163 results about "Intelligence analysis" patented technology

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 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 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 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.

The invention discloses a linkage command decision-making and early warning method fusing grid patrol events and artificial intelligence, and belongs to the technical field of crossing of smart cities and artificial intelligence. The method aims at solving the problems of low efficiency and insufficient early warning caused by the fact that multi-source data are difficult to fuse and event association depends on manpower in existing urban governance. According to the scheme, multi-source data such as grid patrol, Internet of Things monitoring and hotline are accessed and standardized to form a standardized event in a unified format; performing time-space and semantic fusion analysis on the events by using an artificial intelligence analysis engine, intelligently merging associated events and identifying high-risk events; early warning is automatically generated based on a preset rule base, and a processing task is distributed to a corresponding service system; and finally, the command system carries out unified scheduling. According to the method, intelligent association analysis, active early warning and cooperative command of urban governance events can be realized, and the management efficiency is improved.

The invention discloses an event causal reasoning method for intelligence analysis and electronic equipment. The method comprises the following steps: acquiring a plurality of evidence fragments related to a query text from a plurality of sources, and taking the plurality of evidence fragments as an evidence fragment set; extracting entities from the evidence fragments, and linking the entities into a knowledge graph; event elements are extracted from the evidence fragments, event nodes and causal edges are generated according to the event elements, and one or more candidate causal edges are generated based on the event pairs; retrieving from the evidence fragment set to obtain one or more binding evidences of each candidate causal edge; and calling a large language model for each candidate causal edge to generate a causal assertion, performing consistency verification on the binding evidence of the causal assertion and the same candidate causal edge, combining into a traceable causal evidence chain, and performing event causal reasoning analysis. According to the method, the event-level representation is constructed, and the event-event candidate causal relationship is generated, so that a reasoning object is improved from entity co-occurrence to a reasonable and maintainable event causal structure.

The invention relates to the technical field of artificial intelligence analysis and optimization of a power system, and discloses a line loss variable separation method, system and device based on dynamic time sequence vector fitting, and a medium, and the method comprises the steps: fusing multi-source heterogeneous data, and constructing an enhanced feature vector sequence through an attention mechanism and a physical equation constraint; establishing a time-varying causal graph model based on a dynamic Bayesian network, introducing an attention mechanism to calculate time sequence feature similarity, calculating time-varying causal strength in combination with physical constraint loss and topological distance, and deducing dynamic causal; a theoretical line loss curve is dynamically fitted in a physical-data double-track modeling mode, and key nodes in a causal graph model are used as correction factors; the contribution degree of each factor to the line loss difference is quantified based on a Shapley value algorithm, and accurate separation and attribution of line loss components are realized.

The invention belongs to the technical field of intelligence analysis, and particularly discloses a knowledge graph driven trend intelligence prediction method, which comprises the steps of collecting multi-source heterogeneous intelligence data and constructing a target trend knowledge graph, generating and screening a prediction index group based on the knowledge graph, performing classification and quantitative weighting on the prediction index group, and obtaining a target trend information prediction result. Drawing a threat curve graph, and constructing a trend intelligence prediction model based on the threat curve graph to output an action trend prediction result of the monitored object; the method has the following advantages that multi-source intelligence can be systematically fused, structural analysis is achieved, the misjudgment risk caused by data noise and strategic deception is effectively reduced, the prediction accuracy and foresight are improved through quantitative weighting and probabilistic reasoning, and therefore intelligent, dynamic and high-reliability technical support is provided for strategic early warning.

The invention discloses a vertical domain intelligence analysis method based on a large language model. The method comprises the following steps: receiving a natural language analysis demand of a user; based on pre-constructed vertical domain specific task planning knowledge, intelligently decomposing the demand into a subtask list corresponding to a preset dimension by using a large language model, and semantically matching a proper information acquisition tool for each subtask to generate a structured task plan; the system schedules and executes tool sets of network search, webpage capture, internal query and the like according to a plan, and performs cross verification and cleaning on collected fragmented information; and inputting the verified data into a large language model for integration analysis, and optionally carrying out multi-round iteration self-evaluation optimization to automatically generate a high-quality vertical domain intelligence analysis report. According to the method, full-process and high-intelligence automatic processing of information analysis in the vertical field from demand understanding to report generation is realized, and the method has excellent planning capability, process interpretability, result reliability and excellent modular expansibility.

The invention relates to the technical field of data processing, and discloses a fresh agricultural product full-link traceability management system, which comprises a data acquisition module used for acquiring original data representing the state of the data acquisition module; the artificial intelligence analysis module is used for processing the original data to generate an analysis result and a credibility score corresponding to the analysis result; an intelligent contract is deployed in the data write-in scheduling module, and the intelligent contract is used for receiving the analysis result and the credibility score; and the hierarchical block chain storage module is used for responding to the write-in instruction generated by the data write-in scheduling module. Through intelligent analysis and hierarchical block chain storage, quantitative evaluation and dynamic hierarchical storage management of source credibility of fresh agricultural product traceability data are realized, a main chain is effectively prevented from being polluted by abnormal data, a data closed-loop recheck mechanism is established, and the overall authenticity and reliability of traceability information are improved.

The invention belongs to the field of artificial intelligence, particularly relates to an interpretable artificial intelligence analysis and reasoning system based on image calculation, and aims to solve the problems that the AI image decision process is opaque and the causal logic is non-traceable. The system comprises a multi-scale feature extraction interface, a semantic concept generation interface, a causal inference engine interface, an anti-fact interpretation generation interface and a man-machine collaborative verification interface, and high-credibility image analysis is realized by constructing an interpretable path from pixels to semantic concepts and then to a causal chain.

The invention discloses a strategic recommendation method and device, and relates to the technical field of intelligence analysis tools, the method is applied to a computer device capable of calling a knowledge base and a large model, and the method comprises the following steps: retrieving an obtained text fragment based on an input request of a user; based on the input request, calling a large model to carry out site analysis on the input request to obtain a site analysis result; based on the input request, the text fragment and the standing analysis result, calling the large model to combine with the text fragment to perform qualitative analysis on the standing analysis result, and outputting a qualitative analysis result; based on the input request and the qualitative analysis result of the dictionary format, determining an optimal feature vector of the dictionary format by using an analytic hierarchy process; and on the basis of the optimal feature vector, four-quadrant analysis data of each strategy is calculated by using a four-quadrant analysis method, and then optimal strategy pushing is completed. According to the method, qualitative analysis, an analytic hierarchy process and a four-quadrant analysis method are combined, development strategic intelligence analysis is carried out according to requirements input by a user, and strategic suggestions are generated.

A system for privacy preserving artificial intelligence (AI) comprising: a first and second device interoperability system (DIS) coupled to an artificial intelligence analysis subsystem (AIAS). The first and second DIS receive a first and second plurality of data sets from coupled first and second pluralities of user devices respectively. The first and second DIS create first and second aggregated data sets based on the received first and second pluralities of data sets. The first and second DIS determine first and second subsets of model parameters for an AI model based on the first and second aggregated data sets; then transmit first and second sets of information based on the model parameter subsets to the AIAS. The AIAS creates a set of model parameters for the AI model based on the transmitted sets of information, and transmits the set of model parameters to the first and second DIS for deployment.

The invention discloses an axle industry information acquisition and analysis system based on a large language model, and belongs to the technical field of artificial intelligence and information processing. The system comprises an intelligence acquisition layer, an intelligence processing layer and an intelligence analysis layer, wherein the intelligence acquisition layer is used for acquiring multi-source intelligence data from different information sources; the intelligence processing layer is used for converting an unstructured text of the multi-source intelligence data into structured data with rich semantic marks by utilizing a large language model; and the intelligence analysis layer is used for analyzing the structured data with the rich semantic marks by using a large language model and generating an analysis report. According to the invention, automation of information acquisition, intelligence of analysis and scientization of decision making are realized.

A system and method for optimized airline selection in air travel bookings provides a comprehensive solution for enhancing user experience in choosing airlines. This system integrates an Al-driven web platform, communicably coupled with a database, which analyzes crowdsourced consumer input on airline service experiences across multiple metrics. A user interface of the platform enables users to submit feedback and view a public dashboard of airline evaluations. The Al system processes this data to generate and update performance assessments for airlines, aiding users in making informed decisions. Additionally, the system facilitates the selection and booking of air travel, optimizing the process through up-to-date performance assessments and user preferences, including using a virtual assistant to help with the selection and booking process. The system is industry-agnostic and adaptable to various applications beyond air travel.

This invention provides a truck model and axle count information comparison and export transaction system, relating to the field of intelligent transportation technology. It includes a multi-source data acquisition unit; an information processing and verification unit, communicatively connected to the multi-source data acquisition unit, for processing the multi-source data based on an artificial intelligence recognition model and constructing a dynamic digital twin model of the vehicle; a multi-dimensional cross-verification mechanism; and a hierarchical decision execution unit, communicatively connected to the information processing and verification unit, for executing hierarchical response operations based on the risk score. By introducing multi-source data fusion and artificial intelligence analysis, this invention significantly improves the accuracy of vehicle model and axle count verification, effectively reducing tolling errors caused by identification mistakes. Through the multi-dimensional cross-verification mechanism, the system achieves real-time comparison and risk warning of vehicle information at entrances and exits, enhancing the real-time identification and prevention capabilities for cheating behaviors such as modified vehicles and hidden axles.

The invention relates to the technical field of health monitoring, in particular to a foot multi-area temperature and humidity cooperative monitoring system and method for diabetic foot ulcer risk prediction.The intelligent sensing terminal is integrated with a distributed temperature and humidity sensor array, a motion sensing unit, a microprocessor and a first wireless communication module through a flexible circuit; the mobile relay terminal is internally provided with a second wireless communication module and a terminal application program, the cloud analysis platform is deployed with a time sequence database, an artificial intelligence analysis engine and a message push service, and the medical interaction terminal provides a visual interface for medical staff and patients. According to the system and the method, real early warning is realized, the accuracy and the reliability of risk assessment are improved, a complete collaborative closed-loop system is constructed, efficient energy management and excellent user experience are realized, long-term self-optimization and self-adaptive evolution capabilities are also realized, and the clinical value of the system and the method is continuously improved.

According to the network threat analysis method and system based on the learning evolutionary game provided by the invention, the expected benefit of sharing is analyzed through modeling, quantitative analysis is carried out by adopting the learning evolutionary game, and a reasonable incentive strategy is obtained, so that the sharing and utilization efficiency of network threat intelligence is promoted; each entity enterprise inquires from the open community to obtain the required threat intelligence, analyzes the threat intelligence and deploys own network defense measures, so that the network security defense capability is improved, and the problems that in the prior art, the capability of providing a large number of resources is difficult, the data sharing and exchanging capability is weak, and a large number of continuous network attacks are difficult to defend are solved.

The application discloses a battlefield information processing method based on multi-modal data fusion, comprising the following steps: acquiring multi-modal original data of battlefield information, standardizing different original data to obtain a structured data set; performing time and space alignment on the structured data set to obtain a space-time reference data set; performing feature completion on the space-time reference data set to obtain a complete analysis data set; performing hierarchical encryption on the complete analysis data set to obtain an encrypted transmission data set; performing distributed storage on the encrypted transmission data set to obtain a secure storage data set; performing isolation processing on the secure storage data set to obtain an intermediate result data set; performing real-time detection and protection on the intermediate result data set to obtain a final protection data set; and performing processing detection and credibility judgment on the final protection data set to obtain a final credible information analysis result.

The invention discloses an artificial intelligence-based anti-scattering grating method, which comprises the steps of collecting and preprocessing scattering source parameters, incident light polarization state parameters and grating state parameters, constructing an artificial intelligence analysis model, establishing a coupling relation model of a dynamic scattering source and an incident light polarization state, calculating scattering compensation demand parameters, and calculating the scattering compensation demand parameters. Generating a grating parameter dynamic adjustment instruction, controlling an adjustment mechanism to execute operation, and feeding back an actual anti-scattering effect parameter correction instruction; the method solves the problem that the anti-scattering effect is attenuated along with the environment due to the fact that the coupling effect is not considered in the prior art, and the anti-scattering effect is stable in the complex environment.

This invention relates to the field of intelligent video surveillance and artificial intelligence analysis technology, specifically to a method for identifying abnormal behavior in gas stations by integrating cross-camera behavior chains. The method includes: collecting multiple video streams, historical trajectory data, a 3D spatial topology map containing blind spot nodes, and operating procedure text; extracting target appearance and movement features, combining topological connectivity probability and dynamic tolerance for cross-camera joint matching to generate an identity identifier; stitching trajectories according to timestamps, and inserting blind spot abnormal dwelling nodes in timed-out cross-node segments to form a spatiotemporal behavior path; inputting the path text and video stream slices along with the procedures into a visual language model for behavioral intent reasoning; determining the abnormality category, calculating a dynamic risk score, and triggering a graded alarm; this invention organizes fragmented actions and blind spot dwellings into a reasonable behavior chain, achieving dangerous logical behavior identification.

The invention belongs to the technical field of positron detection equipment, and particularly relates to a positron detection equipment performance optimization device and system based on artificial intelligence. Comprising a base, a lower hollow column, an upper hollow column, a connecting cylinder, an upper sliding block, a lower sliding block, a lifting adjusting assembly, a steering adjusting assembly, a circular box, a supporting moving assembly, a guiding reset assembly, a data acquisition module, a parameter adjusting module and an artificial intelligence analysis module. The system is reasonable in design, can collect equipment operation data in real time, analyzes the data in combination with a deep learning algorithm, rapidly identifies potential problems, automatically adjusts related parameters, remarkably improves the operation efficiency of equipment, greatly reduces the manual maintenance cost, and improves the working efficiency. And meanwhile, the device has the advantages of flexible data acquisition, stable movement and support and efficient data processing capability, so that the device can be quickly deployed in different application scenes, and diversified requirements are met.

Systems and methods for providing artificial intelligence analysis of total organic carbon ("TOC") sensor data are provided. A TOC measurement device includes a fluid containment portion for containing a sample fluid, and a sensor and an oxidation device located at the fluid containment portion. A controller operates the oxidation device to cause the sample fluid at the fluid containing portion to undergo different degrees of exposure to the oxidation device for a time period during which measurements are received from the sensor, each measurement being acquired at a different time, in one embodiment, the method includes determining an error-compensated TOC measurement for a sample fluid such that each of the measurements reflects a different level of exposure of the sample fluid to an oxidation device, analyzing the measurements, including determining an initial TOC measurement for the sample fluid by applying a deterministic model, and applying an artificial intelligence module to the initial TOC measurement to determine an error-compensated TOC measurement.

The embodiment of the invention provides a multi-source threat intelligence analysis method and device, electronic equipment and a storage medium, and relates to the field of network security. The method comprises the steps of obtaining multi-source heterogeneous threat intelligence data, and performing normalization processing on the multi-source heterogeneous threat intelligence data to obtain a standardized data set; partitioning the standardized data set to obtain a plurality of data blocks, and dynamically partitioning based on a preset word segmentation device to obtain structured integrated data; extracting dominant threat information and attack intention tags through global analysis of a preset large language model to obtain a preliminary analysis list; and constructing a knowledge graph according to the preliminary analysis list and storing the knowledge graph in a preset graph database, and combining the knowledge graph and the preliminary analysis list to generate an intelligence analysis result. The method is used for realizing end-to-end structured processing and effective association analysis of multi-source heterogeneous threat intelligence, improving intelligence analysis efficiency and accuracy, and providing reliable support for network security defense.

This invention discloses a container shipping route analysis method based on artificial intelligence analysis, belonging to the field of shipping intelligence technology. The method includes: collecting route operation data during the operation and management of a target container shipping route, performing time alignment and normalization processing to generate an actual route event sequence; embedding route governance rules into corresponding route segments based on the actual route event sequence to construct a route governance semantic representation; outputting the arrival time series of each port of call and the reliability evolution characteristics as the route progresses through an artificial intelligence analysis process based on the route governance semantic representation; and generating a counterfactual route trajectory sequence using the arrival time series and reliability evolution characteristics. This invention reconstructs the theoretical evolution process of a shipping route under given operational constraints by constructing a counterfactual route trajectory with route governance semantics, providing a predictable and alignable expressive basis for the operational state of the route in both time and space dimensions.

The invention relates to the technical field of data fault diagnosis and early warning, and discloses an artificial intelligence-based industrial equipment multi-dimensional operation data fault diagnosis and early warning system, which comprises a data fault diagnosis and early warning system, and is characterized in that the data fault diagnosis and early warning system comprises a data acquisition layer, an artificial intelligence analysis layer and an application service layer; the data acquisition layer is used for full-dimensional data sensing and preprocessing and is responsible for acquiring high-quality and multi-dimensional original data; and the artificial intelligence analysis layer is used for carrying out data feature extraction, fault diagnosis and health prediction. According to the industrial equipment multi-dimensional operation data fault diagnosis and early warning system based on artificial intelligence, a three-layer collaborative architecture of data acquisition-AI analysis-application service is constructed, so that a business closed loop from multi-source heterogeneous data perception to intelligent diagnosis decision is realized, the problems of single data dimension and analysis chain breakage of a traditional system are solved, and the fault diagnosis and early warning of the industrial equipment multi-dimensional operation data is realized. And the comprehensiveness and systematicness of fault diagnosis are obviously improved.

This invention relates to the field of cyberspace security and threat intelligence analysis technology, and discloses a sparse anomaly behavior detection method and system based on privacy-preserving semantic evidence. First, irreversible semantic abstraction is performed on collected multi-source security data to extract semantic evidence elements related to anomalies, which are then semantically encoded to obtain semantic evidence units. Next, based on the semantic evidence elements and their semantic feature representations, a semantic evidence hypergraph containing intra-event and cross-event hyperedges is constructed. Finally, based on the semantic evidence hypergraph, feature aggregation is performed on semantic evidence nodes and their relationships, anomaly scores for each behavioral event or semantic evidence are calculated, and sparsity is determined by combining the frequency of behavior occurrence to identify anomalies. This invention can construct cross-event semantic behavior representations without exposing the original data and model multi-entity collaborative relationships based on the hypergraph structure, achieving effective detection of low-frequency, cross-stage recurring anomalies.

This invention relates to the field of computer network security technology, specifically to a CWE (Cost Evidence-Based Weapon) segmentation method, device, and storage medium based on a community discovery algorithm, aiming to improve the benefits of vulnerability classification for vulnerability intelligence analysis. The proposed CWE segmentation method based on a community discovery algorithm includes: constructing a knowledge graph based on the relationships between different CWEs; assigning each CWE in the knowledge graph to a separate community; and using a modularity-based community discovery algorithm to progressively merge communities until the modularity no longer increases, resulting in the final segmented CWE communities. Furthermore, this invention proposes, from an attack benefit perspective, constructing a joint CAPEC and CWE knowledge graph, and using a community discovery algorithm to segment CWEs into communities, significantly improving the benefits of vulnerability classification for vulnerability intelligence analysis.

The application provides an intelligent central control screen supporting artificial intelligence detection analysis algorithm, comprising: a data acquisition module, which is used for continuously collecting multi-category business data in an application scenario; a data transmission module, which is used for transmitting the obtained multi-category business data to an artificial intelligence analysis module; the artificial intelligence analysis module, which is used for automatically recognizing and matching the multi-category business data based on a multi-dimensional artificial intelligence detection analysis algorithm, and performing strategy analysis on the business data of the corresponding category based on the automatic recognition and matching result; a decision module, which is used for performing business coordination and association on the strategy analysis result in the application scenario, and performing decision analysis based on the business coordination and association result; and a control module, which is used for performing control operation on the equipment corresponding to each category of business based on the decision analysis result. The reliability and accuracy of the analysis on different categories of business data are ensured, and the intelligent level and processing efficiency of the intelligent central control screen are improved.