3950 results about "Data model" patented technology

The invention belongs to the technical field of knowledge maps and artificial intelligence, and discloses a building safety risk identification method of a big language model assisted knowledge map. According to the technical scheme, the overall process comprises the steps of data collection and preprocessing, construction of urban building structured table data, construction of an urban building safety knowledge graph, integration of the structured table data and knowledge graph data, model fine adjustment, model training, model performance evaluation and model recognition effect verification. According to the technical scheme, the large language model with the high semantic modeling capacity and the knowledge graph with the high graph structure expression capacity are integrated, the related knowledge of urban building safety is automatically extracted, constructed and integrated, high-risk events such as fire disasters and structural hidden dangers are recognized, and the informatization level and the intelligent level of urban building safety management are improved.

The invention relates to the technical field of financial data processing, in particular to a multi-source heterogeneous fund data processing method and system, and the method comprises the steps: recording source information through building a data source registry, and marking a unique identifier for data; performing differential analysis on the fund data in different formats to generate standardized column type storage data; traversing column type storage data to extract statistical characteristics, performing field classification through metadata analysis and financial dictionary matching, analyzing business connotations of fields difficult to classify in combination with a localized large language model, and mapping the business connotations to a header fusion knowledge graph; generating a mapping rule from the source field to the enterprise-level data model by applying a rule engine template on the basis of field classification and semantic recognition results; converting the data structure according to the mapping rule and executing standardization processing; the quality is further optimized through data cleaning; and finally, the data are verified, standardized fund data supporting data traceability are output, and the strict requirements of financial supervision application are met.

The invention relates to the technical field of data analysis, in particular to an operation decision intelligent analysis method and system based on cross-domain data fusion. The method comprises the following steps: firstly, based on an enterprise multi-domain ontology knowledge base, performing entity identification and relation mapping on heterogeneous data from different business systems through a semantic mapping-based multi-source heterogeneous data dynamic fusion algorithm, and establishing a unified data model; then, a causal reasoning and deep learning fused hybrid intelligent decision engine is adopted to analyze and process the model; then, a multi-level causal relationship network among business variables is constructed through a causal relationship discovery algorithm by utilizing an analysis result of the hybrid intelligent decision engine, and an adaptive business scene analysis model based on reinforcement learning is used to dynamically adjust an analysis strategy according to business environment changes; generating a Pareto optimal decision scheme set through a multi-objective optimization algorithm, and outputting operation decision suggestions; according to the invention, the comprehensiveness and accuracy of intelligent analysis of enterprise operation decisions are improved.

Activity monitoring of a cloud compute environment based on container orchestration data may be performed by a data platform. For example, the data platform may obtain audit log data generated by a container orchestrator within a cloud compute environment, generate a data model based on the audit log data, and use the data model to monitor the activity for a security issue. The data model may be indicative of activity occurring with respect to one or more containerized applications executing within the cloud compute environment and the monitored activity may be activity with respect to the one or more containerized applications. Corresponding methods, systems, and products are also disclosed.

The invention discloses a multi-source process parameter mapping supervision system and method based on a big data model, and relates to the technical field of process parameter analysis. Initial process parameters in a process production line are collected, the initial process parameters are processed to obtain standardized process parameters, and the process production line is subjected to process stage division; analyzing a stage product deviation degree of the process stage, calculating correlation between the standardized process parameters and the product deviation degree of the process stage, analyzing the stage product deviation degree of the process stage, predicting the product deviation degree of the process stage in production, and predicting a product reject ratio in production based on the product deviation degree of the process stage. According to the method, the product deviation of each stage and the reject ratio of the whole product are continuously predicted, the production state is judged in real time, a dynamic quality control closed loop is constructed, and the pertinence and effectiveness of supervision are improved.

The embodiment of the invention provides a government affair digital human dynamic interaction method and system based on a multi-mode large model. The method is applied to the technical field of government affair intelligent services, and comprises the following steps: acquiring a policy announcement text, and performing cleaning and structuring processing to obtain a structured policy data set; and extracting old and new policy data, performing difference comparison, marking key change fields, and generating policy change data. Abstracting and element extraction are carried out on the change data to form structured semantic fragments, and the structured semantic fragments are incrementally embedded into the policy knowledge graph. And generating a question and answer pair sample based on the updated knowledge graph, and carrying out self-supervised fine tuning training on the multi-modal large model. A user inputs multi-modal data, and the model generates a government affair response and feeds back the government affair response. According to the scheme, the multi-modal large model can continuously keep the latest policy knowledge; the model is enabled to generate accurate government affair response with consistent context while understanding multi-modal input such as text, voice and image, and timeliness, accuracy and interactive experience of policy interpretation are improved.

The invention relates to the technical field of water pump energy-saving control, and discloses a water pump energy-saving control system based on an intelligent data model, which comprises a data sensing module, a modal analysis module and the like. The data sensing module collects flow, lift and power data and is provided with a dynamic monitoring window; the modal analysis module divides operation modes and generates an energy efficiency characteristic matrix; the rule construction module extracts a core energy efficiency factor and constructs an energy-saving regulation and control strategy; the priority scheduling module analyzes the control priority matrix and evaluates an energy efficiency deviation index; a mapping calibration module fits the optimal energy efficiency reference curve to generate an energy efficiency deviation gradient map; the control execution module generates a regulation and control instruction set according to the map. The system realizes fine management and energy efficiency optimization of water pump operation through multi-mode energy efficiency analysis and dynamic regulation, solves the problems of poor adaptability of traditional control working conditions, low energy-saving efficiency and the like, is suitable for water pump energy-saving control in the fields of industry, municipal administration and the like, and is accurate and efficient.

The invention discloses an ocean holographic environment comprehensive digital twinning system, and relates to the technical field of ocean monitoring, the digital twinning system comprises a data layer, a model layer, an application layer, an environment layer, a governance layer and a service layer, multi-scale spatio-temporal data is used as a substrate, and a virtual-real mapping and intelligent simulation technology is used to simulate the ocean holographic environment comprehensive digital twinning system. A full-dimension virtual mirror image covering the seabed, the middle sea and the sea surface is constructed, and the system serves core scenes such as a supervisor, scientific research cooperation and ocean engineering. According to the digital twin system, a six-layer layered architecture is adopted, edge computing and cloud computing collaboration are combined, a data-model-service-governance-environment multi-dimensional collaboration system is formed, data-driven decision making and dynamic optimization are achieved, and the marine monitoring efficiency and accuracy are improved.

The invention discloses a data import and field intelligent matching method for a low-code platform, and particularly relates to the technical field of low-code platform data processing. Constructing a field initial feature vector, extracting a standard field and historical matching features thereof from the target data model, and calculating a semantic matching score matrix by using a multi-source feature fusion model; screening the initial mapping table based on a dynamic threshold value, performing statistical deviation analysis in combination with a field value distribution difference measurement feature delta, and when delta exceeds a preset threshold value, calling a value mapping correction model to complete rematching to obtain an optimized mapping table; if it is detected that user history adjustment exists in the same source file type, re-correction is carried out based on a user behavior log, and a final field mapping table MP3 is output; finally, the MP3 is applied to data import, and automatic matching and binding are achieved. The method improves the accuracy and automation level of field matching, and is suitable for a multi-source heterogeneous data integration scene.

The invention relates to the technical field of nondestructive testing, and discloses a steel structure weld defect ultrasonic detection and identification system. A data acquisition module of the system acquires an original ultrasonic signal of a steel structure welding seam through ultrasonic detection equipment and acquires geometric attribute data of the welding seam; the model construction module constructs a welding seam three-dimensional digital model based on the data; a feature extraction module performs feature mining on the three-dimensional digital model and extracts a weld defect feature index set; the difference analysis module carries out deviation calculation on the characteristic index set and a reference index set in a standard welding seam characteristic database, and an abnormal area is identified; the risk assessment module calculates a defect sensitivity index according to the abnormal region in combination with real-time environmental parameters, and assesses a defect risk level; and the report generation module formulates a detection scheme according to the defect risk level, generates a detection instruction, executes ultrasonic scanning, collects performance data and generates a defect detection report. The system has the advantages of high detection precision, high reliability, automatic and standardized process and the like.

The invention relates to the field of human resource management, and discloses a tobacco enterprise human resource management auxiliary calibration method based on big data, and the method comprises the steps: obtaining multi-source heterogeneous data related to enterprise internal human resources, and constructing a structured human resource data model in combination with a data standardization processing mechanism and an abnormality elimination strategy; post portrait modeling is carried out on the structured human resource data model, a capability dimension nesting analysis method is introduced, key capability factors and weight distribution required by each post are extracted, and a post capability demand graph is constructed; based on the post capability demand map, fusing the staff portraits and the historical job data, and identifying the deviation between posts and the staff through a multi-dimensional feature matching algorithm to form a preliminary calibration suggestion set; and a dynamic service association analysis method is introduced, and key matching parameters in the preliminary calibration suggestion set are dynamically corrected in combination with latest service demand data and real-time task assignment information. The method has the advantage of improving the management efficiency.

The invention discloses an internet of things multi-protocol fusion limo intelligent control system, which relates to the field of limo intelligent control and comprises a hardware irrelevant layer, a multi-protocol adaptation layer, a data abstraction layer, a programmable logic layer, a security and redundancy mechanism and an edge AI module. According to the invention, a physical bus is packaged into an independent module, an MCU / SoC and a protocol transceiver are built in, hot plugging is realized through a standardized PC I e / EdgeConn backboard, a core processing board only exposes a unified backboard interface, module drive localization execution is realized, master control firmware does not need to sense protocol details, only the module needs to be replaced when a protocol is newly added, and the master control firmware or upper software does not need to be modified. The defect that the whole vehicle firmware needs to be refreshed when a bus is replaced in a traditional system is overcome, each protocol corresponds to an independent drive micro-service deployed in a containerization mode, the drive micro-service analyzes a message into a unified data model and uploads the unified data model to a data abstraction layer, a downlink control instruction is packaged and issued according to a target protocol, and it is guaranteed that the protocols are diversified and interfaces are consistent.

The invention discloses a multi-modal fusion real-time environment monitoring visual robot system, and relates to the technical field of real-time vision. The system comprises a multi-mode sensing module, and is equipped with various sensors such as a binocular stereo camera and a laser radar to collect environment data. The heterogeneous data preprocessing unit cleans and downsamples data such as images and point clouds; the space-time alignment fusion module realizes multi-source data space-time registration and synchronization; the environment semantic understanding engine constructs an environment semantic graph through a multi-branch network in combination with an attention mechanism; the abnormal event detection unit identifies abnormity based on a historical data model; the path planning and decision-making module integrates multiple targets to generate an optimal path; the autonomous movement execution module controls the robot to move and operate; and the cloud cooperative control center supports model updating and remote intervention. According to the invention, through cooperative work of all the modules, full-process intelligentization of environment monitoring data acquisition, processing, analysis and decision making is realized.

The invention provides a robot self-adaptive grabbing and assembly track planning method for complex working conditions, and relates to the technical field of robot control, and the method comprises the steps: building a workpiece three-dimensional feature data model, constructing a deep reinforcement learning network comprising a working condition recognition layer, a strategy generation layer and a reward calculation layer, and generating an optimal grabbing strategy. And an assembly task is decomposed into three stages of pre-alignment, initial assembly and accurate assembly, so that parameter dynamic optimization is realized. The manipulator can adapt to complex working conditions such as workpiece pose uncertainty, shielding and deformation, and the grabbing precision and the assembly success rate are improved.

The embodiment of the invention provides a computer communication method and system based on the Internet of Things, and the method comprises the steps: constructing a multi-level system architecture, carrying out the preprocessing and feature extraction of an original data flow, recognizing the data characteristics through a time sequence analysis method, and constructing a dynamic data model; deploying a monitoring agent at a transmission node to collect network performance indexes in real time, and constructing a network quality evaluation model; for an incomplete sensor data flow, prior probability distribution is constructed based on a dynamic data model and a network quality grade, and an optimal estimation value of missing data is calculated by adopting a Bayesian reasoning framework and an iterative algorithm; establishing a mapping relation between a network state and an optimal parameter through reinforcement learning to realize self-adaptive adjustment and optimization; and grading the data according to reliability, extracting high-reliability data points as anchor points, designing an iterative refinement algorithm to realize information propagation, and fusing to obtain a complete sensor data stream. According to the method, the problems of poor data recovery accuracy, static parameter configuration and insufficient adaptability in a complex network environment are solved.

The invention discloses an intelligent storage system supporting multi-source heterogeneous data fusion management, which relates to the technical field of multi-source heterogeneous data fusion, and comprises a data source adaptation module, an intelligent data source adapter is configured in a DataWorks data integration module, multi-source heterogeneous data is accessed through the intelligent data source adapter, and the data source adaptation module is connected with the DataWorks data integration module; extracting a semantic feature vector and a technical feature set; the neural symbol hybrid inference module inputs the semantic feature vector and the technical feature set into a neural symbol hybrid inference engine, calculates a similarity matrix between multi-source heterogeneous data fields through a BERT-based neural network, and imports the technical feature set into a field knowledge graph constructed by Neo4j for symbol logic verification to generate a unified metadata model; according to the method, a semantic similarity matrix calculated by a BERT-based neural network is combined with symbol logic verification of a Neo4j knowledge graph through a neural symbol hybrid inference engine, so that automatic semantic alignment and logic consistency verification of multi-source heterogeneous data are realized.

The invention relates to a bridge structure vibration monitoring and analysis method based on artificial intelligence, and the method specifically comprises the following steps: setting an acceleration sensor network at a key part of a bridge, collecting and marking a vibration signal sample, and forming a data set; a high-resolution time-frequency matrix of sample adaptive optimal kernel time-frequency distribution is calculated, a damage sensitive resonance frequency band is positioned according to spectrum kurtosis, and the time-frequency matrix is enhanced by the resonance frequency band subjected to adaptive gain enhancement; dividing a matrix frequency axis multi-scale binary tree, calculating and normalizing sub-band average energy, quantifying energy distribution uniformity through information entropy, and splicing multi-scale entropy values into feature vectors; then constructing and training a bridge detection data model; and finally, preprocessing new data, inputting the preprocessed new data into the trained model, and automatically analyzing to obtain a bridge health monitoring result. Through time-frequency optimization, multi-scale entropy and deep learning technologies, the problems of large noise, difficult feature extraction and low automation and accuracy of traditional monitoring can be solved.

The invention relates to the technical field of data processing, in particular to a computer feature extraction processing system and method fusing multi-source heterogeneous data, and the system comprises a structure correlation mapping module, a density mutation recognition module, a disturbance path analysis module, a path logic matching module and a risk logic output module. According to the method, by optimizing a data processing mode, important features in data can be accurately recognized, manual intervention and experience dependence are reduced, large-scale data analysis and processing efficiency is improved, the accuracy of a data model is enhanced, and by deeply analyzing the fluctuation amplitude and the change trend of the data, potential associated areas and abrupt change grids can be accurately positioned; key disturbance information is effectively identified, path logic matching can reveal a risk behavior mode in data evolution, potential early warning events are marked in time, the efficient processing capacity of the model for a complex data mode is ensured, and particularly, the problems of low efficiency and insufficient precision in a traditional scheme are solved in the face of large-scale data.

The invention relates to the technical field of pipeline monitoring and artificial intelligence, in particular to a multi-source data fusion pipeline monitoring method and system. The method comprises the steps of performing field sorting, structure unification and risk segmentation processing by obtaining pipeline line data, historical operation archives and strategy update configuration records, and generating a session primary key configuration table; a multi-source acquisition time window is configured, an acquisition task is issued, time anchor point registration and field aperture unification are completed, and a multi-source session data packet set is generated; performing session and risk unit association, performing multi-modal feature extraction and cleaning aggregation based on artificial intelligence, and constructing a pipe network risk map structure by using a map structure data model; and calling a multi-task reasoning model and a rule component based on the atlas, and performing risk type reasoning and grade judgment to obtain a risk assessment result and a strategy updating record. According to the invention, intelligent fusion of multi-source data and closed-loop optimization based on machine learning can be realized, and intelligence and reliability of pipeline safety management are effectively enhanced.

The invention relates to an aviation equipment manufacturing digital main line engine system supporting multi-source heterogeneous data access, and belongs to the technical field of aviation manufacturing data management. The system comprises a multi-source heterogeneous data access adaptation module, a data fusion processing module and a full-process data link construction module. The multi-source heterogeneous data access adaptation module passes through a data interface protocol and format conversion component; the data standardization and fusion processing module completes association fusion through an aviation heterogeneous data collaborative computing framework by means of an aviation manufacturing field data element standard library, a unified heterogeneous data model is generated, and the framework comprises a data fragmentation layer, a parallel node layer and a result aggregation layer; and the full-process data link construction module constructs a data link covering the full life cycle of the product based on a time sequence association algorithm and a product unique identifier mapping mechanism. The system can effectively solve the integration and management problems of multi-source heterogeneous data in aviation equipment manufacturing, and improves the data processing efficiency and the whole-process data tracing capability.

The embodiment of the invention discloses a double-truck-crane cooperative hoisting monitoring method and device, electronic equipment and a storage medium. The method comprises the steps that component parameters and operation environment parameters of hoisting operation are collected, a reference data model is constructed, and an action expected value and a safety control threshold value are set; state acquisition devices are arranged on the two truck cranes to acquire multi-source data in real time and transmit the multi-source data to a central control system so as to realize synchronous sensing of the two trucks; processing multi-source data based on a data fusion algorithm, and constructing a dynamic attitude model and a collaborative operation state model; a state prediction correction value in the model is compared with the reference data model in real time, deviation is judged, and safety early warning is triggered; and generating a feedback control instruction, dynamically adjusting the hoisting operation, correcting the coordination deviation, and realizing the coordination control of the hoisting posture. In this way, through multi-source data fusion and real-time feedback control, the safety and coordination of cooperative lifting operation of the double truck cranes are remarkably improved.

The invention relates to a work order material intelligent tracking and dynamic updating system based on multi-source data fusion. The system comprises an acquisition module, a tracking module, an updating module, a man-machine interaction module and a storage module, the acquisition module is used for acquiring work order information from a bill of material, scheduling information and inventory and supplier data, and constructing a material data model; the tracking module monitors material inventory, consumption and material shortage risks based on the model, and realizes automatic state updating and graphical path recording in combination with bar code recognition and ambiguity analysis; the updating module predicts material requirements through a machine learning algorithm, optimizes a distribution plan and outputs a dynamic report; the interaction module supports multi-format data import, unstructured field classification and interrupted operation control; the storage module ensures credible synchronization and tamper-proofing performance of data among different systems through a block chain technology; according to the invention, high-efficiency management and dynamic allocation of work order materials can be realized in an environment without an MES system, and the production transparency and the resource utilization efficiency are improved.

The invention relates to the technical field of software development tools, and discloses a micro-application generation system and method based on natural language interaction, and the system comprises four core components: a natural language understanding module, a demand modeling module, a code generation engine and a deployment and integration platform. The natural language understanding module generates structured semantic representation through intention recognition, entity extraction and context analysis; the demand modeling module constructs a data model, a process logic and an interface prototype based on the semantic representation, and optimizes model parameters by utilizing reinforcement learning; the code generation engine generates a cross-platform code of the micro-service architecture through template matching and modularization assembly; and the deployment and integration platform realizes seamless integration of the external system and the Internet of Things equipment through a standardized interface. According to the method, the problems that a traditional low-code platform depends on visual dragging operation, demand analysis is rough and cross-domain adaptation is insufficient can be solved.

The invention relates to the technical field of sewage treatment, in particular to an intelligent municipal sewage resource utilization decision support method and system, and provides a method for realizing real-time monitoring and historical data analysis of a sewage treatment system by constructing a multi-domain knowledge coupled heterogeneous decision model; the method comprises construction of a knowledge graph in the sewage treatment field and coupling with real-time data, and a hierarchical decision scheme is formed. On this basis, a collaborative mechanism of microcosmic, mesoscopic and macroscopic layer decisions is established, and risk awareness decision optimization is executed; dynamically dividing risk levels through a Bayesian probability decision framework, and realizing closed-loop self-evolution of data-model-decision; meanwhile, a resource value quantitative model is constructed, and decision evaluation of multi-target balance is carried out; according to the invention, the problem of'knowledge isolated island 'of a traditional system is effectively solved, the decision accuracy is improved, and collaborative decision-making of expert knowledge and data driving is realized.

Access to secured items in a computing system is requested instead of being persistent. Access requests may be granted on a just-in-time basis. Anomalous access requests are detected using machine learning models based on historic patterns. Models utilizing conditional probability or collaborative filtering also facilitate the creation of human-understandable explanations of threat assessments. Individual machine learning models are based on historic data of users, peers, cohorts, services, or resources. Models may be weighted, and then aggregated in a subsystem to produce an access request risk score. Scoring principles and conditions utilized in the scoring subsystem may include probabilities, distribution entropies, and data item counts. A feedback loop allows incremental refinement of the subsystem. Anomalous requests that would be automatically approved under a policy may instead face human review, and low threat requests that would have been delayed by human review may instead be approved automatically.

The invention relates to the technical field of computers, and discloses an integrated information system for clinical test archives and a data processing method thereof, and the method comprises the steps: constructing a standardized data model covering a whole process; multi-source data dynamic structured intake and integrity verification are carried out; performing multi-dimensional semantic association analysis based on a Bayesian network and a correlation coefficient; block chain type version tracing and difference comparison are carried out; performing role-sensitivity-operation-context four-dimensional access control; and task-driven collaborative sharing and auditing log records. The system comprises a unified modeling module, a data intake module, a semantic analysis module, a version tracing module, an access control module and a collaborative auditing module. The problems of data islands, semantic segmentation, poor dynamic adaptability, weak safety protection and the like in the prior art are solved, intelligent management, semantic interconnection, safety controllability and efficient collaboration of the whole life cycle of clinical test files are achieved, and data quality, audit compliance and multi-role collaboration efficiency are remarkably improved.

The invention discloses an intelligent file service system generation method based on a low-code development mode. The method comprises the following steps: constructing a file service core data model, defining file metadata fields, a storage structure and an association relationship, and forming a standardized data architecture. According to the method, the core data model, the visual page component library and the drag-and-drop workflow template are constructed, linkage binding of data-component-process is achieved in combination with a low-code engine, and the efficiency bottleneck of a traditional development mode is broken through; through a three-way real-time synchronization mechanism, real-time hot repair and dynamic branch judgment, page layout and flow rule adjustment can take effect immediately through visual configuration, and the problem of dynamic demand response lag is solved; through the intelligent resource scheduling logic compiled through environment adaptation, a differentiation scheme is generated for different deployment environments, the configuration difference influence under a private cloud and server mixed deployment scene is remarkably reduced, the deployment failure rate is greatly reduced, and the system online stability is improved.

The invention discloses an industrial communication protocol conversion system and conversion method. The method comprises the following steps: acquiring data of an industrial communication source protocol; protocol conversion: converting the source protocol data into target protocol data according to a sequence of protocol analysis, standardized modeling and protocol repackaging; according to protocol analysis, original data transmitted from different hardware interfaces are analyzed according to corresponding industrial protocols; the standardized modeling parses the data into a data model in a unified format; protocol repackaging: recoding the data model into a data frame conforming to a communication specification according to a target protocol rule; and data output: pushing the obtained target protocol data to a third-party server through an MQTT protocol or directly storing the target protocol data in a database for a third-party system to access. Through three core technologies of protocol analysis, unified data modeling and protocol packaging, lossless conversion among different industrial protocols is realized, and an efficient and reliable data communication infrastructure is provided for an industrial automation system.

The invention discloses a marketing copywriting generation model optimization method and device and a storage medium, and the method comprises the steps: obtaining a to-be-optimized data model, and carrying out the incremental pre-training of a base model through marketing knowledge data, and obtaining the to-be-optimized data model; subjective indexes are screened through a preset evaluation index system, and screening results are integrated into objective indexes; constructing a sample data set based on the objective indexes, and training the data model by adopting the sample data set to obtain a target model; and generating a generation logic path of the target model by using a preset thinking chain decomposition model, and optimizing the target model through the generation logic path. According to the method, efficient injection and objective quantitative evaluation of marketing knowledge are realized by constructing a technical path combining a vertical field evaluation index system and incremental pre-training, and the technical effects of improving the brand integrating degree, the selling point accuracy and the commercial availability of the generated copywriting are realized in combination with an iterative optimization mechanism guided by a thinking chain.

The invention relates to the technical field of side slope displacement monitoring, and discloses a side slope displacement monitoring method based on computer vision, which comprises the following steps: step 1, acquiring multi-temporal remote sensing image data of a monitoring area, constructing a time sequence based on a vegetation index, analyzing a disturbance trend of a sub-area, and generating a disturbance risk level map; 2, initial image feature points are extracted based on the slope monitoring image, the feature points are constructed into a graph structure according to the spatial proximity relation, and a graph data model with nodes connected with edges is formed; and step 3, based on the disturbance risk level graph and the graph structure, inputting the graph neural network model to carry out feature point stability modeling. The technical scheme of disturbance risk level map guidance, map neural network stability modeling and feature point stability score screening is adopted, and the technical effect that stable and traceable key feature points can be effectively recognized and screened out under the dynamic disturbance conditions of vegetation disturbance, climate change and the like is achieved.