample image feature vector, and a sample first multimodal feature vector which are obtained by processing image information and text description information of a sample commodity by a pre-trained multimodal large language model, training the pre-trained multimodal large language model to obtain a multimodal large language model; and processing a sample search word, the image information and the text description information on the basis of the multimodal large language model to obtain a sample search word feature vector, a first multimodal feature vector and a sample second multimodal feature vector, and training the multimodal large language model to obtain a trained multimodal large language model. The trained multimodal large language model of the present disclosure can simultaneously learn image features related to the search word and the text description information, and thus the generated second multimodal feature vector is more accurate.

The invention relates to the technical field of radar signal processing, in particular to a radar target analytical calculation method based on multi-dimensional data fusion and a radar device. Comprising the following steps: deploying a multi-band radar sensor array comprising an X band, a C band and a Ku band in a radar monitoring area; performing pulse compression and Doppler processing on the time domain echo signal, and extracting a time domain feature; spectral analysis is carried out on the frequency domain signals, and frequency domain features are extracted; performing angle estimation on the spatial signals, and extracting spatial features; a dynamic weight distribution model is constructed, a fusion weight is calculated through an adaptive algorithm based on three-dimensional quality indexes of a real-time signal-to-noise ratio (SNR), feature stability (SI) and data integrity (CI), and a joint representation vector containing time domain, frequency domain and space multi-dimensional information is generated. According to the invention, by deploying the multi-band radar sensor array, the recognition capability of the subtle feature difference of the target is improved.

The invention provides a power grid multi-agent large model safety evaluation index calculation method, and relates to the technical field of intelligent control. The method comprises the following steps: acquiring a voltage change rate and a frequency change rate, dividing four stages according to disturbance starting and extreme points, pairing curve trends, judging delay and synchronization characteristics, extracting a continuous change structure, and generating a disturbance subsection evolution structure; according to the method, through staged analysis of node voltage and frequency change trends and combination of trend consistency judgment and abnormal segment identification, dynamic structure reconstruction and trend mapping of a disturbance process are realized, and coherence and precision of index response state identification are improved through trend boundary collection and frequency classification; cooperative characteristic identification is enhanced through the coupling direction and delay analysis of inter-node multi-parameter change, the node adjustment priority is defined through regulation and control response sorting, an adjustment path under multi-dimensional linkage is constructed, and the refined management and control capacity of disturbance response and the pertinence of control decision are improved.

Disclosed in the present invention is a foundation deformation calculation method based on numerical model stress extraction. First, a numerical model is established to calculate the vertical additional stress distribution and horizontal additional stress distribution of foundation soil, wherein the elastic modulus of the foundation soil is selected within a range of two to five times the compression modulus on the basis of engineering experience; a layer-wise summation method is used, in which layered settlement is directly calculated for a natural foundation, settlement in a reinforced zone and settlement in an underlying layer are both calculated for a composite foundation, and layered settlement amounts are superimposed and multiplied by a settlement empirical correction coefficient to obtain a final settlement amount; the projection of a horizontal displacement calculation point on the ground and a vertical soil column below the horizontal displacement calculation point are taken as a vertically placed finite elastic long beam, the cross section of the soil column is square, the side length of the soil column is preferably 0.1 m or below, the modulus of the beam is the same as that of the soil, one end is located in a ground surface plane, and the other end is located in a plane where a calculation depth is located; and a distributed load is dispersed into several concentrated loads, a foundation bed coefficient is determined on the basis of a specification, and the horizontal displacement of the calculation point is obtained by means of a beam-on-elastic-foundation method.

The invention relates to a high and cold wetland dynamic monitoring and health evaluation method and system based on multi-source remote sensing. The method comprises the following steps: step 1, data acquisition and preprocessing; step 2, multi-modal data feature extraction; step 3, performing multi-modal data fusion; step 4, dynamic monitoring; step 5, evaluating wetland health; according to the invention, the calculation method of the SAR polarization decomposition model and the optical wetland index is optimized due to the unique seasonal change and earth surface complexity of the high and cold wetland, a scientific basis is provided for the management of the wetland protection area, and the ecological restoration efficiency is improved; alpine region water resource management and ecological protection policy making are supported; and data support is provided for researching the wetland carbon sink function and the climate change influence.

The invention discloses a data-mechanism dual-drive-based rock burst early warning method and system, and the method comprises the following steps: deploying a multi-modal sensor network to collect coal and rock stratum data, building a rock burst disaster precursor information sample database, providing a rock burst disaster multi-modal data precursor feature recognition algorithm, and carrying out the recognition of rock burst disaster multi-modal data precursor features. Mining the relevance between the multi-modal data and disaster-causing key risk indexes, and establishing a rock burst disaster multi-modal data prediction model; establishing a three-dimensional geological geometric model, fusing a multi-field coupling dynamics constitutive model and a catastrophe criterion, constructing a PINN physical information neural network prediction model of the rock burst disaster, and obtaining a time-space evolution rule of an energy field of a target area; providing a loss function coupling calculation method of a multi-modal data driving sample error and a physical driving control equation residual error, dynamic data and mechanism prediction result weight, comprehensively calculating a risk score, and accurately judging a top disaster danger level.

The invention relates to the technical field of artificial intelligence and natural language processing, in particular to a key value cache compression and sparse attention calculation method and system for large language model reasoning, and the method comprises the steps: an offline calibration stage; the online reasoning stage comprises the following steps: a pre-filling step; an autoregression generation step: for each newly generated lexical element, projecting a current query vector Q and a key vector K in a key cache to a low-dimensional space to obtain Q'and K '; calculating an approximate attention score based on Q'and K ', and selecting an index I of the first k most relevant lexical elements which are ranked from high to low; and calculating an accurate attention score based on Q and K [I], and calculating with the value vector V [I] to obtain the output of the current lexical element. According to the scheme, the memory and calculation bottleneck of large model reasoning in a scene of long text sequence input are solved, and the method has the advantages of reducing video memory occupation and calculation complexity at the same time.

The invention relates to the technical field of unmanned aerial vehicle positioning, in particular to an unmanned aerial vehicle flight path positioning method and system based on multi-source information fusion. The method comprises the following steps: acquiring an original observation data stream of an unmanned aerial vehicle in real time through a sensor array, and preprocessing to output a multi-source data sample set; calculating relative motion increment between adjacent key frames through an IMU pre-integration model, and extracting local pose estimation through a visual geometric calculation method; evaluating the confidence of each sensor in real time to dynamically generate an adaptive weight coefficient; and constructing a tight coupling fusion filter taking position, speed and attitude errors as core state quantities, and outputting a global optimal trajectory of the unmanned aerial vehicle. According to the invention, the weight is dynamically distributed through the real-time confidence of the sensor, the multi-source data association is fully mined, and the flight path positioning precision and environmental adaptability of the unmanned aerial vehicle are improved.

The invention discloses an attention state recognition neural network modeling and reasoning method based on an electroencephalogram sequence. The core is to construct and train a deep neural network model suitable for electroencephalogram signals so as to realize intelligent recognition and classification. Firstly, wavelet transformation and time-frequency feature extraction are carried out on electroencephalogram time sequence signals, and a multi-dimensional input tensor is generated in combination with channel position information; and inputting the feature into a deep network fusing spatial convolution, gating circulation and a residual connection structure, and extracting spatio-temporal joint features. A cross-time-step attention mechanism and a dynamic loss adjustment strategy are introduced in a training stage, so that the discrimination capability of the model on an alertness state transition region is improved. The final model can conduct reasoning on electroencephalogram data of any length, and a classification label and a confidence score are output and used for measuring classification reliability. The method focuses on construction and optimization of a specific calculation model, reflects application characteristics of an intelligent algorithm in cognitive state recognition, and belongs to an intelligent calculation method with a neural network as a core.

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 relates to the technical field of slope stability data identification, and particularly provides a multi-sensor fusion slope stability intelligent analysis system and an edge calculation method.The system comprises an environment sensing subsystem, a data processing subsystem and an edge calculation subsystem, the environment sensing subsystem monitors the change of the soil moisture content through a soil humidity sensor, and micro movement data of a slope are obtained through a displacement sensor; using a pressure sensor to sense the change of the ground pressure; meanwhile, pre-screening out abnormal data to form a fused data set; the data analysis subsystem is used for converting abnormal data of the fusion data set into a quantitative index of slope stability, and deducing a potential sliding risk of the slope by analyzing the relevance of the soil water content, the tiny movement data or the ground pressure; and the decision support subsystem provides specific reinforcement countermeasures according to the analysis result of the potential sliding risk. According to the method, the potential sliding risk of the side slope is comprehensively deduced by analyzing the relevance of the soil water content, the tiny movement data and the ground pressure.

The invention discloses a government affair processing method based on a big language model of a knowledge graph and electronic equipment. The method comprises the steps of performing named entity and relation extraction on government affair related texts by utilizing the big language model; performing named entity classification and entity disambiguation on the named entities and the relationships, and constructing a knowledge graph; performing community detection on the constructed knowledge graph by using a Leiden graph calculation method to generate a hierarchical community structure, and generating a community abstract from bottom to top by using a large language model; and extracting entities from government affair questions input by a user by using the large language model, performing mixed retrieval on the entities by using the constructed knowledge graph and the retrieval model to obtain retrieval results related to the entities, and enhancing the retrieval results by using the large language model to generate result answers. According to the method, information extraction is carried out by utilizing the large language model, so that the information of the constructed knowledge graph is more complete, and the knowledge graph is combined with a retrieval enhancement generation method, so that the interpretability and comprehensiveness of content generated by the large language model in the field of government affairs are improved.

The invention discloses a deep covering layer panel rock-fill dam rheological numerical calculation method considering the seepage effect. The method comprises the steps that firstly, contact deformation between a panel and a dam body is simulated; then simulating the unstable seepage process of the rock-fill dam and the foundation, and solving the seepage problem with a free surface; carrying out seepage-stress coupling calculation on the dam body material, and establishing a seepage-stress coupling control equation of the dam body material; carrying out seepage-stress coupling calculation on the overburden foundation, establishing a seepage-stress coupling control equation of the foundation, and finally obtaining a relationship among stress, strain and pore pressure to realize the seepage-stress coupling calculation; numerical simulation is conducted, and a concrete faced rockfill dam rheological numerical simulation result considering the seepage effect is obtained. The problems that in the prior art, the stress deformation rule of the concrete faced rockfill dam under the seepage-rheology coupling effect is not clear yet, and meanwhile, a seepage-rheology coupling calculation method is not perfect enough are solved.

The embodiment of the invention discloses a numerical calculation method for water outlet icebreaking of a navigation body and related equipment, and the method comprises the steps: generating a floating ice field geometric model according to a working condition, and constructing a navigation body model of the navigation body; grid division is carried out on the floating ice field geometric model and the navigation body model, and a water outlet icebreaking calculation model is obtained; fluid-solid coupling relation definition is conducted on the effluent icebreaking calculation model, and an optimized effluent icebreaking calculation model is obtained; the optimized water outlet icebreaking calculation model is used for simulating the water outlet icebreaking conditions of the navigation body at different moments, and water outlet icebreaking numerical values of the navigation body at different moments are obtained. The fluid-solid coupling relation definition can more accurately describe the interaction between fluid and solid, and in the water outlet icebreaking process, collision and extrusion between a navigation body and an ice layer and subsequent icebreaking behaviors all relate to a complex fluid-solid coupling phenomenon, so that the fluid-solid coupling relation definition is carried out on the water outlet icebreaking calculation model, and the calculation accuracy is improved. Therefore, an accurate value of water outlet icebreaking of the navigation body is obtained.

The present invention relates to the technical field of mechanical property testing and characterization. Disclosed are a method and apparatus for calculating a crack propagation life of an engine structure in vibration fatigue, which method and apparatus aim to obtain reliable fracture mechanics parameters and a crack propagation rate curve and provide an accurately quantified crack propagation life, so as to provide methodological guidance for the life assessment of reusable engines. The calculation method comprises: acquiring a time-domain random stress spectrum of stress responses in an assessment section in a finite element model of an engine structure; on the basis of the time-domain random stress spectrum, performing finite element analysis processing on a crack model of the engine structure, so as to determine fracture mechanics parameters of a cracked structure; performing a vibration fatigue performance test on a crack propagation rate model, which is set up according to a preset crack propagation mode, and determining, by using parameter identification, a target parameter value corresponding to the crack propagation rate model; and in combination with the parameter value corresponding to the crack propagation rate model and a preset critical crack length value, performing crack propagation life calculation, so as to obtain a target crack propagation life.

The invention provides a progressive privacy calculation method and system for a virtual-real fusion scene of an intelligent sweeper, and relates to the technical field of sweepers, and the method comprises the steps: collecting indoor space data through a multi-modal sensor, and constructing a scene knowledge graph through an entangled state registration network and a graph neural network; then, based on deep reinforcement learning and zero-knowledge proof, hierarchical encryption is carried out on the atlas, and scene data versions with different privacy protection intensities are generated through federated learning; and finally, performing path planning in a secure multi-party computing environment, and optimizing a cleaning strategy by using neural symbol reasoning and a graph attention network. According to the invention, scene understanding and path planning of the intelligent sweeper can be realized on the premise of guaranteeing the privacy of the user, the sweeping efficiency is improved, and the user experience is improved.

The present application provides a digital twin-based power distribution network power supply reliability calculation method and system. The method comprises: obtaining basic data and operation data of a power distribution line; constructing a digital twin power grid on the basis of the digital twin technology as well as the basic data and the operation data; obtaining scores in respect of power distribution network reliability indexes of the power distribution line on the basis of the digital twin power grid, wherein the power distribution network reliability indexes comprise an element reliability index, a load point reliability index, a system reliability index, a series system reliability index, and a parallel system reliability index; and on the basis of a weighted fusion result of the scores of the power distribution network reliability indexes, determining the power distribution network power supply reliability to be calculated. The present application solves the problem that current methods for analyzing the power distribution network power supply reliability relate to treating entire power distribution networks as research objects and implementing manual sorting, and thus are time-consuming and labor-intensive, are difficult to ensure the accuracy of calculation results, and fail to conduct statistical analysis for weak links in the network distribution networks.

The invention relates to a neuromorphic computing system and computing method based on a spiking neural network, and the system comprises a neuromorphic processor which is used for constructing a hardware-based spiking neural network through employing a plurality of physical neurons, and carrying out the pulse coding and target recognition of a real-time input image; the central processing unit is realized on the basis of an RISC-V instruction architecture and is used for executing a control instruction required for constructing the pulse neural network; the neuromorphic processor is also used for carrying out online learning on the pulse neural network under the control of the control instruction; and the memory is used for storing program instructions required by the operation of the central processing unit and data transmitted between the central processing unit and the neuromorphic processor. The invention further provides a neuromorphic calculation method. According to the technical scheme of neural morphology calculation, high balance among precision, calculation speed and energy efficiency is achieved, and dynamic and complex task data can be processed accurately and efficiently with low power consumption.

The invention discloses a CAD drawing engineering quantity automatic identification and calculation method based on a large language model and image segmentation, and the method comprises the steps: extracting project annotation information in a CAD drawing, understanding and reasoning a project introduction through a large language model, and carrying out the structural expression of the project annotation information; the method comprises the following steps: preprocessing a graph in a CAD drawing by utilizing computer vision, analyzing a pipeline drawing of the CAD drawing by utilizing machine learning, identifying the type and position of a component in the pipeline drawing, and realizing semantic segmentation and instance segmentation; and fusing the structured project annotation information, the semantic segmentation result and the instance segmentation result, counting and calculating the project amount of each pipeline component in the CAD drawing image, and outputting a report according to classification. According to the method, OCR recognition, natural language processing, graph semantic recognition, engineering logic calculation and other technologies are integrated, the CAD drawing processing efficiency and the engineering quantity calculation accuracy are greatly improved, and intelligent support is provided for design, construction, drawing examination, budget and other links.

The invention relates to an unmanned aerial vehicle multi-modal visual positioning method and system in a GNSS denial environment, and the method comprises the steps: constructing a visual inertial mileage calculation method based on deep learning feature extraction and matching, calculating the relative positioning of an unmanned aerial vehicle, and outputting to obtain high-frequency 6-DoF pose information; performing scene matching of infrared RGB image multi-modal fusion based on the Officient-LoFTR, and obtaining absolute positioning in a world system by solving a relative pose between a current key frame picture and a matched satellite image; a factor graph optimization mode is adopted to carry out self-adaptive weight information fusion according to the confidence coefficient of absolute positioning and relative positioning under a world system, and 6-DoF pose information with high frequency and higher precision is output; based on high-frequency and higher-precision 6-DoF pose information, after the unmanned aerial vehicle is recovered, all aerial images and satellite images are fused and updated. The system comprises a calculation module and a fusion updating module. According to the invention, more accurate and high-frequency pose output can be provided.

The invention relates to the field of private data calculation, and discloses a private data calculation method and system based on federated learning, and the method comprises the steps: carrying out the preprocessing of local data, carrying out the secret state feature alignment of the preprocessed data, selecting a federated mode, and carrying out the gradient injection of dynamic differential private noise based on a local model, encrypting the gradient of the injected noise to obtain a safety gradient; aggregating the obtained security gradients and verifying the consistency of the data, establishing a federal transfer learning component to adapt to the feature distribution difference of a minimized source domain and a target domain, establishing a security promotion tree model through multi-party collaborative decision to generate a final prediction tag, and performing zero-knowledge audit evidence storage; the privacy protection is dynamically enhanced, the sensitivity of local data is graded, the dynamic differential privacy noise intensity is verified and adjusted, the privacy security is optimized by using dual-channel encryption transmission, and a federal transfer learning component is strengthened.

The invention relates to the technical field of data processing, in particular to a data processing method and system for a flexible regulation and control terminal, and the method comprises the steps: determining a fuzzy entropy value of each voltage data point in voltage data according to a fuzzy entropy calculation method, and carrying out the abnormality judgment according to the fuzzy entropy value, determining a voltage data point window where the voltage data is located at the sampling moment; calculating a first stability degree according to the quartile distance of the voltage data in the voltage data point window and the number of extreme points; calculating a second stability degree according to the similarity of the voltage data segment and the current data segment in the voltage data point window; and according to the second stability degree, calculating to obtain the sub-window length in the voltage data point window. According to the scheme, the accuracy of fuzzy entropy calculation and the reliability of anomaly detection are remarkably improved.

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 a seat comfort optimization calculation method based on a sensor and AI modeling, and the method comprises the steps: collecting the pressure distribution data of the sitting posture of a passenger and a spine contact area in real time through an attitude sensor and a pressure sensor which are disposed on a seat; a seat supporting area is divided into a cervical vertebra area, a thoracic vertebra area, a lumbar vertebra area and a sacrococcygeal area, and a pressure distribution prediction model of each area is established; constructing an input vector based on the average pressure, the maximum pressure, the pressure gravity center, the body type parameter and the pathological weight factor, and outputting a target support pressure value, a pressure avoiding curve and a neighbor cell migration proportion of each partition by using an artificial intelligence model; dynamic adjustment is achieved through the partitioned air bag and the driving unit, and secondary fine adjustment and protection are achieved in combination with feedback monitoring. The system comprises a posture sensing module, a pressure sensing module, a data processing module, a model building module, a regulation and control execution module and a feedback monitoring module, and self-adaptive regulation and control of different areas of the spine of a passenger can be achieved.

The invention relates to the technical field of remote sensing data processing and glacier area calculation, in particular to an unmanned aerial vehicle and satellite remote sensing data fused glacier area calculation method, which comprises the following steps of: cooperatively acquiring a satellite multispectral image and unmanned aerial vehicle high-resolution optical and LiDAR data, performing time synchronization, high-precision space registration and data enhancement processing, and calculating the glacier area through the unmanned aerial vehicle and satellite remote sensing data fusion. A satellite image glacier macroscopic feature and an initial mask are extracted by using a convolutional neural network and an NDSI / NDWI algorithm, and unmanned aerial vehicle image microscopic texture, edge and topographic features are acquired through a local binary pattern, edge detection and LiDAR point cloud; based on pyramid layering and a conditional random field, adopting a variance weighting algorithm to realize multi-scale feature level fusion; and after segmentation through an Otsu algorithm, calculating the area through a pixel counting method and introducing gradient correction, and evaluating the reliability through three types of precision. The method breaks through the limitation of a single data source, fuses macroscopic and microscopic features, improves the boundary positioning precision and calculation efficiency, and is suitable for glacier dynamic monitoring in a complex environment.