134 results about "Semantic dependency" patented technology

The embodiment of the invention relates to the technical field of data analysis, in particular to a national secret log auditing system which is characterized in that firstly, an operation behavior record sequence generated in the running process of a national secret application system is collected by the national secret log auditing system, and the sequence is composed of log entries containing identity verification information, resource access path information and state transition description information; performing context semantic association analysis on the operation behavior record sequence to obtain a semantic element extraction result and a semantic dependency relationship; then performing multi-level compliance verification based on a national secret security audit rule system, and generating a compliance judgment conclusion of a log entry level and a cross-entry-level abnormal behavior pattern recognition report; and finally, according to the judgment conclusion and the identification report, constructing a national secret log security auditing result set which comprises a risk level evaluation result, a violation evidence chain association graph and a security reinforcement strategy suggestion list, thereby effectively improving the accuracy and comprehensiveness of national secret log auditing.

The invention provides a context-based intelligent voice semantic understanding analysis method and system, and relates to the technical field of voice interaction, and the method comprises the steps: obtaining an input voice signal, extracting an acoustic feature, and decoding the acoustic feature to obtain a candidate text; constructing context semantic representation to carry out disambiguation processing; establishing a semantic dependency graph and carrying out multi-level correlation analysis; spreading context constraint information to perform multi-hop reasoning; and finally generating intention recognition and slot filling results and updating the session state. The semantic comprehension accuracy and the intelligent degree of the voice interaction system are improved by introducing the context information and the multi-level semantic dependency relationship.

The invention relates to the technical field of information retrieval, in particular to a financial knowledge retrieval method and system based on a vector database. The method comprises the following steps of obtaining financial text data, and performing association processing according to the financial text data to obtain financial index association data; performing numerical association reasoning on the financial index association data to obtain numerical association data; semantic dependency reasoning is carried out on the financial index associated data to obtain semantic dependency data; performing multi-head attention calculation according to the numerical association data and the semantic dependency data to obtain financial text feature data; and performing vector index generation according to the financial text feature data to obtain financial vector index data so as to perform financial knowledge base construction auxiliary operation. According to the method, the deep semantic dependency and index logic relationship between financial entities can be accurately captured, the integration and feature expression ability of cross-sentence information is enhanced, and the accuracy of financial knowledge retrieval is improved.

The invention relates to the technical field of social data processing, in particular to a personalized recommendation-oriented big language model social data tag attribute generation method. Comprising the following steps: constructing a multi-modal social feature incidence matrix; generating an initial label set adapted to the social scene; and establishing a tag hierarchical relationship through an improved semantic dependency tree, calculating an aging attenuation coefficient based on a behavior occurrence time interval, and counting a proportion of users with the same tag in a core social circle as a group association value. According to the method, the complexity of the multi-modal data in the social scene can be adapted by constructing the multi-modal social feature incidence matrix, so that the generated tag system is more fit with the multivariate features of the social data. According to the method, the tag hierarchical relationship is automatically constructed through the improved semantic dependency tree, the aging attenuation coefficient is calculated, and the deep analysis capability of social semantics is improved.

The invention relates to the technical field of online approval management, and discloses an online employee approval management system based on an edge calculation model. An employee terminal data acquisition layer of the system captures a multi-modal examination and approval data flow based on a priority dynamic focusing mechanism, generates an examination and approval behavior characteristic thermodynamic diagram through an edge characteristic pyramid network, and divides an abnormal region; the examination and approval knowledge graph construction layer loads a domain rule base, extracts an entity relationship through semantic dependency analysis, and constructs an examination and approval rule path graph with a weight; the map path cross validation layer synchronizes an edge node clock, and generates approval risk confidence through multi-head map attention network fusion data; the dynamic strategy optimization layer converts the risk confidence into an executable strategy and issues the executable strategy to an edge execution engine; and the approval efficiency feedback layer monitors the flow state and generates an efficiency index to dynamically optimize a data acquisition strategy. According to the system, precision, high efficiency and dynamic optimization of approval management are realized.

The invention provides a cross-modal semantic attention collaborative enhancement video subtitle generation method and system, and belongs to the field of video subtitle generation. The method aims at solving the problems that an existing video description model mostly stays in first-order relation modeling on the attention mechanism level, high-order semantic dependence is difficult to capture, and noise is easy to introduce in multi-modal feature fusion. The invention provides a cross-modal semantic attention collaborative enhancement module, the module comprises two key components of context semantic modulation of attention enhancement and cross-modal structure alignment, and the fine modeling ability of the generative model for visual and text semantics is effectively improved by dynamically modulating attention weight and optimizing a modal alignment structure. And integration is carried out based on a non-autoregression coarse-to-fine video description model. Experimental results show that the method can significantly improve the accuracy and diversity of video description generation on the premise of keeping the model scale and the calculation overhead basically unchanged.

The invention discloses a graph-driven self-attention compressible memory management method, and belongs to the technical field of penetration testing. According to the graph-driven self-attention compressible memory management method, an attention graph is constructed, semantic dependence and attention flow directions among multiple Agent nodes are accurately described by utilizing edge weights, the relevance and the accessibility of information are improved, and in the aspects of screening and loading, the expandability of the memory is improved. According to the method, key memory fragments are screened on the basis of concerned graph edge weights and task related features, only the loaded information is loaded, redundancy is effectively eliminated, the data volume processed by a system is reduced, and the system operation efficiency is improved; the semantic compression module can perform abstract processing on historical information based on various elements, and on the premise of ensuring that key semantic information is not lost, the abstract length is controlled within the window limit, so that the model can normally process information, and the adaptability and stability of the system to different data volumes are improved.

The invention belongs to the technical field of education artificial intelligence and deep learning model optimization, and discloses a thinking tree enhancement-based test question knowledge system intelligent labeling method and system.The thinking tree enhancement-based test question knowledge system intelligent labeling method comprises the steps of collecting question data by introducing a thinking chain enhanced data generation mechanism, and generating a high-quality training sample; and a control mechanism is set, a question generation process is optimized, and the generated question is ensured to accord with teaching specifications in the aspects of knowledge point coverage, grade adaptability, difficulty matching and the like. Through a text encoder based on comparative learning optimization, the model can accurately carry out semantic alignment on questions and knowledge point labels, and label path information is fused through a double-coding mechanism, so that the hierarchical relationship between the labels and the capture capability of semantic dependence are improved. Finally, the constructed multi-label prediction model significantly improves the accuracy and generalization ability of knowledge point labeling, and can provide accurate support for personalized learning recommendation and teaching resource allocation.

The invention provides a three-dimensional hand posture estimation method fusing hand and object features, relates to the technical field of hand posture estimation, and constructs a three-dimensional hand posture estimation network fusing the hand and object features. Comprising a feature extraction module, a double-flow hand feature pyramid sub-network, a hand feature dynamic adjustment module, a double-flow attention collaborative feature enhancement module and a decoder. Establishing a loss function of the three-dimensional hand posture estimation network fused with the hand and object features, training the three-dimensional hand posture estimation network fused with the hand and object features, and obtaining a hand posture estimation result based on the trained three-dimensional hand posture estimation network fused with the hand and object features. According to the method, local details and global semantic dependence are captured through bidirectional cross-scale information aggregation, and the problem of channel information loss of a traditional feature pyramid is relieved; and in combination with hand-object geometric constraint and semantic complementation characteristics, the cross-modal feature alignment capability is enhanced, and deep fusion of hand-object geometric constraint and semantic complementation is realized.

The invention discloses an SQL (Structured Query Language) generation optimization method and system based on a semantic dependency graph and reinforcement learning. The method comprises the following steps: acquiring a natural language query and database mode, and constructing a semantic dependency graph containing entity nodes and mode nodes; extracting a meta-path sub-graph based on a preset meta-path mode, combining a graph neural network with a cross-view contrast learning mechanism, and fusing global and local view features to obtain a query semantic enhancement vector; inputting the enhancement vector into a reinforcement learning model to generate a prediction SQL; constructing a multi-dimensional mixed reward function containing an AST tree editing distance to update the model; and calculating a confidence attenuation coefficient by utilizing reward feedback, and dynamically adjusting the weight of a connecting edge in the semantic dependency graph to reconstruct a graph structure. Through explicit semantic modeling and an execution feedback driven graph structure reconstruction mechanism, the problems of difficulty in semantic dependency capture and nonstandard generation logic in a complex query scene are solved, and the SQL generation accuracy and the system adaptive capacity are remarkably improved.

The invention relates to the technical field of data processing, particularly discloses a data blood relationship automatic tracking system based on a large model, and aims to solve the problems that a traditional method is difficult to adapt to complex business logic, cannot capture deep semantic dependency and is low in cross-system automation degree. The system comprises a data perception and acquisition module, a metadata semantization reconstruction module, a multi-granularity dependency analysis module, a dynamic causal reasoning engine and a blood relationship map self-evolution maintenance module, metadata semantics are analyzed through a large model, a multi-granularity dependency relationship is identified, and the blood relationship map self-evolution maintenance module is established in combination with a causal reasoning verification and map self-updating mechanism. Full-link, high-precision and self-adaptive data blood relationship automatic discovery and maintenance are realized, and the coverage range, the accuracy and the automation level of blood relationship tracking are effectively improved.

The invention provides an intelligent processing method and system from structured data to a text based on a natural language, and relates to the technical field of data processing.The method comprises the steps that the text chunk analysis process is optimized and adjusted according to analysis optimization parameters, sentences are segmented into non-overlapping phrases with syntactic function labels, and the text after chunk analysis is obtained; performing syntactic and semantic structure analysis on the text subjected to block analysis, and establishing a semantic association relationship between internal structures of sentences through component analysis, dependency analysis and semantic dependency graph analysis to obtain structured semantic information; performing multi-sentence logic association analysis on the structured semantic information on a chapter level to obtain semantic information of the whole chapter; and based on the semantic information of the overall chapter, obtaining a target natural language text by utilizing a pre-trained large language model. According to the method, the accuracy and fluency of conversion from the structured data to the text are improved.

The invention discloses a road crack segmentation method and device based on deep learning, electronic equipment and a program product. According to the method, two collaborative and functional complementary processing paths are adopted to perform parallel processing on a to-be-segmented image so as to obtain corresponding feature information: on one hand, the to-be-segmented image is converted into a feature sequence containing spatial position information, and global semantic modeling is performed on the feature sequence; global semantic features representing the overall shape of the crack and long-range semantic dependence are obtained; and on the other hand, local texture and edge detail information of the crack is directly extracted from the to-be-segmented image, and detail enhancement features are obtained. Thirdly, fusing the global semantic features and the detail enhancement features to obtain target fusion features considering crack positioning, connectivity and boundary and texture expression; according to the segmentation result output based on the target fusion features, even tiny cracks can be effectively depicted, and then the continuity, boundary definition and robustness of tiny crack segmentation under the complex road background can be improved.

The invention discloses a multi-span question and answer knowledge perception method and system based on big and small model collaboration, and the method comprises the following steps: S1, executing knowledge prompt generation and chain logic reasoning through a big language model, and carrying out the semantic extension and knowledge reconstruction of an input question, obtaining a knowledge perception representation containing a potential answer entity, a reasoning link and a semantic dependency relationship; s2, based on the obtained knowledge perception representation, calculating a knowledge coverage rate between a standard answer in the training sample and the knowledge perception representation; screening the training samples according to the knowledge coverage rate, and constructing a training set with consistent coverage for performing supervised training of coverage matching constraint on the small model; and S3, based on the knowledge perception representation in the S1 and the small model obtained by training in the S2, fusing the knowledge perception representation and the original question, inputting the fused knowledge perception representation and original question into the small model, and executing collaborative reasoning to realize multi-span answer positioning and generation. According to the method, unification of knowledge perception and structured reasoning is realized, and the accuracy and generalization of multi-answer extraction are improved.

The invention relates to the technical field of language recognition, and discloses a voice-to-text optimization method based on Ai assistance, which realizes global collaborative optimization through multi-scale feature adaptive extraction, joint modeling and a continuous learning mechanism. Compared with the problem of misrecognition amplification caused by a modular processing flow in the prior art, the method has the advantage that the influence of environmental noise on the upstream module is reduced by dynamically adjusting the feature extraction parameters and performing joint coding. Meanwhile, by introducing domain knowledge adaptation and context memory mechanisms, the defects of a traditional method in long-distance semantic dependency capture and personalized adaptive capacity are overcome, and therefore the overall performance of a voice-to-text system is improved.

The invention provides a multi-scene target detection and behavior recognition method and system based on deep learning, and relates to the technical field of software development behavior analysis, and the method comprises the steps: obtaining a code editing operation sequence, an interface interaction event sequence and a file access record sequence in a software development environment, and forming a multi-modal development behavior data flow; performing development scene identification on the multi-modal development behavior data flow to obtain a scene state identifier; constructing a semantic perception feature extractor, performing syntax structure analysis and semantic dependency modeling on the code editing operation sequence, extracting an abstract syntax tree structure and a program execution path diagram, and combining to obtain code structured features; performing code mode matching and abnormal mode positioning to obtain a target code segment detection result; extracting operation time sequence features and carrying out cross-modal feature alignment on the operation time sequence features and the code structured features to obtain a unified behavior vector; and performing time sequence dependence reasoning and behavior pattern classification to obtain a behavior recognition result.

This invention relates to the field of Internet of Things (IoT) technology and is a cascading threat detection method for automated rules in IoT. It utilizes a large language model to parse rule text, extract semantic elements, and construct a semantic representation model. A semantic alignment model is built to measure the functional similarity of different rules in a rule set. A TAP rule heterogeneous graph is constructed, establishing explicit and implicit relationships respectively. A dual-attention context encoder is constructed to obtain rule embedding representations containing semantic dependency information. A global relation attention mechanism is introduced to measure the importance of explicit and implicit relation spaces under different node types. The invention determines whether cascading threat paths exist in the rule set and generates threat detection results. This invention can automatically parse rule semantics, construct explicit and implicit dual-relation graph models, and utilize multi-layer graph neural networks to achieve multi-hop inference, thereby comprehensively detecting potential rule-cascading threats.

The present application relates to the technical field of semantic processing, and more particularly to a word element aggregation and distribution method and system based on multi-level cache and semantic preheating, which obtains historical word element request interaction logs, analyzes the access characteristics of different business scenarios and context fragments; in combination with semantic dependency relationships, identifies regular repetition and sudden hotspot characteristics of word element access, determines a preloading target set; real-time acquisition of context semantic data of the current inference request, analysis of word element prediction demand parameters to be generated for output; fusion of word element prediction demand parameters and preloading target set, dynamic generation of word element aggregation parameters and distribution scheduling strategy. The present application realizes forward-looking preheating and accurate scheduling of word element resources by deeply mining historical rules and real-time semantic intent, effectively solves the problems of weak semantic perception, low hit rate and response lag of traditional cache strategies, significantly reduces the inference delay of large models, and improves the throughput and stability of the system in high-concurrency scenarios.

The invention discloses a tea garden identification method and system based on an unmanned aerial vehicle image. The method comprises the steps that an improved UNet model is adopted as a segmentation network, and the improved UNet model comprises a visual Transform module and a cavity convolution module in an encoder: A, the visual Transform module is utilized to segment an input image into patch sequences, and global semantic dependency features are established through a multi-head self-attention mechanism; b, extracting multi-scale local features by using the cavity convolution module and adopting a multi-stage expansion rate combination; and C, the decoder fuses the global semantic dependency features and the multi-scale local features output by the encoder through jump connection to generate a tea garden segmentation result.

The application particularly relates to a nuclear power plant isolation safety measure information automatic identification method based on semantic analysis, comprising the following steps: classifying nuclear power plant isolation safety measure requirement text corpus; preprocessing nuclear power plant isolation safety measure requirement text corpus; automatically identifying whether a nuclear power plant work order task corresponding to the nuclear power plant isolation safety measure requirement text corpus needs to be isolated based on semantic matching; automatically identifying isolation boundary equipment involved in the nuclear power plant work order task corresponding to the nuclear power plant isolation safety measure requirement text corpus based on semantic dependency analysis; automatically identifying an isolation target state corresponding to the isolation boundary equipment of the nuclear power plant work order task corresponding to the nuclear power plant isolation safety measure requirement text corpus based on semantic dependency analysis; and comprehensively grasping the system and equipment isolation conditions of a whole unit in a specific time window according to the nuclear power plant work order task isolation safety measure automatic identification result. The method can automatically identify the nuclear power plant isolation safety measure information.

The present application relates to the technical field of relation extraction, in particular to an invoice relation extraction method based on natural language analysis, comprising the following steps: through analyzing multiple features such as image seal, two-dimensional code and handwritten notes, matching text and image space information, checking the consistency of two-dimensional code and tax control code, identifying field combination and amount and payee abnormality. In the present application, through multi-level fusion analysis of seal, two-dimensional code and handwritten marks and other detail features in the invoice image, spatial joint discrimination of image and text content is realized, the credibility of information verification is improved by using character security comparison and coding consistency, with the help of context semantic dependency structure, the deep connection between payment behavior and entities is strengthened, elements such as amount and payee are dynamically classified and aggregated, automatic recognition of complex transaction relations is realized, further through linkage clustering and difference tracking, intelligent judgment and real-time label generation of abnormal risk behavior are realized, and the active risk control ability of financial management is improved.

The invention discloses an SQL (Structured Query Language) fuzzy test case generation method based on dynamic metadata dependence. The method comprises the following steps: firstly, extracting structural metadata from a database system directory in a dynamic introspection manner during operation to construct a base layer of a hierarchical semantic model, and initializing an enhancement layer of the hierarchical semantic model for storing explicit semantic rules and implicit semantic rules learned during operation; secondly, converting the structural metadata into a semantic dependency graph to describe a semantic relationship; in the test case generation stage, ANTLR is adopted to analyze SQL and mutate SQL intermediate representation, a semantic dependency graph is utilized to analyze semantic requests and detect conflicts, and restored semantic effective queries are automatically generated through a search algorithm. In the execution stage, the next round of SQL generation is guided according to operation feedback. Through the dynamic metadata extraction and runtime learning mechanism, the semantic validity of the SQL test case and the database defect discovery efficiency are effectively improved.

The invention discloses a multi-modal large model problem data tracing method based on semantic association rule mining, and the method comprises the steps: obtaining multi-modal input data, extracting an intermediate semantic feature, constructing a semantic chain structure representing a semantic dependency relationship between modals, and applying an association rule mining algorithm on a semantic chain, so as to achieve the multi-modal large model problem data tracing. And generating a semantic rule set with high confidence. The system comprises a semantic feature extraction module, a semantic chain construction module and a problem data positioning module, and can effectively improve the interpretability of a large model in an exception processing path and the automatic identification capability of problem data. According to the method, the technical problems that a traditional multi-modal model depends on manual analysis in the problem data tracing process, the visualization ability is weak, and the diagnosis efficiency is low are solved, and accurate positioning of problem fragments under complex semantic logic is achieved; the method is suitable for the field of question data analysis and model debugging in multi-mode intelligent systems such as image-text question answering, voice understanding and video analysis.

The invention relates to a key technical scheme intelligent screening and positioning method and system oriented to integrated circuit research and development, and the method comprises the steps: respectively extracting technical entities representing specific process steps and device structures and legal entities representing legal protection boundaries through building a semiconductor field heterogeneous data stream and utilizing a large language model; constructing a bimodal semantic topology network comprising technical association, legal association and cross-modal alignment edges; heterogeneous graph attention representation learning is executed, and deep semantic dependence between nodes is captured; generating a global semantic representation vector representing the logic consistency of the technical scheme based on the topological connection density and the cross-modal semantic alignment strength of the nodes; and finally, accurately positioning a key technical scheme based on the logic consistency confidence coefficient. According to the method, the process-protection logic consistency can be automatically verified, the problems that traditional keyword retrieval is loud in noise and a semantic model lacks logic support are solved, and objective and high-reliability data support is provided for technology pre-research, design avoidance and research and development path planning.

The application relates to the field of natural language processing, and relates to a campus safety emergency extraction method based on multi-task learning, which comprises the following steps: S1: obtaining original news text of a campus safety emergency; S2: establishing a campus safety emergency extraction model, which comprises a text feature representation module, an event type classification module, a trigger word extraction module and an argument role classification module; S3: executing the text feature representation module to obtain a word vector fused with semantic dependency information; S4: executing the event type classification module to obtain a predicted event type feature vector; S5: executing the trigger word extraction module to obtain a trigger word feature vector; and S6: executing the argument role classification module to obtain an argument role. The method solves the problem that the sequence labeling method leads to labeling conflicts and cannot extract overlapping argument roles; and the method solves the problem that it is difficult to fully extract semantic knowledge and dependency relationships in a sequence in a professional field. The feature expression capability of trigger words is strengthened, and the argument role extraction capability is improved.

The invention relates to a large language model driven NL2SQL intelligent query system in the financial field, and belongs to the technical field of SQL query. The system comprises a domain semantic analysis module, a query planning and disassembling module, an SQL generation and optimization module, a query assembly and verification module and a continuous learning and feedback module. The domain semantic analysis module is used for realizing conversion from natural language query to structured semantic representation, and constructing an explicit calculation dependency relationship graph through domain self-adaptive pre-training and semantic dependency analysis; and the query planning and disassembly module is used for identifying calculation nodes and types by analyzing the dependency graph, extracting atomic calculation units, planning an execution sequence through topological sorting, and generating a structured disassembly plan. According to the method, the financial semantic understanding capability is constructed through field self-adaptive fine tuning, the calculation dependence graph explicit modeling query logic is utilized, reliable sub-queries are generated through modular disassembly and optimization, and finally a complete SQL is formed through combination of a verification mechanism.

The invention discloses a semantic guidance multi-label image classification method based on hierarchical prototype injection. Input multi-label images are classified by adopting a classification model. The classification model is a hierarchical prototype visual converter (HP-ViT) model. According to the method, label co-occurrence statistics is analyzed offline, and a hierarchical semantic community which can reflect high-order semantic dependence and is from coarse to fine is mined; secondly, instantiating the semantic concepts into a group of learnable hierarchical prototypes, and performing hierarchical injection at different depths of a Transform encoder by adopting a coarse-to-fine strategy; and finally, the prototypes are supervised through an auxiliary classification task corresponding to each level under a multi-task learning normal form, so that dynamic and structured semantic guidance of the backbone network feature extraction process is realized. The method shows potential in the aspects of improving classification performance, data efficiency and model interpretability, and provides a new technical path for solving a complex multi-label classification problem.

The invention provides a test case generation method and system based on multi-modal machine learning and dynamic optimization, and the method comprises the steps: carrying out the cross-modal feature association of a demand semantic text, a source code instruction stream and a historical defect event record in a software test scene, generating a multi-modal association feature space, inputting a dynamic attention alignment network, and carrying out the multi-modal feature association. Weight distribution is carried out on the business rule semantic dependency relationship, the code execution path dependency relationship and the defect propagation link association relationship, a joint representation vector is generated, double-task cooperative training of test case generation and defect positioning prediction is executed on the joint representation vector, feature extraction weights of double tasks are adjusted to generate a test case generation parameter set, and the test case generation parameter set is subjected to defect positioning prediction. Inputting the boundary test scene into a generator of the generative adversarial network, strengthening feature expression of the boundary test scene, generating a boundary test case set, performing priority ranking on the boundary test case set to calculate case coverage weight, and generating a target test case set. According to the invention, the effectiveness and comprehensiveness of the test case and the utilization efficiency of test resources are improved.

The application discloses a power transaction knowledge extraction method based on semantic coding and feature enhancement, comprising the following steps: cleaning and term standardization on unstructured text in the power transaction field to obtain preprocessed text; using a pre-training language model based on an error correction mask mechanism to code semantic features of the preprocessed text to obtain a semantic coding sequence; according to the semantic coding sequence, sequentially performing bidirectional long-distance semantic dependence capture, dynamic attention weight distribution and local semantic detail fusion to obtain an enhanced feature sequence; based on a layered pointer network, performing entity and relation joint extraction on the enhanced feature sequence to obtain a triple composed of a head entity, a relation and a tail entity; and performing deduplication and completion processing on the triple to complete the knowledge extraction of the power transaction. The application realizes the goal of automatically and high-quality extracting structured knowledge of the power transaction from unstructured text.

The invention discloses a forest and grass science and technology data analysis and prediction method and system based on cloud computing, initial forest and grass data are transmitted to a cloud computing platform, a Spark framework is utilized to perform real-time denoising, format conversion and semantic analysis on the data to generate a unified metadata tag, and forest and grass processing data are obtained; capturing a long-distance semantic dependency relationship of the forest grass processing data through a multi-head attention mechanism, and classifying project data to obtain classified forest grass data; establishing an LSTM prediction model based on an LSTM long short-term memory network, and optimizing hyper-parameters of the LSTM prediction model by using an improved GWO grey wolf optimization algorithm to obtain a target prediction model; and performing trend prediction on the classified forest and grass data by using a target prediction model, and outputting a species distribution change trend. The improved algorithm is adopted to adapt to various scenes, the accuracy of data analysis and processing can be improved, and accurate analysis and prediction are provided for forest and grass data of different classifications.