859 results about "Information Harvesting" patented technology

The invention provides an intrusion detection and response method and system for a satellite internet target range, and relates to the technical field of network security technologies, and the method comprises the steps: carrying out the multi-dimensional real-time collection of the network flow of the satellite internet target range, carrying out the self-adaptive multi-scale feature extraction, constructing a network behavior baseline model, and positioning and recognizing the abnormal flow. Deploying an anomaly detection node based on edge calculation, performing preliminary screening at a track end, performing multi-angle association, and outputting an anomaly event report; a distributed intelligent honeypot is activated, deep interaction is induced, the information collection amount is maximized, multi-modal data is obtained, and multi-dimensional attack feature vectors and behavior sequences are generated; historical defense empirical data are integrated, a method of combining reinforcement learning and a genetic algorithm is utilized, a defense strategy is coordinated, a defense process is generated, and the defense process is recorded through a block chain and fed back to a network behavior baseline model.

The invention relates to methods and systems for integrating generative artificial intelligence (AI) capabilities into Software as a Service (SaaS) platforms. It comprises maintaining AI agents with varying credentials, enabling their interaction with alphanumeric data in table structures, and implementing a hierarchical access control scheme. The system displays table structures, provides interfaces for user inputs, and allows AI agents to be added as platform users. The generative AI agents can analyze data, identify actions, and perform tasks autonomously. The invention also includes methods for proactive information gathering, interactive analysis of AI outputs, and management of AI resources as limited assets. This approach enhances SaaS functionality by enabling AI-driven task completion, data analysis, and decision-making while maintaining data security and user-specific access controls.

Embodiments of the disclosure provide systems and methods for detecting malicious software packages. Detecting malicious software packages can include collecting information identifying one or more known malicious software component classifiers, collecting information identifying one or more known suspicious community behavior classifiers associated with the one or more known malicious software component classifiers and receiving a software package including software components. The method also includes identifying one or more software components of the software package as malicious based on a comparison between the software components of the software package and each of the collected one or more known malicious software component classifiers and the collected one or more known suspicious community behavior classifiers, generating a malicious probability for each of the identified one or more software components and evaluating whether the software package is malicious based on the generated malicious probability for each of the identified one or more software components.

The invention relates to methods and systems for integrating generative artificial intelligence (Al) capabilities into Software as a Service (SaaS) platforms. It comprises maintaining Al agents with varying credentials, enabling their interaction with alphanumeric data in table structures, and implementing a hierarchical access control scheme. The system displays table structures, provides interfaces for user inputs, and allows Al agents to be added as platform users. The generative Al agents can analyze data, identify actions, and perform tasks autonomously. The invention also includes methods for proactive information gathering, interactive analysis of Al outputs, and management of Al resources as limited assets. This approach enhances SaaS functionality by enabling Al-driven task completion, data analysis, and decision-making while maintaining data security and user-specific access controls.

The invention discloses a science and technology fast message intelligence perception system based on a large language model, and belongs to the field of intelligence perception, the system comprises an information collection module, a data standardization module and a database, the information collection module obtains multi-mode science and technology fast message data; the data standardization module is used for receiving the multi-modal data output by the information collection module and executing data cleaning, structured processing and semantic standardization operation; after standardization processing, storing the data into a database; the system further comprises an intelligence value screening module, an evaluation system module and a visualization module. And the intelligence value screening module is combined with the evaluation system to identify the intelligence value, and multi-dimensional visual presentation is realized through the visualization module. The method improves the efficiency and accuracy of information recognition and perception, and has significant advantages in the aspects of processing mass information and enhancing the information perception ability.

The invention relates to the field of resource scheduling, in particular to a cloud-side collaborative computing power dynamic distribution system, which comprises a task management module, a cloud-side collaborative computing power dynamic distribution module, a cloud-side collaborative computing power dynamic distribution module and a cloud-side collaborative computing power dynamic distribution module, the edge information management module is used for collecting information of edge equipment at an edge end and collecting calculation basic information and resource utilization rate of the edge equipment by issuing a calculation power test standard task; the computing power collaboration module is used for acquiring data of the edge information management module and distributing the tasks to the corresponding edge ends according to the computing power requirements of the tasks and the residual available computing power of the edge devices; the calculation feedback module is used for acquiring the isomerism and task calculation type of the edge equipment through the cloud edge communication module; the parameter dynamic optimization module sets a weight coefficient according to the heterogeneity of the edge end and the task calculation type, and the cloud edge collaborative computing power dynamic distribution system flexibly, efficiently and reliably carries out data distribution and optimizes a cloud edge collaborative scheduling distribution strategy.

The invention provides a task scheduling method and system based on an unmanned aerial vehicle, electronic equipment and a storage medium, and relates to the field of task scheduling. The method comprises the following steps: acquiring tasks, resources and user behavior data from a user side and a cloud server through an information collection module, and storing the tasks, the resources and the user behavior data into an information base; the task evaluation module analyzes the data and generates a task value score; the scheduling strategy generation module formulates a preliminary execution scheme in combination with a preset rule, and optimizes through a reinforcement learning algorithm to obtain a dynamic optimal scheduling decision; the task redundancy analysis module is used for identifying mergeable redundant tasks and generating a report for further optimizing a scheduling decision; the dynamic priority adjustment module adjusts task priorities and resource allocation according to the real-time monitoring data; and the scheduling execution module is responsible for performing specific execution and generating a state report. The multi-module collaborative design realizes the intellectualization and adaptivity of task scheduling, and improves the utilization rate of system resources.

The invention discloses a transaction platform management system based on cloud service, and relates to the technical field of cloud computing, and the system comprises the following components: a user behavior analysis module which collects the browsing track, purchase record and evaluation information of a user through an e-commerce platform, collects the attribute data and sales data of a commodity, and sends the collected data to a server; the user behavior analysis module accurately extracts key features of user activeness and commodity popularity to provide powerful support for personalized recommendation, the intelligent recommendation module generates a personalized recommendation list based on real-time behavior data and interests of the user, and the intelligent customer service module realizes personalized recommendation through natural language processing and machine learning technologies. According to the system, the user problem is quickly and accurately responded, the customer service efficiency and the user satisfaction degree are remarkably improved, the predictive maintenance module identifies potential risks and abnormities in advance through real-time data analysis, an early warning and solution scheme is provided for an administrator, and the system failure rate and the maintenance cost are effectively reduced.

The invention discloses an intelligent bid evaluation expert extraction and management system, and relates to the technical field of expert extraction systems. Comprising a data cleaning and compression module, a database logic and table relation design module, an expert information collection standard and automation module, an information real-time updating and principal management module, an extraction algorithm design module, a safety and authority management module and a real-time monitoring and auditing function module. Through a self-adaptive sparse auto-encoder and a dynamic clustering technology in the data cleaning and compression module, abnormal values in a bid evaluation expert database can be detected and cleaned, data compression is performed by adopting a self-adaptive dictionary coding algorithm, and the storage efficiency and query performance of the database are improved; based on a multi-modal information fusion and reinforcement learning method, accurate collection, dynamic updating and intelligent management of expert information are realized. The randomness and fairness of the bid evaluation expert extraction process are ensured by using a mode of combining deep reinforcement learning and a quantum random number generator in an extraction algorithm design module.

The invention provides an intelligent emergency decision support method and device based on multi-Agent cooperation. The method comprises a task planning module, an information acquisition module, a data fusion module and an execution monitoring module. The task planning module adopts a hierarchical decision-making mechanism, performs task decomposition in a plan making stage, generates a plurality of candidate execution paths by using thinking tree reasoning in a plan execution stage, and selects an optimal scheme. The information acquisition module acquires multi-source information such as network search, knowledge graph and geographic data through a plurality of professional Agents. And the data fusion module adopts a blackboard mode to manage heterogeneous information, and realizes intelligent abstract and correlation analysis through a large language model. And the execution monitoring module realizes dynamic optimization and fault self-recovery of the system through a multi-dimensional progress evaluation and cooperative monitoring mechanism. According to the invention, the problems of insufficient information processing capability, low decision-making efficiency and poor system stability of a traditional emergency decision-making system are solved. The information collection and processing efficiency is improved through large language model multi-Agent cooperation, the decision quality and accuracy are improved through a hierarchical decision mechanism, and long-term stable operation of the system is guaranteed through self-adaptive monitoring. The method is suitable for complex emergency decision-making scenes such as natural disasters, safety accidents and public health events.

The invention discloses a noise sensor operation monitoring system and method based on multivariate data fusion processing, and relates to the technical field of data processing. The information collection module is used for acquiring historical environment data and historical noise data; and the information processing module is used for exploring the influence relationship between the environment and the noise and the corresponding information through an exploration method to obtain a target relationship and reference information. According to the method, the set compensation model is matched with the dynamic analysis method, whether the environment and the noise are changed or not is analyzed according to the real-time environment data to obtain the change data, and compensation is performed through the compensation model according to the change data to obtain the corrected noise; according to the method, the monitored noise data with deviation of the noise sensor caused by environment change can be compensated and corrected, so that the accuracy of the monitored noise data is improved, the predicted noise is obtained through matching of the set prediction matching method according to the user demand and the storage library, the user can conveniently know the required future noise, and the user experience is improved. Therefore, corresponding measures can be taken to control the noise.

To increase the amount of information to be utilized.SOLUTION: An information processing system includes an information processing apparatus, a first terminal device that holds first user characteristic information, and a second terminal device that holds second user characteristic information. The information processing apparatus includes: an information collection unit which collects, from the first terminal device, first anonymized information generated by anonymizing the first user characteristic information, and collects, from the second terminal device, second anonymized information generated by anonymizing the second user characteristic information; a learning unit which generates a model configured to learn, by machine learning, a relationship between the first user characteristic information and the second user characteristic information, using the first anonymized information and the second anonymized information, as learning data, and output, on receipt of the first user characteristic information, estimated user characteristic information estimated from the relationship between the first user characteristic information and the second user characteristic information; and a model output unit which outputs the model to the first terminal device.SELECTED DRAWING: Figure 3

The invention provides a network flow regulation and control system and method and a storage medium, and relates to the technical field of network communication. The network traffic regulation and control system comprises: an information collection unit, which is used for obtaining historical traffic data of all media plane network elements connected with a control plane network element and a topological relation of the media plane network elements; the AI model updating unit is used for updating the traffic regulation and control model of the control plane network element according to the historical traffic data and the topological relation, and the strategy generation unit is used for obtaining a traffic distribution strategy of the control plane network element; and the strategy execution unit is used for dynamically adjusting the media traffic of each media plane network element according to the traffic distribution strategy. According to the method, the network data is collected and analyzed in real time, and dynamic updating of the AI model is combined, so that dynamic regulation and control of the network traffic are realized, the method can quickly adapt to changes of network conditions, and the flexibility and efficiency of traffic regulation and control are improved.

The invention discloses an intelligent digital bank monitoring system based on risk control, and particularly relates to the technical field of risk control, comprising a multi-source information collection module, a multi-level risk assessment module, a model optimization module and a decision response module, the multi-source information collection module is used for collecting transaction factor information, behavior factor information, external factor information and internal factor information; the multi-level risk assessment module is used for detecting abnormal transactions and comprises a primary risk assessment sub-module, an intermediate risk assessment sub-module and an advanced risk assessment sub-module, and the model optimization module periodically checks the performance of multi-level risk assessment and verifies the effectiveness of the multi-level risk assessment. And the decision response module generates a comprehensive risk index according to a periodic inspection result of multilevel risk assessment in combination with abnormal detection of risk control, and performs interactive response, so that the running effectiveness of a black box model based on a deep learning algorithm can be effectively inspected, and excessive dependence of a digital bank on an automatic monitoring system is reduced.

The invention discloses an Internet of Things equipment rule generation method which comprises the following steps: receiving a rule generation demand of Internet of Things equipment, and performing retrieval in the Internet and different databases by a large language model according to the rule generation demand based on an MCP protocol to obtain context information related to the rule generation demand; wherein the rule generation demand comprises an information collection demand of the accessed Internet of Things equipment and a service operation demand of the Internet of Things equipment; and the big language model automatically generates a service operation rule and / or an alarm rule which are / is related to the service operation demand of the Internet of Things equipment and can be called by an Internet of Things platform execution engine according to the context information. Based on the MCP protocol and the large language model, the multi-source Internet of Things equipment document is automatically archived and analyzed, automatic generation and self-adaptive optimization of the service rule and the alarm rule are realized, and the operation and maintenance intelligence and automation level of the Internet of Things system is improved.

The invention belongs to the field of data collection, and provides an additive manufacturing industry information collection method and system based on artificial intelligence, and the method comprises the steps: obtaining original data related to the additive manufacturing industry from a multi-source heterogeneous data channel; the original data is preprocessed; performing representation learning on entities in the map to obtain an analogy score relationship among the entities for reasoning a potential technical evolution direction; fusing the generated new terms and the inferred potential technical evolution direction with originally extracted industry knowledge entities and relationships, and constructing a dynamic semantic knowledge graph containing dominant entity relationships and new concept inference edges; periodic updating and reasoning calculation are conducted on the dynamic semantic knowledge graph, and structured industry intelligence information is output and comprises new terms, high-potential technology paths, cross-domain technology fusion trends and concept evolution trajectories. The method can adapt to additive manufacturing industry information collection with rapid information change.

The present invention relates to the technical field of industrial evaluation, and in particular to a data analysis method and system for regional industrial evaluation. The method comprises: obtaining basic information of all enterprises in a to-be-evaluated region within a set time period from a database, and collecting industrial development planning information within the same time period; constructing a characteristic time series of industrial development planning; constructing an enterprise characteristic time series to reflect changes in enterprise information during the execution of each industrial development plan; extracting a keyword one and a keyword two from the basic information of the enterprise and the industrial development planning information respectively to form an enterprise keyword set and an industry keyword set; setting a weight value one by comparing keywords in the enterprise keyword set and the industry keyword set through a similarity measurement method; constructing a set of weight values, and constructing multiple heat maps in combination with enterprise latitude and longitude information; obtaining the business data information of the enterprise, establishing a correlation with the weight value one, and evaluating the development trend of the regional economy and the health status of the industry.

The invention relates to the technical field of wireless communication, and discloses an unmanned aerial vehicle information acquisition system rate optimization method based on communication and inductance integration, and the method comprises the steps: determining a baseband equivalent channel from an unmanned aerial vehicle to ground Internet of Things equipment; determining a data signal received by the unmanned aerial vehicle through a receiving antenna based on the baseband equivalent channel; determining the data rate of the ith ground Internet of Things equipment collected by the mth unmanned aerial vehicle; establishing an optimization problem based on the constraint condition and the data rate; and solving the optimization problem through a multi-agent reinforcement learning model, and determining the maximum total rate of collecting information from the I ground Internet of Things devices by the M unmanned aerial vehicles. According to the invention, while the sensing precision requirement, unmanned aerial vehicle track limitation, power limitation and distance limitation between unmanned aerial vehicles are met, joint optimization is carried out on the track, transmitted beam forming and sensing signals of the unmanned aerial vehicles, so that the purposes of high-speed signal transmission and efficient information collection are achieved, and the total transmission rate of an unmanned aerial vehicle information collection system is maximized.

Techniques are described for providing users of an IT and security operations application with the ability to enable the collection and display of playbook run statistics. Users can selectively enable the generation of playbook run statistics for individual playbooks. Once enabled for a playbook, the IT and security operations application automatically adds source code to the playbook or otherwise enables the collection of function block-level statistics during playbook executions. Users can view the statistics collected for a playbook to compare the performance of individual blocks against one another, to compare the performance of individual playbook runs against other playbook runs or against an average of all playbook runs, and so forth. The ability to obtain playbook run statistics enables users to learn how their playbooks are performing and to troubleshoot potential issues, thereby improving the performance of playbooks and the security and operation of the IT environments in which playbooks are deployed.

The invention provides an article generation method, an article generation device, a computer program product and electronic equipment, and relates to the technical field of artificial intelligence. The article generation method comprises the steps of inputting writing demand information into an information collection agent, so that the information collection agent performs information collection based on the writing demand information to obtain a writing outline and a material matched with the writing demand information; and inputting the writing outline and the material matched with the writing demand information into an article generation agent, so that the article generation agent generates a first article based on the writing outline and the material matched with the writing demand information. The article is generated according to the writing outline and the material determined by the information collection agent, the richness, logicality and accuracy of the article content can be improved, and the automation and intelligence of article generation can be improved through multi-agent cooperation.

A system may comprise a data interpolation platform that comprises a data collector, a pre-processor, an artificial intelligence (AI) engine, an interactive graphical user interface (GUI) engine, a data monitor and one or more servers. The data collector may be configured to collect, from among one or more data sources, data and information (“collected data”). The AI engine may be configured to generate, train, validate, test and / or deploy one or more AI models. The interactive GUI engine may be configured to generate and dynamically update an interactive GUI. The one or more servers may comprise one or more processors, a memory and computer-readable instructions that, when executed by the one or more processors, cause the data interpolation platform to determine, display and dynamically update predictive and interpolated pricing data.

The invention belongs to the technical field of artificial intelligence, embedded systems and action confrontation command, and discloses an action model learning system and method based on embedded AI. The system comprises an information collection module used for collecting historical action information and carrying out data preprocessing, and the historical action information comprises action environment data, confrontation situation information and decision data; the feature extraction and model construction module is used for extracting features from the preprocessed historical action information by using a deep learning algorithm to obtain action dynamic change features and enemy action mode features, and matching the action dynamic change features and the enemy action mode features with corresponding decision data results to form a training set; training the embedded AI model by using the training set to generate an action model; according to the invention, dynamic data from actions can be processed and learned in real time, so that the action model is continuously optimized, and the command decision-making capability is improved.

The invention discloses an attack path planning and key node identification method based on a large language model. The method comprises the following steps: firstly, carrying out information collection on a network environment through a network security tool to obtain a network information graph; constructing an attack path planning model based on a large language model, and specifying an action output format; a few-sample learning method is adopted to guide an attack path planning model based on a large language model to learn a network information graph, and a planning result conforming to expectation is output according to an action output format; generating an attack graph according to a planning result; the nodes in the attack graph are scored through weighted calculation of node degrees and asset importance degrees, and key nodes are identified. According to the method, a large amount of text data can be automatically analyzed and processed, potential attack paths and key nodes can be identified, and the requirement of manual intervention is reduced, so that the occurrence rate of human errors is reduced, and the planning efficiency is remarkably improved.

Disclosed herein are network information collectors, methods, computer-readable media, and systems for generating network traffic statistics. For example, the network appliance is to receive, via a network, an accumulating map from a network appliance device, wherein the accumulating map comprises a condensed and aggregated version of web traffic flow information to the network appliance device; receive, via the network, additional accumulating maps from other network appliance devices; populate an accumulation map database with the accumulating map and the additional accumulating maps; and generate a report of the network traffic statistics hosted by the network appliance device and the other network appliance devices.

The invention discloses a multi-task-oriented unmanned aerial vehicle group convening method, and the method comprises the steps: dividing information interaction into fine-grained neighbor perception and coarse-grained remote estimation through a layered information collection and diffusion mechanism, and respectively sharing detailed resource information and regional summary information; based on a task selection and decision-making mechanism of neighbor information and resource scarcity, after the unmanned aerial vehicle receives multiple pieces of task information, resource requirements, distance and time constraint factors of tasks are comprehensively considered, and resource availability of neighbors and scarcity degrees of required resources in a scene are combined; and selecting candidate tasks and deciding whether to add the tasks or not. According to the method, the communication efficiency and the scheduling precision are both considered, the task response efficiency and the resource scheduling precision of the unmanned aerial vehicle group in the multi-task environment are improved, the adaptive capacity of the system to dynamic environment changes is enhanced, good expansibility and robustness are achieved, and the method is suitable for large-scale popularization and application. The method is suitable for complex unmanned system cooperation scenes with limited resources and dense tasks.

The invention relates to the technical field of data processing. The invention discloses a monitoring system and method for a distribution network construction process and a computer readable storage medium. The monitoring system for the distribution network construction process comprises a power failure information collector, a power failure event analysis unit, a power failure event sending unit and a power failure event monitoring unit. The power failure information collector is used for acquiring power failure event data. And the power failure event analysis unit is used for obtaining the corrected power failure event data. And the power failure event sending unit is used for sending the business processing items with the updated processing priorities to a corresponding application system. And the power failure event monitoring unit is used for monitoring service processing items and obtaining fault node information and the latest state of service processing, and the power failure event monitoring unit displays the fault node information and the information of the service processing items in real time through a visual interface. According to the monitoring system, unified management of power failure event information can be realized, and the safety operation risk of a power grid is reduced.

The invention discloses a commodity recommendation method and system based on agent and hotspot information collaboration, and relates to the technical field of intelligent recommendation, and the method comprises the steps: obtaining structured hotspot data, commodity dynamic information and current user data in parallel through a hotspot information collection tool, a commodity information collection tool and a user information collection tool; performing multi-source information fusion based on a large language model, and generating a structured recommendation demand including a target user, a hotspot association direction, a commodity constraint condition, a user preference matching point and a recommendation priority; and matching and screening the commodities meeting the requirements, and performing multi-dimensional priority ranking according to the hotspot association closeness, the user preference matching degree, the commodity cost performance and the inventory sufficiency to generate an optimized commodity recommendation list. According to the method, semantic comprehension and information fusion are carried out based on a large language model through deep collaboration of hotspot information and user requirements, and the accuracy, timeliness and adaptability of commodity recommendation are improved.

Systems and methods are disclosed for automated industrial hygiene assessment and display comprising receiving sampling results for a stressor, such as a harmful environmental artifact in a physical environment; deriving one or more codes for the stressor from a digital record via an indexing module and / or from other data sources; generating a health effect rating (HER) based on the code; generating an exposure rating (ER) based on the sampling results; generating an uncertainty rating (UR) based on the sampling results; displaying, an interactive UI to facilitate approval or selection of at least one of the HER, the ER, or the UR; generating at least one of a risk rating (RR) or an information gathering priority rating (IGPR) based on a selection of the at least one of the HER, the ER, or the UR; and displaying via the interactive UI at least one of the RR or the IGPR.

A system may comprise a data interpolation platform that comprises a data collector, a pre-processor, an artificial intelligence (AI) engine, an interactive graphical user interface (GUI) engine, a data monitor and one or more servers. The data collector may be configured to collect, from among one or more data sources, data and information (“collected data”). The AI engine may be configured to generate, train, validate, test and / or deploy one or more AI models. The interactive GUI engine may be configured to generate and dynamically update an interactive GUI. The one or more servers may comprise one or more processors, a memory and computer-readable instructions that, when executed by the one or more processors, cause the data interpolation platform to determine, display and dynamically update predictive and interpolated pricing data.

Computer-based generation of information-gathering questions in response to a user query can include parsing the user query using natural language processing and extracting from the user query one or more phrases corresponding to a predetermined category. A knowledge database can be accessed, and entities semantically related to each phrase can be extracted therefrom. A query sub-graph representing a relationship between each of the one or more phrases and the entities extracted from the knowledge database can be generated. An expanded user query can be generated by traversing the query sub-graph. Passages semantically related to the expanded user query can be retrieved from one or more passages databases and ranked. A neural question generator can generate a set of information-gathering questions based on the expanded user query and a group of select passages selected from the plurality of passages in accordance with each selected passage's ranking.