144results about How to "Implement depth" patented technology

The invention relates to the technical field of image semantic segmentation, in particular to an intelligent landslide identification method based on multi-source remote sensing image fusion. Comprising the following steps: calculating a gradient matrix and a water flow convergence index based on a digital elevation model, searching and determining a physical prior interval of cohesive force and an internal friction angle based on geological lithology data, and constructing an input tensor; extracting a surface semantic feature map of the optical image by using visual perception branches through a double-branch coupling network; based on a physical prior interval, mapping branches by using physical parameters, and carrying out nonlinear mapping to obtain a limited rock-soil mechanical parameter diagram; and calling a micro infinite slope stability layer, executing forward derivable calculation based on a limit equilibrium equation, generating a slope stability coefficient field, constructing physical attention gating, executing Hadamard product operation, and generating a semantic feature tensor. Through deep coupling integration and collaborative optimization of the visual perception branch and the physical parameter mapping branch, the true effectiveness of the obtained position data is ensured.

The invention relates to an engineering potential safety hazard detection method based on a multi-modal large model. The method comprises the following steps: acquiring an on-site monitoring image of a target scene; identifying a key entity in the field monitoring image, and extracting positioning information of the key entity; generating a depth map based on the on-site monitoring image; based on the positioning information of the key entity, combining with the depth map, associating the positioning information and the depth information for the key entity, and generating a scene description text; based on the on-site monitoring image, the depth map corresponding to the on-site monitoring image and the scene description text, constructing a multi-modal input packet; inputting the multi-modal input packet into a learned engineering safety domain knowledge base and a visual language large model subjected to instruction fine tuning, and outputting a hidden danger judgment result; the instruction fine tuning of the visual language large model is based on a hidden danger inference chain data set, the data set has hidden danger images corresponding to hidden danger items in an engineering safety field knowledge base and annotation data of each hidden danger image, and the annotation data comprises step result data corresponding to each hidden danger detection step.

The application relates to a basketball video highlight automatic generation method and system based on multi-modal feature fusion, and belongs to the technical field of computer vision and multimedia intelligent analysis. The method comprises the following steps: S1, extracting and aligning a visual feature sequence and an audio feature sequence; S2, using bidirectional LSTM to perform time sequence coding on the audio-visual feature, constructing a cross-modal attention mechanism taking audio as a query and vision as a key value, and obtaining multi-modal fusion features; S3, inputting the fusion features into a 1D CNN to output a frame-by-frame highlight event probability sequence; S4, performing peak value searching based on an adaptive threshold of the probability sequence to obtain a coarse-grained timestamp; S5, taking the coarse-grained timestamp as a center to intercept local features, inputting the local features into bidirectional LSTM to predict starting and ending offsets, and obtaining accurate start and end time boundaries; and S6, clipping and splicing to generate a highlight video according to the accurate boundaries. The application realizes audio-visual semantic depth alignment through an audio-guided visual attention mechanism, and improves boundary positioning accuracy through a coarse-precision two-stage positioning architecture.

The control method comprises the steps that the rotating speed of a high-temperature-stage inverter compressor is mainly comprehensively determined by the high-temperature-stage exhaust temperature, the high-temperature-stage condenser temperature, the high-temperature-side outlet temperature of an intermediate heat exchanger and the high-temperature-stage return air temperature; through dynamic adjustment of the first fuzzy PID controller, the working condition and energy efficiency of the high-pressure side of the system are optimized; the rotating speed of the low-temperature-stage frequency conversion compressor is mainly comprehensively determined by the temperature in the box, the real-time rotating speed of the high-temperature-stage compressor, the low-temperature-stage exhaust temperature, the temperature of the intermediate heat exchanger and the low-temperature-stage return air temperature, and the rotating speed is dynamically adjusted through the second fuzzy PID controller so as to ensure that the temperature in the box is accurate and stable and is matched with the high-temperature-stage capacity in real time. Through cooperative control of the parameter groups, the problems of low efficiency and unstable operation caused by mismatching of two-stage capabilities of the cascade system are solved, and efficient, stable and reliable operation of the system under all working conditions is realized.

The invention discloses a financial load balancing method and system based on cooperation of an application layer and a network layer, and belongs to the technical field of networks. The method comprises the following steps: deploying an application layer load balancing node, an SDN controller, a collaborative decision module, a traffic prediction module and a state acquisition module, and establishing communication connection; collecting an application layer server state index, a network layer link state index, a financial transaction priority, a protocol type and whole network flow data in real time, and predicting a flow change trend; the collaborative decision-making module performs linkage analysis on the multi-dimensional indexes to generate an application layer scheduling instruction and a network layer scheduling instruction which are synchronous; and the instruction is issued to an application layer load balancing node and an SDN controller for execution, indexes after execution are collected and fed back to the collaborative decision module, and the instruction is dynamically adjusted according to the index difference. According to the invention, index intercommunication and instruction synchronization of the application layer and the network layer are realized, closed-loop control is formed, and the service quality and operation stability of the financial SDN network are improved.

The invention relates to a residential energy system low-carbon scheduling method considering a hydrogen-terrestrial heat coordination effect. The residential energy system low-carbon scheduling method comprises the following steps: step (1), constructing a basic physical model for representing energy flow and equipment physical characteristics in a residential energy system; (2) converting the basic physical model into a coupling constraint model based on a dynamic coupling constraint between energy supplies of a set type in the residential energy system; step (3), on the basis of the coupling constraint model, according to a set scheduling period, constructing a low-carbon scheduling objective function to obtain a complete optimization model; and step (4), solving the optimization model to obtain an operation strategy of each device in the residential energy system in the set scheduling period.

The present application relates to the technical field of environmental monitoring and pollution tracing, and discloses a basin-scale perfluorinated compound tracing method and system based on artificial intelligence, which integrates multi-source data in a basin to construct a unified spatial and temporal granularity basin multi-source data cube, identifies perfluorinated compound structures through a virtual spectrum diagram enhanced deep learning model, simulates pollutant spatial and temporal concentration distribution by combining a graph neural network of a physical mechanism, and then inversely propagates and matches source characteristic fingerprints to invert potential emission source contribution, outputs a pollution source contribution heat map and a priority control enterprise list, solves the core problems of difficulty in joint analysis of multi-source data, difficulty in identification of unknown compounds and difficulty in quantitative determination of multi-source superposition contribution in basin-scale perfluorinated compound tracing, and provides support for precise pollution control.

This invention belongs to the field of materials testing technology and discloses a deep learning-based method and system for identifying the interface of SiC modified asphalt. It acquires multi-dimensional interface images through a layered time-series acquisition method using four devices: optical microscope, SEM, EDS-Mapping, and Raman spectrometer. Adaptive algorithms are used for targeted denoising, normalization, and registration, while attention mechanisms and weighted fusion strategies are combined to enhance interface features. Simultaneously, a multi-condition dataset containing a high proportion of minor defects is constructed to achieve deep fusion and accurate identification of multi-modal features, eliminating subjective errors from manual judgment and improving the fine-grained accuracy and detection efficiency of SiC modified asphalt interface identification. The U-Net model is improved for specific scenarios and designed to be lightweight. By optimizing the encoder-decoder structure, introducing a dual-channel attention mechanism and a condition-adaptive branch, and combining a loss function, the feature extraction capability of interface boundaries and minor defects is enhanced.

The application discloses an electric-thermal comprehensive energy system collaborative frequency modulation method and system, the method comprising: obtaining a frequency deviation parameter in an electric power subsystem and a thermal energy storage state parameter in a thermal power subsystem as a real-time frequency modulation parameter; calculating a frequency deviation change rate and combining the real-time frequency modulation parameter to evaluate a thermal energy storage capacity margin value, generating a composite frequency modulation demand index; generating a multi-time scale frequency modulation task; based on the multi-time scale frequency modulation task, generating an electric-thermal weight distribution value by fusing the frequency deviation change rate and the thermal energy storage capacity margin value; generating an electric-thermal collaborative frequency modulation instruction according to the electric-thermal weight distribution value; and executing the electric-thermal collaborative frequency modulation instruction to control frequency modulation devices in the electric power subsystem and the thermal power subsystem. The application adopts a multi-time scale task decomposition and dynamic weight distribution strategy based on a composite frequency modulation demand index, can realize deep collaboration and optimized control of electric-thermal resources, and improves the frequency modulation performance and operation economy of the comprehensive energy system.

The invention discloses a chemical safety production dust removal device which comprises a connecting pipeline, a filtering pipeline, a dust discharging pipeline and a cloth bag piece, the cloth bag piece comprises an upper disc, a lower disc, a cloth bag sleeve and a spring arranged in the cloth bag sleeve and connected with the lower disc, the device is provided with a cleaning pipeline, the spring is driven to swing to assist in dust falling when high-pressure gas is blown in, and a driving device is arranged at the bottom of the dust discharging pipeline. A motor drives a cam to rotate, a cam guide groove and a connecting rod mechanism are used for driving a transverse pressing block to ascend, a spring is compressed through a jacking lower disc, the cam continues to rotate to enable the transverse pressing block to transversely move to give way, the spring is released downwards at the moment when pressure is relieved, and jacking impact and aftershock are generated to shake off dust. According to the invention, the instantaneous release oscillation of the spring is combined with airflow blowing, so that the problems of dust floating pollution and secondary attachment caused by traditional pure airflow back blowing are effectively avoided, the dust cleaning is more thorough, the dust cleaning force can be flexibly adjusted by adjusting the size of the cam, and the practicability is high.

The application discloses a kind of big data processing's augmented reality hospital pharmaceutical scheduling management method and system, it is related to medical information processing technical field, its technical solution key points include the following steps: obtaining the AR space positioning data of hospital pharmaceuticals, real-time inventory data, demand data, distribution path data form scheduling core data set, in scheduling core data set Filtering out the data that deviates from standard scheduling threshold value forms to be optimized scheduling data;Filtering scheduling deviation type, scheduling deviation amplitude, scheduling deviation node in to-be-optimized scheduling data, whether need to be optimized scheduling data is judged according to scheduling deviation type and scheduling deviation amplitude Whether collaborative calibration processing is needed, mark the data processed as target scheduling data;Extract scheduling parameter information of pharmaceutical in target scheduling data;Effect is to realize the dynamic accurate update of scheduling data, avoid the risk of inventory backlog, shortage caused by data deviation, guarantee the comprehensiveness and accuracy of scheduling decision.

The application provides a medicine cold chain logistics transportation whole-process tracking method and system, and relates to the technical field of logistics transportation monitoring. First, the original sensor tracking data stream output by the internal sensor cluster of a transportation carrier in a continuous collection time window is acquired. Then, the transportation carrier operation trajectory line is generated according to the geographic positioning parameters, and the multi-dimensional environment state marker set is generated by combining the physical parameters at the same time of each geographic position coordinate point. Meanwhile, the logistics transfer node topology connection network containing the logistics transfer node unit and the directed transfer path edge is constructed. The operation trajectory line, the multi-dimensional environment state marker set and the topology connection network are input into the pre-constructed space-time state feature fusion tracking model to generate the whole-process state tracking feature tensor. The abnormal state type identification and the space-time distribution feature are obtained by performing the abnormal state mode matching on the whole-process state tracking feature tensor, and the abnormal early warning instruction set is generated. The application can comprehensively and accurately track the whole process of medicine cold chain transportation, intelligently identify the abnormality and timely generate the early warning.

The invention discloses a hyperspectral image super-resolution optimization method based on a fused three-branch network, and belongs to the field of hyperspectral image processing, and the method comprises the steps: firstly obtaining and preprocessing training data, collecting LR-HSI, HR-MSI and corresponding high-resolution hyperspectral truth values, and completing normalization, data enhancement and cross-modal feature dimension matching and interaction; constructing a three-branch network model, inputting the LR-HSI into an HSI branch to extract spectral features, and inputting the HR-MSI into an MSI branch to extract spatial features; performing residual calculation and noise suppression on the spectral and spatial features, inputting the spectral and spatial features into a fusion branch, generating compensation features through convolution and jump connection, fusing three-branch output through learnable weight, generating a fusion image, and training a model through an optimizer. According to the method, a residual difference decoupling fusion strategy is introduced, and directional repair and enhancement are respectively carried out on missing space information in a hyperspectral image and insufficient spectral information in a multispectral image.

The application discloses a metamorphic rock P-T-t trajectory reconstruction method and system based on multi-source data fusion, relates to the field of metamorphic rock P-T-t trajectory reconstruction, and comprises the following steps: acquiring electronic probe micro-area mapping data and internal standard sample data of a metamorphic rock sample, and performing multi-stage data correction; performing mineral phase identification and chemical component quantitative interpretation, generating a mineral classification mapping, an element concentration distribution diagram and a whole rock major component data set; performing thermodynamic phase equilibrium simulation, generating a P-T apparent profile diagram, and extracting a P-T trajectory; performing micro-area in-situ chronology analysis on selected minerals in the mineral classification mapping to obtain chronology data; coupling the P-T trajectory and the chronology data in time and space, constructing a P-T-t three-dimensional trajectory model of the metamorphic rock, and outputting a visualized reconstruction report. The application realizes full-process automation and standardization of the metamorphic rock P-T-t trajectory from data acquisition and processing to model reconstruction, and improves the precision, efficiency and reliability of trajectory analysis.

This application provides a persona retrieval method, apparatus, device, storage medium, and program product. The persona retrieval method includes: using a first language model to perform semantic understanding on the textual description information of a target persona to obtain key tags and persona features of the target persona; filtering candidate persons from the known persons based on the key tags of the target persona and the tag information corresponding to each known persona in the retrieval database; determining reference persons from the candidate persons based on the persona features of the target persona and the feature information corresponding to each candidate persona in the retrieval database; and using a second language model to compare and analyze the textual description information corresponding to each reference persona in the retrieval database with the textual description information of the target persona to obtain retrieval results. Applying the technical solution provided in this application reduces operating costs and improves the efficiency and accuracy of persona retrieval.

The invention relates to an intelligent online patrol comprehensive management platform, and belongs to the technical field of intelligent monitoring and management. The platform adopts a multi-layer distributed architecture and comprises a data acquisition layer, a transmission layer, a platform layer and an application layer. The platform layer integrates a video resource unified access module, an artificial intelligence behavior analysis module, a patrol task scheduling engine, a multi-stage patrol management module and an early warning disposal closed-loop management module. Multi-source video streams are integrated through a standard protocol, video streams of key areas such as examination rooms and test paper storage rooms are analyzed in real time by using a deep learning algorithm, and illegal behaviors such as abnormal number of people and going out with articles are automatically identified and intelligently early warned. Meanwhile, the system supports multi-stage collaborative patrol and disposal process closed-loop management, full-process automation and intelligentization from problem discovery, early warning pushing, task disposal to result feedback are achieved, the supervision efficiency, accuracy and response speed of examination patrol are remarkably improved, and manual dependence is reduced.

The invention relates to the technical field of fuel intelligent management and control, and particularly discloses a sampling and processing integrated automatic control method and system based on batch management. The method comprises the following steps: firstly, creating a full-process dynamic data file with a unique identifier ID for each batch of materials; in the sampling link, a sample is bound with the intelligent container, and the multi-dimensional identity features are associated; in the sample processing process, operating parameters are collected in real time and compared with a preset standard, and the process confidence coefficient of the data file is dynamically calculated and updated; during sample circulation, bidirectional ID verification is carried out through a handshake request, decision making is carried out in combination with the process confidence coefficient, and subsequent equipment is authorized to be started only when verification is passed and the confidence coefficient is higher than a safety threshold value; when the sample is segmented, the data file is also split and completely inherits historical information. Through active inquiry and dynamic evaluation, forced consistency of physical samples and digital information in the whole process is realized, and the reliability of quality detection results is ensured.

The invention relates to a residual soil slope instability intelligent identification method and system fused with multi-source monitoring data. The method comprises the steps of generating a plurality of historical health multi-source spatial-temporal feature tensors according to historical original monitoring data to obtain a training sample set, performing unsupervised training on a deep convolution auto-encoder model to obtain a health reference reconstructor, and calculating health error distribution reference parameters; generating a real-time multi-source spatial-temporal feature tensor according to the real-time original monitoring data, and calculating a real-time anomaly score in combination with a health reference reconstructor; and comparing the score with a dynamic early warning threshold, and when the judgment result is abnormal, generating structured early warning information based on the real-time multi-source spatial-temporal feature tensor and the reconstruction tensor. According to the method, multi-source monitoring data are fused, unsupervised learning is carried out by using a deep convolution auto-encoder model, and the like, so that the accuracy and reliability of instable judgment of the residual soil slope are improved, and the real-time performance of slope stability monitoring and the dynamic adaptability of early warning are enhanced.

A laser-plasma synergistic surface treatment method for CFRP / titanium alloy heterojunctions belongs to the field of heteromaterial bonding technology. First, nanosecond pulsed lasers are used to etch the surfaces of CFRP and titanium alloy to be bonded, constructing a micro-nano composite structure. Then, atmospheric pressure cold plasma is used to activate the etched surfaces, grafting polar functional groups. Finally, the two are bonded and cured using an adhesive. This invention achieves a dual enhancement effect of "mechanical interlocking" and "chemical bonding" by organically combining "laser etching to construct micro-nano physical structures" and "plasma activation to introduce chemical functional groups," significantly improving the interfacial bonding strength of CFRP / titanium alloy heterojunctions. Compared with single treatment methods, this invention can increase the joint shear strength by more than 38%, and the failure mode changes from interfacial failure to cohesive failure. It has advantages such as environmentally friendly process, good controllability, and significant effects, and has broad application prospects in aerospace, high-end equipment manufacturing, and other fields.

The application discloses a multi-source education data attribution analysis system and method based on a large model Function Call, and belongs to the technical field of computers. The system comprises: a multi-source data access module for constructing a unified education data warehouse; an intelligent semantic routing module for identity recognition and intent analysis; a function registration and execution module for triggering atomized analysis functions; and a comprehensive attribution analysis module for generating an attribution report in combination with a knowledge base. The method comprises the following steps: accessing multi-source education data and preprocessing, guiding the large model to identify the intent by using the prompt engineering, outputting a structured instruction to trigger the function call, and finally performing causal reasoning. The application realizes the deep fusion and accurate attribution of multi-source education data, reduces the interaction threshold, and provides a scientific basis for teaching decisions.

This application discloses a voltage optimization control method, device, terminal, and medium for distribution networks, relating to the field of distribution network technology. By introducing a dynamic zoning adjustment mechanism and an adaptive optimization algorithm, this application achieves deep coupling and synergy between the zoning strategy and the optimization algorithm. This method can effectively address the uncertainties and fluctuations brought about by distributed power source access in distribution networks, improve the reliability of reactive power control, and meet the requirements for efficient and stable operation of distribution networks.

The invention discloses a concrete pump pipe fixing system and an assembling and monitoring method thereof. The system comprises a vertical fixing device, a bent pipe fixing device, a horizontal fixing device and a monitoring and control system. The assembling method comprises the following steps of positioning and paying off, installing the vertical fixing device, installing the bent pipe fixing device, installing the horizontal fixing device, installing the pipeline, and clamping and fixing. The monitoring method comprises five main steps of system initialization, data acquisition, edge calculation processing, cloud analysis and early warning, and decision support and feedback. According to the invention, lossless installation, intelligent monitoring, active early warning and predictive maintenance are realized, the construction safety is remarkably improved, the service life of equipment is remarkably prolonged, and the management efficiency is remarkably improved.

This invention provides a campus hardware and software integrated management method and system supporting remote operation via mobile devices. It collects status parameters and business data from heterogeneous devices in the campus teaching, security, and logistics domains, synchronizes them with a hybrid clock, and pushes them to a campus digital twin virtual model incorporating geographic information, achieving full-domain resource virtual-real mapping and 3D visualization. A sliding time window combined with a business weight correction mechanism is used to determine adaptive warning thresholds and cross-device linkage strategies based on device operating time and business scenarios. Mobile devices access the system via an end-to-end encrypted link, enabling remote hierarchical handling based on dynamic hierarchical permissions. Hierarchical warnings are generated when a single parameter exceeds limits or multi-dimensional correlation anomalies are detected. If remote handling is ineffective, maintenance work orders are automatically generated and resources are intelligently scheduled, forming a closed-loop management process. This solution breaks down campus hardware and software data silos, enabling deep mobile control and intelligent operation and maintenance, significantly improving campus management efficiency and emergency response capabilities.

This application relates to the field of data processing technology and discloses an integrated sky-ground geological environment monitoring and assessment method and system. The method includes: integrating sky remote sensing, surface monitoring, and underground exploration data into a monitoring matrix, processing the data to generate a raw database; constructing a spatiotemporal standard field based on the database and fusing it to generate a correlation spectrum; decomposing the correlation spectrum to obtain element feature chains; constructing a coupled dynamic field based on the feature chains and analytically obtaining an evolutionary parameter set; establishing a risk assessment matrix based on the parameter set and analyzing to generate an early warning indicator system; and designing a governance plan by combining the early warning system and historical cases. This application achieves deep integration of space-based remote sensing data, surface monitoring data, and underground exploration data by constructing a unified spatiotemporal standard field, a multi-level feature transfer network, and a dynamic coupling model, thereby improving the accuracy and reliability of geological environment monitoring and assessment.

The present application relates to the technical field of medical film, and particularly relates to a medical film quality detection system based on multi-parameter fusion, which comprises an acquisition module, a discrete determination module, an identification module, a risk determination module, an adjustment module and an early warning module. The present application takes a dynamic discrete coefficient as a trigger condition, analyzes the contribution of each process parameter to the current thickness fluctuation to identify the primary root cause, forms a quantitative process risk index by comprehensively considering the out-of-control degree of the root cause parameter and the thickness deviation, and according to the process risk index, an adjustment instruction with clear direction and strength suggestion can be sent, and the adjustment efficiency is evaluated after adjustment, early warning is given when the control loop fails or the problem escalates, and the problems of lagging quality defect discovery and blind and lagging process adjustment caused by the failure to quickly and accurately locate the process abnormal root cause in the complex multi-process coupling production process are effectively solved.

The invention relates to a petrochemical engineering anomaly detection method and system based on multi-modal attention fusion, and the method comprises the steps: obtaining a real-time video frame of a petrochemical plant, and carrying out the preprocessing of the real-time video frame, and obtaining a binary image; and calling a convolutional neural network to extract original features from the binary image, and performing mapping and weighting processing on the original features in combination with the attention weight to obtain fusion features. And performing linear projection on the fusion features to calculate attention output based on the projection matrix of each head, and performing splicing and linear mapping on the attention output of all the heads. Carrying out the splicing and linear mapping of the attention output of all heads, and carrying out the residual connection and normalization of the fusion features, and obtaining a normalization result. And inputting the normalization result into a classification head to output an abnormal alarm of a corresponding class. The multi-head self-attention classification network is combined, the anomaly detection precision is improved, the false alarm rate is reduced, and the alarm real-time performance is high.

The invention discloses a display user personalized setting method and system based on face recognition, and relates to the technical field of data processing, and the method comprises the steps: obtaining a facial feature flow, recognizing the identity of a user, and calling an individual baseline vector as a zero reference; calculating a difference value between the facial feature flow and the baseline vector to obtain a real-time deviation flow; injecting the deviation flow into an energy integration barrel according to dimensions, and performing energy accumulation in combination with a dissipation factor to obtain a real-time liquid level height; monitoring the liquid level height of each barrel, if the liquid level height exceeds a preset overflow threshold value, performing competitive arbitration based on an excess overflow rate, and determining a dominant adjustment vector; and mapping the advantage adjustment vector to a display parameter space, and changing parameters along a linear iteration track until each liquid level is lower than a threshold value. Through an energy accumulation and competitive arbitration mechanism, smooth and accurate adjustment of multi-dimensional display parameters is realized, and the user comfort and the interaction stability are improved.

The application discloses a kind of cable curvature risk assessment method, device, equipment and medium, belong to the risk assessment field of cable curvature.The method is: obtaining the visible image of the cable to be measured and extracting two-dimensional contour data;The cable to be measured is scanned non-contact, and three-dimensional point cloud data is obtained;Two-dimensional contour data is mapped to the three-dimensional coordinate system in which three-dimensional point cloud data is located, to obtain the three-dimensional point set after fusion, and least square method is used to fit spline curve;Finally, the risk assessment of the spline curve is carried out, and the risk assessment result of the cable to be measured is obtained.Therefore, by implementing the present application, the problem of low accuracy of cable curvature risk assessment in the prior art can be solved.

The invention discloses a hyperspectral image classification method based on a self-supervised contrast learning network. The method comprises the following steps: constructing a basic network; a parallel rotation prediction and mask reconstruction dual-task self-supervision module carries out feature learning under the condition of insufficient labels; the double-branch spatial spectrum feature extraction fusion module introduces a double attention mechanism to reduce the model complexity and enhance feature representation; the self-supervised contrast feature learning module guides the network to learn feature information with strong discrimination through rotation, mask and contrast learning of multi-task joint loss. And finally, a pre-trained model is utilized to carry out fine adjustment under a small number of labeled samples and send the labeled samples into a classifier to obtain a classification result. Through the double-task self-supervision module, the double-branch spatial-spectral feature extraction and fusion module and the self-supervision comparison feature learning module, the dependency of the model on labeled data is effectively reduced, discriminative features are learned, and the classification precision and the model robustness in a few-sample scene are improved.