599 results about "Information Harvesting" patented technology

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.

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 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 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 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.

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.

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.

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 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.

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.

The invention discloses an intelligent information collecting and summarizing system, and relates to the technical field of information management. The problems of high cost, potential safety hazards and low working efficiency in a traditional information collection mode are solved. The system comprises a user interface module, a data verification module, a data encryption module, a data storage module, an API interface module, a summary processing module, a data analysis and visualization module, an authority management module and an adaptive learning module. A user inputs data through an interface, the data are stored in a database after being verified and encrypted, then the data are transmitted to a summarizing processing module through an API interface to be processed, and an analysis result is displayed. The authority management module ensures that the system is safe and controllable, and the adaptive learning module optimizes the system according to user feedback. The method has a good application prospect in the field of information management.

A technical problem to be solved by the present invention is to provide a method for processing real-time accumulated data so as to rapidly search for a packet corresponding to a search condition from a large amount of data called a full packet. The method comprises: a network packet information reception step of acquiring at least one piece of information from flow information including 5-tuple information and package information including meta information, content information, threat detection information, asset information, URL information, and IP information; a step of generating a mapping table in which identification information of the package information and identification information of the flow information are indexed; and a step of generating data specialized for the flow information and storing, in a database, the data including the mapping table.

The invention discloses a disaster reporting method, electronic equipment and a computer readable storage medium, and relates to the field of disaster information collection, and the method comprises the steps: collecting original dialogue information through a dialogue agent; the dialogue agent preprocesses the original dialogue information to obtain target dialogue information; the information management agent is controlled to receive the target dialogue information in real time, feature extraction is carried out on the target dialogue information, and disaster information is obtained; respectively storing the disaster information and all the original dialogue information in a database; and controlling the information management agent to generate a report according to the disaster information. According to the application, by introducing the user agent and the disaster information management agent, multi-modal data acquisition, information extraction and multi-round interaction are realized. The system can automatically identify the type, the place and the severity of the disaster situation, and quickly screen and integrate key elements. By means of a multi-agent framework, a dialogue strategy and a data analysis process can be dynamically adjusted, the integrity and accuracy of information are ensured, and support is provided for disaster reporting and rescue decision making.

The invention discloses a Windows domain penetration test system and method based on deep reinforcement learning. The system comprises an information collection module, a vulnerability detection module, an agent generation module and an agent training module. A host in an intranet domain is used as a springboard machine, an information collection module is deployed, and all network nodes in the domain are subjected to comprehensive information collection; generating a configuration file for constructing an intelligent agent according to the information in the domain, and constructing the deep reinforcement learning intelligent agent through the configuration file; and the intelligent agent automatically verifies all possible potential safety hazards in the domain environment by using a vulnerability detection module, learns in detection, and finally generates all possible safety problems in the domain. According to the method, the automatic penetration testing technology is utilized, full-automatic penetration testing and risk assessment of the domain environment are achieved, deep reinforcement learning is combined, various safety problems in the Windows domain are found more accurately, resources are allocated efficiently, high-risk path areas are defended in a centralized mode, and therefore the overall network safety is enhanced.

The invention relates to a project information collection method and system based on a plug-in technology, and aims at realizing automatic collection and processing of project information through the plug-in technology, remarkably improving the efficiency and reducing the error rate. The declarative access design reduces the system invasiveness and simplifies the integration process. The environment-aware routing technology supports multi-environment accurate management and data isolation storage, and data reliability is enhanced. And flexible rule customization and multi-stage filtering pipelines meet diversified requirements, so that the data accuracy is ensured. And the data platform provides millisecond-level retrieval and visual display, so that the user experience is optimized. Adaptive rule optimization further improves the collection efficiency, and an efficient and low-coupling industrial solution is provided for project management.

The invention discloses a photovoltaic roof health monitoring integrated system for a railway station. The system comprises an information collection system which collects photovoltaic roof BIM three-dimensional model information and GIS model data and performs three-dimensional visual display; the equipment management system is used for monitoring states of various monitoring equipment and recording operation data; the early warning management system is used for analyzing the dynamic database set threshold value and the grade early warning abnormal data; the management and maintenance system automatically generates an inspection task according to a period and records an execution condition; and the reporting system is used for summarizing information, generating various reports and supporting export. The system solves the problems of multi-dimensional data integration, intelligent fault prediction and early warning, data transmission processing real-time performance, unified management and the like, improves the operation and maintenance efficiency and management cost, has flexibility and expansibility, adapts to different photovoltaic roof requirements, effectively makes up for the defects in the prior art, and provides comprehensive guarantee for railway station photovoltaic roof health monitoring.

The invention relates to the technical field of project management, and discloses a building project management resource scheduling intelligent system, which comprises an information collection module, an information processing module, an information analysis module and an early warning module. The building project management resource scheduling intelligent system calculates the worker skill matching score to ensure that workers with different skills are allocated to proper tasks so as to improve the working efficiency and quality, and compares the equipment availability and the equipment fault probability with an equipment availability threshold and an equipment fault probability threshold respectively. When the availability of the equipment is lower than a corresponding threshold value, the operation condition of the equipment is judged to be poor, and when the failure probability of the equipment is higher than a corresponding threshold value, the risk of equipment failure is judged to be very high, the risk is fed back to a worker in time, and a reordering point and a real-time inventory are compared with an inventory level and a target inventory respectively; when the reordering point is higher than the inventory level and the real-time inventory is lower than the target inventory, it is judged that the inventory is seriously insufficient, and replenishment needs to be conducted as soon as possible.

The invention discloses an NRO-QMIX-based urban environment multi-unmanned aerial vehicle cooperative target search method, which comprises the following steps: constructing a multi-unmanned aerial vehicle search environment architecture based on a to-be-searched unknown region, designing an unmanned aerial vehicle search target detection rule and a reward function, establishing a region information credible graph for mapping the unknown region, and searching the unknown region according to the region information credible graph. According to the method, the optimization problem of fusing target search and information collection and realizing maximized updating of a regional credible graph is proposed, an improved NRO-QMIX algorithm is designed to solve the optimization problem, the optimal solution of the execution action of each unmanned aerial vehicle is obtained, and cooperative target search and information collection of multiple unmanned aerial vehicles in the urban complex environment are completed. According to the method, the problems that combined optimization tasks of target search and information collection are difficult to perform at the same time in a complex urban environment and the intelligent agent and environment exploration efficiency is low due to the fact that an existing reinforcement learning method uses a greedy strategy to select actions are solved, and multi-unmanned aerial vehicle cooperative target search and information collection in the urban environment are realized.

The invention discloses a hydroelectric generating set data acquisition and analysis method, system, equipment and medium, and belongs to the technical field of data acquisition and analysis, and the method comprises the steps: obtaining various original data of the operation of a hydroelectric generating set, and achieving the information collection of the set and the operation environment; performing multi-level structured modeling and node feature extraction on the acquired data by adopting a mode of combining a knowledge graph and a graph convolutional neural network, and analyzing an operation state and a future trend of a unit; according to a data analysis result, comparing a historical working condition with a scheduling task, judging whether the operation of the unit is in a reasonable interval, and triggering a corresponding regulation and control measure when the operation is abnormal; by monitoring the state and operation data of key equipment in real time, actual and reference parameters are compared, potential fault risks are identified, early warning information is generated, and data archiving and statistical analysis are carried out; and carrying out visual display on data acquisition, analysis, scheduling and early warning results. Collected data are effectively utilized, and scheduling decision and early warning maintenance are powerfully supported.

The invention discloses a method for acquiring server error information by a BMC (baseboard management controller), and relates to the technical field of fault diagnosis, and the method comprises the following steps: mapping a host serial port to the BMC based on a CPLD (complex programmable logic device), assisting in judging a server stage and debugging the serial port through the CPLD, collecting starting data by the BMC in a power-on starting stage, and sending the starting data to the BMC; and data collection and error early warning are carried out according to serial port jack input or BMC in the operation stage. The technical problem that in the prior art, server error information collection has high requirements for hardware support and software deployment is solved, and the technical effect of simplifying the server error information collection process is achieved.