663 results about "Unsupervised clustering" patented technology

The invention relates to the technical field of network security, in particular to a network security big data state evaluation method based on pattern recognition, which comprises the following steps of: extracting multi-modal features from a network flow log, a system event log, a host behavior log and threat intelligence data, generating a feature matrix, performing feature dimensionality reduction by adopting an auto-encoding network, and obtaining a network security big data state evaluation result; carrying out attack behavior classification and abnormal mode identification in combination with unsupervised clustering and a graph neural network; constructing an attack transition probability matrix based on a Markov model; forming a time sequence attack chain; predicting an attack development trend; and a dynamic protection instruction is issued to the safety equipment. According to the method, the unknown attack detection capability can be improved, the time sequence attack traceability is enhanced, the security situation assessment is optimized, and the method is suitable for security situation awareness in cloud computing, industrial internet and large-scale network environments.

The invention is suitable for the technical field of agricultural intellectualization, and provides an intelligent blueberry disease detection method and system based on multi-modal unsupervised learning, and the method comprises the following steps: carrying out the feature extraction and clustering of preprocessed multi-modal data based on an adaptive contrast deep clustering framework, and obtaining a feature extraction result; obtaining a multi-modal preliminary feature and a preliminary clustering result; based on a multi-modal complementary feature fusion mechanism, according to the multi-modal preliminary features and the preliminary clustering result, carrying out adaptive weighted fusion on the multi-modal preliminary features to obtain multi-modal fusion features; performing unsupervised clustering optimization and disease type identification on the multi-modal fusion features to obtain an unsupervised learning model; and performing deployment and incremental learning on the unsupervised learning model, and detecting the blueberry diseases. According to the method, early-stage accurate detection of blueberry diseases is realized through an unsupervised learning algorithm, a new normal form is provided for intelligent accurate management of blueberries, and the disease prevention and control efficiency and industrial economic benefits are effectively improved.

The invention discloses a distribution box fire early warning method and system based on multi-source information fusion, and particularly relates to the technical field of distribution box fire early warning. Temperature, smoke, acoustic vibration and current harmonic signals are synchronously acquired, time sequence alignment and amplitude normalization are performed, and a dynamic baseline is generated in real time; extracting multi-scale statistics and morphological characteristics according to an adaptive window, and calibrating an operation mode through unsupervised clustering; then retrieving a matched baseline signature in a historical feature library, and calculating a multi-modal standardization deviation of a current window; mapping the deviation index into a graph node, combining a covariance edge weight, a phase synchronization index and a smoke discrete index, obtaining a coupling risk coefficient through a fusion model, and outputting a comprehensive risk index; and finally, carrying out multi-scale rate and acceleration analysis on the comprehensive risk index, triggering three-level early warning of attention, warning and danger by adopting a dynamic threshold, and correcting the threshold by utilizing operation and maintenance feedback self-learning, thereby effectively solving the problems of early warning response lag and unknown early warning level.

The invention discloses a semantic understanding-based technical literature multi-dimensional analysis method, which comprises the following steps of: performing text vectorization processing on text data by utilizing a Sension-BERT model to generate dense vector representation of a text; vector dimensionality reduction is carried out by adopting UMAP, and redundant features are removed; performing unsupervised clustering analysis by using HDBSCAN to generate a clustering result; extracting a subject term from a clustering result by adopting a TF-ICF method; and carrying out multi-dimensional analysis on the clustering result. Compared with the prior art, a comprehensive and efficient patent analysis tool is provided through an advanced semantic understanding patent clustering method, so that deeper interpretation and analysis of technical literatures are realized while the cost benefit is ensured.

The system and method for processing a digital pathology image using a machine learning model that includes a self-supervised hierarchical Vision Transformer (ViT) configured to perform unsupervised clustering with multiple classification tokens. The method includes receiving a digital pathology image that depicts a tissue slice stained with histological dyes. The digital pathology image may be processed to generate a result comprising multiple predicted classifications of individual patches of the digital pathology image. The result is generated by a machine-learning model using a self-supervised hierarchical Vision Transformer (ViT) that may further comprise a multi-head self-attention module configured to predict a crosspatch relevance metric using an attention mechanism for each individual patch in the digital pathology image thereby assigning the individual patches to a cluster based on the crosspatch relevance metrics.

The invention relates to an automatic inspection method and system for intelligent fire fighting equipment of an LNG (Liquefied Natural Gas) filling station, in particular to the field of the intelligent fire fighting equipment of the LNG filling station, a dynamic drifting model is constructed by collecting environmental parameters in real time, and the interference of severe conditions on a sensor is accurately quantified; a time sequence prediction network is used for prospectively capturing a drift trend, and a digital twinning technology is combined to simulate an equipment state in an extreme scene; intelligently identifying an abnormal mode based on unsupervised clustering and dynamically optimizing a multi-source data fusion weight; and finally, through reinforcement learning of a closed-loop iteration drift model and a fusion strategy, the adaptability of the system is continuously improved, misjudgment and missed judgment caused by environmental interference are effectively eliminated in the whole process, the real-time performance and reliability of fire-fighting early warning are remarkably enhanced, the safety accident risk is greatly reduced, and all-weather, high-precision and self-evolution intelligent protection guarantee is provided for the LNG filling station.

The invention discloses a multi-technology fusion multi-source data grading and classifying method and system, and the method comprises the following steps: S1, determining a data source, collecting multi-source data, and carrying out the preprocessing; s2, monitoring by using a CDC technology, and extracting monitored target data; s3, performing feature extraction on the target data, and introducing an EMAP method to perform nonlinear cross projection on a multi-modal feature set; s4, inputting the high-dimensional feature vector into a hybrid classification network, and executing main component molecular space compression and unsupervised clustering; s5, mining high-frequency attribute items by adopting an FP-Growth algorithm, and constructing a privacy attribute set and a non-privacy attribute set; s6, calculating the weighted privacy degree of each privacy cluster, and performing multi-layer privacy level division through a hierarchical mapping network; and S7, carrying out encryption and desensitization processing on the data under each type of labels, and carrying out visual output. According to the invention, the intelligence and security of multi-source data classification and privacy grading are improved.

A computer system uses clustering and a large language model (LLM) to normalize attribute tuples for items stored in a database of an online system. The online system collects attribute tuples, each attribute tuple comprising an attribute type and an attribute value for an item. The online system initially clusters the attribute tuples into a first plurality of clusters. The online system generates prompts for input into the LLM, each prompt including a subset of attribute tuples grouped into a respective cluster of the first plurality. Based on the prompts, the LLM generates a second plurality of clusters, each cluster including one or more attribute tuples that have a common attribute type and a common attribute value. The online system maps each attribute tuple to a respective normalized attribute tuple associated with each cluster. The online system rewrites each attribute tuple in the database to a corresponding normalized attribute tuple.

The invention belongs to the technical field of data processing, and particularly relates to a hidden ore body evaluation and positioning method based on multi-source data processing. The method mainly aims at the problems of incompleteness and isomerism of multi-source geological data in acquisition, fusion and modeling. Comprising the following steps: acquiring hyperspectral, geochemical and magnetic anomaly multi-source data of an evaluation area; intelligently complementing missing modal data by using a generative adversarial network based on geological constraints and modal outburst to form a complete multi-source data set; an unsupervised clustering algorithm combining geological correlation and entropy weight analysis is adopted to construct high-confidence-coefficient pseudo-label data, and knowledge mining of unlabeled samples is achieved; feature purification and dimension reduction are carried out through multi-modal feature fusion and hierarchical principal component analysis, and key feature vectors representing the existence of the ore body are extracted; and finally realizing space prediction of the concealed ore body by utilizing the classification model. According to the method, a high-quality data basis and a unified processing framework are provided for intelligent recognition of the hidden ore body, and efficient and accurate positioning of the hidden ore body is achieved.

The invention discloses a composite material wind power blade damage detection method, and aims to solve the problems of difficulty in quantitative analysis and low distinguishing precision of various damage types in composite material wind power blade damage detection in the prior art. Comprising the following steps that acoustic emission signals in the operation process of the wind power blade are obtained in real time and preprocessed, and the acoustic emission signals comprise parameter data and waveform data; performing unsupervised clustering on the preprocessed parameter data to obtain a preliminary clustering result; training the preprocessed waveform data by adopting an improved multi-branch convolutional neural network, and outputting an accurate classification result; the unsupervised clustering result is verified by using the supervised learning classification result, and the accuracy of the clustering result is evaluated; and fusing the quantitative index accumulated energy, the damage type and the frequency, and constructing a comprehensive damage evaluation model to determine the damage degree. According to the method, the unsupervised clustering method and the supervised learning method are combined, and accurate identification and quantification of the damage are realized.

The invention discloses a data labeling method and system based on user behaviors and attention tracking, and the method comprises the steps: synchronously collecting multi-source behavior signals of a mouse, a keyboard, eye movement and the like of a doctor in real time, combining identity and interface metadata, and carrying out the standardized normalization, abnormality elimination and short time sequence behavior unit division. And extracting individual behavior micro-modes by using unsupervised clustering, and constructing a behavior portrait library. Through multi-modal time sequence modeling and a self-adaptive space-time attention mechanism, behavior characteristics, an interface area and a report text are deeply fused, a multi-level correlation probability is output, and high-precision automatic tagging of content and an image area is realized.

The invention belongs to the technical field of smart cities, and discloses a point cloud-based urban building roof wireframe reconstruction method, which comprises the following steps of: fusing a fast point feature histogram and a multi-scale roof geometric descriptor, generating robust point-by-point features, screening candidate angular point clusters in combination with a classification head, and adaptively segmenting the candidate point clusters based on density parameters by utilizing DBSCAN (Density-Based Spatial Clustering of Applications with Noise), so as to reconstruct a point cloud-based urban building roof wireframe. Initial inflection points are extracted through unsupervised clustering, noise is effectively suppressed, and irregular distribution is adapted; then multi-scale geometric information of an initial inflection point neighborhood is aggregated through an inflection point correction network, offset is learned to correct position deviation, and inflection point positioning precision is improved; and finally, the edge classification network automatically deduces the topological connection of the roof wireframe based on the geometrical relationship and feature relevance of prediction inflection points, so that error accumulation caused by dependence on manual rules in a traditional method is avoided, the generalization ability of a complex roof structure is enhanced, the correction network learns offset through a multi-scale context, and the inflection point positioning precision is remarkably improved.

The invention provides a driving style characteristic classification and identification method under an urban typical steering condition. The method comprises the following steps: constructing a plurality of urban typical driving experiment scenes and collecting driving steering data and car-following data; respectively processing the driving data under each working condition by using Kalman filtering, a sliding time window, an analytic hierarchy process and a principal component analysis method, and obtaining multi-dimensional characteristic parameters; providing a multi-working-condition characteristic parameter coupling method considering subjective driving style evaluation of the driver to couple the multi-dimensional characteristic parameters; using a k-means + + algorithm optimized by a Cuckoo Search algorithm to perform unsupervised clustering on the data samples, and dividing driving styles into a cautious type, a general type and a radical type; training a BP neural network optimized by a genetic algorithm by using a data set containing a driving style, and constructing a driving style characteristic identification model under an urban typical steering working condition; and the identification accuracy is verified in a multi-working-condition coupled city scene. According to the invention, the identification accuracy of the driving style can be improved, and personalized driving requirements are met.

The invention relates to the technical field of data processing, and discloses a flue gas treatment intelligent regulation and control method and system based on artificial intelligence and a medium. The method comprises the following steps: collecting parameter data of a flue gas system through a sensor network and processing to obtain preprocessed data; using unsupervised clustering to identify working condition features and categories; constructing an emission concentration error active switching control model according to the working condition information; different working condition control parameters are optimized by adopting a differential evolution algorithm; optimized parameters are input into a controller, and modes are dynamically switched according to pollutant errors. The control strategy can be automatically switched according to the working condition characteristics of the flue gas treatment system, the complex and changeable industrial production environment can be effectively dealt with, and the system operation cost is optimized while stable and up-to-standard emission of pollutants is guaranteed.

The invention provides an automatic driving safety test system and method, and relates to the technical field of automatic driving, and the system comprises a conventional traffic scene generation subsystem which is used for generating a conventional driving scene data set in a simulation platform; the safety key scene generation subsystem is used for generating a safety key scene by utilizing an automatic driving scene generation network SAGA based on adversarial disturbance according to the conventional driving scene data set; and the safety key scene clustering analysis subsystem is used for dynamically discovering a new category and optimizing a clustering structure by introducing a progressive unsupervised clustering method according to the generated safety key scene to obtain a scene analysis result. According to the method, the problems of insufficient test scene coverage, weak resistance test capability, low key scene analysis efficiency and the like in the safety verification of the existing automatic driving system are solved.

The invention discloses a high and cold arid region slope soil stability safety risk evaluation system and method, and relates to the technical field of slope engineering. The state of each side slope under multi-dimensional indexes such as freeze-thaw cycle frequency, dry-wet alternation index, wind erosion strength, shear strength, water content, porosity and fracture density is quantified, the feature similarity between any two side slopes is calculated, and then a side slope feature similarity matrix is formed. Based on the matrix, an unsupervised clustering algorithm (such as spectral clustering, similarity propagation and the like) can be adopted to divide a plurality of side slopes into similar subsets with structural characteristics similar to environmental response, and category attribution of risks is achieved. On the basis, structural variation analysis, historical instability statistics and central risk difference extraction are performed on similar slope samples, so that the internal instability tendency of the slope can be identified, and a risk prediction model suitable for the type of slope can be constructed through feature training.

The present disclosure relates generally to natural language understanding. An exemplary computer-enabled method comprises: receiving first utterance data corresponding to a first plurality of intents; identifying a first plurality of intent clusters using an unsupervised machine-learning algorithm, wherein each intent cluster of the first plurality of intent clusters comprises a respective subset of the first plurality of intents; training, based on the first utterance data, an intent cluster classification model, wherein the intent cluster classification model is configured to receive a user utterance and identify an intent cluster of the first plurality of intent clusters; and training, based on the first utterance data, an intent classification model for each intent cluster of the first plurality of intent clusters to obtain a plurality of intent classification models, wherein each intent classification model is configured to receive the user utterance and identify an intent from the respective intent cluster.

The invention discloses a lithium battery surface defect intelligent detection method based on deep learning, and the method comprises the steps: S1, carrying out the unsupervised clustering auxiliary defect marking of a collected lithium battery surface image, carrying out the defect enhancement processing, and constructing a lithium battery surface image data set; s2, performing self-supervised pre-training on the YOLOv8 model by using an unlabeled defect image in a related field, and reserving a pre-training weight of a backbone network of the YOLOv8 model; s3, constructing an improved YOLOv8 model, and migrating the pre-training weight of the backbone network to the improved YOLOv8 model; s4, training the improved YOLOv8 model by using the lithium battery surface image data set to obtain a defect detection model; and S5, inputting the surface image of the lithium battery to be detected into the defect detection model to obtain a defect detection result. According to the method, high precision and high efficiency of lithium battery surface defect detection are realized.

The invention discloses a lightweight knowledge graph rapid construction method and system based on an NLP technology. The method comprises the steps of preprocessing an unstructured text and segmenting the unstructured text into semantic segments; unsupervised clustering is combined with the contour coefficient to determine the optimal clustering number, and a semantic association fragment cluster is obtained; performing word segmentation, part-of-speech tagging, NER and entity linking on the fragment cluster, extracting an entity and initial relationship, and fusing semantic similarity, TF-IDF word frequency collaboration degree and co-occurrence frequency to calculate a relationship edge weight; constructing a lightweight knowledge graph; in the question and answer stage, questions are disassembled through a process engine, related sub-graphs are retrieved, and answers with reasoning links are generated. The system correspondingly comprises a text preprocessing module, a clustering module, an entity relation processing module, a graph construction module, a question and answer reasoning module and a storage module. According to the method, the construction cost is reduced, the interpretability and the module coupling degree are improved, multiple scenes such as government and enterprise public opinions and medical assistance are adapted, and the problems of weak generalization, poor real-time performance and'black box 'in the traditional technology are solved.

The invention discloses a federated learning backdoor attack defense method based on layer perception detection, which relates to the technical field of artificial intelligence security, and comprises the steps of client training and uploading, malicious client identification, backdoor key layer detection and deletion, robust aggregation updating and adversarial training for enhancing robustness. According to the method, the model parameters uploaded by the clients are subjected to clustering analysis, and the maximum benign cluster is identified by adopting an unsupervised clustering method, so that instability caused by single threshold judgment is avoided, and the benign client and the malicious client can be distinguished; and meanwhile, layer perception detection and elimination are used in the scheme, so that the backdoor introduced by a malicious client can be effectively identified and eliminated on the premise of ensuring the global model precision, and the influence of backdoor attack on the model is inhibited.

The invention discloses an environmental microbial community state evaluation method based on unsupervised clustering, and the specific implementation mode is as follows: obtaining metagenome sequencing data in an environmental sample, and carrying out microbial species annotation and abundance calculation; based on abundance data, sample microbial species richness and Shannon diversity indexes are calculated, microbial functional gene annotation and abundance calculation are carried out, standardization processing, principal component analysis, Euclidean distance calculation, Ward clustering and contour coefficient evaluation are carried out on the data, and dimensionality reduction and clustering are carried out on the multi-dimensional features. Therefore, comprehensive indexes with obvious differences are provided. According to the comprehensive indexes obtained through the method, original data can be reserved, the complexity of the data can be greatly reduced, the analysis process is simplified, and an intuitive and operable means is provided for overall state evaluation of the environmental microbial community, so that the analysis efficiency and accuracy are improved, and the method is suitable for popularization and application. And determining the overall influence of the environmental change on the microbial community state.

The invention belongs to the technical field of stratum recognition, and particularly relates to a shield parameter geological recognition calculation method and system based on an unsupervised clustering algorithm, and the method comprises the steps: selecting a plurality of groups of data containing factors affecting stratum category recognition; pCA converts a group of complex related variables into fewer and irrelevant variables, and the new variables are called as main components and can capture most of information in original data; determining the clustering number of K-means + + by adopting an elbow method; determining the clustering number of a hierarchical clustering method by adopting a hierarchical tree; k-means + + and hierarchical clustering are respectively used for clustering the data, and evaluation indexes such as identification precision or contour coefficients of the data and the data are calculated, so that potential modes in the data are analyzed and mined more comprehensively. The invention aims to deeply explore the internal relevance between shield tunnel parameters and geological types, provides an innovative and robust solution for real-time detection of geological features in shield construction by overcoming the limitation of the prior art, and has wide application potential.

The invention relates to a pathological image morphological subtype classification method based on an optimal transmission prototype network, and belongs to the technical field of medical image processing. The method comprises the steps of constructing a pathological image data set, performing preliminary grouping by using an unsupervised clustering method, and performing cooperation with a pathologist to optimize the data set; preprocessing the image through a data enhancement strategy; performing feature extraction on the enhanced image through a pre-training model; the method comprises the following steps of: constructing and training an OT-ProtoNet model; setting a prototype, determining dimensions and quantity, realizing a category specific prototype based on a soft distribution mechanism, calculating similarity based on an OT distance, setting training parameters and selecting an optimization algorithm to train a model, and ensuring that the model effectively learns key features in data; and carrying out image morphological subtype classification on the basis of an OT-ProtoNet model. According to the pathological image morphological subtype classification method, the pre-training features can be fully utilized, the classification accuracy can be improved, and interpretability can be kept.

The invention discloses a photovoltaic power station supervised data generation method based on a generative adversarial network, which is used in the technical field of data augmentation, and comprises the following steps: initializing a generator and a discriminator, and randomly extracting training samples; training a generator and a discriminator by using the training sample, and generating a synthetic data set conforming to photovoltaic power station data set distribution through the generator; and clustering the features of the synthetic data set and the photovoltaic power station data set through a K-means clustering algorithm, observing a clustering center and feature distribution, evaluating the feature distribution difference between the generated data set and the real photovoltaic power station data set through an unsupervised clustering method, and evaluating the quality of the generated data set. According to the method, through the steps of density value sorting, initial center selection and the like, fine division of clustering is achieved, and initial center selection deviation in the clustering process is effectively avoided by combining density value calculation and a grid division method.

The invention provides a scanning method and system for hidden asset identification in an electric power industrial control network, and aims to solve the problem that silent, non-registered or illegal access equipment is difficult to discover by the existing asset surveying and mapping means. According to the method, multiple protocol induced detection messages are injected into a target network, equipment response data are collected, feature vectors are extracted, and abnormal equipment behaviors are identified by using an unsupervised clustering and anomaly detection algorithm; and meanwhile, in combination with a graph neural network and a hidden Markov model, modeling is performed on a network communication chain structure and a historical behavior sequence, and potential hidden asset nodes are deduced. According to the method, active discovery and risk reasoning of hidden assets can be realized on the premise of not influencing industrial control services, and the method is suitable for industrial control scenes such as transformer substations and dispatching centers in the power industry and has high safety, intelligence and feasibility.

The invention relates to the technical field of intelligent driving, and particularly provides a personalized driving behavior identification method based on deep embedded clustering. Extracting a low-dimensional depth feature vector of the driving time sequence segment through a deep neural network auto-encoder, performing end-to-end joint optimization on the auto-encoder and unsupervised clustering by using a deep embedded clustering method, generating a driving style clustering result, and labeling a clustering result label; freezing the trained auto-encoder parameters, constructing a driving style classification model, and establishing a mapping relation between driving time sequence fragments and style labels; and distributing semantic tags for clustering results by extracting interpretable features. According to the end-to-end joint optimization technology based on deep embedded clustering, the convergence speed and generalization ability of the model are remarkably improved, and the driving style identification precision is far higher than that of a traditional machine learning method.

The invention discloses an LCD display screen production quality collaborative management and control method based on a detection data chain, particularly relates to the technical field of industrial production quality management, and is used for solving the problem of quality root cause misjudgment caused by complex association of multi-source data in the existing liquid crystal display screen manufacturing process. The method comprises the following steps of: constructing a detection data chain covering an array, a box forming process and a module process, firstly performing multi-source data correlation analysis to obtain a variable pair set, then analyzing an intermediary influence path between variable pairs to eliminate false correlation, and then determining a real causal direction by applying a causal discovery algorithm based on conditional independence. Carrying out unsupervised clustering analysis on the basis of defect characteristic indexes, verifying the consistency of causal directions in different data subsets, and finally outputting a stable quality root cause analysis result to realize accurate positioning and effective management and control of key process parameters.

The invention relates to the technical field of federated learning security, and discloses a federated learning poisoning defense method based on time-frequency spectrogram and comparative learning, which comprises the following steps: receiving model update uploaded by each client, grouping and vectorizing parameters according to model layers, and generating a time-frequency spectrogram by applying short-time Fourier transform to parameter vectors of each layer; based on the time-frequency spectrogram, constructing a positive sample pair through data enhancement, carrying out difficult negative sample mining, and training an encoder by using a contrast loss function to extract an embedded vector with high discriminant power; and performing unsupervised clustering on the embedded vector by using a DBSCAN clustering algorithm, judging the maximum cluster as a benign client, performing final judgment in combination with historical malicious records, and only aggregating model parameters of the benign client to update a global model. According to the invention, high-precision detection of attack features can be realized, and a more universal, more efficient and more practical federal learning poisoning attack defense method is realized.

The invention discloses an automatic cable surface defect detection method and system based on a convolutional neural network, and belongs to the technical field of computer vision and deep learning, and the method comprises the following steps: collecting an initial qualified segment image during the production of a new batch of cables, and extracting a library texture feature vector through a pre-trained texture feature coding network, unsupervised clustering is carried out, and cluster centers left after the abnormal clusters are removed and statistics of the cluster centers are stored in a normal texture self-adaptive reference library of the batch; collecting a cable surface expansion image on line, dividing the image into image blocks, inputting the image blocks into the texture feature coding network to obtain a detection texture feature vector, obtaining a texture deviation degree according to the detection texture feature vector and each cluster center in the normal texture adaptive reference library, and judging whether the texture deviation degree is a texture abnormal point or not; and carrying out connected domain analysis on texture abnormal points of the same-frame expanded image to obtain candidate abnormal regions. The problem that real defects and instantaneous noise are difficult to distinguish in the prior art is solved.

The invention belongs to the technical field of remote sensing, and relates to a multispectral remote sensing atmospheric correction method based on fusion of a physical model and a neural network, which comprises the following steps: S1, classifying aerosol parameter data obtained based on foundation observation, remote sensing inversion or meteorological model and satellite data fusion inversion by adopting an unsupervised clustering algorithm, extracting a typical aerosol mode with physical representativeness; s2, in combination with the typical aerosol mode, simulating the apparent reflectivity of an observation channel under a plurality of atmospheric states and observation geometric conditions by using a radiation transfer model, generating a lookup table covering a wide parameter space, and constructing a training data set; and S3, constructing a nonlinear regression model, taking the training data set as a training sample, learning a mapping relation among an observation angle, an aerosol condition and surface reflectance, performing atmospheric interference correction on an actual multispectral remote sensing image under a pollution condition, and outputting a surface reflectance result.