1536results about How to "Guaranteed timeliness" patented technology

The invention relates to the technical field of ecological environment protection, in particular to an ecological environment damage intelligent identification management method based on multi-source data fusion, which comprises the steps of data acquisition and preprocessing, data fusion and feature extraction, ecological environment damage evaluation model construction, damage dynamic analysis and trend prediction, restoration suggestion generation and visual display. The method comprises the following steps: collecting water quality, atmosphere, biological and social economic data in real time through a sensor network deployed in an ecological sensitive area, preprocessing the data, extracting key features by adopting a multi-modal data fusion technology and a deep learning model, and constructing an ecological damage assessment model; the change rule of ecological damage and the potential high-risk area are predicted based on a time sequence analysis method, and an optimal ecological restoration scheme is generated in combination with a particle swarm optimization algorithm. According to the method, the evaluation result and the restoration suggestion are displayed through the visual interface, a scientific basis is provided for decision makers, and the efficiency and the effect of ecological environment restoration work can be improved.

The invention discloses a mirror image type industry linkage engine system and method based on a knowledge graph, and relates to the technical field of industry linkage engines, and the system comprises a data collection module which collects multi-industry data in multiple modes, and the cleaning accuracy is 90% or above; the knowledge graph construction module is combined from bottom to top and from top to bottom, and is used for distributed storage and block chain evidence storage; the industry mirror image model generation module is used for generating a model through machine learning, and the association accuracy is 95% or above; the linkage analysis and decision module is used for finding a linkage strategy through an intelligent algorithm; and the user interaction module has a graphical interface, is fast in response, can feed back optimization, and supports multi-language and VR / AR display. The system is accurate and efficient in data processing, integrates multi-source data to construct the knowledge graph, is deep and comprehensive in industry analysis, simulates industry relations to assist decision making, is intelligent and reliable in decision making support, provides strategies and evaluation, is convenient and friendly in user experience, improves interactivity through multiple technologies, promotes accurate decision making of enterprises, and promotes collaborative development and innovation of the industry.

The invention discloses a multi-terminal vehicle management system based on an intelligent algorithm, relates to the technical field of vehicle management, and is used for solving the problem of insufficient real-time performance and cooperative efficiency of vehicle management. According to the method, a multi-modal sensor fusion technology is combined with Gaussian process regression, complex environment data is modeled in real time, dynamic target and static background features are accurately extracted, environment changes are analyzed and predicted in combination with a time sequence, accurate input is provided for path planning and task allocation, a path is dynamically adjusted through an incremental optimization algorithm, emergencies are coped, and the path planning and task allocation efficiency is improved. According to a distributed path optimization mechanism, multi-vehicle task path conflicts are eliminated, the cooperation efficiency is improved, a priority model is constructed based on the task emergency degree, the time constraint and the resource matching degree, the weight is dynamically adjusted, vehicle and task matching is optimized in combination with reinforcement learning, the task execution efficiency is ensured, resource competition and path conflicts are solved, and the task execution efficiency is improved. Efficient cooperation in a complex dynamic environment is realized, and the real-time performance and management efficiency of vehicle management are improved.

The invention relates to the technical field of artificial intelligence, and discloses an AI large model enterprise knowledge base system based on cue words, comprising a front end interaction module; a large model module; a knowledge base module; a vector database module; a fine tuning module; a function calling module; and a monitoring and optimizing module. According to the AI large model enterprise knowledge base system based on the cue words, knowledge documents in an enterprise are stored and managed in a centralized mode, efficient knowledge retrieval and matching are conducted through the vector database module, the knowledge base module can be customized according to enterprise requirements, document storage of various formats is supported, a convenient document management function is provided, and the knowledge management efficiency is improved. And the fine tuning module integrates enterprise knowledge into the large model, so that the large model can understand and process special knowledge and implicit knowledge in the enterprise, thereby improving the specificity and knowledge application capability of the system, effectively integrating and managing the enterprise knowledge, converting the enterprise knowledge into operable information, and providing powerful support for decision and operation of the enterprise.

The invention provides a semantic element-based knowledge question and answer accuracy improving method, which comprises the following steps of: extracting information from text data, and constructing semantic nodes; word segmentation processing is performed on a question text input by a user, a word sequence is obtained, and semantic element representation of a question is formed; in the knowledge recall stage, a golden investment algorithm is applied to achieve knowledge retrieval; matching the candidate answers with semantic elements of the user question, and scoring and sorting according to keywords; and according to the query intention of the user question and the sorted candidate answers, constructing cue words of the large model, calling the large model to generate a final answer, and presenting the output answer according to a specified format. By integrating semantic analysis, knowledge recall, natural language processing and other technologies, the intellectualization level and response accuracy of the knowledge question-answering system are remarkably improved, and higher-quality service is provided for users.

The invention relates to the technical field of business adaptation, in particular to a business adaptation degree intelligent analysis method and system combined with a customer portrait. The method comprises the following steps: firstly, collecting customer data from a plurality of data sources, then processing the collected data, extracting interest labels of customers, constructing a customer-interest label matrix, comprehensively evaluating the purchase ability and loyalty of the customers, updating the portraits of the customers according to the interest preferences of similar customers, and then collecting the business demand data of the customers. The method comprises the following steps: constructing a business demand model, quantifying the demand intensity of a customer, generating a demand score vector, matching a customer portrait with the demand score vector, calculating an adaptation degree score, generating personalized recommendation for the customer, preferentially recommending a product or service with the highest matching degree with the customer demand, and pushing related recommendation through multiple channels. And the satisfaction degree and the conversion rate of customers are improved. According to the invention, the resource utilization efficiency and the customer satisfaction are improved, so that more efficient enterprise operation and customer service are realized.

The invention discloses an evaluation method based on water ecological environment monitoring, and relates to the technical field of water ecological environment evaluation. Comprising the steps of data collection: obtaining a river basic information data set, a monitoring equipment information data set and a historical monitoring information data set, and building a cloud server to store the river basic information data set, the monitoring equipment information data set and the historical monitoring information data set. According to the method, each region is accurately monitored through region division of river landforms and a targeted monitoring method, the timeliness of monitoring data is ensured, the water quality condition can be evaluated in real time and the water quality of the corresponding region under unique weather can be evaluated by acquiring a region monitoring data set and a weather information data set in real time to cooperate with an evaluation model, and the water quality evaluation accuracy is improved. In addition, weather changes can be monitored and early warned, timely support is provided for dealing with emergencies, and by summarizing the method, related departments can formulate more reasonable treatment schemes, the water quality management efficiency and effect are improved, and the water quality problem is found and solved in time.

The invention discloses a vehicle-mounted sign retroreflection brightness coefficient detection method and system, and the method comprises the steps: obtaining the image data and point cloud data of a to-be-detected road region shot by an observation module at the top of a detection vehicle, and carrying out the detection of the to-be-detected road region according to a pre-trained first deep learning detection model; obtaining a relative distance between the to-be-detected signboard in the to-be-detected road area and the detection vehicle; when the relative distance meets a preset shooting distance, triggering the detection module to continuously shoot the signboard to be detected with light source illumination and without light source illumination, and respectively inputting two continuous frame images obtained by continuous shooting into a deep learning classification recognition model to obtain a target signboard image, the difference between the average gray values of each target signboard image is calculated to obtain an interference-free gray value, the retroreflection brightness coefficient of the signboard to be detected is obtained according to a preset retroreflection conversion formula, the continuous frames of images are used, interference brought by the external environment during measurement of the retroreflection brightness coefficient of the signboard is eliminated, and the measurement accuracy is improved.

The invention discloses an industrial firewall isolation method of a DCS (Distributed Control System). The objective of the invention is to solve the problem that existing industrial network security protection has defects for a DCS system. According to the method, firstly, DCS architecture analysis and risk assessment are carried out, and protection key points and data flow directions are accurately determined; then, a firewall strategy fitting the characteristics of each area (such as a core control unit, an operation station and an external network interface) is customized, and mechanisms such as multiple data encryption are covered; and then firewall deployment and configuration optimization are carried out to ensure protection continuity and rule timeliness. And abnormal conditions are timely handled through a real-time monitoring and dynamic adjustment mechanism, and system recovery under a security event is guaranteed through an emergency response and backup recovery mechanism. And a regular safety drill and evaluation mechanism is arranged to continuously strengthen the protection capability. According to the method, the network security level of the DCS is comprehensively improved, stable and safe operation of industrial production is guaranteed, and the method has remarkable practical value.

The invention relates to the technical field of geological disaster monitoring, and discloses a debris flow monitoring and early warning system based on Internet of Things, comprising a multi-source data acquisition module used for acquiring environmental data related to debris flow; the data transmission module is used for transmitting the environment data to a data center or a cloud end from a collection point; the data preprocessing module is used for carrying out abnormal value cleaning, time synchronization and normalization processing on the environment data; the deep learning analysis module is used for analyzing the preprocessed data so as to predict the occurrence risk of debris flow; and the dynamic early warning and publishing module is used for generating a multi-stage early warning signal according to an output result of the deep learning analysis module and publishing early warning information to a user in multiple modes. The accuracy, the real-time performance and the adaptability of the debris flow early warning system are improved through multi-source data fusion, edge calculation and a dynamic learning mechanism so as to cope with debris flow disasters in a complex geological environment.

The invention discloses a defect small target detection method and system for a high-resolution image of an electric power scene, and relates to the technical field of electric power vision, when a small defect detection model is trained, a progressive training mode is adopted, the detection effect of a small target in a common electric power scene is better, sampling is carried out through a scoring network, and the detection efficiency is improved. The precision of the model is ensured, and meanwhile, complicated and redundant calculation is avoided, so that the timeliness and accuracy of the whole model are ensured, and end-to-end detection of a power scene target can be realized; in the scheme of the invention, the target resolution is adopted to cut the current electric power equipment image to obtain multiple frames of current electric power image slices under the target resolution, and the current electric power image slices are detected based on the small defect detection model formed after training. And after the defect detection result is obtained, the frame coordinate of the defect detection result is mapped to the current power equipment image, so that the detection precision of the small target in the power image is high, and the intelligent inspection task requirement of a power grid can be met.

The invention relates to the technical field of artificial intelligence, and discloses an anti-fraud model training method based on deep learning, which comprises the following steps: step 1, preprocessing and constructing multi-modal data, carrying out abnormal value processing, missing value filling and feature standardization on original data, representing processed transaction data and geographic information through a feature extraction module, and carrying out feature extraction; the equipment information and the social relation graph are embedded through an embedding layer and a graph neural network; step 2, based on data enhancement of self-supervised learning, pre-training unlabeled data by designing a behavior prediction task and a mask filling task so as to initialize a main model by using generated model parameters, and generating a synthetic sample extension training data set; by designing a behavior prediction task and a mask filling task, automatic learning and enhancement of features are realized by using label-free data, the dependence on labeled data is reduced, the detection capability of the model on fraudulent behaviors is remarkably improved, and the diversity of training data is enriched.

The invention discloses a visual network freight platform transportation transaction management system. The system comprises an order management module, a vehicle scheduling module, a transportation monitoring module, a transaction settlement module and a visual display module. Each module has innovative functions, such as order intelligent classification, vehicle optimization scheduling algorithm to minimize comprehensive cost, real-time monitoring and accurate settlement. The corresponding management method adopts advanced technologies to ensure efficient operation of each link, including block chain recording, intelligent contract settlement and the like. The whole system realizes efficient management, real-time monitoring and convenient visualization of transportation transaction of a freight platform, and improves the efficiency and quality of logistics transportation; according to the visual network freight platform management system, efficient and accurate order management is realized, vehicle scheduling is optimized, the cost is reduced, comprehensive and real-time transportation monitoring is guaranteed to be safe, safe and accurate transaction settlement is realized, convenient and visual display is convenient to make decisions, and the logistics transportation efficiency, quality and user experience are improved.

The invention relates to the technical field of data processing and analysis, in particular to a three-dimensional model data management system and method for engineering surveying and mapping. The method comprises the following steps: acquiring engineering surveying and mapping data; preprocessing the engineering surveying and mapping data to obtain standard engineering surveying and mapping data; performing multi-dimensional feature recognition on the standard engineering surveying and mapping data to obtain multi-dimensional surveying and mapping feature data; performing key engineering surveying and mapping information extraction on the multi-dimensional surveying and mapping characteristic data to obtain key engineering surveying and mapping information; performing association analysis on the multi-dimensional surveying and mapping characteristic data and the key engineering surveying and mapping information to generate multi-dimensional engineering surveying and mapping associated data; monitoring the engineering surveying and mapping data in real time to obtain engineering surveying and mapping real-time monitoring data; through a data processing technology, a geographic information system technology and a three-dimensional mapping technology, integrated management of multi-source heterogeneous data is realized, and dynamic updating management of the data is realized, so that the presentation accuracy of the engineering surveying and mapping three-dimensional model is improved.

The invention relates to the technical field of education informatization, and discloses a digital teaching resource library intelligent management and application method and system. The method comprises the steps of constructing a digital resource library, constructing a resource application big data analysis model to calculate a resource quality index, a popularity index and an update index, performing intelligent resource sorting and elimination based on the indexes, analyzing the correlation between a user retrieval request and resources, and performing intelligent recommendation. According to the method, efficient management and accurate application of teaching resources are realized, the resource utilization rate and the teaching effect are improved, the storage space and the management cost are saved, and data support is provided for education decision making.

The invention discloses a one-way optical shutter video stream transmission method based on weak supervision semantic segmentation, and belongs to the technical field of one-way optical shutter information transmission. Comprising the following steps: carrying out key region and background region classification and marking on all video data received by an optical shutter outer end through a weak supervision semantic segmentation model; performing priority division on a key region and a background region in the video frame; dividing the data packets according to time windows, and packaging the data packets; performing dynamic data compression and segmented coding on the packaged data packet; reed-Solomon redundant coding is carried out on all the data packets of the key area after compression coding is carried out on the data packets of the key area; dynamically adjusting the transmission sequence of the data packets to perform link scheduling; and a receiving end carries out de-encapsulation and data restoration, and reconstructs the restored video frame. According to the method, redundancy and compression of data transmission contents are accurately controlled, so that the transmission efficiency and reliability are remarkably improved, the bandwidth utilization is optimized, and the transmission reliability and stability are enhanced.

The invention discloses an official document generation system and method based on a large model, belongs to the technical field of artificial intelligence, and aims to solve the technical problems of low official document format and content accuracy, poor material failure and poor content innovation in official document generation. Comprising a model training module used for performing model training on an official document generation model based on official document samples in an official document sample library; the model fine tuning module is used for performing layered fine tuning on the trained official document generation model; the cue word optimization module is used for carrying out cue word matching on user input based on a built-in cue word set; the outline generation module is used for calling a final official document generation model to generate an official document outline based on the cue word combination and user input; and the official document generation module is used for calling the final official document generation model to generate and output an official document body based on the user input and the final official document outline, and calling the privacy data shielding module to shield the privacy data in the official document body.

The invention relates to the technical field of bearing fault detection, in particular to a cloud-edge collaborative bearing fault detection method and a related product, and the method comprises the steps: carrying out the preprocessing of collected signal data of a bearing in different states; performing feature extraction on the preprocessed signal data by using CNN, inputting the extracted features into a GRU model, and generating a preliminary fault detection result based on the output of the GRU model; according to the cloud detection method, fusion processing and feature extraction are carried out on collected signal data, a local binary residual network model is established, result conversion is carried out on an output result of the local binary residual network model through a multi-layer perceptron and a hidden layer, and a final bearing fault detection result is generated. The bearing fault detection method effectively solves the technical problems that the maintenance time is too long, the production process is influenced and the maintenance cost is increased due to the fact that the bearing fault detection method in the prior art cannot judge the bearing fault type.

The invention discloses a well site fracturing dynamic analysis method based on the Internet of Things. The well site fracturing dynamic analysis method comprises the following steps that fracturing site real-time data is transmitted back; the fracturing process is monitored in real time; wellbore fluid flow state simulation analysis; crack simulation is carried out; performing multi-well crack simulation comparative analysis; based on a grid contour line generation and filling algorithm, a target layer top surface structure map is generated, well points and fractures are projected to the oil reservoir target layer top surface structure map, the distribution situation of the multi-well fractures in an oil reservoir is displayed, area, distance and other measurement tools are provided, the overall condition of fracturing transformation is subjected to multi-dimensional statistical analysis, and fracture design is assisted. The real-time data of the fracturing site are transmitted back based on the Internet of Things, so that various data in the well site fracturing process can be collected and transmitted to the data center in real time; a fracturing well site scene is set up through the digital twinning technology, and the efficiency and safety of construction operation are improved; the accuracy of predicting the distribution of the fracture network can be improved, the fracturing design is optimized, and the oil and gas recovery efficiency is improved.

The invention discloses a threat intelligence aggregation and attack abnormal feature intelligent generation method based on a large language model. The method comprises the following steps: acquiring network security intelligence of an open source threat intelligence community platform in real time; cue words of the large language model are optimized according to predefined multi-dimensional feature attributes, and multi-dimensional feature attribute extraction of threat intelligence is carried out on network security intelligence; according to the extracted multi-dimensional feature attributes of the threat intelligence, constructing a net-shaped graph relation structure database of the threat intelligence; constructing a threat intelligence knowledge graph according to the net-shaped graph relation structure database; and querying the threat intelligence knowledge graph, marking attack behavior features, connecting attack behavior feature points, and generating attack behavior feature sub-graphs. According to the method, the security team can quickly respond to emerging threats, and the capability of coping with network security threats is improved.

The invention provides a distributed photovoltaic output power estimation method, and the method comprises the steps: installing a load sensor and a photovoltaic output sensor at an access point, and collecting original data including load data and photovoltaic output data in real time; processing the collected original data; carrying out time synchronization on the load data and the photovoltaic output data; key features are extracted from the cleaned original data, and photovoltaic output is predicted through a time sequence analysis model; inputting load data and photovoltaic output data which are acquired in real time into the trained time sequence analysis model; the time sequence analysis model performs real-time operation according to the load data and the photovoltaic output data, and estimates the photovoltaic output power at the current moment; and based on the estimated photovoltaic output power and the real-time load data, the intelligent coordination controller performs dynamic adjustment, so that the photovoltaic output is matched with the load power. According to the invention, efficient estimation and optimal control of photovoltaic output are realized.

The invention discloses a big data prediction system based on machine learning, and relates to the technical field of machine learning and big data, and the system comprises a data collection module, a data preprocessing module, a feature engineering module, a machine learning prediction model module, a model training module and a prediction output module. According to the method, a specific algorithm is applied through the feature engineering module, correlation is considered, a penalty factor is introduced, related feature subsets are focused to improve precision, a hidden layer and parameters of the model module can be adaptively adjusted, for example, high precision is obtained in power load prediction, the data acquisition module updates and processes a format in real time, and data timeliness and availability are ensured. The preprocessing module processes abnormal values by adopting a clustering algorithm, corrects and normalizes parameters after determining the parameters, meets the requirements of the algorithm and improves the stability and accuracy, and the characteristics act together, so that the system is excellent in performance in a prediction task, and a solid foundation is laid for accurate prediction.

The invention belongs to the technical field of low-voltage distribution cable fault identification, and particularly relates to a method for judging a series arc fault of a low-voltage distribution cable based on disturbance current characteristics, which comprises the following steps of: S1, acquiring current data of three-phase current of the cable; s2, analyzing the similarity of the current waveforms of the adjacent current periods by adopting a Pearson correlation coefficient PCC, and judging whether the cable has current disturbance or not; s3, when it is judged that current disturbance occurs, intercepting current data of a disturbance starting point and a plurality of current periods after the disturbance starting point, and recording the current data as disturbance current; calculating the time-frequency domain characteristic quantity of the intercepted disturbance current; and S4, inputting the time-frequency domain characteristic quantity of the disturbance current into a preset fault identification model, carrying out detection and identification on a cable series arc fault, and judging whether the fault causing current disturbance is the series arc fault or not. According to the method, the low-voltage distribution cable series arc fault can be efficiently, accurately and timely monitored and identified, and the safety and stability of a power system are improved.

The invention relates to the technical field of territorial space planning, in particular to a territorial space planning index calculation method and system based on a large language model. Deriving and outputting the name of the calculation tool and the actual value of the input parameter of the calculation tool through the large model, and generating a corresponding space calculation instruction; executing the space calculation instruction to obtain a corresponding index calculation result; and inputting the index calculation result and the corresponding first task cue word into the large model, and interpreting through the large model to obtain a corresponding territorial space planning result. By implementing the method, the accuracy, timeliness and reliability of land space planning index calculation can be improved.

The invention provides a knowledge graph dynamic updating method and device based on a crop growth process, and the method comprises the steps: inputting environment factor data and a crop growth stage into a trained Transform model, and obtaining a time sequence feature; performing relation graph construction based on the environmental factor data and the crop growth indexes through a graph convolutional network to obtain an environmental crop growth relation graph; performing feature extraction based on the environment crop growth relation graph through a graph attention network to obtain a spatial feature matrix; performing feature extraction based on the spatial feature matrix through a spatial graph convolutional network to obtain high-dimensional spatial features; performing alignment and conversion based on the high-dimensional spatial features through a spatial transformation network to obtain geographic spatial features; performing feature fusion based on the time sequence features and the geographic space features through a deep fusion model to obtain comprehensive features; and performing incremental updating on a preset knowledge graph based on the environmental factor data, the crop growth indexes and the comprehensive characteristics.

The invention discloses a networked measurement and control data transmission task scheduling method for a giant low-orbit satellite constellation, and solves the problem of low completion rate of measurement and control data transmission tasks caused by incapability of global station distribution in the prior art. Obtaining a resource attribute, a ground station state and a low earth orbit satellite state by using the task layer, the resource layer, the equipment state and the constellation state, performing data conversion to obtain input data, inputting the input data into the trained multi-agent network to obtain an action corresponding to the current time slot, and determining an entry low earth orbit satellite and an associated ground station according to the action; obtaining a routing path between the entry low earth orbit satellite and the task low earth orbit satellite according to the inter-satellite shortest path routing table, and determining a scheduling sequence according to the routing path and the associated ground station; the mutual influence between global tasks and resources is considered, the effectiveness of decision making during execution is reduced, and meanwhile, the scheduling efficiency of measurement and control data transmission tasks in a giant low-orbit satellite constellation scene is improved.

The invention discloses a safe and timely transmission method of an RIS-assisted ISAC system based on a short packet. According to the method, a meta-reinforcement learning method is adopted for the first time to uniformly allocate communication resources and sensing resources of the RIS-assisted ISAC system based on short packets, and meanwhile, communication security, sensing security and information freshness of users in the system are guaranteed. Through solving a non-convex multi-objective optimization problem, joint optimization of communication performance and perception performance is realized.

The invention belongs to the technical field of ground unmanned platforms, and particularly relates to a sky-air-ground integrated cross-country environment high-precision map construction method. Comprising the following steps: 1, acquiring a cross-country environment high-precision map; and 2, making a cross-country environment high-precision map. According to the invention, automatic extraction of elements is realized by combining an automatic extraction technology; in the aspect of map making, making specifications are provided, and the running efficiency of a high-precision geographic information system is improved while it is guaranteed that the cross-country maneuvering requirement of a ground unmanned platform in the cross-country environment is met; incremental data updating is carried out on the high-precision map based on a real-time mobile environment perception result of the ground unmanned platform so as to ensure the timeliness of the high-precision map. Through the method, the blank of a high-precision map technology in the field of automatic driving in the cross-country environment is filled up, and support is provided for improving the cross-country maneuverability of a ground unmanned platform.

The invention discloses an item type self-growth and self-evolution and classification method based on a large model. The method comprises the steps that item type catalogs are added to a large language model, and corresponding item classification cues are constructed; inputting the government affair hotline work order text and the item classification prompt word into the fine-tuned large language model, and quoting an item type directory to output an item type corresponding to the government affair hotline work order text; and outputting the item type output by the large language model as a classification result of the to-be-classified government affair hotline work order text. The item type catalog needs to be periodically updated based on the large language model. Prediction of government affair hotline item types is achieved in the mode that the large language model is combined with the item classification prompt words, meanwhile, fine adjustment is conducted on the large language model so that the large language model can be more focused on the field of government affair hotlines, and item type self-growth and self-evolution of the item type catalog are achieved through the large language model. Therefore, the timeliness and accuracy of government affair hotline item type prediction are improved.

The invention discloses a dynamic structured data classification method, device and system and a storage medium, and the method comprises the steps: S1, obtaining structured field attributes according to structured data; s2, forming a data classification subdivision rule; s3, the structured field attributes and the data classification strategy detailed rules are matched, and structured data fields which are successfully matched and structured data fields which are not successfully matched are obtained; meanwhile, data classification marking is conducted on the successfully matched structured data fields; s4, training a decision tree model by using the successfully matched structured data fields; and S5, according to the trained decision tree model, carrying out classification marking on unsuccessfully matched structured data fields. By adopting the technical scheme of the invention, the problems of inaccurate structured data classification result and incapability of automatic classification are solved.