375 results about "Intelligence analysis" patented technology

The invention provides a talent evaluation management method and system based on AI intelligence, and relates to the technical field of artificial intelligence analysis, and the method comprises the steps: S1, converting a handwritten resume image into structured text data through OCR image recognition; converting the interview record into a dialogue text with a time sequence mark through voice transfer; performing format analysis on the electronic document to extract original text content; performing coding standardization processing on the structured text data, the dialogue text and the original text content to generate a standardized text data set containing semantic tags; and S2, performing context semantic coding on the standardized text data set, performing node alignment and semantic disambiguation processing on professional terms through a domain knowledge graph, and generating a text feature vector containing an entity association relationship. According to the method, through multi-source data integration, semantic deep analysis and dynamic matching calibration, the talent ability is accurately evaluated, the result is scientific and quantitative, and intelligent and interpretable talent evaluation and decision support is provided.

The invention relates to a regional pollution process real-time monitoring, regulation and control system and method based on artificial intelligence. The system is composed of a data acquisition module, a communication module, an artificial intelligence analysis module, an intelligent regulation and control module, an execution terminal and a user interaction module. The data acquisition module acquires multi-source data such as pollutant concentration and meteorological parameters by means of a multi-modal sensor, the data are processed and transmitted by the data communication module, and the artificial intelligence analysis module realizes pollution source tracing and accurate prediction by using technologies such as a space-time diagram convolutional network and an LSTM-Transform hybrid neural network. The intelligent regulation and control module generates a regulation and control strategy based on an NSGA-II algorithm, and the execution terminal is responsible for implementation. The user interaction module provides a visual interface and a manual intervention channel. The method comprises the steps of data acquisition and processing, model construction and prediction, regulation and control strategy formulation and execution and feedback optimization closed-loop operation. According to the invention, comprehensive real-time monitoring and accurate regulation and control of regional pollution are realized, the prediction accuracy is improved, the environmental, economic and social benefits are balanced, and an efficient technical means is provided for regional pollution treatment.

The invention discloses an interdisciplinary scientific research potential assessment method based on a dynamic multi-modal knowledge graph, relates to the technical field of scientific research management and intelligence analysis, and aims at the dynamic requirements of college scientific research ecology by relying on an incremental assessment framework. Comprising the steps of multi-source data acquisition and incremental knowledge graph construction, time sequence trend identification, online and transfer learning optimization, scene simulation and multi-scene strategy verification and multi-modal data fusion. The map structure is updated in real time through concept drift detection and ontology extension, and emerging hotspots are identified by combining time sequence difference indexes such as citation acceleration and a cooperative network; and the accuracy and continuous iteration capability of the evaluation model are kept in multiple environments by means of online learning and a cross-domain migration strategy. Finally, potential evolution is quantified through scene simulation, strategy suggestions are output, the sensitivity to the cross domain is further enhanced with the assistance of multi-mode embedding, a closed-loop evaluation system from data to decision is formed, and college subject cross layout and resource allocation are assisted.

The invention discloses an intelligent dynamic triage system and method for emergency patients. The triage system comprises a data acquisition module used for acquiring basic information, physiological parameters and historical records of patients; the artificial intelligence analysis module generates a preliminary triage result; the Internet of Things connection module collects hospital resource data in real time; the central control processing unit is connected with each module to receive data; the dynamic grading decision-making module divides the patients into critical symptoms, severe symptoms, subcritical symptoms and common symptoms; the resource allocation module dynamically generates a resource allocation scheme according to the grading result; the diagnosis and treatment process management module tracks the whole process of the patient and updates state data; when the state of the patient changes, the system transmits data to the artificial intelligence analysis module again for evaluation, triggers the grading decision and resource allocation module, dynamically updates the triage priority and the resource allocation scheme, and achieves closed-loop feedback and continuous optimization.

The invention discloses a 5G base station power supply state intelligent monitoring system and method, and belongs to the technical field of intelligent monitoring. According to the system, operation parameters of a 5G base station power supply are acquired in real time through the data acquisition module, and after the operation parameters are processed by the signal conditioning and conversion module, the master control processing unit analyzes digital signals in real time, detects abnormal states and generates alarm information. The communication module is responsible for data transmission and instruction receiving, and the power management and safety protection module provides overvoltage, undervoltage, overcurrent and short circuit protection and triggers emergency measures. The artificial intelligence analysis module performs fault prediction and performance optimization based on historical data and real-time data, and the remote monitoring terminal realizes data visual monitoring and management. According to the invention, the real-time performance and accuracy of monitoring are improved, unified management of multiple base stations is realized, the operation and maintenance cost is reduced, and stable operation of a 5G communication network is guaranteed.

The invention provides a network information security adaptive threat intelligence analysis and response method and system. The method comprises the following steps: acquiring an encrypted traffic load byte stream, and segmenting the encrypted traffic load byte stream into a time sequence traffic matrix according to a time window; and analyzing the communication metadata, matching the features of the threat intelligence library, and generating a dynamic threat fingerprint based on protocol compliance and behavior abnormality. And performing multi-stage wavelet packet decomposition on the matrix, extracting the energy spectrum density, the information entropy and the time-frequency variable coefficient of the high-frequency component, and performing weighted fusion to generate a frequency-domain composite abnormal index representing heartbeat signal characteristics. And identifying hidden heartbeat periodicity and protocol violation modes through a multi-mode fusion mechanism in combination with the frequency domain index and the threat fingerprint, and outputting a threat score. And if the attack exceeds the threshold value, intercepting the flow, verifying a new attack feature, and then reversely updating the threat intelligence library to form a defense self-evolution closed loop. According to the method, automatic interception and closed-loop updating of the threat intelligence library are realized, and the defense adaptability is remarkably improved.

The invention provides an artificial intelligence-based optical coating quality monitoring method, which comprises the following steps of: 1, acquiring visual image data of the surface of a coating substrate in real time by using a common industrial camera in an optical coating process; 2, preprocessing the collected image data, and extracting visual feature information representing the state of the film layer; 3, inputting the extracted feature information into a pre-trained artificial intelligence analysis model for simulating artificial experience judgment; step 4, outputting an evaluation result of the current coating quality by the artificial intelligence analysis model, wherein the evaluation result comprises identification of film layer uniformity, thickness trend and potential defects; and 5, generating a process adjusting instruction according to the evaluation result, feeding back the process adjusting instruction to a control system of the coating equipment, and dynamically adjusting process parameters to realize closed-loop optimization control.

The invention relates to the technical field of marine disaster monitoring and early warning, in particular to an AI-based marine disaster prevention monitoring and early warning system. Comprising a multi-source data acquisition module for acquiring multi-dimensional data through a sensor network, remote sensing data access and a meteorological data interface; the data preprocessing and fusion module is used for carrying out cleaning, space-time calibration and feature fusion on the collected data; the artificial intelligence analysis module is used for realizing accurate analysis and prediction of different levels of storm surge by using a multi-scale prediction model cluster unit; the risk assessment and early warning generation module is used for calculating risk indexes according to a related index system and dividing early warning grades; and the interaction and release module is used for realizing visual display and multi-channel release of the early warning information. According to the invention, advantages of multi-source data and artificial intelligence technology are integrated, and real-time and accurate monitoring and early warning can be carried out on storm surge and coast submerging conditions.

The invention relates to the technical field of artificial intelligence, and discloses a motor manufacturing quality intelligent detection system which comprises a sensor data acquisition module, a data processing module, an artificial intelligence analysis module, a decision feedback module and a report generation module. Aiming at electromagnetic interference and mechanical noise generated by multiple processes, a self-adaptive signal filtering mechanism is established, transient interference components of data collected by a sensor can be reduced in real time, the extraction integrity of key characteristic parameters such as vibration and temperature is ensured, the misjudgment risk caused by signal distortion is reduced, the problem that the omission ratio is too high in traditional detection is solved, and the detection accuracy is improved. And a dynamic association rule base is constructed, so that the system can capture early fault symptoms which cannot be recognized by a traditional single-dimensional model, prediction blind areas caused by multi-field data splitting are reduced, and the product life prediction precision is improved.

The invention discloses a multi-source threat intelligence privacy fusion processing method and system, and relates to the technical field of network security threat intelligence analysis and processing, and the method comprises the steps: collecting threat intelligence, and carrying out the cleaning, standardization and desensitization; establishing a threat index semantic model and aligning cross-modal features; the central coordination module completes model aggregation after organization node local training; and based on the aggregation model fusion intelligence, semantic association and attack link reconstruction are established, and a result is generated and fed back for optimization. The technical problems that in cross-organization fusion processing of multi-source heterogeneous threat intelligence, data formats are not uniform, and privacy protection and intelligence collaborative analysis contradictions exist, so that threat detection is not comprehensive and inaccurate, and accurate evaluation and effective management and control requirements are difficult to meet are solved. The technical effects that the multi-source heterogeneous threat intelligence is effectively fused on the premise of privacy protection, the comprehensiveness and accuracy of threat detection are improved, and the requirements for accurate assessment and effective management and control of threats in a cross-organization scene are met are achieved.

The invention provides a science and technology information deep analysis method and system based on cross-modal semantic enhancement, and relates to the technical field of science and technology information analys.The method comprises the steps that firstly, a cross-modal semantic anchor point set is constructed, and the cross-modal semantic anchor point set comprises text theme anchor points extracted from science and technology information texts, visual object anchor points extracted from images and the association mapping relation of the text theme anchor points and the visual object anchor points; constructing a semantic conduction path between anchor points based on the cross-modal semantic anchor point set, realizing bidirectional information transmission, generating a cross-modal semantic enhanced representation, performing hierarchical semantic analysis on the enhanced representation to obtain a topic association rule, a technical element dependency relationship and a concept evolution sequence, and integrating the topic association rule, the technical element dependency relationship and the concept evolution sequence into an analysis conclusion; the analysis conclusion is reversely mapped to adjust the association mapping relation strength, an updated set is obtained, finally, a structured science and technology information analysis report is generated based on the updated set, logic connection of all modules is achieved, and comprehensive and accurate science and technology information analysis is provided for users.

The invention relates to the technical field of intelligent customer acquisition, in particular to a cross-border supply chain financial service AI intelligent customer acquisition system based on big data analysis. Functional components such as a big data access engine, an artificial intelligence analysis model, a customer portrait modeling engine, a preference setting interface, a public opinion and risk identification module, an intelligent screening engine and a docking output module are integrated; multi-source data such as customs data, trade data, enterprise industry and commerce data, judicial public data, international manufacture and cutting lists and internet public information from all places around the world are widely fused. The system performs modeling calculation on industry categories, regional distribution, export history, transaction records, public opinion trends and compliance risks of import and export enterprises, establishes customizable customer optimization rules, accurately outputs a target customer list which can be covered by credit insurance and financing service in combination with a machine learning algorithm, and attaches contact information. Various import and export related data resources can be accessed, automatic screening and output of credit insurance and account receivable financing adaptive exporters are realized through preference setting and AI analysis, the customer obtaining efficiency is remarkably improved, the marketing and risk control cost is reduced, and the economic benefit is improved. The problems that traditional cross-border supply chain financial service customer obtaining depends on manual recommendation, offline visit and a static customer library, the efficiency is low, and small and medium-sized export enterprises are difficult to cover are solved.

The invention relates to the technical field of security management and control, in particular to an intelligent security dynamic identification management and control system, which comprises a multi-mode sensing module, a high-definition camera array, a millimeter-wave radar, an infrared sensor, an RFID reader-writer and a data processing module, and is used for receiving data of the multi-mode sensing module, carrying out time-space alignment and data fusion processing, and sending the data to the multi-mode sensing module. And personnel features, article features and environment features are extracted. The system also comprises an artificial intelligence analysis module and a user interaction module. According to the intelligent security dynamic identification management and control system, an artificial intelligence analysis module constructed by using a deep learning algorithm can perform real-time analysis and prediction on personnel behaviors, article states and abnormal events, discover potential safety hazards in advance and take corresponding management and control measures, thereby realizing conversion from passive defense to active prevention and control. For example, the system can give an alarm in time according to multi-dimensional information such as temperature change, smoke concentration and abnormal behaviors of people at the initial stage of a fire, so that precious time is won for people evacuation and fire extinguishment.

An exemplary system and method are disclosed that is configured to detect cognitive impairment (e.g., early cognitive impairment) or assess cognitive function by analyzing, via machine learning and artificial intelligence analysis, behavioral metadata collected from a smart app during the course when a subject is using a cognitive test instrument for cognitive tests that incorporate motor activity (e.g., drawing or writing). The machine learning and artificial intelligence analysis can execute features associated with the test taker's metadata (e.g., time spent on task or questions, changing answers, referring back to the previous question), drawing qualities (e.g., line straightness, completeness), among others.

The invention discloses a multi-agent collaborative scheduling method and system oriented to science and technology intelligence analysis, and relates to the technical field of science and technology intelligence analysis, and the method comprises the steps that a task issuing layer outputs a standardized task description by executing task demand element analysis; after the intelligent coordinated scheduling layer receives the standardized task description, closed-loop collaborative decision making is carried out, and a task distribution instruction is output; and the professional agent execution layer drives the data acquisition agent group to acquire science and technology information data through a data access adapter of the data resource layer, executes a science and technology information analysis task, outputs a structured processing result, and visually displays the structured processing result to a user through the task release layer. The technical problem that in the prior art, a science and technology information processing system lacks intelligent task allocation, and consequently the information processing efficiency is low is solved, and the technical effects that four-layer architecture closed-loop cooperation of science and technology information analysis tasks and efficient dispatching of specialized intelligent agent groups are achieved, and the task allocation accuracy and the information processing efficiency are improved are achieved.

The invention belongs to the technical field of network security, and discloses a countering measure strategy construction method and system based on attack intelligence. According to the method, firstly, multi-source attack intelligence is fused, and a causal time sequence attack knowledge graph is constructed; then, mapping the atlas on a digital twinborn model based on the IEC 62443 standard, constructing a Bayesian attack graph, and quantifying the risk of each attack path; then, taking the risk, the cost and the operation influence as multiple targets, and adopting an NSGA-II algorithm to generate a Pareto optimal countering strategy set; further, an optimal robust strategy is selected from the strategy set by solving the Stackelberg safety game model; and finally, explaining the decision process by using an interpretable AI technology. According to the method, the problems of passive threat intelligence analysis, static risk assessment, sub-optimal strategy selection and opaque decision-making process in the prior art are solved, and active, quantitative, optimal and credible defense decision-making for the key infrastructure is realized.

The invention relates to a network threat report attack knowledge graph automatic construction method and system based on a large language model, a storage medium and a program product, and the method comprises the steps: carrying out the iterative processing of extracted entities and relationships through a clustering method based on the large language model, and generating an initial attack knowledge graph; according to the invention, aggregation processing is carried out on a plurality of technology example threat reports belonging to the same attack technology through an attack technology graph template generation mechanism, a standardized attack technology standardized template library is established, and automatic attack technology tagging of new threat reports is realized by adopting an attack technology alignment method based on Word2Vec and WordNet, so that the automatic attack technology tagging of the new threat reports is realized. And a complete attack technology knowledge graph is constructed, so that a security analyst can quickly understand an attack path and a key threat point, and the automation degree and the accuracy of threat intelligence analysis are remarkably improved.

The invention discloses a target detection method based on an image semantic matching algorithm, and the method comprises the steps: carrying out the multi-source data collection through an image sensor, an SAR (Synthetic Aperture Radar) radar and infrared remote sensing equipment which are carried by an unmanned plane, and obtaining a visible light image, a high-resolution radar image and thermal imaging data in a low-light environment; carrying out target identification on different data sources by utilizing an integrated learning method; judging whether the target is a tank or not by adopting a soft voting fusion strategy; after the tank is detected, the crossing unmanned aerial vehicle is dispatched to adjust a flight path, and image data with higher resolution are obtained; a deep learning model is trained based on an image semantic matching technology, semantic analysis is performed on a high-precision image, and weak parts of a target are automatically identified and labeled by matching standard part features. According to the method, the multi-modal source data is fused, the reliability of tank recognition is improved, the weak part of the target is accurately positioned by combining the image semantic matching technology, the method can adapt to a complex battlefield environment, and the intelligent level of battlefield intelligence analysis is improved.

The present invention relates to the technical field of cross-border e-commerce supply chain, and discloses a cross-border e-commerce supply chain data analysis method and system based on artificial intelligence. The method has the following effects: The embodiment of the present invention provides a cross-border e-commerce supply chain data analysis method and system based on artificial intelligence. The method is executed by a processor and includes obtaining cross-border e-commerce supply chain data; analyzing through artificial intelligence algorithms to obtain supply chain analysis results for each type of product; obtaining a product number set, matching the product number set with the supply chain analysis results to obtain product number supply chain analysis results; establishing a product supply visualization network model; determining main product nodes in the product supply network model according to the product supply visualization network model; and determining the product supply route between the target product node and the main product node. This method provides an effective high-efficiency transportation method for cross-border e-commerce supply chain through artificial intelligence analysis methods.

The invention discloses a high-precision intelligent quality inspection system for diamond grains, and relates to the technical field of defect detection and contour measurement, and the system comprises a multi-mode sensing device which is used for obtaining a two-dimensional image, a three-dimensional shape and spectral data of the grains; the module is used for receiving manufacturing process parameters and constructing a theoretical three-dimensional model; the interface is used for acquiring actual manufacturing process data; the unit is used for processing the fused measured data and comparing the fused measured data with a theoretical model to generate deviation data; the hierarchical artificial intelligence analysis engine comprises a data analysis and coordination control AI model based on multi-head attention, the AI model is coupled with an attribution analysis layer, and the engine fuses and analyzes measured data, deviation data, process parameters and actual manufacturing process data. And performing deep cross-modal association, trend analysis, manufacturing data association analysis and accurate attribution by using an attention mechanism, and finally generating a quality evaluation result containing application performance prediction and process optimization suggestions with attribution information.

Conventionally, the road was visually inspected from patrols etc., and it was determined whether to respond to road disasters in the event of a disaster or the like by observing the actual road conditions. However, there was a problem that it took time and effort to make a judgment only by visual patrol, and it was difficult to respond quickly. 【Solution means】 An information acquisition unit that acquires information related to disasters, an analysis unit that analyzes two or more pieces of information acquired by the information acquisition unit, and a determination unit that determines the necessity of road disaster response from part or all of the results analyzed by the analysis unit, A road opening unit that registers a pre-formulated road opening plan and formulates an implementation plan for road opening, and a robotics unit in which a disaster response robot performs a road opening operation using artificial intelligence analysis and robotics technology, A road disaster response support system, program, and road management method, characterized by comprising:

A modular open system architecture for common intelligence picture generation is disclosed. The system receives intelligence requirements through a multimodal artificial intelligence system and calculates collection feasibility across multiple intelligence sources based on physical and temporal conditions. The system develops integrated collection plans through the containerized analytics workbench using containerized analytics modules and processes intelligence through GPU-accelerated deep learning models for automated target recognition. Satellite collection is orchestrated through satellite data acquisition optimization platform by evaluating weather conditions, orbital parameters, and sensor capabilities, while space domain awareness is maintained through space domain awareness system for real-time collection asset management. Multi-source intelligence data is fused through multi-source intelligence fusion system to populate a common intelligence picture. The system implements automated workflows for intelligence analysis and dissemination while maintaining security controls, with the containerized analytics workbench providing pattern of life analysis and dynamic exploitation through containerized microservices.

The invention discloses a spatio-temporal data driven consumption behavior prediction method and system, and relates to the technical field of text travel data intelligent analysis, and the method comprises the steps: respectively defining a user node, a business district node and an event node according to user attribute data, business district attribute data and a historical external event, performing analysis in combination with user historical consumption data and business district POI data to determine a corresponding user-business district consumption edge, a business district-event influence edge and an event-user trigger edge so as to construct a heterogeneous ternary graph; performing multi-scale Motif sub-graph sampling on the heterogeneous ternary graph to extract a multi-granularity sub-graph, and extracting multi-scale spatial features of corresponding user nodes from the multi-granularity sub-graph; and the two are organically fused into a highly expressed space-time fusion feature, and the business district consumption behaviors of the user in different business districts are predicted according to the space-time fusion feature. Therefore, the prediction precision and robustness of the commercial district consumption behavior of the user are greatly improved.

The invention discloses a network threat intelligence analysis method and system, and the method comprises the steps: carrying out named entity recognition, designing a DeBERTa-CRF-BiGRU composite model for named entity analysis, extracting key entities from massive threat intelligence data, and laying a foundation for subsequent intelligence analysis; the method comprises the following steps: performing accurate classification of attack behaviors in intelligence, quickly identifying and responding to potential network threats based on basic data extracted by named entity identification, further refining and classifying intelligence, and providing structured information for constructing an attack intelligence chain; constructing an attack intelligence chain, on the basis of an attack chain framework, constructing the attack intelligence chain from the network threat information extracted in the first two steps, and displaying attack source, path, target and attack type information by using a graphical method; through connection of the nodes and the edges, the relation between the attacker and the target and the relation between different attack types are visually displayed.

The invention provides a power grid energy efficiency optimization and load prediction management method and device, relates to the field of energy and power, and solves the technical problem of data storage and processing delay caused by poor database expansibility and low algorithm efficiency. The method comprises the following steps: integrating various sensor data and intelligent sensing terminal data to form multi-dimensional data; and sending the multi-dimensional data to an edge computing node for preprocessing, and storing the multi-dimensional data in a distributed storage cluster. Load data and multi-dimensional influence factors in the multi-dimensional data stored in the historical distributed storage cluster are acquired, and a prediction model is constructed. And analyzing the multi-dimensional data stored in the distributed storage cluster through an artificial intelligence analysis engine, capturing nonlinear and non-stationary change characteristics of the load, and obtaining real-time prediction data. And acquiring real-time operation data of each link of the power grid from the distributed storage cluster, and optimizing a scheduling strategy in combination with a dynamic loss characteristic model of the equipment. The method and the device are used in the power dispatching process.

The invention relates to the technical field of power utilization monitoring, and discloses a photovoltaic user power utilization abnormity monitoring method and system based on artificial intelligence. The photovoltaic user electricity consumption abnormity monitoring system based on artificial intelligence comprises a data acquisition module which is used for acquiring photovoltaic power generation data, electricity consumption data and environment data of a user; the data preprocessing and feature engineering module is used for cleaning, aligning and normalizing the original data acquired by the data acquisition module and constructing a feature data set for model training and reasoning; and the artificial intelligence analysis engine module comprises an unsupervised learning unit, a supervised learning unit and a deep learning unit. According to the invention, the false alarm rate and the missing report rate can be effectively reduced, the accurate diagnosis of the abnormal type can be realized, and the intelligent and accurate operation and maintenance requirements of power grid enterprises on the power utilization monitoring of photovoltaic users are met.

The invention discloses a carbon neutralization target setting and tracking system based on big data and an artificial intelligence technology, and relates to the technical field of carbon neutralization. The system comprises a plurality of key modules, a data acquisition module widely collects multi-source data of energy, industry, traffic, building, weather and the like, a satellite remote sensing data acquisition technology is introduced, and the carbon emission condition is comprehensively reflected; the data preprocessing module adopts a transfer learning technology to improve the processing efficiency; the big data storage module is combined with a block chain technology to guarantee data credibility; a reinforcement learning mechanism is integrated into a target setting sub-module of the artificial intelligence analysis module, a knowledge graph technology is introduced into a tracking analysis sub-module, and a carbon neutralization target is accurately set and tracked; the visual display module provides immersive experience by applying VR and A technologies; and the decision support module gives comprehensive decision suggestions in combination with a simulated annealing algorithm and a multi-objective decision theory. The system can scientifically set targets, accurately track the progress and intelligently assist in decision making, and provides powerful technical support for carbon neutralization.

The invention discloses an intelligent dynamic prediction method for a coal and gas outburst dangerous area of a working face, and the method comprises the steps: building an integrated early warning information analysis platform, dynamically collecting key parameters in a mine, and constructing a dynamic data set; and calculating a coal seam firmness factor and a gas emission factor by utilizing a data fusion technology and combining historical data and regression analysis. Furthermore, in combination with a static data set, such as geological and ventilation system distribution data, a multi-parameter coupling prediction model is constructed. The model is trained and verified through a machine learning method so as to predict the coal and gas outburst risk of the working face in real time. The system automatically triggers early warning according to a model prediction result and a preset threshold value, a scientific basis is provided for coal mine safety management, and coal and gas outburst accidents are effectively prevented.

The invention belongs to the field of Internet information processing, and particularly relates to a live broadcast information analysis method based on a knowledge graph, which comprises the following steps: acquiring a certain amount of various types of live broadcast data of a specified platform, and respectively establishing a violation database and a normal database; selecting a certain amount of live broadcast data from the violation database and the normal database, selecting violation and violation keywords, and establishing a knowledge graph of each category according to the violation and violation keywords; according to the method, live broadcast information is analyzed and sorted in a knowledge graph mode, corresponding keyword information is acquired, a target model is established, and illegal information auditing is performed, so that illegal information in daily live broadcast can be audited in real time without too many administrators, the success rate of auditing is far greater than that of manual auditing, and the auditing efficiency is greatly improved. Hidden dangers in the platform can be greatly eliminated, and the live broadcast quality is effectively controlled.

The invention discloses a target recognition system and method based on a large language model and a knowledge graph, and the system comprises a data collection module, a data preprocessing and feature extraction module, a data fusion module, a knowledge base module, an LLM reasoning module, and a result output and presentation module, and relates to the technical field of target recognition and tracking. According to the target recognition system and method based on the large language model and the knowledge graph, an intelligent system capable of effectively integrating multi-source heterogeneous intelligence data and automatically carrying out high-precision identity and intention recognition can be constructed, so that the efficiency and depth of intelligence analysis are improved, and decision making is better supported.