662 results about "Effective solution" patented technology

The present disclosure generally relates to a two-stage system for detecting malicious nodes in Wireless Sensor Networks (WSNs), enhancing network security and resilience. The system employs a distributed approach, leveraging Cluster Heads (CHs) and a central server for efficient and accurate detection. Initially, sensor nodes are monitored for comprehensive node and network metrics, statistically ranked by significance in identifying malicious behavior. CHs perform a resource-aware first-stage detection based on their resource weight, filtering potential threats locally. Results are then aggregated at a server for a second-stage analysis using a hybrid Machine Learning (ML) and Deep Learning (DL) approach. This advanced analysis, combined with statistically relevant metrics, significantly improves detection accuracy. By integrating resource-conscious CH operation with powerful server-side ML / DL, this system offers a scalable, energy-efficient, and highly effective solution for securing WSNs against malicious node attacks, surpassing traditional detection methods in both speed and precision.

Disclosed in the present invention is a salient contour matching-based method for target measurement in a severe imaging environment. The method specifically comprises: (1) acquiring a binocular image of a target; (2) establishing a global-local joint constraint-based background light estimation model, and removing a scattering effect of a medium in an imaging environment to obtain a restored left eye image and a restored right eye image; (3) learning an original image, and on the basis of a residual between a network reconstructed image and the original image, obtaining target localization prediction maps of the left eye image and the right eye image; and (4) respectively extracting contour lines of the target in the left eye image and the right eye image, constructing feature matching descriptors of contour points, performing stereo matching on the two sets of contour lines by minimizing matching cost, and performing three-dimensional reconstruction on the contour lines in light of calibrated intrinsic and extrinsic parameters to complete the measurement of a key size. According to the present invention, the key sizes of different targets in a severe environment can be accurately measured, thereby providing an effective solution for the problem of measuring the sizes of targets in a severe environment.

The invention discloses a labeling task assignment method and device based on artificial intelligence, and the method comprises the steps: obtaining historical behavior data, and constructing a multi-dimensional user portrait; receiving a task description document, a data sample and a quality requirement document to obtain a multi-dimensional task feature vector; based on the multi-dimensional user portraits and the multi-dimensional task feature vectors, a matching degree score is calculated through a multi-objective optimization algorithm, and an optimal task allocation scheme is generated; optimizing the task structure through a fireworks algorithm based on student t distribution, and generating an optimized task unit structure; real-time monitoring is carried out through the anomaly detection model and the quality prediction model, and quality control measures are triggered; model parameters are updated through a reinforcement learning algorithm, and a personalized feedback and capability improvement strategy is generated. According to the method, accurate matching between the annotators and the tasks is realized, the processing efficiency of complex tasks is improved, the annotation quality is improved, the expansibility and the response speed of a platform are enhanced, and an effective solution is provided for large-scale and high-quality data annotation.

The invention discloses a carbon emission prediction and optimization method, and the method comprises the steps: obtaining multi-source heterogeneous data including historical carbon emission data, meteorological data, economic indexes, energy consumption data, and Internet of Things sensor data, and constructing a three-dimensional feature matrix through employing an improved spatial-temporal feature extraction algorithm; based on a mixed architecture of a graph neural network GNN and a long and short term memory network LSTM, a prediction model is established in combination with an attention mechanism, and training is performed through an adaptive learning rate optimization algorithm; a prediction result is input into an improved NSGA-III algorithm, and three targets of total carbon emission, economic cost and social benefits are optimized at the same time; and establishing a feedback closed loop through reinforcement learning RL, and updating the model and the strategy on line according to real-time monitoring data. According to the method, multiple advanced technologies such as accurate data processing, dynamic prediction, multi-objective optimization and cross-domain collaboration are integrated, and a comprehensive and effective solution is provided for carbon emission management.

The invention discloses a multi-level storage control method based on access popularity, and relates to the technical field of information, the method comprises the following steps: building a storage resource monitoring module, and collecting data volume change, read-write frequency and capacity occupation proportion information of each node from a storage system in real time; the method comprises the following steps: collecting original data, carrying out preliminary cleaning and formatting processing on the collected original data to obtain a standardized resource state data set, comparing predicted data with operation parameters of a current storage system, and if a predicted demand exceeds a current capacity limit, automatically generating a capacity expansion scheduling task to obtain a final resource optimization configuration scheme; according to the multi-level storage control method and device based on the access popularity, intelligent monitoring, load balancing and capacity planning of storage resources are achieved, the resource utilization efficiency and expandability of a storage system are improved, and an effective solution is provided for stable operation and performance optimization of a large-scale storage system.

The invention discloses a reef limestone strength prediction method based on pore feature machine learning, and belongs to the technical field of crossing of geotechnical engineering and artificial intelligence, and the method comprises the steps: obtaining a reef limestone three-dimensional digital core, and extracting pore features after preprocessing; establishing a multi-source pore-physical coupling feature data fusion framework based on the pore features; integrating a random forest and a gradient boosting tree, dynamically weighting parameters in the pore features, designing a composite loss function, performing hierarchical training, and embedding physical constraints to construct a reef limestone strength prediction machine learning model; layered K-fold cross validation is adopted, the robustness of the reef limestone strength prediction machine learning model is tested, and interpretability is verified. According to the method, through data processing, the reef limestone strength prediction machine learning model is constructed and optimized, the relationship between the pore characteristics and the reef limestone strength is deeply excavated, the model prediction accuracy, reliability and interpretability are improved, and an effective solution is provided for reef limestone strength prediction.

The invention discloses a sparse reward environment-oriented collaborative knowledge fusion reinforcement learning method, and relates to the field of collaborative knowledge fusion reinforcement learning methods. By constructing a lightweight collaborative knowledge fusion model and a dynamic reward remodeling mechanism, the problems of low intelligent agent exploration efficiency and difficulty in strategy convergence in a sparse reward environment are solved. The method comprises the following steps: constructing a reinforcement learning framework comprising a policy network and a value network; designing an action space mutation supervision mechanism and a lightweight collaborative knowledge fusion model, and generating a smooth substitution action when a strategy is detected to be unstable; and a reward function is designed in combination with the task target and the dynamic constraint, and reward remodeling is realized by activating rewards through sub-target potential energy difference and knowledge fusion. According to the method, effective intermediate feedback can be provided for the agents in the sparse reward environment, the exploration efficiency is improved, the convergence time is shortened, the stability and cross-scene migration ability of the strategy are enhanced, and an effective solution is provided for sparse reward scenes such as robot control and multi-agent game.

The invention relates to the technical field of PCB welding spot defect detection, and discloses a PCB welding spot defect detection system and method based on image recognition, and the system comprises an image preprocessing module which is used for obtaining an original image flow and dividing an interested detection area; the feature fusion module is used for extracting multi-modal features to form a fusion set; the defect judgment module is used for establishing a mapping index and obtaining a judgment result; the parameter calibration module is used for verifying the detection parameters and adjusting the mapping index; and the report output module is used for generating a defect detection report. The method comprises the steps of image preprocessing, feature fusion, defect discrimination, parameter calibration, report generation and the like. According to the system and the method, the PCB welding spot defects can be efficiently and accurately detected, the detection precision and stability are improved, a structured report is generated, and an effective solution is provided for PCB quality detection.

The invention discloses an automatic fastening system and method for overhead line system cantilever bolts. The system comprises a maintenance car, a visual ranging parking unit, a laser radar sensing unit, a mechanical arm execution unit, a force feedback control unit, a tail end execution unit, a digital twin unit and an operation management platform. The method comprises the following steps: S1, guiding a maintenance car to stop below a cantilever of the overhead line system; s2, scanning an overhead line system cantilever, generating a three-dimensional model and positioning a bolt; s3, planning a collision-free path; s4, smooth alignment of the bolt sleeve is achieved; s5, bolt fastening is completed according to the preset torque; s6, synchronously displaying the operation progress, and storing whole-process operation data; and S7, performing anomaly detection and task continuous transmission on the whole process, and performing data archiving and early warning. Intelligent and automatic bolt fastening operation is achieved for the first time in the field of railway contact network maintenance, and an effective solution is provided for improving the railway operation and maintenance efficiency and safety.

The invention relates to the technical field of unmanned aerial vehicles, in particular to an intelligent dynamic pilot frequency networking system and method for an image transmission module MESH ad hoc network. The method is based on spectrum fingerprint features, combines a real-time interference topological graph constructed by interaction of neighborhood nodes, identifies a conflict frequency set needing to be evaded through multi-dimensional weighted evaluation, and dynamically generates a pilot frequency strategy table containing main and standby frequencies and a switching threshold value by using a frequency hopping decision engine according to the conflict frequency set and a preset legal frequency pool. Synchronizing the pilot frequency strategy table to an associated relay node, generating a link stability evaluation matrix and feeding back the link stability evaluation matrix to a frequency hopping decision engine; and correcting a frequency switching threshold value and an alternative frequency weight in the pilot frequency strategy table based on the link stability evaluation matrix. According to the invention, the spectrum utilization efficiency of the unmanned aerial vehicle image transmission system can be improved, the stability and reliability of a multi-hop link are ensured, the networking time delay is reduced through a distributed decision-making mechanism, and an effective solution is provided for high-quality wireless transmission in a dynamic topology environment.

The invention discloses a configurable number-theory transformation parallel computing acceleration method and device for a post quantum cryptographic algorithm, and relates to the technical field of cryptographic algorithms. The number theory transformation is a calculation bottleneck in lattice-based post-quantum cryptography and PQC; a hardware accelerator specially designed for number theory transformation (NTT) is an effective solution for improving the execution speed. At present, a mainstream design neglects the influence of the scale of a calculation array, so that the design of an NTT calculation circuit is poor in flexibility and low in memory utilization rate, and a two-dimensional reconfigurable number theory transformation acceleration circuit is provided; the method is applied to a key generation stage, an encryption stage and a decryption stage of the post-quantum cryptographic algorithm. The NTT reconfigurable computing circuit provided by the invention can keep the utilization rate of hardware resources, and is suitable for mobile terminal equipment with limited resources; the NTT circuit adopts a low-complexity memory mapping scheme, so that the address control logic is greatly simplified, and the hardware overhead is reduced.

The invention discloses a mine small target detection method based on deformable convolution and a residual structure, belongs to the technical field of underground small target detection, and further improves the detection precision and robustness of a small target by introducing multi-scale feature fusion and an attention mechanism. The method comprises the following steps: a backbone network reinforces cooperative perception of channel, space and position information in a feature extraction stage by fusing an MLCA attention mechanism, and effectively inhibits background noise interference; on the basis of a DPC-Block multi-scale feature fusion network, the relevance between low-order detail information and high-order semantic features is reserved through cross-layer feature interaction; the detection layer adopts a ShapeIoU loss function to optimize bounding box regression precision and accelerate model convergence; according to the invention, high target detection precision can be realized, and the problem of missing detection of small targets is effectively improved; and an effective solution is provided for small target detection in a complex scene in collaborative optimization of an attention mechanism and feature fusion and a geometric perception loss function.

A system and method for adaptive data processing combining compression and encryption. The system analyzes input data characteristics, compares probability distributions, and creates a transformation matrix to convert data into a dyadic distribution. It generates a main data stream of transformed data and a secondary stream of transformation information. The system dynamically selects and applies processing techniques, including transformation, encoding, compression, and encryption algorithms, based on analyzed characteristics and real-time performance metrics. It compresses the main data stream using Huffman coding and implements security measures to protect the output. A feedback loop monitors technique effectiveness, updates a knowledge base, and influences future selections. The system can operate in lossless, lossy, or modified lossless modes, adapting to different application requirements. This approach offers an efficient solution for scenarios where both data reduction and security are critical concerns.

The invention relates to the technical field of unmanned aerial vehicle countering, and discloses an unmanned aerial vehicle countering method and system based on image depth recognition, and the method comprises the steps: carrying out the real-time monitoring of a target airspace through a camera device, and obtaining a monitoring image; identifying the monitoring image according to the target identification model, judging whether an unmanned aerial vehicle exists, and if so, analyzing the monitoring image according to a feature matching algorithm, and judging whether the unmanned aerial vehicle is a suspicious unmanned aerial vehicle; if the unmanned aerial vehicle is a suspicious unmanned aerial vehicle, calculating a three-dimensional coordinate of the suspicious unmanned aerial vehicle by adopting a triangulation method, and predicting a flight path of the suspicious unmanned aerial vehicle according to the three-dimensional coordinate to obtain a predicted flight path; and performing threat assessment on the suspicious unmanned aerial vehicle according to the predicted flight path, determining a threat level, and triggering a corresponding countering strategy. The unmanned aerial vehicle identification and positioning accuracy is effectively improved, the suspicious unmanned aerial vehicle is accurately hit, and an effective solution is provided for airspace safety protection of an important area.

The invention discloses a steel defect detection method based on multi-scale edge enhancement, and relates to the field of industrial detection. The method comprises the following steps: constructing a steel defect detection data set and carrying out preprocessing, and dynamically interacting high and low layer features by constructing a Compute-ConvNeXt and an edge enhanced feature fusion structure (EEFF) so as to enhance semantic expressions of edges and small target defects; according to the method, an improved loss function (Focal-MPDIOU) and a data equalization strategy are combined, the sensitivity of the model to fuzzy edges and tiny defects is improved, meanwhile, the overfitting problem caused by sample distribution unbalance is restrained, and automatic defect detection of production line steel is achieved. A defect detection task with high precision, high robustness and high generalization is realized in a complex industrial scene, and an effective solution is provided for intelligent detection of metal material surface defects.

The invention provides a comprehensive scene map path planning method based on an improved A * algorithm, and the method comprises the steps: 1, carrying out environment modeling for a comprehensive scene map: segmenting a two-dimensional plane scene into more than two square grids, and carrying out the assignment of a single grid in a binarization manner to represent the occupation condition of the grids; constructing a protection area formed by a single-layer grid around the obstacle, and filling the protection area into gray; step 2, planning a path by adopting an improved A * algorithm: step 2-1, establishing an improved bidirectional search strategy; step 2-2, establishing a heuristic function with a barrier linear density weight; step 2-3, adopting a variable neighborhood node expansion mode to complete node expansion; and step 2-4, carrying out path secondary optimization. According to the method, the planning efficiency and the path performability in a complex environment are remarkably improved, and an effective solution is provided for navigation systems in the fields of service robots, intelligent warehousing and the like.

The invention discloses a few-sample target detection method and system based on an aggregation variational prototype. The method comprises the steps of constructing a data set containing a base class and a new class, dividing the data set into a support set and a query set, generating a class prototype by utilizing a P-VAE module in combination with CLIP semantic features and a feature discriminator, realizing bidirectional fusion of the support set and the query set features by means of an MFM module, fusing the query features and the class prototype, and inputting the fused query features and the class prototype into a detection head to complete target detection. The system comprises a data set construction module, a priori variational automatic encoder P-VAE module, a mutual fusion module MFM, a feature fusion module and a target detection module. According to the scheme, by introducing semantic priori, optimizing prototype generation and feature interaction, the problems of data imbalance and insufficient new class feature representation in a few-sample scene are solved, improvement of new class detection precision is verified on PASCAL VOC, MS COCO and other data sets, and an effective solution is provided for target detection of sample scarce scenes such as medical images and rare species monitoring.

The invention discloses an unsupervised domain adaptive medical image segmentation method based on multi-view alignment and pseudo label optimization, and aims to solve the problems of insufficient segmentation precision and low pseudo label quality caused by domain offset. According to the technical scheme, firstly, image level alignment is executed through a frequency domain smooth fusion module, and a class target domain image is generated; pre-training a segmentation network by using the image and generating an initial pseudo tag; then, through a two-stage optimization process, the integrity and the structural rationality of the pseudo tag are improved through prototype-based potential foreground completion and SAM-based structural perception enhancement in the process; and finally, on the basis of the optimized high-quality pseudo tag, constructing a multi-view prototype contrast learning framework to carry out final feature level alignment training. According to the method, the segmentation precision of the model on the label-free target domain is improved, and an effective scheme is provided for solving the challenge of scarcity of annotation data in medical image segmentation.

The invention discloses a cloud type inversion method and system based on cavity window attention, and the method comprises the following steps: obtaining satellite remote sensing image data, extracting multi-dimensional spectral features, converting the latitude and longitude information of an image into spatial features through a position coding module, carrying out the feature splicing of the two features, and obtaining a fusion feature; a convolutional layer is input to extract basic features of image edges, textures and the like, a plurality of cavity window attention modules are stacked, each cavity window attention module comprises a cavity window division module and a weighted attention recovery module, the cavity window attention modules are used for extracting multi-scale image features, image resolution is recovered step by step through up-sampling, feature representation is refined, and a multi-scale image is obtained. According to the method, the features are mapped to the category space, the classification probability is output, cloud type inversion with higher accuracy and robustness is achieved, and an effective solution is provided for cloud information processing in the remote sensing image.

The invention discloses an intelligent logistics scheduling method based on Beidou positioning, and the method comprises the steps: obtaining the real-time position information of a vehicle, and constructing a traffic state matrix in combination with road congestion data; judging a weather influence area based on the weather change trend, and obtaining a weather risk area in combination with the traffic state matrix; obtaining inventory state data, and generating a resource demand priority list in combination with the weather risk area; adopting a multi-objective optimization algorithm, taking the resource demand priority list, the traffic state matrix and the weather risk area as input, obtaining an optimal path set, and generating a preliminary scheduling scheme; if a dynamic task insertion request is received, the task insertion feasibility is judged, the resource demand priority list is adjusted, the multi-objective optimization algorithm is operated again, and a corrected scheduling scheme is generated. Through multi-dimensional data fusion and intelligent algorithm optimization, the real-time performance, flexibility and reliability of logistics scheduling are improved, and an effective solution is provided for resource allocation in a complex environment.

The invention relates to the technical field of image rain and snow removal, in particular to a feature-driven GAN-based self-adaptive snow and rain removal method, which comprises the following steps: S1, constructing a feature extraction network architecture oriented to complex degradation features; s2, a spatial self-adaptive layered constraint loss system is established; s3, establishing a double-path reasoning framework of multi-stage collaborative optimization; and S4, constructing an industrial-grade credible evaluation system. The rain / snow removal frame based on the generative adversarial network is innovated in the aspects of generator architecture, loss function design and cooperative training of the generator and the discriminator, and by introducing a double-generator structure, an attention mechanism and space guide loss, the recovery performance of the rain and snow degraded image is remarkably improved; an effective solution is provided for practical application, and through the innovations, the model shows an excellent image recovery effect in a complex rain and snow scene.

The invention relates to the technical field of big language models, in particular to a document polishing method and system based on a big language model and a document uploading and preprocessing module, and a user uploads a document to be polished through a platform and inputs a global command at the same time. Tasks such as grammar correction and expression optimization are automatically completed, so that the manual editing time is saved, and a solution with high cost performance is provided; individuation and high quality: the system can carry out individualized polishing according to the document draft uploaded by the user and the requirements thereof, and meanwhile, high quality of the polishing effect is ensured; and user friendliness: the system displays the retouching and modifying part in the document in a highlight manner, and marks the retouching and modifying part according to the modifying type, so that the user is helped to better understand the retouching process and the modifying result. In addition, the user can finely adjust the polishing result on the interface by himself, the interactive interface is simple and easy to use, and it is ensured that the user finally obtains an ideal document under the assistance of the system.

The invention relates to the technical field of cloud computing and identity authentication security, and discloses a cloud environment intelligent identity authentication cross-domain docking method and system.The cloud environment intelligent identity authentication cross-domain docking method comprises the steps that heterogeneous identity data of multiple cloud platforms is collected and preprocessed, and a standardized identity data set is obtained; constructing a unified identity semantic model based on the standardized identity data set and generating a feature embedding vector; performing identity mapping conversion on the feature embedding vector by using a deep neural network to obtain a mapped identity feature vector; performing multi-factor adaptive trust evaluation in combination with the mapped identity feature vector and the real-time behavior data to obtain a dynamic trust evaluation result; generating an adaptive security token based on the dynamic credibility evaluation result; according to the method, the problems of cross-domain isomerism and dynamic trust evaluation of identity authentication in a cloud environment can be solved, and an effective solution is provided for cloud computing security.

The invention discloses a motion artifact elimination method and system based on PPG signals, and relates to the technical field of electronic digital data processing, in particular to a high-accuracy and high-robustness artifact elimination method oriented to the PPG signals in wearable equipment. Firstly, artifact detection is carried out through a multi-base learner fusion model based on meta-learning, and efficient identification of different individual motion artifacts is realized; afterwards, for the detected artifact signals, the system introduces an AVAE model to carry out artifact removal, through multi-loss function joint optimization, high-quality PPG signals with time sequence details reserved are recovered, an effective solution is provided for high-quality perception and robustness health monitoring of the PPG signals in the wearable device in a complex dynamic environment, and the method has the advantages of being high in accuracy and high in robustness. Good popularization and application prospects are realized.

The invention discloses a method for deploying a regular expression based on P4 in an intelligent network card / DPU. The method specifically comprises the following steps that the regular expression defined by a user is compiled into a finite-state machine; a matching table structure and a state register are defined in the P4 program and used for storing a state conversion rule of the regular expression, and a current matching state is recorded; and mapping a matching logic and a state register defined by the P4 into a hardware module of the intelligent network card / DPU, and realizing high-speed state conversion and matching by utilizing the parallel processing capability of the intelligent network card / DPU. According to the method for realizing regular expression matching based on P4 in the intelligent network card or the DPU, the regular expression is converted into the finite-state machine, and the programmability and the hardware acceleration capability of the P4 language are combined, so that the network flow detection and analysis efficiency is greatly improved, and the network flow detection and analysis efficiency is improved. And an effective solution is provided for next-generation network security and performance optimization.

The invention provides a cellular-free mMIMO (multiple input multiple output) system computing power resource allocation and beam forming optimization method and a cellular-free mMIMO system computing power resource allocation and beam forming optimization system, and belongs to the technical field of wireless communication. The method specifically comprises the following steps of: respectively establishing a double-queue model based on a first-in first-out criterion at a central processing unit (CPU) and an access point (APs), taking a dequeue rate at the CPU as an enqueue rate at the AP, constructing a data volume relationship in a queue of a current frame and a previous frame, and analyzing system downlink end-to-end time delay based on an M / D / 1 queuing model. In a first layer queue, a binary search algorithm is provided to design computing power resource allocation at a CPU, and in a second layer queue, a path tracking algorithm is provided to optimize a beam forming vector at an AP. According to the method, the instantaneous channel state information is utilized, the worst user time delay is minimized under an alternating iteration algorithm framework, the calculation complexity is reduced, and meanwhile an effective solution is provided for ultra-low time delay communication.

Various embodiments provide a robust backdoor attack on machine learning (ML)-based detection systems that can be applied to demonstrate and identify vulnerabilities thereof. In various embodiments, an artificial intelligence (AI)-based Trojan attack is generated and implanted inside a ML model trained for classification and / or detection tasks, and the AI-based Trojan attack can be triggered by specific inputs to manipulate the expected outputs of the ML model. Analysis of the behavior of an ML model having the AI-based Trojan implanted (and / or triggered) then enables identification of vulnerabilities of the ML model and further enables the design of ML models with improved security. Various embodiments of the present disclosure provide a fast and cost-effective solution in achieving 100% attack success rate that significantly outperforms adversarial attacks on ML models, thereby improving applicability and depth in testing ML-based detection systems.

The invention discloses an improved defect detection method for a medicine bubble cap plate by using YOLOv12, and relates to the technical field of medicine packaging detection, and the method comprises the steps: S1, taking a defect data set of the medicine bubble cap plate, carrying out data enhancement processing on the data set, expanding the scale of the data set, and improving the generalization ability of a model; s2, images of the data set are input into an improved YOLOv12 model, the model adaptively changes the weight of each convolution kernel through a dynamic convolution module, and the features of the images are efficiently extracted; and S3, inputting the original features into a partial convolution module, performing convolution operation on a subset of an input channel to realize model distillation, and combining point-by-point convolution to realize cross-channel feature recombination. According to the improved defect detection method, a DynamicConv reconstruction network basic unit is introduced to replace a conventional convolutional layer of an original model, and in order to further lighten the model, partial convolution is used to replace an A2C2f module at the tail end of a backbone network, so that an effective solution is provided for real-time detection of medicine packaging quality control.

The invention relates to the technical field of land classification, in particular to a land utilization classification method based on a space-time fusion technology, which comprises the following steps: firstly, acquiring original remote sensing images, classifying the original remote sensing images, and then respectively creating buffer areas for the divided original remote sensing images; the method comprises the following steps of: acquiring an original remote sensing image, dividing a buffer area into buffer area groups according to the category of the original remote sensing image, carrying out space-time fusion on different buffer area groups through a space-time fusion algorithm, splicing the fused image to generate a final image, and finally carrying out land utilization classification by adopting a supervised classification method according to the final image and a historical image. According to the method and the device, an effective solution is provided for improving the land utilization classification precision by integrating the time dynamic information of the low-resolution image and the space details of the high-resolution image based on the space-time fusion technology.

The invention relates to the technical field of camera imaging, and particularly discloses a hyperspectral camera semi-automatic focal plane adjustment method based on PI control. In order to solve the problem that the pose relationship between a sensor and a focal plane has great influence on performance indexes during the adjustment of the hyperspectral camera, the method proposes to realize semi-automatic adjustment by using PI control software. By controlling imaging of the hyperspectral camera, displaying light and shadow on a software interface in real time, calculating indexes such as MTF and signal-to-noise ratio and combining feedback adjustment of a PI control algorithm, the optimal pose relation between the focal plane and the detector is rapidly and accurately obtained. According to the method, the installation and adjustment efficiency and precision are improved, the installation and adjustment period is shortened, the dependence on the experience of installation and adjustment personnel is reduced, and standardized focal plane installation and adjustment of the hyperspectral camera are realized. Reliable image support is provided for subsequent hyperspectral application, an effective solution thought is provided for similar engineering requirements, and the method has wide application prospects and value.