464 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 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 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.

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.

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 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 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 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 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 digital latent social robot generation and specific data acquisition method and device. The invention belongs to the technical field of computers. The method comprises the following steps: generating a social robot: training a multi-modal large model based on general recognition to obtain the social robot with semantic understanding and simulated dialogue reply generation capabilities; understanding and generation of multi-modal information: understanding the multi-modal information, and generating and replying information; emotion analysis and calculation: carrying out emotion state modeling, analyzing the emotion and understanding of the social contact object, and carrying out dynamic reply; specific personality shaping: carrying out personality shaping to maintain personality, maintaining behavior consistency of roles, and carrying out active identity defense and latency; establishing a specific relationship: identifying a specific target and establishing the relationship; and specific data acquisition: acquiring and storing specific social data in private chat and group chat scenes. According to the technical scheme, information can be replied in time, information collection and analysis efficiency is improved, and comprehensive acquisition of communication data is realized.

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 the field of data analysis, in particular to an information collecting and processing method for heat stroke pathology data integration based on deep learning, which comprises the following steps: determining the data state of user analysis data according to the data richness of the user analysis data and the image conversion completion degree; when the keyword selection mode is associated phrase selection generation, determining the number of the selected keywords according to the phrase influence coefficient, and selecting the keywords according to the sensitivity coefficient from large to small; when the keyword selection mode is image conversion supplementary generation, determining that a keyword generation mode is generated according to a median point or an outlier according to the feature representation degree of the divided paragraph; under the condition that the initial search is completed, optimization analysis is conducted, the search optimization mode is determined to be combined search or sequential search according to the repeated keyword analysis state, and the pathological data integration search efficiency is improved.

The invention provides a resource management method and system for a high-performance computing cluster, equipment and a medium. The resource management method comprises the following steps: calling the queue queuing job number of each computing queue collected by a scheduler device, and summarizing the queue queuing job number of the high-performance computing cluster to generate a cluster queuing job number; calling node resource use data on each computing node collected by a performance information collection device; according to the node resource usage data, determining a queue resource usage rate corresponding to each computing queue and a cluster resource usage rate of a high-performance computing cluster; generating resource management information according to the queue queuing job number, the queue resource utilization rate, the cluster resource utilization rate and the cluster queuing job number; and sending the resource management information to a resource reminding device to trigger the resource reminding device to generate a reminding message. And automatically summarizing and analyzing the node resource use data to further generate matched resource management information so as to manage and optimize the resources in the cluster.

The invention relates to the field of e-commerce data collection. The e-commerce transaction information collection method based on big data comprises the following steps: collecting user mobile terminal interaction behavior data through an embedded SDK, synchronously calling an e-commerce platform API to obtain structured transaction data, and generating an original transaction data set; performing dynamic hierarchical labeling on the original transaction data through a hierarchical rule engine to obtain a transaction data set with hierarchical labels; dynamically adjusting the sampling weight of the high-risk transaction, and generating an optimized sampling instruction set; analyzing transaction data corresponding to the optimized sampling instruction set, and converting unstructured data into feature vectors; according to the method, differential privacy processing is performed on sensitive fields in a time sequence transaction knowledge graph, an auditing statistical report is generated through a homomorphic encryption algorithm, and a desensitized multi-dimensional analysis result is output, so that the precision of a user portrait and a commodity recommendation model is improved, the transaction fraud omission ratio is reduced, and the problem that a traditional encryption technology is incompatible with privacy compliance requirements is solved.

The invention discloses an automatic resume generation system based on a large language model. The automatic resume generation system comprises an information collection module, a data processing module and a resume generation module. After a user enters the system, the user information JSON is obtained through the information collection module; and the user information is transmitted to the data processing module to optimize and generate a text in the user information, so that the text better meets the requirements of the application posts of the user. And in the data processing module, auxiliary information is generated according to the post applied by the user, a subjective description text in the user information JSON is optimized by using a large language model, and the optimized user information JSON is obtained. And the resume generation module is used for generating a user resume in a Markdwon format by using the optimized user information JSON based on a resume template in a database selected by the user. And after checking and correcting the initial version of the user resume, generating a final pdf format resume. The system has the advantages of being efficient, personalized, simple, easy to use and professional.

The invention particularly relates to a PCS real-time online monitoring system based on an intelligent gateway. The PCS real-time online monitoring system comprises the following parts: a multi-source information collection module for collecting temperature information data, cooling fan information data and ventilation information data in equipment; the information analysis module is used for analyzing the temperature information data to obtain a temperature evaluation coefficient; analyzing the information data of the cooling fan to obtain a fan aging coefficient; analyzing the ventilation information data to obtain a blockage coefficient; and a comprehensive diagnosis module. According to the invention, through the distributed temperature sensing network, fan state monitoring and airflow channel analysis, collaborative evaluation of the thermal state, the mechanical performance and the ventilation efficiency of the equipment is realized; the limitation of the traditional single-point threshold alarm is broken through by combining the different-temperature proportion, the duration and the temperature difference fluctuation; rotation speed deviation and noise characteristics are fused, and the heat dissipation failure caused by air duct blockage is effectively avoided through combination of cone model quantitative aging degree pressure difference analysis and image recognition.

The invention relates to the technical field of data processing, in particular to a natural language processing method and system based on deep learning, and the method comprises the steps: 1, information collection and initial processing; step 2, information processing; step 3, dynamic weight distribution; 4, dynamically adjusting the mode and matching a database; and step 5, semantic understanding and result presentation. According to the scheme, the database containing various dialect keywords is constructed, so that dialect vocabularies can be accurately recognized during speech recognition, and the dialect vocabularies are converted into standard semantics. The system can effectively process different dialects, the recognition accuracy and the understanding ability of the natural language processing system in the dialect environment are remarkably improved, the application range of the natural language processing system is greatly widened, and users in the dialect area can smoothly use products and services related to natural language processing.

Systems, apparatus, and methods for access resolution based on situational awareness. The system adapts to the behaviors of users and provide more secure, more natural operation. Unlike existing solutions that are focused on a single factor / multi-factor authentication and binary outcomes (grant / deny), the exemplary “physical access resolution” system assesses the entire access event (not just identity) and may responsively launch multiple resolutions at points throughout the access event (not just a binary decision). Various entities employ layers of abstraction and integration between logical layers to allow for integration across nodes. Information collection may be staged and handled with varying levels of privacy protections. Access resolution may be implemented according to multi-modal and / or confidence-based metrics.

The invention discloses a remote intelligent control system for multimedia equipment and an implementation method thereof, and belongs to the technical field of network communication, and the implementation method comprises the following steps: acquiring network environment information of the multimedia equipment, judging an NAT (Network Address Translation) type, and generating a mixed penetration protocol selection rule to determine an initial penetration protocol; the method comprises the following steps: acquiring port allocation characteristics of NAT equipment based on an initial penetration protocol, generating port prediction parameters through linear increment prediction and LSTM neural network training, and formulating a port prediction and keep-alive strategy; executing UDP (User Datagram Protocol) penetration according to a keep-alive strategy, starting a TCP (Transmission Control Protocol) standby mechanism when the UDP penetration fails, and generating a penetration scheme by combining HTTP tunnel encapsulation and firewall inbound rule configuration; equipment identification information collection, signaling interaction, penetration attempt and connection establishment are completed based on a penetration scheme, successful feedback is generated after verification is passed, and remote control is realized; according to the invention, the stability and efficiency of remote connection of the multimedia equipment in a complex network environment are improved.

The invention relates to the technical field of marketing, and discloses a laundry customer marketing system based on big data, which comprises the following steps: firstly, collecting customer information, specifically collecting customer name, contact information, address, gender and age information in a customer registration filling mode, and collecting evaluation of customers on laundry service, environment and price; third-party data acquisition is carried out to obtain customer data provided by a third-party platform, and the customer data comprises calculation of the average consumption amount, consumption frequency and evaluation scores of customers; carrying out data mining, specifically mining and discovering a hidden mode and a rule in customer data; according to requirements, marketing target customer crowds are analyzed from the angles of transaction channels, transaction amounts, transaction frequencies and member identities, and information of target customers is accurately returned and exported. Through the accurate customer portrait and the self-service analysis function, the laundry can accurately identify the target customer crowd and formulate the personalized marketing strategy, the marketing accuracy is improved, and the marketing effect is improved.

An information collection system includes a collection apparatus, and one or more proxy apparatuses, in which the collection apparatus collects information from a terminal by communication using a first communication method, in a case where the information is not collected from the terminal in the communication, transmits an information collection request for the terminal to the one or more proxy apparatuses by communication using a second communication method, and collects the information of the terminal with which any of the one or more proxy apparatuses responds in accordance with the information collection request, and the proxy apparatus responds to the collection apparatus with the collected information in a case where the information is collected from the terminal by communication using the first communication method in accordance with the information collection request from the collection apparatus.