1019 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 supply chain management, and discloses a product supply chain carbon footprint accounting method, which comprises the following steps: (1) data collection and arrangement: collecting time sequence activity data of each link of a supply chain, including production data, transportation data and supplier data; acquiring corresponding emission factors including emission factors with specific areas and time; (2) constructing a dynamic life cycle evaluation model: establishing the dynamic life cycle evaluation model containing time dynamics, and taking activity data and emission factors as time-dependent input parameters; by introducing dynamic life cycle evaluation, the method can capture the time change of carbon emission of each link of the supply chain, including seasonal fluctuation, policy change, market demand change and other factors, and considers the influence of geographic positions, transportation routes and regional energy structures on carbon emission in detail in combination with the GIS technology, so that the method has the advantages of high efficiency and high reliability. The supply chain carbon footprint accounting precision is significantly improved, and the result is more accurate and reliable.

The invention relates to the technical field of natural resource investigation, in particular to a GIS-based natural resource investigation system and method, which comprises data acquisition, data preprocessing, spatial data integration, dynamic monitoring and analysis, resource utilization evaluation and decision support. Firstly, environment data, human activity data and resource distribution data are collected in real time by arranging multiple kinds of sensors, remote sensing satellites, unmanned aerial vehicles and other devices, and cleaning and denoising processing is carried out. And then, spatial integration is performed on data from different sources by utilizing a GIS platform, and a geographic information data set is formed by combining regional map information. Through a dynamic monitoring system, in combination with spatial-temporal change analysis, resource change and fluctuation of environmental factors are monitored in real time, and resource consumption and environmental influence trends are predicted. Based on the analysis results, the system generates a resource optimization utilization scheme and environmental protection measures, scientific decision support is provided for managers, and sustainable utilization of resources and protection of the ecological environment are achieved.

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 provides a multi-platform internet marketing activity effect evaluation and prediction model construction method, and relates to the technical field of data analysis. The method comprises the following steps: collecting marketing activity data from a plurality of internet platforms; performing cleaning, normalization and feature engineering processing on the data; constructing a multi-level evaluation and prediction model based on a machine learning algorithm, wherein the multi-level evaluation and prediction model comprises a data fusion model, an effect evaluation model and an effect prediction model; calculating a click rate, a conversion rate, a user retention rate and a user life cycle value by using an effect evaluation model so as to comprehensively evaluate a marketing activity effect; and performing effect prediction by using the effect prediction model, and evaluating the model through a root-mean-square error, a mean absolute error and a mean absolute percentage error. According to the invention, the accuracy and comprehensiveness of marketing activity effect evaluation and prediction can be improved, and an important basis is provided for optimizing a marketing strategy.

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.

The invention discloses a patient pain prediction method and device based on wearable equipment. The method comprises the steps that historical physiological data and activity data of a patient are collected through the wearable equipment; acquiring a pain assessment report of the patient through an electronic medical record system, wherein the pain assessment report comprises a pain intensity score, the frequency and duration of pain attack and a treatment and intervention scheme; establishing a pain multi-modal evaluation model according to the physiological data and the pain evaluation report; wherein the pain multi-modal evaluation model comprises a learning identification module, a 1-all strategy module, a time sequence prediction module and a multi-modal fusion evaluation module; inputting the current physiological data into the pain multi-mode evaluation model, and predicting the pain intensity and the pain mode of the patient. Through the model comprising rule learning, the 1-all strategy, time sequence prediction and multi-mode fusion evaluation, the prediction precision of the pain intensity and the pain mode of the patient is improved, and the diagnosis and treatment effect of the patient, especially the chronic pain patient, can be remarkably improved.

The invention discloses a gas turbine cascade pressure field sparse sensing and testing method based on sparse representation, and the method comprises the steps: constructing a three-dimensional numerical model according to a gas turbine cascade geometric model, and setting a corresponding boundary condition; performing unsteady numerical calculation by using CFD software, and outputting unsteady pressure field data; taking the unsteady pressure field data as a sample set, and learning from a given sample set through a dictionary learning method of the sample set to obtain a dictionary; sparse representation of the flow field is carried out by using the dictionary; dividing the flow field area into a plurality of clusters by adopting a K-means clustering method, and selecting sparse pressure measuring points in each cluster; and performing data reconstruction on the corresponding local flow field according to the pressure value of each sparse pressure measuring point, and superposing the local flow field of each cluster to realize the reconstruction of the global flow field. According to the method, sparse measured data and simulation data in the cascade can be cooperatively utilized, and high-precision flow field reconstruction in a whole-field state can be carried out only by relying on rare sensor information under a noise background.

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.

Various embodiments of the present disclosure provide universal machine learning tracking and control techniques for enforcing universal standards across a plurality of disparate machine learning projects within an enterprise. The techniques include generating a canonical representation of a machine learning model. The techniques include receiving model activity data from a third party computing resource in response to user activity within a third party workspace. The techniques include generating relative progress data for the machine learning model based on the model activity data and modifying the canonical representation of the machine learning model based on the model activity data and the relative progress data. The techniques include generating and providing a model interface point for the machine learning model in response to the canonical representation satisfying a publication threshold.

In some implementations, a system enables an administrator to customize a set of rules to dynamically adjust the configuration and output of an application provided to users. A configuration interface for setting rules that dynamically adjust output of an application is provided. Data indicating one or more rules are received through the configuration interface. Activity data indicating user interaction with the application or sensor data for at least some of a plurality of users of the application are then received from multiple client devices. A determination relating to the activity data satisfying at least one condition or trigger is then made. Instructions to adjust output of the application according to one or more system actions of the one or more rules are then communicated to client devices associated with the users in the first subset of the plurality of users.

A system and method for determining device attributes using a classifier hierarchy. The method includes: sequentially applying a plurality of sub-models of a hierarchy to a plurality of features extracted from device activity data, wherein the sequential application ends with applying a last sub-model of the plurality of sub-models, wherein each sub-model includes a plurality of classifiers, wherein each sub-model outputs a class when applied to at least a portion of the plurality of features, wherein each class is a classifier output representing a device attribute, wherein applying the plurality of sub-models further comprises iteratively determining a next sub-model to apply based on the class output by a most recently applied sub-model and the hierarchy; and determining a device attribute based on the class output by the last sub-model.

Systems, methods, and computer-readable storage media for performance indicator operations are disclosed. One method includes identifying user activity data and one or more performance indicators of a user, and modeling the user activity data to generate one or more performance products including a plurality of performance parameters corresponding to a future performance indicator of the user. The method further includes generating and presenting a graphical user interface (GUI) including one or more actionable events associated with the plurality of performance parameters, and monitoring the user activity data and the one or more performance indicators of the user based on receiving new activity data corresponding to the future performance indicator from a user data source. The method further includes presenting one or more content items on the GUI including an indication of whether the user satisfies the plurality of performance parameters of the one or more performance products.

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.

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 discloses an urban garbage cleaning path dynamic adjustment method and device, equipment and a medium, and relates to the technical field of artificial intelligence, historical garbage collection data, urban activity data, seasonal change data and weather condition data in a city are acquired and preprocessed; establishing a dynamic garbage generation quantity prediction model based on a long short-term memory network, and training and testing the dynamic garbage generation quantity prediction model by using the processed data; inputting the to-be-predicted urban activity data and the meteorological data into the target dynamic garbage output prediction model to output a garbage output prediction value; an urban garbage cleaning path is planned based on a garbage output predicted value by using an ant algorithm and a local search strategy, and the urban garbage cleaning path is dynamically adjusted and optimized based on real-time garbage output and traffic condition data, so that the cleaning path of the garbage cleaning vehicle is dynamically adjusted. The resource allocation flexibility and adaptability and the garbage treatment efficiency are improved, and the urban environmental sanitation level is improved.

A storage node maintains storage objects that contain decoy datasets that are used to detect cyber-attacks. The decoy datasets may be deployed at logical block addresses (LBAs) including the first and last LBAs of the LBA range of the storage object. Real active data may be stored on the storage object with the decoy datasets. A cyber-attack is detected based on receipt of a suspicious IO command that accesses decoy data. Access to the decoy data may be detected by maintaining an IO access bitmap, calculating digital signatures of decoy datasets, or monitoring for decoy data that is marked for remote replication.

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.

A system for monitoring animal activity, the system comprising an animal tag having a tag identifier attached to an animal to generate animal data packets comprising timestamped location data having tag latitudinal and longitudinal coordinates and timestamped activity data relating to activity of the animal over a window of time and a processor configured to evaluate the animal data packets to determine an activity classification for the animal wearing the animal tag over the window of time. The processor is also configured to calculate a pasture performance value of the area based on the animal data packets received from the animal tag.

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 provides a big data-based load prediction method for a power system, and relates to the technical field of power system dispatching control. The big data-based load prediction method for the power system comprises the following steps: data collection: collecting historical load data, weather data, economic activity data and special event data of the power system; data preprocessing: cleaning and normalizing the collected data to ensure the data quality; feature extraction: extracting key features influencing the load from the original data by using a big data analysis technology; model construction: adopting a machine learning algorithm to construct a load prediction model; and model training and verification: training the model by using historical data, and verifying the accuracy of the model through a verification method. Through multi-source data fusion, the historical load data, the weather data, the economic activity data and the special event data are integrated, and the factors influencing the power load can be captured more comprehensively, so that the prediction accuracy is improved.

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.