813 results about "Active data" patented technology

Systems, methods, and devices that relate to monitoring and managing autonomous agents are disclosed. In one example aspect, the method includes receiving activity data from autonomous agents in an operational environment, deploying static and dynamic observing agents to monitor expected behavior and deviations, detecting a deviation by an autonomous agent, determining the cause through analysis, performing a mitigative action based on the cause, and executing a preventative action to block similar future deviations. The method may also involve configuring observing agents with different observation modalities, periodically modifying observation parameters unpredictably, facilitating direct communication between observing agents, resolving conflicts in observations, and updating observation policies. Mitigative actions can include disabling credentials, rerouting communications, and logging actions. Preventative measures may involve updating behavioral policies and adjusting agent parameters to disincentivize problematic behaviors.

The invention relates to the technical field of data processing, and discloses a comprehensive management method and system for a smart park, and a storage medium. The method comprises the steps that environment, energy consumption and people flow data are collected through the Internet of Things, and a basic data set is obtained through edge node processing and semantic alignment; constructing a digital twin system by using a spatio-temporal data cube and a graphical mode; constructing a land digital identity system by adopting space-time coding and block chain technologies; performing anomaly identification and risk analysis on the activity data to obtain supervision early warning data; performing multi-dimensional analysis on the enterprise operation data to form an enterprise portrait; and presenting resource distribution through mixed reality, and analyzing cross-department data to obtain operation efficiency data. According to the invention, holographic perception, intelligent supervision and cooperative service of park elements are realized.

A method for using a local large language model (LLM) within a user's secure computing environment is disclosed. The LLM operates behind a firewall to prevent transmission of sensitive data, and utilizes an encrypted vector database and artificial intelligence techniques for content retrieval, response generation, and task anticipation. This system can be used on mobile, wearable, vehicle, or IoT devices and offers various services such as health monitoring, financial advice, automated communications handling, and personalized daily activity optimization. It also has the ability to detect fraud, fine-tune responses using augmented user data, assist in negotiations, identify personal interests, and provide health recommendations based on dietary and physical activity data.

The invention relates to the technical field of risk assessment, in particular to an AI enterprise financial risk dynamic assessment method and system. The method comprises the following steps: obtaining enterprise multi-dimensional financial data, operation activity data, industry environment data and macroeconomic index data, carrying out cleaning, standardization and time sequence alignment processing, and integrating the data into a financial risk assessment multi-dimensional feature matrix; constructing a dynamic weight distribution model based on the financial risk assessment multi-dimensional feature matrix, endowing differentiated weights, and generating a time sequence weighted feature vector; risk level mapping and comparative analysis are carried out on the time sequence weighted feature vectors, risk points exceeding a threshold value are identified, and feature sources of the risk points are traced; and constructing a risk conduction path map based on the risk points and feature sources thereof, simulating risk evolution trends under different intervention measures, and evaluating and generating a dynamic evaluation report containing risk early warning levels, key influence factors and intervention measure suggestions. According to the invention, the enterprise financial risk assessment efficiency can be improved.

The invention discloses a nursing pressure real-time monitoring system and method based on multi-modal data. The system comprises a data acquisition module, an intelligent analysis module, an analysis module and a real-time feedback module. The data acquisition module synchronously acquires physiological data, behavior activity data and environment situation data of nursing personnel and forms a nursing signal; the analysis module receives the nursing signal from the data acquisition module, receives the dynamic situation information from the intelligent analysis module, and generates a pressure state signal containing pressure event evaluation; the real-time feedback module receives the pressure state signal containing the pressure event evaluation from the analysis module, and generates and outputs a real-time feedback signal based on the pressure state signal. According to the nursing pressure real-time monitoring system and method based on the multi-modal data, the problem that traditional wearable equipment only collects physiological behavior data and cannot be associated with specific nursing situations such as drug administration operation and patient first aid can be solved.

A method for using a local large language model (LLM) within a user's secure computing environment is disclosed. The LLM operates behind a firewall to prevent transmission of sensitive data, and utilizes an encrypted vector database and artificial intelligence techniques for content retrieval, response generation, and task anticipation. This system can be used on mobile, wearable, vehicle, or IoT devices and offers various services such as health monitoring, financial advice, automated communications handling, and personalized daily activity optimization. It also has the ability to detect fraud, fine-tune responses using augmented user data, assist in negotiations, identify personal interests, and provide health recommendations based on dietary and physical activity data.

The invention belongs to the technical field of communication, and discloses a network transmission management method and system, and the method comprises the steps that a data collection module collects multi-dimensional data in real time; the decision planning module optimizes a network route; the decision planning module performs protocol and encryption switching; the execution module dynamically adjusts the transmission strategy; the safety protection module performs four-dimensional dynamic protection on the data; the hybrid cloud deployment module guarantees the confidentiality of core data; the multi-active data center module realizes data synchronization among a plurality of data centers; the cross-domain collaborative module realizes privacy protection collaborative training between edge nodes through federated learning; and the block chain auditing traceability module records the key operation log. According to the method, routing and flow prediction are optimized through a mixed quantum classical calculation engine, multi-protocol heterogeneous scheduling and neural mimicry congestion control are combined, the network transmission efficiency is greatly improved, the data transmission speed is improved, and the data leakage risk is reduced through application of a zero-trust security matrix and a quantum key distribution technology.

The invention belongs to the technical field of information technology and intelligent health service, and discloses a community old-age care service resource optimal configuration method based on edge computing, which comprises the following steps: collecting physiological data, environmental data and activity data of old people to form original heterogeneous data, uploading the original heterogeneous data to a data warehouse through an urban and rural community network adaptation mechanism, generating a space-time tagging multi-modal data set; constructing a dynamic digital twinborn model, generating a space thermodynamic diagram, identifying different level priority requirements, and generating a time period allocation scheme through a conflict priority scoring mechanism; an MILP model is constructed to dynamically optimize edge node computing power and functional area space resources, and a resource optimization report and a block chain transaction list are generated; desensitizing the original data of the old people, creating a digital identity card, generating an encrypted service log, and synchronously recording migration behaviors of the old people; and triggering network strategy switching and priority threshold self-adaption, and carrying out resource scheduling and strategy closed-loop optimization.

One or more computing devices, systems, and / or methods that provide an interactive reporting system for analyzing and reporting on campaign related data and mining insights using generative AI are provided. In an example, a user interface is configured to receive a natural language request and display a natural language response. A serving system is coupled to a data store housing campaign data sets and comprises a dedicated campaign large language model (LLM) configured to convert the natural language request to a suitable query capable of being run against the campaign data sets and to run the query against the data sets, to receive output from the query, to encode relevant query output together with the natural language request and / or the suitable query into an encoded output, and to decode the encoded output to generate a natural language response to the natural language request.

Disclosed herein are systems, methods, and media for emissions data analysis. The disclosed embodiments include accessing emissions activity data from at least one emissions activity data source. The emissions activity data may correspond to an entity and the at least one emissions activity data source corresponds to an activity region. The disclosed embodiments include extracting structured emissions data from the emissions activity data by applying the emissions activity data to a machine learning model configured to standardize data. The machine learning model may be trained with emissions training data. The disclosed embodiments include accessing an emissions factor database containing a plurality of emissions factors. The disclosed embodiments include selecting, from the emissions factor database, at least one emissions factor corresponding to the activity region. The disclosed embodiments include generating an emissions line item based on the structured emissions data and the at least one selected emissions factor.

The invention provides a road construction carbon emission monitoring method, which belongs to the technical field of electric digital data processing, and comprises the following steps: establishing a monitoring network through a multi-source sensor, collecting equipment operation, material consumption and construction activity data by using an Internet of Things terminal, and calculating carbon emission in combination with a carbon emission factor database to form a fingerprint spectrum. Edge calculation is adopted to carry out time sequence analysis to recognize abnormal points, a neural unit jump algorithm and a multi-head attention architecture are applied to carry out fine adjustment on a pre-training model, and a jump threshold value is calculated through a specific function. And introducing a distributed gradient descent optimization equation to integrate data of multiple construction sites to protect privacy, and generating a monitoring report including total carbon emission, intensity and emission reduction suggestions. And by adaptively adjusting the monitoring time interval, the carbon emission monitoring precision and efficiency are improved. The pre-training model adopts a multi-layer time convolutional network structure, and the adaptability to a specific construction scene is improved through unsupervised pre-training and supervised fine tuning.

The invention relates to the technical field of e-commerce, in particular to an e-commerce data processing method and system.The e-commerce data processing method comprises the following steps that based on user search data, the similarity between keywords and commodity titles is recognized, stable behavior fragments are extracted to generate a matching relation set, keyword sorting is optimized to form a semantic triggering sequence, and the matching relation set is extracted; and analyzing behavior chain identification jump nodes to extract switching points, reconstructing partition frequency output acquisition priorities, and aggregating regional active data streams. According to the method, the matching relation is constructed through the similarity of the keywords and the commodity titles, the semantic logic of the search path is enhanced, the user intention recognition precision is improved, the keyword sorting is optimized by analyzing the click sequence and frequency, the path turning point extraction capability is improved, and the attention is guided to be dynamically adjusted through the commodity partition click change; regional data streams are classified and aggregated according to active time periods, refined recommendation and efficient data scheduling are realized, and the accuracy and personalization of e-commerce data processing are enhanced.

The invention discloses an open-pit mine slope risk prediction method and system based on multi-source data, and relates to the technical field of intelligent geological engineering, and the method comprises the steps: collecting geological data, displacement monitoring data and environmental activity data of an open-pit mine slope, carrying out the preprocessing, and constructing a three-dimensional digital twinborn body; carrying out fusion calculation on the preprocessed displacement monitoring data and environmental activity data through a physical information neural network, predicting a displacement value and a stress value of the strip mine slope, and obtaining full-field physical quantity prediction data; according to the strip mine slope slip crack surface damage evolution path, the stability risk levels of the whole strip mine slope and different areas in the strip mine slope are judged, high-risk areas are positioned, and a strip mine slope risk prediction report is generated. According to the method, the damage evolution path of the slip crack surface of the strip mine slope is dynamically deduced, so that space-time continuous modeling of the damage evolution process, early recognition of the slope instability precursor and accurate insight of the risk evolution trend are realized.

The invention discloses a soil heavy metal distribution prediction method and system based on machine learning, and the method comprises the steps: obtaining topographic factor land use industrial activity data, carrying out the fusion remote sensing information processing, and determining a multi-source data set; performing standardization processing according to the multi-source data set, and performing space-time registration if the scale difference after standardization processing exceeds a preset threshold value to obtain data in a unified format; key features are extracted according to the unified format data, and a dimension reduction feature set is obtained through principal component analysis; a random forest model is constructed according to the dimension reduction feature set, parameters are optimized, and a heavy metal content prediction model is determined; inputting the sampling finite point location data into a heavy metal content prediction model, judging an industrial activity influence area, and obtaining a preliminary distribution estimation result; based on the preliminary distribution estimation result, high-resolution grid data are obtained by fusing topographic factors for the space complex region; and generating a pollution distribution diagram according to the high-resolution grid data, judging a low-prediction-precision region, and obtaining a final optimized layer.

The invention discloses a green e-commerce platform intelligent decision-making system and method based on multi-objective optimization, and belongs to the field of e-commerce logistics scheduling and operation optimization, and the method comprises the steps: obtaining supply chain data, logistics paths, packaging attributes and user behavior data, and generating an original operation data set; constructing a dynamic environment influence evaluation model to output real-time environment index data; generating a coupled user portrait in combination with user historical behaviors and environmental protection activity data; adjusting the dynamic environment influence evaluation model based on a green strategy and external changes, and generating an environment compensation factor; calculating a cooperative gain factor and outputting a multi-objective decision scheme; collecting feedback data to construct an effect-strategy association map; and a closed loop optimization link is formed through adaptive parameter tuning. According to the method, the dynamic environmental impact assessment model and the real-time cooperative gain factor are adopted, and the coupling user portrait and the environmental compensation factor are combined, so that the improvement of the logistics path optimization rate and the precise decision of green logistics can be realized.

The invention relates to the technical field of operation management, in particular to a resource optimization-oriented operation activity data quantitative analysis method and system, and the method comprises the steps: obtaining multi-modal data to construct enterprise operation digital twins; on the basis of the twin data, inferring a causal relationship between variables through a causal discovery algorithm, quantifying a causal effect by using a graph neural network, and constructing a high-fidelity simulation environment on the basis; and training and generating an optimal resource allocation strategy model in the environment by adopting a deep reinforcement learning engine. In the decision-making stage, causal intervention simulation is carried out on the candidate schemes through a do operator, so that the net effect of the candidate schemes is evaluated, and the optimal scheme is selected. And finally, the scheme is deployed and continuously monitored, and a new data iteration optimization model is utilized to form a self-adaptive closed loop of'data-insight-decision-optimization '. According to the resource optimization-oriented data quantitative analysis method and system, the problems that a decision-making link is split and depends on artificial experience in the prior art are solved.

A system and method for inferring device types. A method includes selecting a device type inference model from among a plurality of device type inference models based on a manufacturer of a device, wherein each device type inference model corresponds to a respective manufacturer and is trained using training data of devices manufactured by the respective manufacturer, wherein each device type inference model is trained to output a device type prediction; and determining an inferred device type for the device, wherein determining the inferred device type for the device further comprises applying the selected device type inference model to a plurality of features, wherein the plurality of features is extracted from device activity data indicating ports used by the device and at least one volume of traffic communicated via each port used by the device.

The invention relates to the technical field of preventive medicine, and discloses a mouse-borne disease intelligent monitoring and early warning device based on multi-source data fusion, which comprises an environment sensor module, a mouse activity monitoring module, a pathogen detection module, a human case data acquisition module, a data fusion processing unit and an early warning and decision support unit, the data fusion processing unit is used for integrating and analyzing multi-source heterogeneous data from the above modules, and the early warning and decision support unit generates early warning information and prevention and control suggestions based on an analysis result. According to the method, the environment data, the mouse activity data, the pathogen detection data and the human case data are integrated, a multi-dimensional monitoring system is constructed, space-time correlation analysis is performed in combination with a deep learning algorithm, the prediction accuracy of disease outbreak is remarkably improved, the mouse recognition accuracy is high, and the pathogen detection specificity is excellent.

A system and method for anomaly detection. A method includes recursively partitioning a sample of device activity data including deterministic characteristics of a population of devices over iterations in order to create partitions. Each iteration includes determining a split density metric for a candidate subpopulation created by splitting a portion of the population with respect to a corresponding type of deterministic characteristic. The split density metric for the candidate subpopulation is determined based on a density value of the candidate subpopulation and a coverage value of the corresponding type of deterministic characteristic. The partitions include each candidate subpopulation meeting a split density metric threshold. A baseline for each of the partitions is established based on device activity for devices represented in device activity data of the partition. An anomaly is detected based on behavior of a device and the baseline established for a partition corresponding to the device.

An analyzer module forms a hypothesis on what are a possible set of cyber threats that could include the identified abnormal behavior and / or suspicious activity with AI models trained with machine learning on possible cyber threats. The Analyzer analyzes a collection of system data, including metric data, to support or refute each of the possible cyber threat hypotheses that could include the identified abnormal behavior and / or suspicious activity data with the AI models. A formatting and ranking module outputs supported possible cyber threat hypotheses into a formalized report that is presented in 1) printable report, 2) presented digitally on a user interface, or 3) both.

The present disclosure relates to a communication method and system for converging a 5th-Generation (5G) communication system for supporting higher data rates beyond a 4th-Generation (4G) system with a technology for Internet of Things (IoT). The present disclosure may be applied to intelligent services based on the 5G communication technology and the IoT-related technology, such as smart home, smart building, smart city, smart car, connected car, health care, digital education, smart retail, security and safety services.Embodiments herein provide a method for switching between a plurality of SIM networks by a multi SIM UE in a wireless communication network. The method includes determining, by the multi SIM that the multi SIM UE needs to initiate an active data session on a second SIM network based on one of a paging message received by the multi SIM UE from the second SIM network and at least one MO activity which needs to be delivered using the second SIM network. Further, the method includes sending, by the multi SIM UE, a leaving operator message to the first SIM network indicating that the multi SIM UE is leaving the first SIM network; and switching, by the multi SIM UE, from the first SIM network to the second SIM network by triggering procedures to initiate the active data session on the second SIM network.

The invention discloses an activity rule management system for improving output efficiency. The system is composed of an intelligent rule generation module, a dynamic rule adjustment module, a rule conflict detection module, a task parallel processing module, a user behavior prediction module, a visual rule editor, a rule execution efficiency evaluation module, a rule version management module, a cross-platform compatibility module and an integrated AI assistant module. The method is used for integrating AI assistants to provide real-time rule consultation and optimization suggestions for users, and the AI assistants can also automatically optimize rules according to activity data and user feedback.

The invention discloses a carbon emission accounting system and method based on the whole process of a power system. A data acquisition module is used for historical activity data of various energy main bodies and desulfurizer consumption, historical operation data of a power system grid topological structure and a power system, power consumption data, load data and SF6 equipment parameters; the source side carbon emission conversion module is used for obtaining the total carbon emission of the source side; the electric power carbon emission flow calculation module is used for obtaining direct carbon emission and indirect carbon emission of a network side; and the load side power carbon emission calculation module is used for calculating the carbon emission of the load side according to the load side node carbon potential and the power consumption data. According to the method, the whole processes of the source side, the network side and the load side of the power system are comprehensively considered, the node carbon potential and the load side node carbon emission factor are dynamically calculated through the power system carbon emission flow method, and the problem that a traditional carbon emission accounting method cannot cover the whole process of source-network-load and cannot adapt to the dynamic power system is solved.

The invention discloses a child activity data desensitization processing method and system based on differential privacy, and relates to the technical field of privacy data protection, and the method comprises the steps: carrying out the time synchronization and unit normalization of multi-source data from a wearing device and an environment sensor, and building a unified multi-dimensional time sequence matrix; then, introducing a change point detection method to dynamically segment the time series data, so that the data is divided into event segments with clear behavior semantics; a sensitivity scoring mechanism is designed, factors such as activity types, time, places and behavior frequencies are comprehensively considered, privacy risk quantification is performed on each event segment, adaptive privacy budget allocation is implemented based on a scoring result, and key protection of sensitive data segments and efficient utilization of conventional data segments are realized; after differential privacy noise is injected, time sequence consistency correction and cross-feature correlation correction are combined. Therefore, differentiated and refined protection of the child activity data is realized, and privacy protection and data availability are effectively balanced.

The invention discloses an AI-based distributed big data intelligent storage management method, and relates to the technical field of storage management. The method comprises the steps of generating an initial feature matrix by obtaining observation data of all storage devices in a distributed storage cluster, and inputting the initial feature matrix into a pre-training life prediction model to obtain residual life; equipment with the service life lower than a first safety threshold value is judged as high-risk equipment, and data of the high-risk equipment is included in a to-be-migrated source data set; the devices with the service life higher than a second safety threshold value are included into a to-be-migrated target device set; and generating a migration plan according to the source and target data sets and executing migration. By predicting the residual life of the equipment, advanced identification and active data migration of the high-risk equipment are realized, the data loss risk is reduced, and the utilization rate of the health equipment and the overall cluster storage resource efficiency are improved.

The invention relates to the technical field of infectious disease monitoring and early warning, and discloses an intelligent influenza early warning system based on community multi-modal data fusion. The community-level multi-modal data fusion architecture is constructed, medical health data, environmental data, crowd activity data and network behavior data are integrated, spatial-temporal features are dynamically extracted and fused in combination with a deep learning model, and the problem of community monitoring blind areas caused by a single data source of an existing early warning system is solved; a long short-term memory network and convolutional neural network cascade architecture is utilized to capture a localized propagation rule, and the defect that a region-level prediction model cannot adapt to community heterogeneity is overcome; the risk score is generated in real time, the grading response instruction is triggered, a'monitoring-early warning-intervention 'closed loop is established, the early warning timeliness is remarkably improved, a basic-level response chain scission gap is filled, early prevention and control of flu outbreak are finally achieved, and public health resource consumption is reduced.

Embodiments of the present invention provide a system for leveraging distributed register technology to securely monitoring, tracking, and recommending utilization of resources. The system is configured for gathering one or more input parameters from one or more entity systems, collecting activity data from one or more third party systems, analyzing the activity data collected from the one or third party systems, generating one or more recommendations based on the one or more input parameters and analyzing the activity data, wherein the one or more recommendations are associated with one or more activities, estimating resource usage for the one or more recommendations, and allocating resources to the one or more recommendations.

The invention discloses a production simulation prediction optimization system based on artificial intelligence. The system comprises a data acquisition module, a data processing and analysis module, a model construction module, a prediction and optimization module and a decision support module. The data acquisition module is responsible for acquiring various data in a dry bulk cargo wharf production process; comprising cargo types, production equipment operation states, storage yard use conditions, ship docking information, berth occupation conditions, transportation equipment operation data, production personnel work records, data of all links in the production process, production activity data and market analysis data, and the collected data are stored in a database; and the data processing and analyzing module is used for cleaning, sorting and analyzing the acquired data, removing noise data and abnormal values and extracting valuable information. According to the invention, intelligent management and optimization of wharf production are realized, and the operation performance and core competitiveness of enterprises are improved.

A dynamic grid allocation system for power distribution in the development of integrated circuits in the sub-five-nanometer range, wherein the system comprises the following: a placement processor unit configured to receive a synthesized netlist comprising a variety of standard cells and macros, each associated with switching activity data derived from simulation or synthesis activity profiles, and to generate a spatial cell placement arrangement by grouping cells into microzones based on activity correlation, time sensitivity, and connection proximity; a power density calculation unit coupled to the placement processor unit and configured to calculate the local power density and instantaneous current demand for each microzone based on the spatial distribution of switching operations, capacity load, and effective switching frequencies; a dynamic network generation unit coupled to the power density calculation unit and configured to generate a multilayer power distribution network with variable network topology, where the width, spacing, via density and metal layer assignment of the power network are adaptively determined as continuous functions of the localized power density and power demand calculated for each microzone; a topology control processor configured to monitor voltage drop and electromigration data obtained from signoff analyses or predictive models in real time and dynamically adjust the power grid topology parameters to ensure compliance with predefined reliability thresholds for voltage drop and electromigration; a predictive current modeling unit configured to receive historical switching and voltage data, train a predictive model for transient current peaks, and provide predictive power boost instructions to the dynamic grid generation unit before power integrity violations occur; and A feedback synchronization controller is communicatively connected to the placement processor unit and the topology control processor, whereby the feedback synchronization controller continuously exchanges updated power density information and placement constraints, so that cell placement and network topology adapt together in real time, thereby minimizing routing congestion, ensuring a uniform voltage distribution, and guaranteeing compliance with electromigration and IR drop regulations under various activity conditions.

The invention relates to an optical network topology dynamic adjustment method and system, computer equipment, a medium and a product. The method comprises the following steps: acquiring a computing resource association degree parameter according to equipment resource allocation information, acquiring a data transmission delay parameter according to global topology information of a network topology structure, and transmitting the data transmission delay parameter to a computing node under the condition that communication activity data generated by computing equipment of each computing node in a target iteration process passes verification, acquiring a communication traffic matrix according to the communication activity data, acquiring actual communication traffic according to the communication traffic matrix and the global topological information, acquiring a network load balancing parameter according to the actual communication traffic, and acquiring an evaluation function value based on a computing resource association degree parameter, a data transmission delay parameter and the network load balancing parameter; and according to the evaluation function values corresponding to all the network topology structures, determining a target network topology structure, and adjusting the optical connection path to realize optical network topology adjustment. By adopting the method, the network performance can be improved.