2010 results about "Data harvesting" patented technology

A value chain network control tower system comprises a processor and memory configured to execute a know your model system that manages the complete lifecycle of Al models in enterprise environments. The know your model system performs model intake and registration actions including model documentation collection, registration procedures, metadata collection, input / output interface standardization, legal and licensing validation checks, and security validation. The system conducts comprehensive model evaluation and risk assessment actions by analyzing foundational properties, task performance, safety and risk management, alignment and compliance characteristics, operational metrics, and tooling transparency capabilities. The know your model system executes model deployment actions through automated environment validation, predeployment approval processes, and controlled production deployment with continuous monitoring.

The invention discloses a method for simulating and forecasting flood in a cold highland area based on hydrological and hydrodynamic coupling, and belongs to the technical field of disaster forecasting. The method specifically comprises the following steps: S1, multi-source basic data collection and preprocessing: collecting multi-type and multi-scale basic data for a target cold and cold mountainous area drainage basin; and S2, deep learning correction and fusion of the satellite rainfall data: aiming at the local overestimation and underestimation problems of the satellite rainfall data, a deep learning algorithm is adopted to carry out hour scale correction and fusion. Four types of core data of satellite remote sensing, reanalysis, ground observation and geographic space are collected, total factors of'rainfall-runoff-terrain-underlying surface 'required by flood simulation in the cold and cold mountainous area are covered, simulation one-sidedness caused by lack of data types in traditional modeling is avoided, rainfall and runoff abnormal values are eliminated by adopting a 3-sigma criterion, data formats and spatial-temporal scales are unified, and the modeling efficiency is improved. A standardized data set is formed, and interference of abnormal values, format incompatibility and space-time mismatching on subsequent model input is avoided.

The present invention provides a system and method for generating proposals for infrastructure modalities, such as electrical substations, using advanced artificial intelligence. It includes an input interface for data collection, a lightweight generative or rendering pipeline for creating preliminary 2D designs, and a generative model selected from diffusion, transformer-based, GAN or other architectures for refining these into detailed 3D models and generating preliminary designs. The system evaluates designs against predefined criteria to ensure compliance and feasibility. Supported by a cloud-based infrastructure for robust data processing and integration with third-party services, this system enhances the efficiency, accuracy, and compliance of modality planning and proposal generation.

The various embodiments herein provide a system and method for networked digital twins with autonomous collaborative decision-making. The system comprises a Digital Twin Engine for real-time data acquisition, model synthesis, and simulation, an AI Module for advanced data analysis, an autonomous collaborative decision-making module for optimized decision-making, a communication layer for secure data exchange, and supporting modules for coordination, storage, security, and user interaction. The method for generating and deploying digital twins comprises data collection, transmission, preprocessing, model synthesis, simulation, validation, and deployment. The method for networking and collaboration comprises AI-based data processing, complex event processing, autonomous decision-making, task distribution, decision communication, real-time monitoring, and continuous improvement. This system enhances operational efficiency, scalability, and security, reducing the need for human intervention and providing a comprehensive management solution for complex systems.

A computer-implemented system for multidimensional generative value assessment and applicant selection, consisting of: a data collection unit configured to electronically receive applicant data consisting of structured academic records, work experience records, digital documentation, and unstructured narrative responses generated from generative self-assessment instruments and contextual interviews; a feature extraction unit coupled to the data acquisition unit, configured to apply computer-assisted text processing, semantic analysis, and token-level attribute identification to transform narrative responses and structured data into multidimensional feature vectors that represent generative indicators of innovation, mentoring, collaborative performance, resilience, social contribution, ethical consistency, and predicted institutional impact; a weighting calculation unit configured to assign weight values ​​to the extracted feature vectors based on a digital generative profile definition matrix that includes dimensions, sub-criteria, indicators, documentation requirements and importance coefficients, with the weighting being distributed across the generative dimensions defined in the digital matrix and configurable according to the institutional context; a quantitative rating unit configured to calculate a generative rating score by aggregating weighted feature vectors derived from self-assessment inputs, interview-based ratings, document analyses, and authenticity predictions, with the aggregation including normalization, nonlinearity correction, conflict handling, and artifact frequency balancing to obtain a consolidated score; a proof verification unit configured to electronically validate referenced digital evidence by performing content extraction, metadata verification, pattern matching, and cross-document correlation to determine authenticity, credibility, and contextual relevance with respect to the calculated feature vectors; a classification determination unit configured to assign a classification level to an applicant by comparing the generative assessment score with a set of system-defined calculation thresholds, including at least a lower threshold, a middle threshold and an upper threshold, the classification levels representing different generative maturity states and determining subsequent eligibility for selection; a decision generation unit configured to produce a digital output data set that includes classification level, feature aggregation summaries, evidence validation results, and recommended organizational actions, wherein the decision generation unit encodes the data set in a digitally signed, tamper-proof format and stores it on a non-volatile storage medium; and A system control unit acts as an operational interface to all other units and is configured to orchestrate data flow, scheduling, process state transitions, and event logging to ensure verifiable traceability, consistency, and auditability of the evaluation and selection processes.

The invention relates to the technical field of artificial intelligence and data processing, in particular to a policy and enterprise intelligent matching method and system based on deep semantic understanding. The method comprises the following steps: acquiring multi-source policy information data and enterprise information; generating an enterprise multi-dimensional portrait according to the enterprise information; performing first matching and second matching on the multi-source policy information data and the enterprise multi-dimensional portrait respectively; and constructing a comprehensive matching model, and based on the first matching result and the second matching result, using the comprehensive matching model to complete policy and enterprise intelligent matching. According to the policy and enterprise matching method, the problems of low efficiency, inaccurate matching and important information omission in a traditional policy and enterprise matching method are solved through matching policies and enterprise portraits. Through automatic data collection, preprocessing and matching calculation, manual intervention is greatly reduced, the policy and enterprise matching efficiency is improved, and an enterprise can quickly find a policy suitable for the enterprise.

A system to personalize a shopping experience in a conversational commerce platform is disclosed. The system includes a processing subsystem having a user interface module for consumer input and an input conversion module that processes and translates this input using a large language model (LLM) engine. The engine module features a catalog facet creation module that structures product information, a facet enrichment module for detailed descriptions, images and buyers' profile, and a customer profiling module utilizing natural language processing to understand customer needs. An AI merchandising module presents optimal product facets to customers based on profiles and historical data. Additionally, a conversational commerce module facilitates product selection through guided conversations, while a personalization module tailors recommendations. The system also includes a data collection and analytics module for performance tracking and a training and optimization module for continuous improvement of the LLM.

The invention discloses a property operation report generation method and device based on multiple agents and a related medium, and the method comprises the steps: calling an intention recognition agent to carry out intention analysis, and then calling a task planning agent to carry out path construction and scheme solving; determining an execution sequence of tasks in combination with the task planning data to obtain task processing data; calling a data collection agent to carry out data collection on the task processing data to obtain primary processing data; and calling a code calculation agent to carry out code generation and model operation on the primary processing data, calling an outline making agent to carry out outline arrangement and report generation processing, calling a report writing agent to complete report packaging, and outputting a property operation report. According to the invention, the structured analysis result obtained through calculation is subjected to outline arrangement and report generation processing, and the property operation report is packaged and output, so that manual report arrangement is not needed, the process is simplified, and the report output efficiency is improved.

Systems and methods are disclosed for anomaly detection using a “detect and collect” cybersecurity monitoring approach. Initially, a cybersecurity monitoring system obtains and analyzes a baseline subset of telemetry data from computing resources to detect potential anomalies indicative of cybersecurity threats. Responsive to identifying such anomalies, the system selectively determines additional, contextually relevant telemetry data for targeted collection. This selective data collection significantly reduces telemetry volumes, enhancing efficiency and scalability. An intelligent data fabric and dynamic security knowledge graph are employed to enrich telemetry data in real-time, enabling comprehensive anomaly characterization, risk scoring, and automated security responses. The disclosed techniques support multimodal and multiresolution anomaly detection, adaptive learning, and rapid threat response within diverse distributed computing environments.

An AI-powered rating system includes a central artificial intelligence (AI) agent framework configured to orchestrate a rating process and automatically utilize available tools for data gathering and extraction; a data processing pipeline configured to simultaneously handle multiple streams of information from diverse sources, including structured data and unstructured data; a machine learning infrastructure comprising multiple specialized artificial intelligence models, each configured to evaluate a specific type of entity; a generative AI integration layer configured to provide a natural language explanation of a rating result for the rating process and enable conversational interaction with the system, where the generative AI integration layer is further configured to transform the unstructured data into quantifiable risk signals that are integrated with a structured data processing stream; and an output generation and reporting engine configured to deliver the rating result through one or more channels and formats.

The invention relates to the technical field of agricultural engineering and ecological environment protection, in particular to a saline-alkali soil desalination improvement system and method based on brackish water recycling. The system comprises a data collection module which collects soil salinity data, meteorological data and crop growth data of the saline-alkali land in real time; the data processing and analyzing module is used for receiving and preprocessing the soil salinity data, the meteorological data and the crop growth data of the saline-alkali land, constructing a soil salinity prediction model and outputting a soil salinity prediction result; the irrigation strategy decision module is used for generating an optimal irrigation strategy by adopting a reinforcement learning algorithm according to the soil salinity prediction result and the preprocessed crop growth data; and the irrigation control system is used for performing brackish water irrigation operation on the saline-alkali soil according to the optimal irrigation strategy. Precise regulation and control of saline-alkali soil salinity can be achieved, brackish water is adopted as irrigation water, water resources are saved to a certain degree, and therefore the improvement cost of saline-alkali soil is reduced.

The invention discloses a multi-modal quantifiable security threat reasoning method and system based on causal reasoning, and relates to the technical field of network security, and the method comprises the following steps: S1, data collection; s2, data preprocessing; s3, carrying out multi-modal fusion; s4, carrying out cross-modal causal association; s5, a causal inference engine; s6, real-time monitoring and response are carried out; and S7, performing CSA security proxy. According to the multi-modal quantifiable security threat inference method and system based on causal inference, comprehensive monitoring and response to network security threats are realized by fusing causal inference and a multi-modal dynamic sensing technology; the core problems that in traditional network security threat detection, the false alarm rate is high, attack root cause positioning is fuzzy, and cross-modal data relevance is weak are solved.

Systems, methods, and other embodiments associated with a framework for multi-entity detection, data sourcing, and processing are described. In one embodiment, a multi-entity processing method includes parsing an electronic document being processed in a processing flow for a first entity to detect that the document also concerns a second entity based on entity identifiers in the document. In response to the detection, the method determines an inter-entity agreement that governs processing between the first entity and second entity by matching attributes of the document against stored agreement data structures. The method automatically orchestrates processing of an inter-entity transaction between the first entity and the second entity by generating execution steps according to sequential and parallel dependencies specified by the inter-entity agreement. And, the method generates electronic records in a system of the first entity and a system of the second entity that reflect execution of the inter-entity transaction.

The present disclosure discloses a system (106) and a method (400) for energy management in a network (108). The method may include initialization by defining input and output parameters (baseline energy cost, optimized energy cost). The method may include performing real-time data collection, systematically gathering real-time metrics and calculating the baseline energy cost. The method may include conducting AI / ML-based prediction for traffic and load forecasting and coverage demand analysis, using historical and real-time data to predict future patterns, and allowing for resource optimization and proactive adjustments. The method may include dynamically adjusting network parameters and calculating and optimizing real-time energy cost (EC), continuously monitoring the network's current EC based on dynamically adjusted parameters. Compare optimized EC with baseline EC, adjusting parameters as needed.

The invention discloses a large ship autonomous berthing path planning method, and relates to the technical field of channel control. The method comprises the steps of collecting and processing data, establishing a ship motion model, setting a path planning target, selecting and solving an optimization algorithm, verifying and adjusting a path, obtaining hydro meteorological historical data and real-time sensing data of port wind, wave and flow, ship basic parameters and under-actuated dynamic characteristic data, and preprocessing the data. Obtaining a ship motion model; minimum berthing time, minimum energy consumption and maximum safety are set, and a comprehensive objective function is obtained; defining constraint conditions of kinematics, dynamics, port environment and collision avoidance; solving by using an optimization algorithm, performing dynamics simulation verification and collision detection verification on the planned path, adjusting parameters of the optimization algorithm, and modifying a target function or a constraint condition; according to the method, the optimal berthing path can be planned, the collision risk is effectively avoided, the safety and efficiency of large ship berthing are improved, and the method has universality and flexibility.

An equipment system optimization method driven by deep reinforcement learning comprises the following steps: sequentially constructing an equipment system architecture model and a combat scene simulation model based on a DoDAF framework, then carrying out parameter space sampling in the equipment system architecture model by using a uniform design method, and according to capability items and equipment elements defined by the equipment system architecture model, carrying out parameter space sampling on the equipment system architecture model; constructing a combat effectiveness evaluation model and a system cost evaluation model for evaluating the system; constructing a parameter-modulated deep reinforcement learning (PM-DRL) model to explicitly embed system parameters into an agent state space, and performing data collection and evaluation through the trained PM-DRL model to obtain lt; system parameter-evaluation result gt; a data set; and finally, constructing an optimization model by taking the agent model as a target function, and determining an optimal solution, namely system parameter configuration, by combining the solved Pareto frontier with the preference of a decision maker. According to the method, under the same combat effectiveness requirement, system parameter configuration with lower construction cost can be obtained through optimization.

The invention provides a BIM (Building Information Modeling)-based engineering electromechanical installation method. The method comprises the following steps: establishing an engineering electromechanical BIM model; carrying out model collision check and optimization; construction simulation and scheme making are carried out; field installation and implementation; and installation acceptance and model filing. According to the method, the geometric and attribute integrated BIM model is constructed through data collection and digital drawing review, full-dimensional collision detection is carried out, key pipeline specification requirements are preferably guaranteed, model data reliability is guaranteed, construction disjunction caused by drawing errors is avoided, the problem of pipeline collision is solved in advance, and the construction efficiency is improved. Material waste and construction period delay of dismantling and reassembling in the construction stage are reduced, meanwhile, the risk of illegal acceptance of key pipelines is reduced, and the limited space utilization rate is increased.

The invention discloses a power distribution cabinet intelligent early warning system and method based on multi-modal data, and relates to the technical field of power distribution cabinet intelligent early warning, and the method comprises the steps: carrying out the recognition of environment parameters of a position where a power distribution cabinet is located, and obtaining macroscopic environment data, including environment wind power data, environment humidity data, environment temperature data and environment pollutant data; constructing a three-dimensional coordinate system, and performing grid unit division on the surface of the power distribution cabinet to obtain grid units of the power distribution cabinet; performing data collection on the grid units of the electric cabinet to obtain grid unit parameters; constructing an electrolytic layer prediction model, identifying the macroscopic environment data and the grid unit parameters, and predicting to obtain an electrolyte film risk index of the grid unit of the power distribution cabinet based on coupling analysis of environmental dirt retention and condensation infiltration on the outer surface of the power distribution cabinet; and performing abnormal early warning on the power distribution cabinet based on the spatial distribution of the grid units of the power distribution cabinet and the corresponding electrolyte film risk index.

The invention provides a project risk dynamic prediction method and device, electronic equipment and a storage medium, and the method comprises the steps: collecting multi-dimensional original data in a whole life cycle of a target project, carrying out the preprocessing of the multi-dimensional original data to generate a standardized data set, building a case decision model based on the standardized data set, and carrying out the prediction of a project risk. And inputting real-time project data into the case decision model to obtain a prediction result, finally performing risk assessment according to the prediction result, and triggering early warning when the risk assessment result exceeds a risk threshold value, so that the problems of poor data quality and low risk caused by the fact that only single-dimensional data collection can be realized and data lacks standardized processing in the prior art can be solved. The problems of low project risk prediction precision and early warning lagging caused by a static evaluation mode are solved, and the technical effects of improving project risk prediction accuracy and realizing project full life cycle risk management real-time performance and effectiveness are achieved.

The present disclosure discloses a system (106) and a method (400) for energy cost (EC) optimization based on one or more beamforming actions in a network (108) The method may include initialization by defining input and output parameters (baseline energy cost, optimized energy cost). The method may include performing real-time data collection, systematically gathering real-time metrics and calculating the baseline energy cost. The method may include conducting AI / ML-based prediction for traffic and load forecasting and coverage demand analysis, using historical and real-time data to predict future patterns, and allowing for resource optimization and proactive adjustments. The method may include dynamically adjusting network parameters and calculating and optimizing real-time energy cost (EC), continuously monitoring the network's current EC based on dynamically adjusted parameters. Compare optimized EC with baseline EC, adjusting parameters as needed.

The invention discloses a traffic anomaly monitoring method and system based on big data and a storage medium, and belongs to the field of network security. The method comprises the following steps: determining a monitoring target and a monitoring range, and defining a network range in which traffic monitoring needs to be carried out; data collection and arrangement: using a traffic monitoring tool to collect network traffic data, and performing data cleaning and preprocessing; analyzing traffic characteristics, including statistical analysis, protocol and port analysis and traffic mode identification; setting a baseline threshold, setting the baseline threshold based on the flow characteristic analysis result and the service demand, and establishing an adaptive threshold adjustment mechanism; and continuous verification and optimization: monitoring whether the network flow exceeds a baseline threshold in real time, triggering an anomaly detection mechanism, generating early warning information, executing a response strategy, and optimizing the baseline threshold according to feedback. By establishing the network flow base line and monitoring the deviation condition in real time, network abnormity can be found in time, the network security protection capability is improved, and the method is suitable for security monitoring of various network environments.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for managing and adapting monitoring programs. In some implementations, a system communicates with a set of remote devices involved in a monitoring program that involves collection of data from the remote devices over a communication network. The system determines a set of attributes associated with an outcome or condition that has occurred for multiple of the remote devices. The system generates parameters to adapt the monitoring program, and the system selects a second group of remote devices to involve in the adapted monitoring program based on profiles or sets of attributes of users associated with the remote devices. The system configures the remote devices in the selected second group to perform monitoring for the adapted monitoring program, including acquiring data for the adapted monitoring program and providing the acquired data to a server over the communication network.

The invention discloses an unmanned aerial vehicle group elastic path planning method and system for resisting an intelligent jammer, which can ensure the accurate synchronization of an interference strategy while realizing the efficient compression and transmission of information and reducing the communication bandwidth demand by an interference party by means of an encoder-decoder communication mechanism, and cooperate with a preferential experience playback technology to realize the flexible path planning of the unmanned aerial vehicle group. Learning is rapidly carried out from past experiences, convergence of an interference strategy is accelerated, efficient cooperative interference is achieved, and a strong threat is formed for a data collection task of the unmanned aerial vehicle group; at the defense party, an attention reviewer module is constructed based on an advanced attention mechanism, information having key influence on unmanned aerial vehicle decision making is accurately focused, the attention weight between intelligent agents is calculated, the weight is reasonably distributed for a state-action joint vector, and a more targeted decision is made in a complex environment; a double-commentator module is introduced, a value function is independently estimated by using two commentator networks, and the reliability and the stability of the algorithm are improved through smooth function fusion output and stable value function estimation.

The invention relates to the technical field of natural language processing, in particular to a dynamic intention chain modeling and causal cleaning method and system under a multi-round dialogue scene. According to the method, through multi-round dialogue data collection and preprocessing, one-hot codes are allocated to each sub-word or word group, intention recognition and sequence modeling are carried out based on the codes, and the intention of the user can be accurately recognized; quick mapping for directly obtaining user intentions from voice input data by skipping text data conversion is established, so that the intention recognition efficiency is improved; according to the method, the causal relationship graph is constructed based on the user intention sequence, the Bayesian network is utilized to perform causal modeling, the causal relationship between intentions is derived, and the probability of reasoning the next intention through the previous intention is calculated, so that the next intention of the user and the interactive operation to be executed are accurately reasoned; and collecting user feedback data to calculate a satisfaction score, and correcting the priority ranking rule of the rule engine based on the score.

The invention provides a financial research report generation method based on a large model. Comprising a data collection module, a data analysis module and a report writing module. The data collection module is used for receiving a predefined data collection task and an autonomously generated data collection task, then inputting the tasks into a deep search agent, and constructing a structured multi-modal memory for a search result for storage; the data analysis module constructs a data analysis agent, and realizes the generation of a structured multi-modal small report through cooperation of a financial calculation tool, a chart generation tool and a reference addition tool in an actor-reviewer iteration mode in which a large language model is matched with a visual language model; and the report writing module constructs a report writing agent to obtain a chapter first draft and then inputs the chapter first draft into the post-processing agent to obtain a final report. The problems that an existing deep research system and a large language model agent in the financial field are lack of financial field knowledge, limited in multi-modal ability, limited in research and report generation depth and comprehensiveness and the like are solved.

The invention relates to the technical field of heat supply pipe networks, in particular to a small sample learning pipeline leakage identification method based on multi-dimensional acoustic feature fusion, which comprises the following steps of: acquiring sound pressure data based on a main line of a heat supply pipe network, analyzing signal amplitude and frequency features by a sliding window, constructing a multi-dimensional acoustic feature set, screening and aggregating leakage and normal state samples, and identifying leakage and normal state samples. Comparing the difference between the new data and the prototype, quantifying the feature distance, and judging the leakage state according to the minimum attribution principle. According to the method, multi-angle behavior expression is established, a typical category prototype is generated through feature induction, multi-category feature center aggregation and dynamic measurement are combined, the high-reliability judgment capability of the leakage state is improved, compared with a traditional technology, leakage signals are comprehensively represented through multi-dimensional features, and the reliability of the leakage state is improved. The adaptability to environmental noise, sudden interference and a complex pipe network structure is effectively enhanced, misjudgment is reduced, the dependence on a large amount of labeled data is greatly reduced through the small sample learning technology, and the data collection and labeling cost is remarkably reduced.

A method, system and apparatus are disclosed. A wireless device is provided. Wireless device is configured to perform measurements on at least one first reference signal resource and at least one second reference signal of a plurality of reference signal resources indicated by a reference signal configuration, the measurements being performed based on a measurement report configuration associated with the plurality of reference signal resources. The wireless device is configured to store the measurements for at least one of training and monitoring a machine learning, ML, model configured to predict at least one of at least one best beam and at least one K-best beam associated with at least one downlink, DL, reference signal transmitted by the network node.

An apparatus may include a policy acquisition circuit configured to interpret a vehicle policy data value including an external event trigger condition. An apparatus may include a policy processing circuit configured to generate parsed policy data including the external event trigger condition. An apparatus may include a policy execution circuit configured to collect vehicle data from one or more vehicle end points in response to the parsed policy data.

There is disclosed a method and system for retinal stimulation and retinal signal data collection. The system comprises a housing. The housing supports one or more light sources that are configured to expose a retina of an eye of an individual to a flash of light. The housing supports a spectrometer that is configured to record a light spectrum and an intensity of the flash of light. The housing supports a camera configured to capture image data of the eye while exposed to the flash of light.

A platform for facilitating an automated IT audit. The platform may have a frontend allowing users to access the platform, a backend configured to perform processing, and a data collection system equipped to interface with connectors. The backend may include at least one server equipped to send, receive, store, and process data; a testing and analyzing system that may make use of algorithms, machine learning, and artificial intelligence in order to test and analyze the collected data against pre-configured best practice standards and policies, and a reporting system that may be configured to transmit the tested and analyzed data to the frontend. The backend system may be configured to opine on the data and generate specific recommendations about future developments of an auditee's IT infrastructure, allowing an audit to be completed automatically from start to finish by the use of the software, eliminating the need for human intervention.