595 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.

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.

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 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.

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 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.

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.

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.

Systems, methods, and computer-readable media for modeling data using cyber resilience identities and associated metadata are disclosed. A system can include one or more processing circuits configured to generate at least one cyber resilience identity include at least a link or association with metadata corresponding with activity data performed by an entity or third-party or a third-party corresponding with at least one preference, protection, authentication, resilience, or security (PPARS) of a third-party. The one or more processing circuits can associate the at least one cyber resilience identity within a control structure, the control structure being compatible with at least one data structure corresponding with accessing at least a portion of the at least one cyber resilience identity. The one or more processing circuits can and transmit the at least one cyber resilience identity to at least one of (i) a distributed ledger, (ii) a data source, or (iii) an interface.

The invention relates to the technical field of intelligent tourism management, and discloses a tourist behavior dynamic modeling method based on space-time big data, which comprises the steps of collecting and preprocessing multi-source positioning data, inputting the multi-source positioning data into a multi-source positioning fusion engine, and combining statistical optimization and deep learning model fusion to obtain a high-precision positioning model. Track reconstruction and time sequence aggregation are carried out on continuous position points of tourists based on a high-precision positioning model, a structured activity data graph is generated, activity data of the tourists at all positions are obtained, activity preference characteristics are extracted through an association rule mining and clustering algorithm, and a Markov chain and a spatio-temporal evolution model are combined to predict a tourist flow trend. And forming a flow prediction result, and generating a visual dynamic decision support by using the prediction result. According to the invention, visual and dynamic decision support is provided, the crowd congestion is relieved, and the operation safety of the scenic area is guaranteed.

Computerized methods and systems obtain threat data generated from activity data using unsupervised learning. The activity data is collected from enterprises and describes activities performed on the enterprises. The threat data indicates likelihood that sequences of activities performed on the enterprises are indicative of malicious intent. A supervised ML model that processes sequential data is trained by providing a training set of sequential data to the supervised ML model. The training set includes at least some of the obtained threat data, and data derived from activity data collected from at least some of the enterprises. The trained supervised ML receives new data that describes a sequence of activities performed on an enterprise, and processes the received new data to produce a prediction of whether the sequence of activities performed on the enterprise will lead to a malicious action on the enterprise. In some embodiments, multiple supervised ML models are used.

The invention relates to the technical field of data acquisition and treatment, and discloses a multimodal compatible global real-time full-amount active data acquisition and treatment method, which comprises the following steps: acquiring data source connection information of a regional medical platform, identifying potential data sources in a region, and constructing a feature portrait; matching an acquisition strategy for each data source and generating an acquisition task configuration; data acquisition is carried out, data of different data forms are analyzed, and data formats are unified; performing data cleaning, standardization and quality verification; carrying out sensitive field identification, and carrying out desensitization and encryption processing on sensitive information; performing association key extraction and entity recognition disambiguation; performing multi-source data intelligent association integration; matching the classified storage strategy and carrying out persistence processing, recording a processing process and carrying out interface development; according to the method, data cleaning, standardization, quality verification and privacy desensitization are pre-embedded into an acquisition process through a streaming collaborative governance mechanism, so that integrated processing of acquisition and governance is realized.

This disclosure is directed to systems and techniques for detecting change in patient health based upon peak and non-peak patient activity data. In some examples, the peak and non-peak patient activity data correspond to one or more peak (time) periods and the one or more non-peak periods, respectively, where at least one peak period and at least one non-peak period corresponds a highest activity level and a lowest activity level, respectively, for a single day. If a change in patient health is detected, the techniques described herein may direct a medical device to generate for display output indicating the detection of the change in patient health.

Methods, systems, and apparatus for training a smaller machine learning model through contrastive learning. The method includes obtaining data specifying a larger machine learning model, wherein the larger machine learning model has been trained through contrastive learning; obtaining a training dataset comprising a plurality of training examples; and training the smaller machine learning model on the training dataset, the training comprising, at each of a plurality of training iterations: generating a batch for the training iteration that comprises a subset of the plurality of training examples, the generating comprising selecting the subset of training examples according to performing an active data selection procedure based on respective contrastive losses of the larger machine learning model on one or more candidate batches that each include a respective subset of training examples from the training dataset; and training the smaller machine learning model on a contrastive loss function using the batch.

Network address migration using a destination compute instance to update network configuration information in a cloud environment is disclosed. A network interface either using a private address within a subnet corresponding to the created network interface or using a floating address outside of the subnet corresponding to the created network interface is created. A first node of the HA pair with a service provider interface. The first node is an active data server of the HA pair and the second node is a backup node of the HA pair. Requests are serviced the first node using the created network interface. Upon failure of the first node, the second node performs a failover, wherein if the first node was utilizing a floating address, the second node registers the second node with the service provider interface by adding an address of the second node to the route table.

Devices, systems, methods, and processes for data plane redundancy management in network devices are described herein. A linecard in a network device may classify a plurality of packets into a first category or a second category based on whether a packet is a control packet or a data packet. The linecard may transmit all control packets and data packets to an active data plane. The linecard may selectively transmit the control packets and a sampled subset of the data packets to a standby data plane. Thus, the standby data plane is equipped with dynamic information of network using the control packets and Media Access Control addresses using the sampled subset of the data packets. When a failure is detected in the active data plane, the linecard starts transmitting all the data packets also to the standby data plane and starts accepting processed packets from the standby data plane for forwarding.

The invention discloses a vehicle software upgrading information management method and device, equipment and a program product, and relates to the technical field of information management. The method comprises the following steps: acquiring upgrading information of various types of vehicle software, the system comprises a software upgrading management system file, a software upgrading project process recording file, software upgrading configuration information and software upgrading compliance information. Vehicle type authentication information and first information sharing activity data generated inside a vehicle management party and second information sharing activity data generated between the vehicle management party and an external software upgrade related party in a vehicle software upgrade life cycle; carrying out storage management on the various types of vehicle software upgrading information; and in response to the consulting request for the at least one type of vehicle software upgrading information, generating a consulting record for the at least one type of vehicle software upgrading information. The management comprehensiveness of the vehicle software upgrading information can be improved.

An early learning assessment system and method using an interactive assessment device in a culturally sensitive and holistic manner. Each learner has a learner profile with learning assessment data and learner activity data. Learning assessment is done across various learning domains and analyzed in a prescriptive learning engine to assess learning in young learners across a plurality of research-identified core learning skills and provides an indication of skill mastery for particular learners across the core learning skills as well as a learning path with activities for learning appropriate to the learner's skill.

The invention relates to the technical field of intelligent medical treatment, in particular to a chronic disease follow-up visit data management method based on artificial intelligence, which comprises the following steps: collecting physiological parameter data, behavior activity data and medication record data of a patient through a medical equipment terminal; performing time sequence standardization processing on the collected data to generate a data set divided according to a preset time period; setting a first abnormal threshold value based on a clinical medicine guide, and setting a second abnormal threshold value based on historical data statistical analysis; when the data exceeds a first abnormal threshold value, generating a red early warning identifier, and when the data exceeds a second abnormal threshold value, generating an orange early warning identifier; and executing grading response according to the early warning grade. According to the method, a double-threshold cooperation mechanism and clinical threshold and statistical boundary cross validation are utilized, personalized chronic disease data management and dynamic adjustment can be carried out on different patients, monitoring of the patients can be accurately adjusted, and the medical diagnosis effect of the chronic disease patients is improved.

At least one database stores clinical activity data indicative of clinical activities of medical professionals or maintenance activity by medical equipment servicing personnel. Consumption of educational content units related to one or more medical devices by a medical professional (or maintenance thereof by a servicing person) is tracked. Future clinical (or maintenance) activities to be performed by the medical professional (or servicing person) is predicted based on the clinical (or maintenance) activity data. One or more metrics are calculated related to the medical professional's (or servicing person's) knowledge and / or experience for a future time. The metrics may include a knowledge metric based on the tracked consumption of educational content units, and / or an experience metric based on the clinical (or maintenance) activity data and the predicted future clinical (or servicing) activities. One or more refreshment educational content units are recommended based on the one or more metrics.

The invention discloses a fish habitat real-time monitoring system based on Internet of Things. Comprising a data acquisition module used for acquiring fish habitat data, a communication networking module used for transmitting the acquired data, an analysis early warning module used for analyzing the data, a comprehensive management module used for assisting device operation and a propaganda education module used for propagandizing habitat education. Water quality parameters, hydrological parameters, fish activity data and human activity data of a fish habitat are collected, analysis data are generated in an analysis and early warning module, an ecological health index of the fish habitat is calculated, an early warning signal is generated, ecological risks of a fish living environment are predicted, and an ecological health index thermodynamic diagram is generated in a comprehensive management module. And real-time monitoring is completed under cooperation of multiple modules. The fish ecological resource environment is subjected to multi-source collection and analysis through the multi-source sensing technology, the correlation between fish habitat data and fish population distribution is deeply excavated, information of the habitat is accurately grasped, the monitoring accuracy is high, and the risk pre-judgment capability is high.

The invention discloses a dynamic carbon factor prediction and carbon budget management system and method based on machine learning, and the method comprises the steps: collecting activity data through a data collection terminal, and enabling the activity data to comprise energy consumption data and logistics data; performing behavior recognition and standardization processing on the collected unstructured data, and outputting structured activity data; outputting a dynamic carbon factor value by using a carbon factor prediction model based on the situational features; combining the activity data with the dynamic carbon factor, checking the total carbon emission amount, and comparing the total carbon emission amount with a preset carbon budget target or industry standard to generate a checking report; when the over-emission risk is detected, simulating different carbon reduction strategies through a carbon budget optimization algorithm, and generating an optimization suggested path; and continuously training the model by using user feedback and monitoring data, updating carbon factors and optimizing logic.

The invention discloses a cross-regional API asset management method and device, equipment and a storage medium. API activity data collected by region collection agents are obtained, each deployment region is provided with a corresponding region collection agent, the API activity data comprises network boundary traffic, DNS analysis records, service registry data, side vehicle agent logs and probe data during application operation, feature vectors representing API interface feature information are extracted from the API activity data, and the feature vectors are used for representing API interface feature information; the feature vectors are identified, APIs corresponding to the API activity data are identified, the APIs are clustered based on the feature vectors, and the APIs of the same type in the deployment areas are managed in a classified mode. A distributed and decentralized API identification mechanism is adopted, cross-regional deployment is supported, the dependence of a gateway is eliminated, the cost is reduced, an API feature modeling method based on multi-source data fusion is adopted, the API identification accuracy is improved, and cross-regional API identification and classified management are supported.