267 results about "Intelligence analysis" patented technology

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 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.

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 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 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 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.

An artificial-intelligence image processing method based on intelligent transportation. The method comprises the steps of image acquisition and preprocessing, feature extraction, construction and training of an artificial intelligence model, intelligent analysis and decision support, intelligent image processing and feedback, result output and application, etc. In the artificial-intelligence image processing method based on intelligent transportation, information obtained by means of feature extraction is used for the establishment of an artificial-intelligence analysis model, thereby significantly enhancing the intelligent analysis capability of an intelligent-transportation management system. In the step of construction and training of an artificial intelligence model, by means of performing mapping calculation on extracted features and historical data, a constructed artificial-intelligence analysis model can learn and recognize a pattern in a transportation image, thereby predicting an optimal processing method. The establishment of such a model enables the intelligent-transportation management system to not only process current image data, but also perform learning and prediction on the basis of the historical data, thereby improving the intelligence level of the system.

Disclosed are apparatuses, systems, and methods, for generative artificial intelligence analysis and improvement. The systems and methods may analyze a plurality of outputs produced by a first generative AI model for using a plurality of input options for performing each task of a plurality of tasks. The system may then compare first performance data reflecting a first subset of input options selected from the plurality of input options used by the first generative AI model for at least one task of the plurality of tasks and second performance data reflecting a second subset of input options used by a second generative AI model for the at least one task. Based on a comparison of the first performance data and the second performance data, the systems and methods may generate a recommendation related to a use of the first generative AI model.

The invention discloses an agent training data set construction method and system combined with cross-modal learning, and relates to the technical field of agent training, and the method comprises the steps: obtaining multi-modal intelligence data from a multi-source database, extracting feature information, and projecting the feature information to a preset semantic space to obtain cross-modal alignment features; constructing an agent function call syntax tree based on cross-modal alignment features, analyzing the syntax tree into an instruction sequence, constructing an analysis reasoning chain, and generating a sample label and an interaction track; constructing a task dependency graph by using the sample labels and the interaction tracks, decomposing the task dependency graph into a plurality of parallel decision branches, and dynamically adjusting a processing strategy to form a training data set; mapping the training data set to an agent target space, optimizing and analyzing an inference chain through a training feedback channel, and outputting a standardized sample library; and finally inputting to-be-analyzed data into the trained agent, and generating an intelligence analysis report according to the analysis reasoning chain.

The invention discloses an induction detection system for production and manufacturing based on artificial intelligence and a regulation and control method, and belongs to the field of artificial intelligence. Comprising an edge data acquisition module, a depth image recognition module, a dynamic sensor integrated array, an artificial intelligence analysis module, an intelligent abnormity feedback module and a linkage execution control module. The edge data acquisition module is deployed at key nodes of a production line, models pressure, current, temperature, vibration and other signals, and performs cross sampling with the sensor array. The depth image recognition module adopts a structured light laser array and a multi-phase shift interference technology to extract a morphology gradient map in real time. And the artificial intelligence analysis module fuses the multi-scale convolutional network and the time sequence reasoning unit, and performs high-dimensional alignment and modeling on the double-source data to form a multi-resolution sensing field. And the intelligent abnormity feedback module generates a response grade score, and realizes millisecond-level control mapping with the linkage control module based on a state path. The method has the beneficial effect that the accuracy and response speed of abnormal recognition in the manufacturing process are improved.

The invention discloses a domain knowledge-driven threat intelligence characteristic index generation method, and belongs to the field of network security. Aiming at the problems of domain knowledge deficiency, insufficient interpretability, poor dynamic adaptability and the like existing in a traditional data driving method, the invention provides a framework fusing expert knowledge and data driving through domain knowledge graph construction, multi-modal feature fusion and dynamic weight optimization; comprising the steps of domain knowledge modeling, multi-modal feature fusion and index generation and verification. According to the method, the interpretable indexes conforming to the ST IX format are generated, practice verifies that the defects of a traditional method are effectively overcome, and the accuracy, interpretability and dynamic adaptability of threat intelligence analysis are improved.

The invention relates to a self-adaptive firewall rule generation method and device based on real-time threat intelligence, and the method comprises the steps: carrying out the semantic analysis of a threat intelligence text, extracting attack indexes and attack technique and tactics labels, and generating an intelligence triple set and an attack technique and tactics label set; calling a language model to generate candidate rules based on an intelligence analysis result, carrying out credibility scoring, intercepting a low-scoring rule as a high-risk illusion rule, and entering other rules into a to-be-verified set; constructing a digital twinborn shadow sub-domain to perform real-time traffic verification on the rule set to be verified, and dividing the rule set to be verified into an effective rule set and an invalid rule set according to a verification result; constructing a rule dependence graph based on the invalid rule set to perform pollution traceability, and adding nodes with excessive pollution into an isolation list; and cleaning the historical training samples based on the isolation list and the interception rule log, and performing incremental training on the language model to generate an updated model.

The invention relates to the technical field of power system network security, and discloses an AI power industrial control network threat detection and self-evolution defense system, which comprises a data acquisition and preprocessing module used for acquiring and preprocessing multi-source heterogeneous data of a power industrial control network; the time sequence anomaly detection module is used for carrying out time sequence anomaly detection on the standardized fusion feature vector sequence; the topology anomaly detection module is used for carrying out topology anomaly detection on the standardized fusion feature vector sequence; the comprehensive anomaly judgment module is used for calculating an anomaly detection threshold value, calculating a comprehensive anomaly score and comparing the anomaly detection threshold value with the comprehensive anomaly score; the threat intelligence analysis module is used for constructing an electric power industrial control threat knowledge graph and performing threat intelligence analysis based on a comprehensive anomaly detection result; the defense strategy optimization module is used for performing defense strategy optimization; the self-evolution learning module is used for driving continuous evolution of threat detection and defense strategies; the safety protection capability of the electric power industrial control network is effectively improved.

The invention provides an artificial intelligence-based engineering safety assessment method and system, and belongs to the technical field of engineering structure health monitoring. The method comprises the following steps: acquiring physical parameter time sequence data of an engineering structure through a distributed sensor network, synchronously acquiring surface visual data, and processing the data; inputting the processed data into a multi-modal artificial intelligence analysis model; processing time sequence data by an improved Mogrimer LSTM network through four-round iterative optimization and a sensor health sensing mechanism, and realizing damage positioning, classification and crack width quantification by an improved YOLOv8 network through a coordinate attention module and a decoupling detection head; fusing the time sequence and the visual features through a cross-modal attention mechanism, and generating a risk part feature vector; and calculating a safety risk index based on the risk part feature vector, and outputting a three-level risk level and a structured report. According to the method, multi-source data are fused, and the precision and real-time performance of engineering safety assessment are remarkably improved.

The invention discloses an amorphous alloy motor operation efficiency monitoring method and system based on artificial intelligence, and relates to the technical field of electrical engineering, the system is composed of a plurality of functional modules, and the system comprises a multi-dimensional data acquisition module for acquiring motor operation multi-field coupling parameters, environment condition data and motor operation multi-field coupling parameters; comprise electrical coupling parameters, thermal coupling parameters and mechanical coupling parameters; the edge calculation module is used for preprocessing motor operation multi-field coupling parameters and environment working condition data, introducing multi-field coupling features and extracting multi-dimensional features and multi-sensor time sequence features; and the cloud artificial intelligence analysis module constructs a dynamic mapping model based on historical motor operation data by adopting an improved LSTM and Attention mechanism and fusing a Transform architecture, inputs multi-dimensional features into the dynamic mapping model, outputs an energy efficiency level, and fuses multi-sensor time sequence features by using a graph neural network based on the energy efficiency level.

The invention relates to the technical field of operation management, in particular to an airport intelligent global situation awareness and intelligence analysis management method and system. The method comprises the following steps: capturing corresponding global operation data in real time to generate an airport global original data set; performing airport operation map construction and pressure elasticity test analysis on the airport global original data set to generate an airport operation global pressure assessment result; performing airport operation pressure planning and global situation awareness comparative analysis on the basis of an airport operation global pressure assessment result to generate an airport actual operation global situation awareness model; carrying out risk early warning information analysis and airport omnibearing comparison based on the airport actual operation global situation awareness model, and generating an airport global operation process redisk report containing deviation analysis; and performing operation optimization and improvement analysis based on the airport global operation process recovery report to obtain an airport operation global optimization and improvement management scheme. The airport operation efficiency can be remarkably improved.

The invention discloses a cold chain temperature control remote monitoring and early warning system and method driven by the Internet of Things, and the system comprises a monitoring terminal module which is disposed in a cold chain environment and is used for collecting temperature and environment state signals and generating original sensing data signals; the wireless communication module is connected with the monitoring terminal module, receives an original sensing data signal and packages the original sensing data signal into an Internet of Things transmission frame signal; the cloud platform processing module receives the Internet of Things transmission frame signal, analyzes and stores the data, and generates a temperature abnormity early warning signal through an integrated artificial intelligence analysis algorithm; the early warning service module receives the temperature abnormity early warning signal and generates a multi-stage early warning notification signal according to a predefined warning strategy; the terminal execution module is connected with the cloud platform processing module and the early warning service module. According to the cold chain temperature control remote monitoring and early warning system and method driven by the internet of things, the problems of temperature control information lag, inaccurate early warning and difficulty in tracing in the cold chain transportation process can be solved.

The invention relates to a differential privacy-based competitive intelligence analysis system and method. The differential privacy-based competitive intelligence analysis system comprises a differential privacy processing and budget management module, a differential privacy feature engineering module, a privacy protection analysis and prediction module and a differential privacy synthetic data generation module. According to the design of the invention, an end-to-end advanced analysis framework with mathematics provable privacy protection capability is constructed, so that accurate, timely and reliable insight of market dynamics and competitor strategies is realized on the premise of strictly protecting enterprise sensitive commercial confidentials.

The invention discloses a software supply chain vulnerability assessment method based on multi-Agent collaboration and dynamic hypergraph learning, which is used for opening a link between vulnerability assessment and repair and realizing full-life-cycle collaboration linkage. For the problem of vulnerability assessment and repair splitting, the method covers the core capabilities of static analysis (SAST), dynamic detection (DAST), interactive analysis (IAST), intelligence analysis and the like. Evaluation results of various agents are output through standardized vulnerability portraits, and a subsequent repair strategy is driven to be automatically generated and executed, so that whole-process closed-loop linkage and fine control of'discovery-evaluation-repair-verification 'of vulnerabilities are realized.

The invention relates to the technical field of power information network threat intelligence analysis, and provides a power information network multi-source threat intelligence analysis method, system and device and a medium, and the method comprises the steps: obtaining to-be-analyzed multi-source threat intelligence data of a power information network; performing attack entity relationship extraction analysis on the text intelligence data according to a preset attack entity data model and a preset entity association mode table to generate an attack reconstruction main graph; carrying out attack entity relationship extraction analysis on the attack vulnerability codes based on a preset code large model and an attack entity retrieval database to generate a plurality of attack reconstruction sub-graphs; and combining the attack reconstruction main graph and the attack reconstruction sub-graphs to generate a target attack reconstruction graph. According to the method, an attack entity relationship extraction mechanism based on a unified attack entity data model is combined with a multi-source attack graph reconstruction mechanism, so that the accuracy and comprehensiveness of attack entity recognition are improved, the reliability of threat detection and attack tracing is ensured, and the security defense capability of the electric power information network is improved.

The invention relates to a student growth path recommendation method based on a large language model, and belongs to the technical field of artificial intelligence analysis. Comprising the following steps that all information related to student growth is comprehensively and accurately collected, and the information comprises explicit feedback data collection and implicit feedback data; original and disordered data are converted into a clean and regular format which can be used for analysis; converting the preprocessed data into structural features capable of effectively describing trainee characteristics, and constructing a portrait based on the structural features; finding a general and efficient growth path mode from successful student samples; the mined path knowledge and wider domain knowledge are structured, so that machine understanding and reasoning are facilitated; and finally, personalized recommendation is realized for students. According to the invention, an innovative and three-in-one recommendation framework is constructed, so that the fundamental defects in the prior art are overcome.

The invention belongs to the field of artificial intelligence, particularly relates to an integrated artificial intelligence analysis and modeling platform supporting large-scale data processing, and aims to solve the problems of low resource scheduling efficiency, model training reasoning splitting, data algorithm coupling, limited cross-modal modeling and the like under high-dimensional heterogeneous mass data. The platform comprises a distributed data access layer, a unified metadata governance engine, a heterogeneous computing resource scheduling center, a model life cycle management module, a multi-modal fusion modeling framework and a dynamic service orchestration interface, and realizes automatic data access governance, dynamic resource scheduling, model full-cycle management and control and cross-modal intelligent fusion. And the resource utilization rate, the modeling accuracy and the service deployment efficiency are remarkably improved.