1874 results about "Data format" patented technology

The invention discloses a method for simulating and forecasting flood in a cold highland area based on hydrological and hydrodynamic coupling, and belongs to the technical field of disaster forecasting. The method specifically comprises the following steps: S1, multi-source basic data collection and preprocessing: collecting multi-type and multi-scale basic data for a target cold and cold mountainous area drainage basin; and S2, deep learning correction and fusion of the satellite rainfall data: aiming at the local overestimation and underestimation problems of the satellite rainfall data, a deep learning algorithm is adopted to carry out hour scale correction and fusion. Four types of core data of satellite remote sensing, reanalysis, ground observation and geographic space are collected, total factors of'rainfall-runoff-terrain-underlying surface 'required by flood simulation in the cold and cold mountainous area are covered, simulation one-sidedness caused by lack of data types in traditional modeling is avoided, rainfall and runoff abnormal values are eliminated by adopting a 3-sigma criterion, data formats and spatial-temporal scales are unified, and the modeling efficiency is improved. A standardized data set is formed, and interference of abnormal values, format incompatibility and space-time mismatching on subsequent model input is avoided.

The invention provides a data analysis mapping method and system based on context awareness, and the method comprises the steps: carrying out the directional crawling of a target website through a crawler instance, so as to obtain multi-format webpage source data, then extracting the structural features, semantic features and source features of the multi-format webpage source data, and constructing a source data context feature set; dynamically matching an adaptive analysis strategy based on the source data context feature set to accurately extract target field data, performing semantic similarity matching on the target field data and a preset service scene standard field library, and performing data format unified conversion in combination with a context format rule to obtain standardized field data; and finally, performing multi-dimensional verification on the standardized field data, and generating an SQL statement after the verification is passed, so as to solve the problems that an existing data analysis mapping scheme depends on a fixed template and cannot dynamically adapt to page structure change of a target website, manual auxiliary field mapping consumes time, and errors are easy to introduce.

The invention relates to an intrusion detection method based on cross-domain security management and a shared behavior model. The method comprises the following steps: acquiring multi-dimensional original data according to a preset cross-domain data acquisition rule and multi-domain node deployment; performing compliance, integrity and format matching degree verification on the original data, shielding sensitive information by using a dynamic desensitization technology based on a verification result, converting a heterogeneous data format, filtering missing field abnormal data, and obtaining compliance data; the method comprises the following steps: extracting a multi-dimensional feature set of user cross-domain access, constructing a shared behavior feature vector through weighted calculation, constructing a reference behavior model library in combination with a cross-domain security policy, and screening out intrusion behaviors and feature deviation data through feature comparison and behavior deviation calculation; according to the method, cross-domain security audit logs are fused for correlation analysis, intrusion behavior types and risk levels are judged by means of a Bayesian network model, differential security response strategies are generated and executed, and cross-domain intrusion detection and protection are achieved.

The invention provides an ocean live big data fusion method based on deep learning, and relates to the technical field of ocean big data fusion, and the method comprises the steps: obtaining meteorological driving variables, ocean historical reanalysis data, satellite observation data and ocean field observation data, and performing unified format conversion processing on the basis of the acquired meteorological driving variables, the ocean historical reanalysis data, the satellite observation data and the ocean field observation data, and configuring a unified ocean environment feature tensor set. A unified marine environment feature tensor structure is configured, meteorological driving variables, marine historical reanalysis data, satellite remote sensing data and on-site buoy observation data are subjected to space resampling, time interpolation and missing complementation and are unified into a space-time grid under a geographic reference coordinate system, a four-dimensional tensor set is configured, and the four-dimensional tensor set is obtained. The problem of fusion obstacles caused by non-uniform data formats and inconsistent temporal-spatial resolutions in the prior art is effectively solved.

A computer-implemented apparatus, system, and method is disclosed for protecting sensitive data. A cognoscible computing engine is multi-layered and multi-pathed. It includes features for handling different data formats, including structured, semi-structured, and unstructured data. Features are included to support near real-time processing at scale with high accuracy. Applications include redacting or masking sensitive data to comply with data privacy and security standards.

The invention relates to the technical field of medical artificial intelligence. The invention provides a medical AI-oriented multi-modal data feature processing and model training method. The method comprises the following steps: acquiring multi-modal medical data dispersed in a heterogeneous system; performing standardization processing on the multi-modal medical data, establishing a unified data format and a cross-modal mapping rule, and forming a standardized data set; processing the unstructured data in the standardized data set by adopting a mixed feature extraction model; and constructing a cross-modal feature fusion network, carrying out space-time alignment and semantic association on different modal features based on a mapping rule of the cross-modal feature fusion network, and generating a multi-modal joint feature vector. The problems that in the prior art, when medical multi-modal data is processed, cross-system butt joint is difficult, the unstructured data feature extraction capacity is limited, the data quality is insufficient, and the model generalization capacity is weak are solved.

The invention belongs to the technical field of geological disaster monitoring and early warning, and relates to a rainfall type landslide-debris flow chain disaster intelligent dynamic monitoring and early warning method, which comprises the following steps: data acquisition: generating multi-source data including an optical image, an SAR image, an unmanned aerial vehicle image and ground data; preprocessing the multi-source data; performing space-time registration, data format and organization on the multi-source data after data processing; key characteristic parameters are calculated, and data fusion is carried out; constructing a multi-dimensional feature vector, and generating a multi-dimensional feature image; constructing a risk assessment model by using a deep learning model, and performing real-time prediction; dynamically determining the total risk level of each area according to a preset comprehensive research and judgment rule by integrating the real-time prediction result; and when the total risk level reaches a'high risk 'or'extremely high risk' threshold value, generating early warning information, and performing early warning release. According to the invention, comprehensive, whole-process three-dimensional perception, dynamic and self-adaptive intelligent evaluation and disaster prediction are realized.

The invention provides a paper file information input method, system and device and a medium, and belongs to the technical field of file information processing. The method comprises the steps of collecting a paper file image, performing preprocessing in sequence to obtain a preprocessed image, and performing region segmentation on the preprocessed image by using a target detection algorithm to generate a region coordinate mapping table; generating a file template by using a predefined file template library according to the region coordinate mapping table; performing printing form recognition and handwritten form recognition on characters in the image by using a sequence generation model to generate a text sequence, and recognizing a table structure by using a two-dimensional convolutional network; according to the text sequence and the table structure data, a named entity recognition model is used for labeling field types, data formats are checked and corrected according to template rules, errors are corrected in combination with context semantic similarity, and structured data are generated; and mapping the structured data to an information system database table according to a template field mapping rule, and inputting the structured data into an information system.

A system for detecting the motion state of a vehicle behind a mobile maintenance vehicle based on thunder-vision fusion belongs to the technical field of traffic intelligent control and comprises a multi-source sensing module, a target detection module, a trajectory tracking module and a vehicle motion state modeling module. A structured state output module performs standardized integration on vehicle target trajectory information provided by a detection and tracking module based on a unified data expression model to form a multi-dimensional structured data format including fields such as a relative distance, a relative speed, an acceleration, a vehicle type, a lane number, a frame timestamp and the like; consistency analysis and downstream calling can be conveniently carried out among different systems, the problem that a traditional detection system is difficult to integrate due to lack of a structured interface is avoided, the system supports flexible butt joint with a road sensing platform, a vehicle-mounted control system and an upper-layer data analysis module, good expansibility and cross-platform adaptation capacity are achieved, and the system is suitable for being popularized and applied. And the engineering availability and deployment efficiency of the traffic flow state information are obviously improved.

The invention provides a power grid operation analysis method based on a cloud platform, and relates to the technical field of power grid operation analysis, and the method comprises the steps: configuring a semantic event template library, configuring multi-source power grid operation data access and time reference synchronization, configuring an event triggering rule and an event judgment mechanism, and extracting an event fragment data set, and configuring a semantic abstract vector, executing semantic compression, generating a unique analysis task number, registering a task metadata directory, and setting a task priority and priority queue arrangement. By configuring the semantic event template library and realizing the access and time reference synchronization of the multi-source power grid operation data, the unity and timeliness of the event fragment data set can be ensured in a mass data environment, the problem of analysis errors caused by non-uniform data formats and non-uniform time sequences in the prior art is solved, and the analysis accuracy is improved. Therefore, the reliability of power grid operation state identification and analysis results is improved.

The invention relates to the technical field of three-dimensional models, and discloses a GIS data-based low-altitude visual three-dimensional model automatic generation method, which comprises the following steps of: acquiring multi-source GIS vector data from an open geographic data source, performing standardized preprocessing through a data cleaning rule set, constructing a Cesium earth scene basic framework in a UE5 engine environment, and dynamically generating a city L2-level three-dimensional visualization model based on a preprocessed GIS data stream; when a low-altitude visual three-dimensional model is generated, a lightweight data format is adopted, data transmission efficiency and dynamic updating capability are improved, complex light environment changes can be adapted in real time, the problem of color distortion on the surface of the model is reduced, meanwhile, a visual angle deviation detection and correction mechanism is established, the geometric body edge distortion phenomenon is relieved, and the generation efficiency of the low-altitude visual three-dimensional model is improved. And the color rendition precision and the spatial positioning reliability of the low-altitude scene are improved.

The invention provides an AI-based trachinotus ovatus juvenile fish culture water parasite prediction and prevention method and system, and the method comprises the steps: collecting multi-source data from a culture water body through a sensor array, including a nucleic acid signal, a fish school behavior track and an environment parameter sequence, and obtaining an original data set; unifying the data format and the acquisition frequency according to the original data set by adopting a time sequence alignment method, and obtaining an aligned feature flow; aiming at the aligned feature flow, applying a convolutional neural network to extract a space-time pattern of a nucleic acid signal and an abnormal index of a fish school behavior, and determining an extracted feature set; if the abnormal index in the extracted feature set exceeds a preset threshold value, a causal association between environmental parameters and parasitic risks is classified through a support vector machine, and a preliminary risk level is judged; simulating a water body change scene according to the comprehensive causal model to obtain a predicted trajectory of parasite propagation; and determining a quantitative score of the parasite risk and generating a prevention and control instruction sequence by comparing the predicted trajectory with the real-time monitoring data.

The invention provides a multi-platform satellite thermal infrared hyperspectral atmospheric ammonia monitoring method and system, and belongs to the technical field of satellite remote sensing. The method comprises the following steps: collecting thermal infrared hyperspectral data, atmospheric state parameters, earth surface parameters and instrument characteristic parameters of a stationary satellite or a polar orbit satellite; based on a thermal infrared radiation transmission physical mechanism, inputting the preprocessed standardized parameters into a fast radiation transmission forward model to obtain a simulated spectrum consistent with the actually measured data format of a stationary satellite or a polar orbit satellite; constructing a cost function containing observation error constraint and NH3 prior information constraint on the basis of the simulated spectrum and the actually measured spectrum in combination with an optimization estimation theory, solving a minimum value of the cost function by adopting a Levenberg-Marquardt iterative algorithm, and performing inversion to obtain an atmospheric ammonia concentration profile; and performing quality control and column concentration conversion on an inversion result, and verifying the precision by combining multi-source observation data to obtain an atmospheric ammonia concentration data set.

The invention discloses a homomorphic encryption-based cross-industry health insurance data sharing and privacy protection method, and relates to the technical field of health insurance data security, and the method specifically comprises the steps: standardizing medical and insurance industry core data, quantifying key indexes such as a disease clinical risk value and an occupational basic risk coefficient, achieving the format unification of cross-industry data, and achieving the privacy protection of the cross-industry data; further generating ownership, risk and business labels, carrying out encryption processing on data through homomorphic encryption on the risk labels based on three scenes of data sensitivity and business influence grading, business label binding pricing, underwriting and claim settlement prediction, adding an integrity check code to high-risk data, screening ciphertexts based on the labels, and then executing fusion calculation; the comprehensive risk adjustment coefficient ciphertext is generated, the data integrity is verified, the comprehensive risk adjustment coefficient is obtained after collaborative decryption, the comprehensive risk adjustment coefficient is applied to premium pricing, underwriting conclusion generation and claim risk pre-judgment, and the scientificity and accuracy of health insurance pricing and underwriting decision making are improved.

The embodiment of the invention provides a method and a system for processing ocean multi-parameter wide-width imaging data of a Beidou interference altimeter, and relates to the technical field of GNSS-R. The method comprises the following steps: constructing a set of complete and strict data processing chain; through precise calculation of a GNSS-R direct signal and reflection signal delay inequality interference geometric model and correction of actual measurement delay inequality errors, systematic errors in the process of converting original signals into geophysical parameters are remarkably reduced, and the precision and reliability of parameters obtained through final inversion are improved. And on the basis of a self-adaptive data collection strategy of parameter space-time transformation characteristics and a spatial interpolation reconstruction process, discrete strip data is intelligently'woven 'into a continuously-covered wide-swath imaging type data product, so that the data format better meets the analysis requirements in the fields of oceanography, climatics and the like, and the intuition and usability of the product are improved.

The invention provides a humanoid robot data set quality evaluation method and a humanoid robot data set quality evaluation system based on multi-dimensional evaluation, and provides a systematized and standardized evaluation process aiming at the quality problems of the existing robot multi-modal data set in the aspects of normalization, integrity, accuracy, consistency, timeliness, accessibility, availability and the like. According to the method, an automatic tool is combined with manual auditing, the structure, metadata, annotation content, data formats, multi-modal alignment and the like of a data set are comprehensively detected, a deep learning algorithm is introduced to achieve detection of pornographic pictures and sensitive words of visual content, and content safety and compliance are effectively improved. The system can flexibly adjust the weight of each evaluation dimension according to different application scenes, automatically generates a quality evaluation report, and supports continuous monitoring and optimization and improvement. Practical application shows that the method can significantly improve the overall quality of the robot data set, and provides reliable data basis and technical support for intelligent training and application of the robot.

The invention provides a method for systematically monitoring aircraft pilot and air traffic control (ATC) radio communications to detect predefined aviation-specific keywords indicative of operational conditions affecting flight safety or efficiency, such as turbulence, icing, or visibility constraints. Detected keywords and semantic equivalents from voice transmissions are transformed into structured data formats. These structured data are correlated with Automatic Dependent Surveillance-Broadcast (ADS-B) data, including precise aircraft position, altitude, heading, and speed. Leveraging this integrated data, the system generates automated, quantitative visualizations, such as condition-specific heatmaps or other graphical representations, illustrating spatial distributions of reported in-flight phenomena like turbulence, icing, or hazardous conditions. These visualizations enable air traffic controllers, pilots, and aviation stakeholders to rapidly assess and disseminate real-time observations and historical patterns of flight hazards, thereby significantly enhancing situational awareness, operational safety, and decision-making effectiveness without manual input or subjective interpretation, improving overall aviation operational efficiency and safety.

The invention discloses a multi-modal sensor abstract description and packaging method and system based on software definition. Firstly, a unified sensor abstract description model based on software definition is introduced, so that hardware independence is realized, and the differences of a bottom-layer multi-mode sensor in the aspects of interfaces, protocols and data formats can be effectively shielded; secondly, abstract packaging of data is achieved through an autonomous information processing module, and high-bandwidth and high-redundancy original data is converted into low-bandwidth and high-value structured information; in addition, by means of automatic registration, discovery, scheduling and control mechanisms of the sensor management control module on sensor resources, the system realizes a real plug-and-play function. According to the invention, by deploying the autonomous information processing modules at various sensor nodes, the original sensing data is preprocessed, and standardized data packaging with semantic consistency is output, so that the operation load of the central controller is reduced, and the information processing efficiency and the system response capability are also improved.

A train operation intelligent dispatching system and method are provided, where the system includes: a data acquisition and control module, configured to collect multi-source data affecting train operation; a data processing module, configured to perform data aggregation on the multi-source data, and process the multi-source data to form multi-source data in a unified data format; a multi-disciplinary data resource pool, configured to perform real-time storage and updating of the multi-source data in the unified data format, and store historical multi-source data; an intelligent fault analysis module, having intelligent fault analysis models therein, configured to perform fault analysis based on the multi-source data to obtain fault analysis results of corresponding disciplinary systems, and output fault advance warnings of corresponding disciplinary systems according to the fault analysis results; and a process preplan module, configured to provide preplan handling processes corresponding one-to-one with external multi-disciplinary systems.

The invention provides a test data diversification generation method and system based on a data factory, and the method comprises the steps: carrying out the multi-dimensional semantic deconstruction of a basic test data set stored in the data factory, and obtaining a basic concept component set through the recognition of a semantic association relationship and a concept hierarchical structure between data entities; constructing a dynamic semantic association network on the basis, and generating an association weight matrix by calculating semantic association strength among different concept components so as to recombine the basic concept component set and generate a structured semantic template set; the method comprises the following steps: acquiring a target test data format, inputting the target test data format into a pre-trained feature evolution generation network, optimizing a feature evolution direction, obtaining an evolved feature set, performing data form conversion and relevance verification, converting the evolved feature set into a target test data format, and generating a target test data set meeting diversified test scene requirements after verification. According to the invention, the requirements of diversified banking business test scenes on high-quality test data can be better met.

The invention discloses an automatic filling method and system based on YOLO and RPA, and the method comprises the steps: collecting a form interface image through screen capture, inputting the image into a YOLO model for processing, and recognizing form elements to generate bounding box information; reading data from an external data source, matching a corresponding relationship between the data and fields according to a preset mapping rule, verifying a data format, and obtaining a data set passing verification; using RPA to simulate user operation, inputting data passing verification into a form field, and formatting to obtain a filled form interface; checking fields required to be filled and data formats, and determining the validity of the form; simulating and clicking a submission button to complete form operation, and recording a submission result; and monitoring potential errors in the whole process, performing classified processing, recording detailed log information, and obtaining an optimized filling record. According to the method, the automation degree and accuracy of form processing are remarkably improved, the manual operation burden is reduced, the business process efficiency is optimized, and the method is suitable for complex form filling scenes.

The invention discloses a multi-serial-port peripheral automatic identification and protocol adaptive communication method in an embedded system. The method comprises the following steps: monitoring physical connection states of a plurality of serial ports in the embedded system; performing communication parameter self-learning on the target serial port, and determining a baud rate and a data format matched with the access equipment; automatically matching the features of the initial data frame with a plurality of protocol feature templates stored in a protocol feature library preset in the embedded system to obtain one or more candidate communication protocols; constructing a verification instruction by using a frame format of the candidate communication protocol, sending the verification instruction to the access equipment, performing interactive verification, and confirming a final target communication protocol according to a response result of the access equipment; and the self-adaptive communication link establishment aiming at the access equipment is completed. The method has the advantages that the problems that manual configuration is needed, deployment is tedious and errors are prone to occurring during mixed connection of multi-protocol equipment are solved, and the flexibility, expandability and deployment efficiency of an embedded system are remarkably improved.

The invention discloses a mass data automatic label generation method based on a large model and a rule engine, which comprises the following steps: S1, multi-source data acquisition and preprocessing: acquiring data through a unified data interface, and then performing data cleaning, data standardization processing and data format conversion to obtain standard input data; s2, constructing a label system framework; s3, generating a large model label; s4, performing intelligent clustering and label refining; s5, rule engine constraint and optimization; s6, evaluating and optimizing label quality; and S7, automatically expanding and updating the tag system. According to the method, automatic label generation of mass multi-source heterogeneous data is realized through a large model and rule engine technology under the condition of no personnel intervention, a large-scale label system containing thousands of labels is constructed, meanwhile, the label system can be rapidly updated along with product iteration and keep timeliness, and the generated labels are accurate and extensible.

The invention provides an autonomous operation obstacle avoidance method and system for a low-altitude aircraft, and the method is executed by the low-altitude aircraft, and comprises the following steps: S1, data acquisition: collecting data through a multi-source heterogeneous sensor group carried by the low-altitude aircraft, airspace control information, real-time meteorological data and collaborative sensing data of other aircrafts sent by the ground sensing base station are received through the air-ground communication interface; preprocessing the collected original data, wherein the preprocessing comprises data format standardization, abnormal value elimination and time synchronization; and S2, data fusion processing: carrying out credibility evaluation on the preprocessed multi-source heterogeneous sensing data and collaborative sensing data. According to the invention, through three-dimensional credibility evaluation and an air-ground data compensation mechanism, the perception precision is greatly optimized, the perception data efficiency is improved in complex environments such as low illumination, strong electromagnetic interference, rainstorm and the like, and obstacle avoidance decision errors caused by data distortion of a single sensor are avoided.

The invention provides a full-life-cycle carbon emission monitoring method for a zero-carbon park building. The method comprises the steps that original carbon emission data covering all stages of the full life cycle of the building are collected; constructing a unified data model, and standardizing carbon emission data formats of different sources and stages; performing integrity verification and abnormal value correction on the data to generate a standardized carbon emission data set; establishing a dynamic carbon emission reference line according to a zero-carbon park planning target and building type characteristics; comparing the standardized data with a reference line in real time to generate a carbon emission deviation index; triggering an early warning mechanism based on the deviation degree index, and predicting a carbon emission trend in a subsequent stage; integrating the deviation index, the early warning signal and the trend prediction result to generate a visual monitoring report; and generating a carbon emission reduction strategy optimization scheme for the specific building stage according to the abnormal nodes fed back by the report. According to the invention, accurate monitoring, real-time early warning and dynamic optimization of building carbon emission can be realized.

The invention provides a graphic display agent device based on a PCIE (Peripheral Component Interface Express) bus, which is characterized in that an instruction analysis module receives a graphic drawing instruction containing an operation type and a corresponding coordinate parameter through a PCIE bus interface, and generates first pixel data based on the instruction; meanwhile, the data conversion module receives original image data containing pixel position information and sensor echo information through a radar video interface, analyzes the original image data and converts the original image data into second pixel data consistent with the first pixel data in format, and the format difference of the two types of data is eliminated to meet the superposition condition; a graph superposition processing module generates a primary and secondary clear composite image according to the logic that the first pixel data covers the second pixel data; then, the image output control module receives the synthesized image by using a first buffer area and a second buffer area which alternately work to ensure that a complete image frame is transmitted to a subsequent module; and finally, the video signal generation module converts the complete image frame into an analog video signal conforming to a preset video time sequence and outputs the analog video signal.

The invention relates to the technical field of project supervision informatization, and provides a supervision multi-heterogeneous system cloud edge collaborative adaptation method, which comprises the following steps: collecting heterogeneous information such as edge equipment hardware architecture and an operating system to generate records; integrating supervision monitoring data and cloud instructions, classifying the data according to functions and updating frequencies, and marking high-frequency units; unifying a data format and adding Hash verification information; the cloud constructs a three-dimensional mapping table, and edge nodes are grouped in combination with high-frequency data; converting the private protocol of the edge device into a general protocol; by taking minimization of transmission quantity and energy consumption as a target, distributing tasks by using an integer linear programming model; dynamically adjusting the equipment energy consumption; and dynamically optimizing the configuration based on the interface indexes. The cloud edge interaction efficiency can be improved, the energy consumption is reduced, and the reliable operation of supervision business is ensured.

The invention provides a general semiconductor test data full life cycle management system, and the system comprises a test source data access module which is used for uploading a source test file in a test machine to the system; the full test data analysis and standard model conversion module is used for converting the uploaded source test file into a test file in a standard data format and transmitting the test file to the multi-dimensional KPI atomic model operation and persistent storage module; the multi-dimensional KPI atomic model operation and persistence storage module is used for calculating a multi-dimensional KPI atomic result according to the data in the test file in the standard data format and storing the multi-dimensional KPI atomic result to a persistence layer database; and the unified service output module is used for receiving a user query request, performing aggregation calculation on the multi-dimensional KPI atomic result in the persistent layer database according to the user query request, and returning a KPI result meeting the user query request. The system can improve the utilization efficiency of semiconductor test data and reduce the operation cost.

The invention discloses an identification document full-process intelligent generation system and method. The system realizes end-to-end automatic processing from data acquisition to document generation through original report screenshot sampling, file filtering, authorization document analysis, data extraction and structuring, report content generation, equipment information analysis and judicial document template filling. The system adopts a multi-stage fault-tolerant and verification mechanism, integrates a large-model intelligent analysis and extraction technology, can automatically repair a non-standard data format, and generates standardized document content according to judicial specifications. According to the method, the judicial expertise document generation efficiency, accuracy and normalization are remarkably improved, and the evidence effectiveness of the output document is ensured.

The invention provides a mold engineering drawing labeling system, method and equipment, a storage medium and a program product, and belongs to the field of computers. The system comprises an input analysis module used for obtaining three-dimensional model information, two-dimensional engineering drawing information and mapping information of a target mold; packaging the three-dimensional model information, the two-dimensional engineering drawing information and the mapping information into structured data based on a set data format; the semantic understanding module is used for performing semantic understanding based on the structured data by utilizing a large language model to obtain feature functions and annotation requirements; the dynamic annotation engine module is used for generating an annotation strategy based on the feature function and the annotation requirement; and the parameterized annotation generation module is used for generating an annotation instruction code based on an annotation strategy by utilizing a large language model, and writing the annotation instruction code into the target mold engineering drawing. The method is at least used for solving the problems that in an existing method, labeling efficiency is low, deep semantics cannot be understood, and consequently certain limitation exists in feature recognition application.