2820 results about "Negative sample" patented technology

The invention discloses an archive knowledge base construction and retrieval method and system based on multi-modal data fusion. The method comprises the steps that heterogeneous archive data are cleaned, image features are extracted through CNN, text features are extracted through Transform, audio is converted into text and then subjected to similarity, a unified feature vector is generated, and metadata is constructed according to archive code association; creating a graph database instance, defining nodes and relationship types, importing entities and relationships, and storing feature vectors and metadata; the features are mapped to a high-dimensional shared semantic space, positive and negative sample pairs are constructed to update embedded layer parameters, self-attention is used in modalities, a shared attention mechanism is used between modalities, weights are adjusted according to archive features, and unified knowledge representation is generated; segmenting the steering quantity of the multi-modal data, storing the steering quantity into a database, and adopting hierarchical indexing and optimizing as required; related document fragments are retrieved through RAG technology vectors, answers are generated with the help of a large language model, and session feedback is provided. The file retrieval efficiency and accuracy are improved.

The invention discloses an industrial knowledge graph completion and adaptive retrieval method based on a large language model, and belongs to the technical field of computers, an incomplete knowledge graph is divided into a plurality of sub-graphs, and the knowledge graph is completed through negative sample filtering, neighborhood information pruning, information merging, question and answer template mapping, knowledge graph completion large model construction and LoRA fine tuning. The self-adaptive retrieval query module is used for receiving user problems, task decomposition, entity exploration, relation exploration, updating sub-graphs, reasoning paths and sub-target states in the memory sub-module, evaluating the performance and outputting a query result, and in the reflection and self-correction process, when information is insufficient, the self-adaptive retrieval query module is used for performing self-adaptive retrieval query. And the LLM evaluates whether the exploration direction needs to be adjusted or the reasoning path needs to be corrected based on the current information, and finally outputs an answer meeting the user demand according to the retrieved information. According to the method, the capability of a large language model is effectively combined, and the efficiency and the accuracy of knowledge graph completion are improved through adaptive retrieval and an reflection mechanism.

The invention provides an intelligent customer risk assessment system and method based on a large language model, and relates to the technical field of risk assessment, and the method comprises the steps: obtaining multi-modal data of a customer, carrying out the preprocessing, and extracting structured and unstructured features; constructing a hierarchical risk knowledge system, and realizing adaptive evolution of the knowledge system through a graph neural network and a generative model; constructing an initial negative sample library, and constructing a negative sample database in combination with a non-risk mode labeled by an expert and derivative layer analysis; optimizing the large language model by adopting a strong supervision, weak supervision and reinforcement learning cooperative training mechanism under each classification according to the customer type; mining risk features in a text by using the optimized large language model, processing multi-modal data through a multi-level attention network, and generating a positioning report including contradiction type coding, service influence dimension evaluation and risk level quantification; the accuracy, efficiency and flexibility of customer risk assessment are improved, and the risk management strategy is optimized.

The invention discloses a contrast learning unsupervised cross-modal hash retrieval algorithm based on a graph attention mechanism, and relates to the technical field of cross-modal retrieval. According to the graph attention mechanism-based contrast learning unsupervised cross-modal Hash retrieval algorithm, feature extraction is performed on image and text data through a pre-trained CLIP model to obtain image modal features and text modal features, and additional positive samples are generated through data enhancement; the similarity is calculated through Euclidean distance and cosine similarity on the basis of a graph attention network, and high weight is given to positive samples and low weight is given to negative samples by comparing adjacent matrixes. The method has the advantages that the features of the image mode and the text mode can be effectively fused and mapped to the common Hash space, a graph attention mechanism can adaptively adjust the connection weight between the features of the different modes, samples with high semantic correlation are highlighted, and therefore the semantic consistency between the image and the text is ensured.

The invention discloses a domain-adaptive retrieval enhancement generation method and system, and the method comprises the steps: carrying out the document analysis and semantic partitioning of a document through a semantic understanding model and a document rule, and storing document fragments; on the basis of the large language model and a preset question library, generating a question and disassembling the question; performing correlation evaluation and sorting on the initial retrieval results in combination with a retrieval technology; candidate content most matched with the cue words is screened out through a large language model, and question-retrieval fragments are scored and marked; performing fusion on the initial retrieval result and the scoring and labeling result to construct positive and negative samples, and constructing a supervised data set; and based on a preset loss function, utilizing the supervised data set to carry out supervised training on the knowledge semantic search model, replacing the RAG mixed search module, and carrying out continuous iteration. By means of the technical scheme, new domain knowledge and operation feedback data can be flexibly applied, and domain self-adaptive retrieval enhancement generation is achieved.

The invention discloses a multi-label electrocardiogram classification method based on self-supervised pre-training and multi-modal semantic alignment, which belongs to the technical field of artificial intelligence, and comprises the following steps: realizing self-supervised pre-training of unlabeled data through a single-modal contrast enhancement network, generating global and local contrast views by adopting a multi-scale random cutting strategy, and classifying the global and local contrast views in a multi-scale random cutting mode; in combination with a teacher-student network architecture, the potential invariance features of the ECG signals are learned while negative sample dependence is avoided, the problem of annotation data scarcity is effectively relieved, and the feature robustness is improved. A multi-modal fusion mechanism based on label semantic guidance is provided, a time domain signal and a frequency domain time-frequency graph are mapped to a unified semantic space through fine-grained semantic alignment, local feature enhancement and cross-modal complementary information fusion are realized by using a cross attention mechanism, and the problem of semantic difference caused by modal heterogeneity in a traditional method is overcome. A multi-label comparison loss function based on a disease co-occurrence relation is proposed, a category discrimination boundary is dynamically optimized by modeling a label co-occurrence probability, the feature separability of a tail category is improved while the head category discrimination ability is enhanced, and the problem of sample category imbalance in a multi-label scene is remarkably relieved.

The invention discloses an image-text retrieval method based on comparative learning and modal fusion. The method comprises the following steps: giving an original image-text pair; the method comprises the following steps of: 1, respectively carrying out local coding and global coding on an input image based on a target detection model and a deep residual network, and coding an input text based on a pre-training language model; 2, generating more matched image-text pairs (positive sample pairs) and mismatched image-text pairs (negative sample pairs) by adopting a diversified data augmentation strategy; 3, establishing cross-modal hierarchical semantic fusion of the image and the text, and bridging a semantic gap between a visual mode and a language mode; 4, measuring the semantic similarity degree between the image and the text; and 5, based on positive and negative sample pairs generated by data augmentation, carrying out model training through comparative learning. The method solves the problem that an existing image-text retrieval method is insensitive to fine granularity difference between modes.

The invention relates to the field of deep learning and emotion recognition, in particular to an EEG emotion recognition system and method based on multi-scale space-time diagram convolution and comparative learning, and the method comprises the steps: carrying out the preprocessing of an EEG signal; constructing positive and negative sample pairs based on the emotion categories and the identities of the subjects; extracting hidden space representation of the electroencephalogram signal by adopting a stack auto-encoder, and dividing the electroencephalogram signal into a plurality of functional brain regions to obtain a brain source signal after dimension reduction; constructing an undirected graph structure of the brain source signal; constructing a multi-scale space-time diagram convolutional network as a feature extraction network, inputting an undirected graph structure, dynamically learning and updating an adjacent matrix, expressing features of brain source signals in the undirected graph structure, and extracting emotion related features; based on a multi-scale space-time diagram convolution and comparative learning framework, an EEG emotion recognition model is built and trained to eliminate individual differences. According to the method, the multi-dimensional information of the EEG signals is fully utilized, and the accuracy, robustness and generalization performance of emotion recognition are remarkably improved.

The invention provides a medical multi-hop text question and answer retrieval enhancement method based on heterogeneous knowledge. The method comprises the following steps: firstly, constructing a uniform heterogeneous graph structure based on a medical knowledge graph of a medical document, and establishing a semantic bridge through an entity-document mapping relationship; performing semantic decomposition on a complex medical problem input by a user by utilizing the large model, and iteratively generating a series of mutually independent atomic queries; searching a reasoning path in the entity sub-graph of the heterogeneous graph, and calculating a path score by fusing the weighted combination of the entity association text similarity, the entity matching degree and the path edge weight; training a retriever by adopting a marginal sorting loss function, and optimizing a retrieval effect through positive and negative sample comparative learning; and finally, calling a large model to convert the reasoning path with the highest score into a text, and extracting a document fragment corresponding to a path node. According to the method, the problems that an existing retrieval enhancement technology is insufficient in complex problem processing capacity, poor in reasoning interpretability and the like are effectively solved, and high-accuracy medical questions and answers are achieved.

The invention discloses a mineral resource intelligent prediction method and system based on multi-source heterogeneous data fusion and deep learning, and the method comprises the steps: collecting and preprocessing multi-source heterogeneous data, and carrying out the standardization processing to form a structured data set; multi-source heterogeneous data fusion: realizing data layer space registration and feature layer weight dynamic allocation through an attention mechanism multi-modal fusion module, and outputting a high-dimensional metallogenic feature vector; constructing a CNN-LSTM mixed deep learning model and completing initialization training, and outputting an initial mineralization probability graph; and establishing a dynamic updating engine, performing model increment training based on transfer learning, correcting the mineralization probability through positive and negative sample reinforcement learning in combination with a newly added data type, and outputting a time sequence dynamic mineralization probability graph. According to the method, mineralization probability dynamic evaluation and risk quantitative updating are realized, the prediction precision and the model updating efficiency are improved, the method is adaptive to a multi-stage exploration scene, and accurate real-time support is provided for exploration decision making.

The invention discloses a multi-modal retrieval model training method and a multi-modal retrieval method and device, and relates to the technical field of multi-modal retrieval, and the method comprises the following steps: constructing a vector database; based on the vector database, constructing a difficult-to-negative sample training set; the difficult negative sample training set comprises a plurality of query texts and difficult negative sample document pictures corresponding to the query texts; and inputting the difficult-to-negative sample training set into the initial multi-modal retrieval model for training to obtain a target multi-modal retrieval model. Due to the fact that the vector database stores multi-vector representation of multiple documents, negative samples (namely, difficult-to-negative samples) similar to but not related to query vectors can be found through the vector database, the samples are mined through the vector database, a difficult-to-negative sample training set is constructed, the model can learn more subtle distinguishing features, and the classification accuracy is improved. Therefore, the positive and negative sample distinguishing capability of the model is improved, and the retrieval accuracy is improved in the retrieval process.

A computer-implemented method for artificial intelligence (AI) based risk / value assessment of a geographic area includes performing feature engineering to contextually enrich collected data. Three datasets are generated from the contextually enriched data, where a first dataset is generated by combining positive samples of the contextually enriched collected data with hard negative samples of the contextually enriched data, a second dataset is generated by combining the positive samples with soft negative samples of the contextually enriched data, and a third dataset is generated by combining the positive samples, hard negative samples, and soft negative samples. A machine learning model is trained to generate three different types of predictions for the risk / value assessment of the geographic area based on the three generated datasets.

The invention discloses a virtual fitting personalized clothing recommendation method based on artificial intelligence, and the method comprises the following steps: S1, collecting a user image, text description and behavior data, and generating a multi-modal user feature set; s2, constructing a multi-modal heterogeneous semantic map, and carrying out structural modeling and embedded representation; s3, graph embedding and feature fusion are carried out, positive and negative sample pairs are constructed, and semantic alignment training is carried out; s4, optimizing network structure parameters and training hyper-parameters by using a raccoon optimization algorithm; s5, calculating a semantic matching score, generating recommendation candidates, inputting body type parameters, and generating a multi-angle virtual fitting image; and S6, collecting user behavior feedback, updating a map edge weight and a training sample, and executing closed-loop optimization. The method has the advantages that personalized clothing recommendation and virtual fitting image generation based on the multi-modal features and the body type parameters of the user are achieved, a recommendation-fitting-feedback closed loop is constructed, and recommendation accuracy and user experience are improved.

The embodiment of the invention provides a landslide disaster negative sample optimization method based on an improved frequency ratio, and belongs to the technical field of data processing, and the method specifically comprises the steps: collecting disaster-pregnant environment data, and carrying out the preprocessing of the disaster-pregnant environment data, and obtaining raster data; defining two parameters of grading precision and grading bandwidth, introducing information entropy and a maximum frequency ratio to adaptively set parameter values of the grading precision and the grading bandwidth, and calculating a comprehensive frequency ratio; based on the statistical distribution of the comprehensive frequency ratio, taking a peak point as a negative sample screening threshold value, dividing a negative sample screening area based on the threshold value, then realizing calculation of a positive and negative sample ratio, and further determining a negative sample label under the constraint of a buffer area; according to the frequency ratio distribution of each raster data, an attribute interval with a frequency ratio greater than 1 is extracted as a significant feature, then the significant features of each raster data are classified, and an optimal feature element is determined through multi-collinearity analysis. Through the scheme of the invention, the accuracy and reliability of negative sample selection are improved.

The invention discloses a battery fault identification method based on probability labels and identification feature learning. The method is suitable for modeling and discrimination of various fault states in small sample and weak label scenes. The method comprises the following steps: firstly, acquiring key parameters such as voltage, current and temperature in an operation process of a battery system, and constructing standardized time sequence characteristic data; secondly, three types of pseudo labels are generated based on multi-source information such as alarm time difference, prediction residual error and mahalanobis distance, and a unified abnormal probability label is obtained through weighted fusion; constructing positive and negative sample pairs according to the difference between the tags, and introducing difficult samples with similar features but large tag difference to enhance the discrimination ability of the model; then constructing a twin neural network structure composed of shared parameter sub-networks, inputting positive and negative sample pairs for comparative learning, and extracting low-dimensional embedding features with clustering and distinguishability; and finally, through calculating a space distance between a new sample embedding vector and a known fault type, identification of a current fault type and evaluation of an abnormal degree are realized.

The invention discloses a pathological feature recognition and negative elimination method based on microscopic imaging. The method comprises the following steps: S1, collecting a pathological section image and digitally generating original microscopic image data; s2, preprocessing the original microscopic image; s3, constructing a pathological image recognition network fusing converter coding and a gating dynamic receptive field mechanism, and outputting pathological feature vectors; s4, performing context modeling through an attention guidance and category perception decoder, and outputting an image classification result; s5, constructing a discriminant boundary separation model based on positive and negative sample embedding, and performing negative exclusion judgment; s6, performing confidence coefficient weighted evaluation in combination with the uncertainty and the boundary distance, setting a dynamic threshold value, and screening out low-credibility samples; and S7, coding the classification result and the negative label into structured data, and sending the structured data to a diagnosis auxiliary system. According to the method, multi-scale modeling and a negative screening mechanism are fused, and intelligent recognition and credible diagnosis output of the pathological image are realized.

The invention provides a single-view image three-dimensional modeling method and system based on comparative learning, and the method comprises the steps: starting from a single input image, generating a multi-view candidate image through a pre-training two-dimensional diffusion model in combination with score distillation sampling; performing positive and negative sample division on the candidate image pairs based on texture fidelity and structure coherence; constructing a perceptual contrast learning mechanism, calculating perceptual loss between the images, and optimizing texture consistency in a three-dimensional content generation process; proposing a number-aware triple loss function, and dynamically adjusting the contribution ratio of positive and negative samples in training; then, a super-resolution reconstruction module is embedded to perform detail enhancement on the image, and the quality of a contrast gradient signal is improved; and finally, performing joint training on the perception comparison loss and a neural radiation field or volume rendering module to generate a high-quality three-dimensional grid or volume rendering result. The problems that in an existing diffusion generation method, multi-view textures are inconsistent, structure reconstruction is not coherent, and the semantic distinguishing capacity is insufficient are effectively solved.

The invention discloses a cross-modal heterogeneous data processing method and system. The method comprises the following steps: acquiring multi-source heterogeneous data including a vibration signal, a temperature signal, an oil spectral signal and the like, and forming a cross-modal alignment index sequence through event detection and time alignment processing; constructing a heterogeneous graph structure based on the aligned index sequence, and determining an edge weight according to correlation between modal fragments to obtain a cross-modal heterogeneous graph; performing representation decoupling on the heterogeneous graph, generating a cross-modal shared semantic subspace and a modal specific subspace, and reducing redundancy and noise interference through mutual information minimization constraint; establishing a prototype library, dynamically generating positive and negative sample pairs by adopting cross-modal contrast learning to enhance the discrimination ability, and obtaining an optimized cross-modal fusion result; and finally, outputting a diagnosis result including the fault type and severity, generating a diagnosis evidence track, and binding the key cross-modal fragment with the semantic centroid of the prototype library to realize interpretable traceability and self-correction of the result.

The invention discloses an emotion recognition method and system based on cross-modal alignment and matching enhancement. According to the method, firstly, feature extraction is carried out on text, audio and video modalities in a data set, and then a text and audio cross-modal emotion alignment module and a text and video cross-modal emotion alignment module are constructed respectively, so that cross-modal semantic alignment is realized. Constructing an emotion label matching module based on an alignment result, generating modal pairs with similar emotions but different labels by using a difficult negative sample mining strategy, and paying attention to cross-modal emotion consistency through a dichotomy task guide model; performing modal feature fusion on the three modals through a six-layer attention crossing mechanism, finally splicing feature vectors, inputting the spliced feature vectors into a long-sequence context fusion modeling module for deep modal fusion, and capturing cross-modal interaction information; and the fused features are sent to an emotion classification module, and a final emotion category recognition result is output.

A system for increasing the accuracy in summarization techniques is disclosed. The system generates a set of summaries for text. The system determines a label for each summary based on a set of composite metrics. The label for the summary indicates the truthfulness and faithfulness of the summary with respect to the text. The system determines that more than a threshold number of the set of composite metrics indicate that a first summary is assigned with a first label. In response, the system adds the first summary paired with the text as a positive sample to a dataset. The system determines that more than a threshold number of composite metrics indicate that a second summary is assigned with a second label. In response, the system adds the second summary paired with the text as a negative sample to the dataset. The system trains a summarization algorithm with the dataset.

The invention relates to the technical field of power grid intellectualization, in particular to a digital power grid asset classification and evolution monitoring method, system and device based on self-supervised comparative learning and a storage medium. The method comprises the following steps: acquiring network flow data in a digital power grid, extracting multi-dimensional features such as a time interval, a direction, a data packet length, a protocol type and an address port, and constructing an asset behavior feature matrix; then constructing a self-supervised contrast learning model, taking the communication behavior sequences of the same asset in different time periods as a positive sample pair, taking the communication behavior sequences of different assets as a negative sample pair, and training an asset embedding model through an InfoNCE contrast loss function; then, asset communication behaviors are mapped to a semantic embedding space, and asset classification and identification are carried out based on embedded vectors; and finally, carrying out evolution monitoring on the assets by adopting a sliding time window mechanism, generating an asset evolution trajectory sequence, and carrying out abnormity perception early warning. Autonomous learning without manual annotation, high-precision asset identification and real-time state evolution monitoring are realized.

The invention discloses a pseudo-label semi-supervised semantic segmentation method based on a diffusion model, and the method comprises the steps: S1, inputting remote sensing image data into a pre-trained diffusion model, and extracting multi-scale features; s2, based on a student-teacher model, the unmarked feature set obtains an unmarked semantic segmentation result through a student model, and the unmarked feature set generates a pseudo tag through a teacher model; s3, pixels of pseudo labels are screened out through entropy calculation, the pixels are divided into reliable pixels and unreliable pixels according to the pixels, the reliable pixels participate in unsupervised loss calculation of the student model and are compared with unmarked semantic segmentation results generated by the student model, and the unreliable pixels serve as negative samples; s4, performing comparative learning to obtain comparative loss relative to unreliable labels; and S5, determining a loss function optimization target, and training a student-teacher model. According to the method, the complexity of feature learning is improved, and the discrimination capability of feature representation is enhanced.

The invention discloses a medical health cost prediction system and method based on multi-source data fusion, and relates to the technical field of medical data analysis, the system firstly fuses a plurality of heterogeneous medical information sources to generate structured data; then, multidimensional features related to the cost are extracted to construct a feature representation vector; and the cost prediction modeling module constructs a cost prediction model through graph structure modeling and semantic embedding, carries out joint optimization by combining a graph attention mechanism and semantic similarity, and outputs a prediction result by fusing a time sequence and static characteristics. A joint optimization algorithm of graph semantic comparison loss and prediction deviation loss is introduced into model training, and positive and negative sample pairs are constructed through a cost label distance. And the feedback optimization module triggers model updating when the prediction deviation exceeds a threshold value, and dynamically adjusts a model structure and parameters through a deviation index and a sample confidence factor. And the prediction interpretation module analyzes influence factors based on intermediate layer features or gradient propagation, outputs an interpretation report, and improves the transparency and credibility of the model.

The invention relates to the technical field of image forgery detection, in particular to an image forgery detection method based on self-supervised contrast learning. According to the technical scheme, the method comprises the steps that an image to be detected is input into a trained image forgery detection model, and an image forgery detection result of the image to be detected is obtained; the image forgery detection model comprises a feature extractor which is pre-trained through self-supervised contrast learning and is used for extracting an image feature I of an image to be detected; the classifier is used for classifying the to-be-detected image into a real image or a forged image based on the image features I extracted by the feature extractor; the self-supervised contrast learning pre-training takes a real image set as a positive sample, and takes the real image set and a forged image set as negative samples. According to the method, feature distribution of a real image and a forged image is mined through self-supervised comparative learning, and the problem of generalization of a detection model is solved.

The invention discloses an image optimization method based on a CLIP model, and the method comprises the steps: S1, carrying out the coding initial processing of characters and images, inputting a character description and an initial image code into a CLIP model, and obtaining a corresponding semantic vector and a visual vector; s2, cross-modal contrast learning: based on positive and negative sample contrast and an adaptive attention mechanism, aligning the semantic vector and the visual vector obtained in the S1; s3, self-supervised learning is carried out, and precision improvement of semantic-to-visual mapping is completed through self-supervised learning; s4, feature refinement generation: based on the optimized semantic and visual feature vectors, generating a preliminary concept conforming to description; and S5, image output: repeating the steps S1 to S4 until a preset matching value is met, and outputting a conception image matched with the text description. According to the image optimization method based on the CLIP model, semantic mapping from characters to images is improved, so that the semantic mapping is more in line with the expectation of natural language description, and a visual conception graph with rich details is generated.

The invention belongs to the technical field of image processing, and particularly relates to a wood surface defect detection method and device based on multi-view anomaly detection, and the method comprises the steps: obtaining a target image based on an original wood image; constructing a wood surface defect detection model comprising an encoder, a decoder and a defect marking module, constructing positive and negative samples based on the target image, and training the encoder by using the positive and negative samples through contrast learning; the output of the encoder is used as the input of a decoder, a loss function is constructed through a mean square error and a structural similarity index to train the decoder, and the decoder outputs a reconstructed image; obtaining a residual image between the reconstructed image and the target image through a defect marking module to obtain a fused residual image, and marking a pixel region exceeding a dynamic threshold as a defect; and detecting the preprocessed to-be-detected wood image by using the trained wood surface defect detection model to realize wood defect detection and pixel-level positioning.

The invention discloses a method and system for evaluating the susceptibility of avalanches based on GIS and machine learning, and belongs to the technical field of avalanche susceptibility evaluation. The method comprises the following processes: determining the influencing factors causing avalanches in a target area according to the terrain data, meteorological data and avalanche data of the target area; performing correlation detection on the influencing factors causing avalanches preliminarily selected to determine the evaluation factors; constructing a positive sample set and a negative sample set according to the evaluation factors and avalanche point data information of the target area, training a machine learning model according to the positive sample set and the negative sample set, and obtaining an avalanche susceptibility evaluation model after training; producing an avalanche susceptibility zoning map according to the prediction results of the avalanche susceptibility evaluation model, and determining the avalanche susceptibility of each area; the method can improve the rationality and scientificity of the avalanche susceptibility evaluation results, and provide theoretical support and method guidance for the avalanche disaster prevention and control system.

The invention belongs to the field of deep learning technology in computer vision, and discloses a multi-modal pre-training directivity segmentation method. According to the method, a frozen contrast language image pre-training model is adopted to construct an overall framework, and a sample initialization module, a language encoder module, a visual encoder module and a multi-image feature weighted fusion module are included, so that precise segmentation of a specified object in an image is realized cooperatively. The sample initialization module generates text descriptions of positive and negative samples, eliminates interferents through negative words, prepositions and new sentences generated by using a large language model, and ensures the accuracy of the model. The image is processed through a visual encoder, deep features are extracted, incomplete back propagation is carried out, and model parameter freezing is ensured. And finally, in a multi-image feature weighted fusion module, the positive and negative text features and the multi-level features are fused to form unified feature representation to guide image segmentation. According to the method, the understanding ability of the model for the key region and the key object is effectively improved, and the segmentation effect is enhanced.

The invention belongs to the technical field of agricultural machinery track classification, and particularly relates to an agricultural machinery track data classification method and device, electronic equipment and a storage medium. The method comprises the following steps: carrying out preprocessing and feature conversion operation on original agricultural machine GNSS trajectory data to obtain continuous and uniform time sequence trajectory data; the original agricultural machine GNSS trajectory data comprises a plurality of discrete trajectory points, and the category of the trajectory points comprises a field trajectory and a road trajectory; a trajectory classification model comprising a feature extraction module and a classification head is constructed, the time sequence trajectory data serve as training samples to train the trajectory classification model, the feature extraction module is trained in a comparative learning mode, and the classification head is trained through cross entropy loss; and deploying the trained trajectory classification model, and classifying agricultural machinery trajectory data by using the trained trajectory classification model. According to the invention, a contrast learning framework fused with the dynamic negative sample queue is designed, the classification accuracy and generalization ability are improved, and the method is suitable for track classification tasks in various complex scenes.

The invention discloses a multi-modal-based generative generalized zero sample learning method. The method comprises the following steps: S1, collecting and preprocessing image and text data; s2, optimizing image features by adopting self-supervised learning, and generating visual feature vectors; s3, text features are enhanced through context attention, and text feature vectors are generated; s4, constructing a cross-modal embedding space, aligning visual and text features, and constructing a positive and negative sample pair; s5, calculating the similarity of the sample pair, and optimizing cross-modal feature distribution; s6, introducing regularization constraint, modeling known category features by using the improved Gaussian mixture variational auto-encoder, and generating unknown category features; and S7, training a classification model, and performing category prediction in the optimized cross-modal embedding space. According to the method, visual and text features are fused, cross-modal alignment is optimized, the quality of unknown category features is improved, and the defects of an existing zero sample learning method in cross-modal alignment and generalization ability are overcome.