653 results about "Prior information" patented technology

The invention discloses a belt conveyor fault identification and prediction method and system, and relates to the technical field of digital intelligent monitoring of belt conveyors, and the method comprises the steps: collecting the operation data of a belt conveyor in real time through various sensing devices arranged on the belt conveyor, analyzing the collected video data and multi-modal data through an AI algorithm, and carrying out the real-time monitoring of the operation data of the belt conveyor; analyzing the operation state and fault prior information of parts and materials of the conveyor, inputting the information into a dynamic simulation model, simulating the common influence of various faults on the belt conveyor through Monte Carlo simulation, and calculating the probability prediction values of the various faults; finally, an analysis result is displayed in real time in combination with modes of graphic display, probability curves and the like, and working personnel are timely notified of occurred and about-to-occur faults; meanwhile, fault probability prediction and operation data serve as training data again to conduct incremental learning on the AI algorithm, and the accuracy of the AI algorithm is continuously improved.

The present invention relates to the technical field of coal mine water inrush disaster prevention and control, and relates to an artificial intelligence-based method for identifying a water inrush point in a mine and parameters of a simulation model, comprising the following steps: step 1: establishing a numerical model on the basis of observation data, and determining prior information of parameters to be identified including position coordinates of a water inrush point; step 2: on the basis of the numerical model and the prior information of the parameters, generating a training sample dataset and a test sample dataset for an alternative model; step 3: constructing and training an alternative model neural network; step 4: testing the accuracy of the alternative model; and step 5: executing a simulated annealing algorithm to identify the position of the water inrush point and parameters of a simulation model. By using the method provided by the present invention, the problem in accurate positioning of water inrush points in underground coal mines can be solved, which is of great significance in carrying out scientific disaster management and rescue efforts in a timely manner.

The present disclosure provides an artificial intelligence-based method for enhancing mine safety by identifying and predicting locations of water inrush points in a mine, including the following steps: S1: constructing a numerical model to determine priori information of parameters to be recognized based on observation data, including coordinates of locations of water inrush points; S2: generating a training sample dataset and a test sample dataset of an alternative model based on the numerical model and the priori information of the parameters; S3: constructing and training a neural network of the alternative model; S4: testing an accuracy of the alternative model; and S5: performing a simulated annealing algorithm to identify the locations of water inrush points and simulation model parameters.

The invention relates to the technical field of novel power system optimization operation, in particular to a new energy high-uncertainty scene generation method and device based on a CWGAN-GP model, and the method comprises the steps: correcting an initial generative adversarial network through employing a Wasserstein distance and a gradient penalty term, so as to obtain a final generative adversarial network, and the target convolutional neural network is introduced into the final generative adversarial network to obtain a conditional deep convolutional generative adversarial network of improved deep convolution, so that WGAN-GP is introduced into the conditional deep convolutional generative adversarial network of improved deep convolution to obtain a CWGAN-GP model of improved deep convolution. And a target L-ISODATA clustering algorithm is applied to cluster the generated wind and light scenes so as to generate a high-uncertainty new energy power generation output scene. Therefore, the problems that in the related technology, when the GAN processes high-dimensional data, complex space-time characteristics are difficult to effectively capture, and due to lack of prior information, the training direction is prone to deviation, and the scene generation effect and stability are affected are solved.

The invention belongs to the technical field of pointer instrument reading identification, and particularly discloses an industrial pointer instrument reading automatic identification method based on deep learning, which comprises the following steps of: acquiring an image of a pointer instrument by using an industrial camera; detecting an instrument panel in the instrument image by using an instrument panel detection model based on YOLOX-DC; segmenting a pointer, a key scale and a scale reading of the instrument panel by using an image segmentation model based on PM-SwinUnet to obtain a segmented image of the instrument; and based on the segmented image of the instrument, removing edge pixels by using a skeleton refinement and central point extraction algorithm to obtain the position of the pointer, the direction of the pointer and the position of the scale, and further obtaining a final pointer type instrument reading by using an improved angle method. The problems that existing pointer instrument reading recognition depends on prior information, generalization ability is poor and segmentation is incomplete are solved, and accurate recognition of pointer instrument reading is achieved.

The invention discloses an electromechanical equipment abnormal behavior detection and fault prediction method based on a causal time-space Transform, and belongs to the technical field of electromechanical equipment abnormal detection, and the method comprises the steps: S1, constructing an initial causal graph according to the causal relationship between the physical structure and the functional part of electromechanical equipment; s2, correcting the initial causal graph based on historical operation data of the electromechanical equipment to generate a causal graph; s3, introducing the causal graph as prior information into a Transform model, predicting sensor time sequence data of the electromechanical equipment to be detected through the trained Transform model, and outputting corresponding high-dimensional feature representation; and S4, according to the high-dimensional feature representation, calculating the abnormal weight of each component, and carrying out component-level abnormal identification and fault prediction. The method breaks through the limitation that only the data correlation is fitted and the causal relationship is ignored in the electromechanical equipment anomaly detection of a traditional time sequence model, and explicit modeling of an equipment fault chain propagation mechanism is realized by fusing a causal reasoning mechanism and feature modeling.

The invention discloses a document table extraction method and device, equipment and a medium, and relates to the technical field of computer information processing. The extraction method comprises the following steps: performing OCR (Optical Character Recognition) on a to-be-processed document table image to obtain a text block; performing visual feature coding on the document table image to obtain deep visual features; performing semantic feature coding on the text sequence of the text block to obtain a semantic feature vector; performing spatial feature coding on the bounding box of the text block to obtain a spatial feature vector; performing feature fusion processing on the deep visual features, the semantic feature vectors and the spatial feature vectors to obtain multi-modal guide features; and performing structured decoding processing on the multi-modal guide features to obtain structured representation of the table. According to the method, the text and the position information pre-recognized by the OCR are fused with the visual features of the document table, so that the visual features are guided to be expressed again and are actively aligned to the logic structure defined by the prior information, and the extraction accuracy of the table logic structure is improved.

The invention provides a product storage life evaluation method based on multi-stage success and failure data, which evaluates the storage life of a product by adopting a discrete parameter algorithm according to multi-stage success and failure natural storage test data of the product, and comprises the following steps: S1, performing zero-moment failure probability distribution fitting and mean value estimation, and according to prior information and factory data, calculating a failure probability distribution value; performing fitting on zero-moment failure probability distribution by adopting beta distribution, and calculating expected mean estimation of the zero-moment failure probability distribution; s2, abnormal value screening and order-preserving processing of fusion data are carried out, multi-stage data are combined, an error threshold value is set, abnormal points are eliminated, and order-preserving processing is carried out; s3, discretizing position parameters, and carrying out Weibull parameter estimation; the interval range of the position parameters is determined, equal interval division is carried out, and corresponding translation processing is carried out on the data; and S4, life parameter selection and abnormal point consistency judgment. According to the method, multi-stage success and failure data can be fully utilized, and the product evaluation precision is improved.

The invention discloses a large-model-driven cross-modal power insulator defect detection method and system, and the method comprises the steps: constructing an image-text dual-channel feature learning frame based on a pre-trained vision-language large model, introducing fine-grained semantic description as prior information, and carrying out the detection of the defects of a cross-modal power insulator. Feature enhancement and space focusing under semantic guidance are realized through a cross-modal attention fusion mechanism, and the robustness and discrimination ability of the model in a complex scene are further improved in combination with a multi-scale pyramid structure and a channel re-calibration mechanism. The method can effectively solve the practical problems of difficult recognition of tiny targets, strong environmental interference, limited sample data and the like.

The invention provides a path planning method based on machine learning in an easy-to-degrade environment, and relates to the fields of path planning, machine learning and the like. The method comprises the specific steps of firstly processing point cloud data through semantic segmentation and fuzzy evaluation to generate a semantic map containing risk information, then taking a global path generated by an A * algorithm as prior information of a DQN algorithm, then designing a reward function which comprehensively considers safety, efficiency and shortest path, and optimizing a local decision through the DQN algorithm. And finally, fusing the local path and the global path generated by the DQN to generate a continuous, smooth and safe optimal path. Compared with the prior art, the method not only can effectively cope with the problem of performance reduction of the laser radar in a degraded environment, but also can remarkably improve the path planning safety and robustness of the mobile robot in a complex environment, has real-time performance and expansibility, and can be widely applied to the fields of service industry, industry, transportation industry and the like.

The invention relates to a vehicle and unmanned aerial vehicle combined scheduling method for wide-range low-cost inspection. The method comprises the following steps: acquiring prior information; modeling the unmanned aerial vehicle inspection problem of each target area according to the prior information to obtain a mixed integer non-convex optimization problem with the goal of minimizing the weighted sum of the total execution time and the energy consumption of all the inspection unmanned aerial vehicles; performing linearization on a non-convex bilinear term in the mixed integer non-convex optimization problem, and performing discretization processing on a nonlinear function by adopting piecewise linear approximation; an approximate mixed integer linear programming problem is obtained and solved, and an unmanned aerial vehicle scheduling strategy is obtained; modeling according to the unmanned aerial vehicle scheduling strategy and the prior information to obtain an inspection vehicle path planning problem taking the comprehensive driving cost as a target; the routing inspection vehicle path planning problem is converted and modeled into a Markov decision process, a routing inspection vehicle is used as an intelligent agent, a state, an action and a reward function are defined, and a routing inspection vehicle scheduling strategy is obtained. Therefore, combined inspection of the inspection vehicle and the unmanned aerial vehicle is realized, and the inspection range is expanded.

The invention discloses a road obstacle recognition method based on machine vision, and the method comprises the steps: carrying out the precise positioning of the spatial position of an obstacle through a multi-sensor information fusion algorithm according to the azimuth and distance information of the obstacle estimated through a sound source positioning algorithm and the position information outputted by an obstacle detection model, and improving the positioning precision; the method comprises the following steps: acquiring guide mark information, such as road signs and lane lines, extracting guide mark features through a semantic segmentation algorithm, carrying out dynamic fusion on the guide mark features and an obstacle recognition result, and improving the accuracy and robustness of obstacle recognition by using prior information; in order to solve the problem of insufficient parallax analysis precision in long-distance, high-speed motion and other scenes, a monocular depth estimation algorithm based on deep learning is introduced, pixel-level depth information is predicted from a single image through a convolutional neural network, and the precision of obstacle distance estimation is improved.

The invention discloses an interpretable building energy consumption prediction method and system based on a large language model, and belongs to the technical field of building electric power energy consumption prediction. Comprising the following steps: acquiring and processing prior information of building energy consumption prediction, and generating prefix vector representation; energy consumption time series data of the building are obtained, reversible instance normalization processing and patch embedding operation are sequentially carried out on the energy consumption time series data, reediting is carried out in combination with the text prototype vector representation, and embedded vector representation is generated; splicing the prefix vector representation and the embedded vector representation, and performing feature processing on a splicing result through a large language model to obtain a multi-dimensional vector representation; and performing mode conversion on the multi-dimensional vector representation and mapping the multi-dimensional vector representation to an output space to obtain an energy consumption prediction result. The interpretability and the prediction efficiency of building energy consumption prediction are improved, and the problems that existing energy consumption prediction is limited by complex influence factors, the energy consumption prediction time is long, and the result lacks interpretability are solved.

The invention discloses a coordinate system transformation fusion filtering tracking method and system for a dual-base-station radar, and relates to the technical field of radar target tracking. The method comprises the following steps: firstly, establishing a state and observation equation, and determining an initial state of a target based on prior information under Cartesian coordinates; then, obtaining a target motion state through one-step prediction, generating a sigma point by utilizing U transformation, and calculating a mean value and a covariance for constructing state space prediction; thirdly, fusing the measurement data and the prediction data by using a Kalman filter to obtain optimal state estimation and a covariance matrix; and finally, converting the updated state estimation back to the Cartesian coordinate system based on U transformation, and circulating the process until the tracking is finished. According to the method, the nonlinear filtering problem in the updating process can be avoided, and the tracking robustness and precision are improved.

The invention discloses an unmanned aerial vehicle self-adaptive inspection method based on multi-layer characterization learning, which comprises two stages of automatic coarse inspection and self-adaptive fine inspection, and learns the space structure, component characteristics and disease characteristics of a bridge layer by layer. In the automatic rough detection stage, in combination with a spatial clustering post-processing method and a point cloud semantic segmentation network, instance attributes of bridge components are quickly learned. Then, the point cloud space is compressed and converted by using a simulation view field model and a dimension reduction technology, and the unmanned aerial vehicle is guided to carry out efficient and comprehensive space patrol in plane geometry. In the adaptive fine detection stage, a space-channel two-dimensional optimized hybrid convolution structure Light-PVIT is utilized to learn and extract disease features in the automatic coarse detection stage as prior information. The information guides the unmanned aerial vehicle to carry out fine detection on a disease area in a small field of view. According to the unmanned aerial vehicle self-adaptive inspection method, the bridge inspection efficiency and precision are remarkably improved, and reliable technical support is provided for bridge maintenance work.

The invention discloses a discrete wavelet steel rail damage acoustic emission signal detection method based on filtering parameter adaptive search, which comprises the following steps of: firstly, preprocessing acoustic emission data acquired by a sensor, and removing a pure noise segment in the signal to relieve the subsequent operation pressure; then, an evaluation function combining a reduction degree index and a sparseness index is designed, an evaluation index of a filtering parameter represented by each sparrow is calculated in combination with discrete wavelets, and a filtering parameter enabling the evaluation index to be minimum is searched by adopting a self-adaptive sparrow search method, namely an optimal filtering parameter group; and finally, filtering the original signal based on the optimal filtering parameter group, extracting a high-frequency component, obtaining the time-frequency characteristic of the high-frequency component by adopting short-time Fourier transform, and proposing a steel rail damage judgment condition based on the dual-band time-frequency characteristic, which can judge whether the damage exists or not through the time-frequency characteristic and calculate the moment when the damage is captured by a sensor. The problem that a traditional method generally depends on system parameters and prior information is solved.

The invention discloses a generative intention recognition method based on prior information and related equipment, and relates to the technical field of intention recognition, the method comprises the following steps: obtaining entity data and user verbal skill, the entity data comprising entity data in a navigation multimedia domain and entity data in a cabin domain; performing feature extraction on the user verbal skill to obtain a first user verbal skill vector; performing feature extraction on the entity data in the navigation multimedia domain based on the first user verbal skill vector to obtain an entity vector in the navigation multimedia domain; performing feature enhancement on the entity data in the cockpit domain to obtain an entity vector in the cockpit domain; and performing intention recognition based on the first user verbal skill vector, the entity vector in the navigation multimedia domain and the entity vector in the cabin domain to obtain an intention recognition result.

The invention relates to the field of intelligent decision-making, and particularly discloses an intelligent decision-making generation system based on user prior information guidance, which is characterized in that historical decision-making case data and to-be-audited decision-making contents of a target object are acquired, each decision-making case data in the historical decision-making case data comprises a case content and a decision-making label, and the case content and the decision-making label are acquired; semantic coding and global semantic aggregation are carried out on the historical decision case data by adopting a data processing and analysis technology based on artificial intelligence, and semantic understanding is carried out on the to-be-audited decision content; and intelligently obtaining an auxiliary decision-making result of the decision-making type label according to the semantic association matching feature between the historical decision-making case data and the to-be-audited decision-making content. Through the mode, the system can better utilize past decision-making experience to guide the current decision-making process, so that more accurate decision-making suggestions are provided, and the decision-making quality and efficiency are improved.

The invention relates to the technical field of intelligent control and information security crossing, in particular to an anti-error data injection Kalman filtering method based on neural network enhancement, which comprises the following steps: firstly, constructing a neural network auxiliary correction module for identifying and correcting an extended Kalman filtering intermediate variable affected by an error data injection attack; secondly, designing a hybrid filtering architecture, and combining the learning ability of a neural network with the theoretical advantages of traditional Kalman filtering; and finally, realizing adaptive optimization of model parameters through a mixed training mechanism. Accurate state estimation can be achieved only through limited prior information, and meanwhile the calculation efficiency and interpretability of a traditional Kalman filter are kept. And the anti-interference capability of the system in a non-Gaussian noise environment is effectively improved. The application of the method on an induction motor model is obviously better than that of a traditional anti-error data injection scheme, and breakthrough progress is achieved in the aspects of estimation precision and robustness.

The embodiment of the invention provides an underground tunnel geometric parameter remote sensing inversion method, and belongs to the technical field of data processing, and the method specifically comprises the steps: building a sensitivity enhancement model corresponding to ground surface displacement caused by underground tunnel excavation in combination with an empirical model and a physical model; an InSAR geometric projection equation is introduced, and a function relation between underground tunnel geometric parameters and InSAR line-of-sight deformation is constructed; the method comprises the following steps: collecting time sequence I nSAR data of an underground tunnel in an excavation period, and measuring accumulative sight deformation of a research area according to the time sequence I nSAR data; based on the accumulated line-of-sight deformation, extracting tunnel deformation center positions in a plurality of cross sections and surface line-of-sight deformation of the cross section positions by using an image segmentation and morphological algorithm; and optimizing and constraining a parameter space based on known prior information, substituting the earth surface sight line direction deformation into the function relationship, and performing optimal solution by using a genetic algorithm to obtain other geometric parameters of the underground tunnel. Through the scheme of the invention, the reliability and adaptability of inversion are improved.

The invention discloses a two-stage checking method for the node water demand and the pipe section friction resistance coefficient of a water supply pipe network, and is applied to the technical field of real-time online correction of a water supply pipe network model. Comprising the following steps: grouping node water demand and a pipe section friction resistance coefficient based on prior information, calculating a covariance matrix of pressure and flow measuring points, calculating estimated values and covariances of the node water demand and the pipe section friction resistance coefficient through a first-stage sequential data assimilation algorithm according to monitoring data at the current moment, and calculating the node water demand and the pipe section friction resistance coefficient through a second-stage sequential data assimilation algorithm; and then a calculation result serves as an empirical value to be added into a target function of second-stage iterative calculation, an estimated value serves as an initial value of an iterative algorithm to be subjected to fine tuning optimization, an optimal estimated value of parameters at the current moment and a covariance matrix of the optimal estimated value are obtained, the calculation result is brought into the next moment, and the two-stage algorithm is repeated to check model parameters at the next moment. The advantages of the two-stage algorithm complement each other, the checking precision of the parameters is improved, and a new thought is provided for real-time online correction of the water supply pipe network model parameters.

The invention discloses a unified representation and dynamic knowledge reasoning method and device for an intelligent cluster system, and the method comprises the steps: determining a decyclization task sub-graph based on a composite task state space model of the intelligent cluster system through the priori information; constructing a real-time data sub-graph by using a real-time data feedback mechanism, and constructing a traceability mode sub-graph based on the decyclization task sub-graph; candidate traceability sub-graphs with the highest matching similarity with the traceability mode sub-graphs are determined from the real-time data sub-graphs to serve as traceability sub-graphs; constructing a reasoning path set based on the decyclization task subgraph, and calculating a probability value of each reasoning path in the reasoning path set by using a path probability model constructed based on a Bayesian conditional probability chain rule; and screening out the reasoning paths of which the probability values are smaller than a preset probability value in the reasoning path set by utilizing a dual path pruning strategy, and adding the reasoning paths containing abnormal nodes in the reasoning path set into an alarm path set so as to provide support for state prediction and risk management of the intelligent cluster system.

The invention discloses a multi-satellite direct positioning method based on CUDA (Compute Unified Device Architecture) parallel calculation, relates to the field of space-based electromagnetic signal positioning, and aims to be suitable for large-range and high-precision positioning of low-signal-to-noise-ratio signals. According to the technical key points, a positioning area is determined in a satellite coverage range according to prior information, and grid points are divided on the surface of the earth; two or more satellites with time-frequency synchronization obtain observed quantities of signals emitted by the same ground radiation source, a first signal model of the multiple observed quantities is constructed, and down-conversion is carried out on the first signal to obtain a second signal; constructing a cost function by using signal characteristics (such as time difference of arrival, frequency difference of arrival and the like) in a radiation source signal received by a satellite; a cost function corresponding to each grid point is subjected to multi-core parallel computing through the GPU; and summarizing results, and searching the maximum value of the cost function to determine the estimated position of the ground radiation source. According to the multi-satellite direct positioning method based on the CUDA, the characteristic that parallel calculation can be carried out when cost function grid searching optimization is carried out in direct positioning is utilized, calculation is directly carried out on satellites, satellite-ground data transmission is omitted, the positioning time is shortened by utilizing the independence of searching calculation among grids, and the real-time performance of a positioning system is guaranteed.

The invention discloses a physical information neural network hyperspectral band selection method for target identification, which uses physical information SDI as prior information to be combined with a channel attention mechanism added in a deep neural network to guide band selection, and evaluates the advantages and disadvantages of band weights through a reconstruction module after the weights are generated. Wave band selection is carried out accordingly; according to the hyperspectral image target recognition waveband selection method based on the physical information neural network, compared with a traditional method, the process is simplified and the precision is improved through end-to-end learning, the utilization of spatial context information can be enhanced by adding an attention mechanism, and the model has interpretability by adding prior SDI information.

The invention relates to a circuit board defect identification method and system based on multi-dimensional image data, and belongs to the technical field of data identification processing, and the method comprises the following steps: obtaining synchronous image data of a circuit board to be detected in a plurality of imaging modes; performing space-spectrum joint registration on each modal image to generate a multi-dimensional image cube with a unified coordinate system and pixel alignment; inputting the multi-dimensional image cube into a pre-trained multi-branch heterogeneous fusion neural network; generating a pixel-level defect probability graph by utilizing a defect sensing context decoder, and performing geometric constraint optimization on the probability graph by combining prior information of a circuit board design layout; outputting defect types, positions and confidence coefficients, and establishing an interpretable defect fingerprint database according to the multi-dimensional response characteristics of the defects; the method has the beneficial effects that false defect signals generated by image noise and circuit board surface texture interference can be effectively inhibited, the omission ratio and the false detection ratio are greatly reduced, and pixel-level accurate defect positioning is realized.

The invention discloses an unmanned ship state information online modeling and forecasting method based on a simulation technology, and relates to the technical field of unmanned ship simulation testing, an unmanned ship database system is constructed, and simulation data obtained through simulation of an unmanned ship model in a start-ccm + real flow field and a Nometic marine environment and data collected in a real marine test are included; and designing a nonlinear reserve pool calculation algorithm based on a sliding window algorithm to carry out online modeling forecasting on the state information. According to the method, dynamic characteristics of computational fluid mechanics, a physical simulation environment and a real marine environment are combined, online modeling forecasting of state information of the unmanned ship in the real marine environment can be realized by using small sample data, effective prior information is provided for collision avoidance, planning and control of the unmanned ship, and a navigation task is safely and effectively completed.

The invention discloses a visual inertia SLAM (Simultaneous Localization and Mapping) method and system based on point-line fusion, relates to the technical field of image processing, and solves the problem that stable and distinctive features cannot be effectively selected when a current SLAM method processes multi-line features. According to the scheme, point feature extraction and tracking are carried out on visual data based on a tracking algorithm combining forward and reverse optical flows, and line feature selection and optimization are carried out on the visual data based on a line segment detection model after line feature selection and homogenization optimization; marginalizing the visual data and the inertial data based on a sliding window model and converting the visual data and the inertial data into prior information, and improving marginalization steps based on a distributed optimization strategy; special feature detection and matching are set during data preprocessing, marginalized residual processing sliding window optimization is set during back-end nonlinear optimization, the stability and accuracy of point-line features are enhanced, and the operation efficiency of the system is improved.

The invention discloses a ground penetrating radar pipeline intelligent inversion method based on multi-scale deep learning, and belongs to the technical field of ground penetrating radar pipeline data inversion. The method aims at solving the problems that a traditional inversion method is low in resolution, high in calculation complexity, depends on prior information and the like. The method comprises the specific steps that underground simulation model data sets are constructed in batches; forward modeling calculation is carried out based on time domain finite difference, and a matching data pair of the radar B-scan image and dielectric constant distribution is generated; constructing a multi-scale deep neural network comprising a residual attention module, a dual-path space attention module, a multi-scale feature fusion module and a reconstruction module; through end-to-end training and model test optimization, an optimal model is finally stored to realize inversion output from original radar data to dielectric constant distribution. According to the method, through a multi-scale feature extraction and fusion mechanism, the recognition precision of pipelines of different sizes is effectively improved, the inversion efficiency is greatly improved, and an efficient and accurate technical scheme is provided for underground pipeline detection.

A method for sending a short message, a short message terminal and a medium relate to the technical field of satellite navigation, and the method comprises the following steps: when the short message terminal cannot receive a short message downlink signal broadcasted by a satellite end or the quality of the received short message downlink signal is lower than a preset quality threshold value, sending a short message to the satellite end; calculating visual satellites of the short message terminal at the current position to obtain a to-be-selected satellite set; obtaining all accessible satellites of the global short message system, which are determined in advance according to the prior information, and obtaining an accessible satellite set; the prior information represents whether each satellite with the short message access capability allows the short message terminal to access or not; determining an intersection of the visual satellite set and the accessible satellite set; determining an uplink access satellite from the intersection, and assembling an inbound message frame based on a synchronization head of the uplink access satellite; and sending the inbound message frame. The function of non-downlink signal blind sending of the short message is realized, and the function of the short message system is enriched.

A data processing method relates to the field of artificial intelligence, including: processing a target image through a first posture recognition model to obtain first posture information of a target object in the target image; processing the target image through a second posture recognition model to obtain second posture information of the target object in the target image; the first posture information and the second posture information are used to describe the three-dimensional posture of the target object; the second posture information is used to determine the two-dimensional projection information of the predicted posture of the target object; and a loss is constructed according to the first posture information and the second posture information, as well as the two-dimensional projection information and the corresponding annotations, and the loss is used to update the second posture recognition model. The present application introduces a posture information that will not or basically will not be deformed as the prior information (first posture information) when training the posture recognition model to ensure that the posture determined by the posture information output by the updated posture recognition model will not be deformed.