1251 results about "External data" patented technology

The present invention relates to a digital twin processing method and system, and a cloud platform. The method comprises: acquiring production system elements, carrying out abstraction definition and parameterization description on the production system elements by means of digital-to-analog knowledge fusion of a mathematical model and a mechanistic model, so as to construct a digital twin ontology model; analyzing and reconstructing model data to obtain a mapping model of which object variables can be directly accessed and operated by a collective motion control method, so that the model is visualized at the cloud; and using an external data source to drive parameter update and operation matching of the model by means of a motion control method, so as to complete cooperative deployment and synchronous evolution of an actual physical device and the model in the production process on a cloud server. According to the present invention, a model is constructed by means of digital-to-analog knowledge fusion of a mathematical model and a mechanistic model, the model is mapped to achieve motion visualization, model parameter update and operation matching on the cloud are achieved, and then cooperative deployment and synchronous evolution of a physical device and the model are completed.

The invention provides a supply chain sales anomaly detection and root cause analysis system and method fused with a knowledge graph, and the system comprises a demand collection and preprocessing module which is used for connecting an order system, a supply chain system, a customer relationship management system and an external data source, and completing the data cleaning, entity analysis and feature extraction; the supply chain knowledge graph construction module is used for defining an entity type and a relationship type; the real-time anomaly detection module is used for accessing a sales index data stream, performing anomaly detection in combination with lightweight filtering and a graph neural network model, and calculating node and global anomaly scores; and the visual report generation module is used for automatically generating a visual report. According to the method, the dynamic supply chain knowledge graph is constructed, the graph neural network is applied, multi-source heterogeneous data is deeply fused, the complex dependency relationship between entities is effectively captured, the accuracy and timeliness of sales anomaly detection are remarkably improved, automatic positioning of abnormal root causes and evidence chain tracing are achieved, and the analysis efficiency is greatly improved.

Systems and methods for detecting and mitigating prompt injection attacks on a generative LLM are disclosed. A deployment scenario is considered, in which the generative LLM supports a task automation function. Prompts are received and interpreted by the generative LLM, and outputs from the generative LLM are used to trigger automation actions. The prompts are constructed based on a combination of user input and external data and are, therefore, vulnerable to prompt injection attacks though manipulation of the external data. To mitigate this risk, a separate discriminative classification, decoupled from the generative LLM, engine is configured to identify malicious prompts, and filter out any malicious prompts before they reach the generative LLM.

Systems, methods, and devices to detect unauthorized third-party connections within a network infrastructure, such as by analyzing network traffic data using fuzzy matching and machine learning techniques. One aspect includes receiving network traffic data comprising records of communication events involving network identifiers, determining communication relationships between network entities identified by the network identifiers, accessing entity identifiers associated with known third-party systems, and determining associations between the network identifiers and the entity identifiers using a fuzzy matching process. Other aspects include identifying communication relationships involving the third-party systems based on the associations and detecting unregistered or unknown third-party connections within the network infrastructure. Further aspects include normalizing identifiers, computing string similarity metrics, assigning confidence scores, incorporating external data sources, building network association patterns, comparing current patterns to baseline patterns to detect anomalies, and updating security policies or firewall rules in response to detected anomalies. Additional aspects are provided.

The invention discloses a fire risk accurate prediction method and system based on a multi-source data fusion knowledge graph, and belongs to the technical field of machine learning, and the method comprises the following steps: accessing a multi-source heterogeneous data set, integrating sensor data, geographic information, historical data and external data, and obtaining a multi-source heterogeneous data set; a full-size forest three-dimensional model is constructed by means of FDS and SolidWorks, combustion simulation is carried out, environmental parameters are set carefully to ensure that the result is accurate, and a foundation is laid for construction of a multi-source heterogeneous data set; the method comprises the steps of data acquisition, data preprocessing, noise data cleaning, space-time alignment and feature engineering, multi-source heterogeneous data acquisition and preprocessing are carried out, and forest fire related data covering structured, semi-structured and non-structured types are acquired from a multi-source heterogeneous data set. The accuracy and timeliness of fire early warning are remarkably improved, effective fusion of forest fire multi-source heterogeneous data can be achieved, and transparency and traceability in the data fusion process are ensured.

The invention provides a cellular network flow space-time prediction method and system based on a hybrid neural network, and belongs to the technical field of intelligent communication. The method adopts a layered deep neural network architecture, and comprises a data embedding layer, a space-time coding layer, a feature fusion layer and an output layer. The data embedding layer maps a historical traffic sequence, cross-domain external data and metadata into high-dimensional features; the space-time coding layer is used for respectively fusing one-dimensional causal convolution and a Mama neural network to extract multi-scale time features and densely connecting convolution and a multi-head attention mechanism to capture multi-scale space features through time and space modeling branches; the feature fusion layer realizes adaptive weighted fusion of spatial-temporal features, cross-domain features and metadata features by using a gating fusion mechanism; and the output layer performs linear transformation on the fusion features to generate a final prediction result. According to the method, the spatial-temporal dynamic capture of the service traffic is accurate, the prediction curve is highly fit with the true value, and the accurate prediction of the multi-service traffic of the cellular network is realized.

Systems and methods described herein enable adaptive, threshold-based modification of node maps representing ontologies, knowledge graphs, or code development pipelines using generative artificial intelligence. The disclosed platform can retrieve a node map and generate one or more candidate perturbations that modify nodes or relationships within the node map. The disclosed platform can evaluate the effect of the perturbations by comparing respective outputs against ground-truth data. Perturbations can be automatically determined based on changes in external datasets, compliance policies, or operational requirements. The perturbations can be implemented when a computed perturbation quality value satisfies a threshold quality criterion. As such, the system enables efficient, policy-compliant evolution of relational system architectures in dynamic environments.

The invention belongs to the field of data processing, and particularly discloses a method and system for automatically generating a document according to a template and structured data, and the method comprises the steps: processing an archive template file according to a document object model analysis method, extracting the position, the type and the associated tag of a placeholder, and obtaining a placeholder metadata set; according to the placeholder metadata set, traversing a document hierarchical nesting relationship by adopting a depth-first search algorithm to obtain a hierarchical placeholder mapping table; obtaining the structured data of the document repair record through an external data source, judging whether the structured data field corresponds to the tag in the mapping table according to the hierarchical placeholder mapping table, and if so, generating a matching pair list from the field to the placeholder to obtain a preliminary matching data set; the objective of the invention is to solve the problem of a business scene in which complex nested data and file template placeholders cannot be accurately matched in a document repair record in the prior art.

The invention provides a digital employee robot performance intelligent evaluation method and system, and relates to the technical field of data processing, and the method comprises the steps: carrying out the feature extraction of user interaction contents in all scenes; performing fluctuation detection, identifying a time period of abnormal fluctuation, and extracting user request content and robot response content in the time period; restoring the current session path based on a preset business process template, performing path consistency comparison in combination with a response rule of the robot under the path, and generating an abnormal node set if the path is interrupted or the response is offset; extracting a user request format, a robot response behavior and an external data calling state corresponding to each abnormal node, performing abnormal source analysis, calculating user input abnormity, and identifying an attribution probability of abnormity and data interface abnormity; according to the invention, autonomy and accuracy of robot performance intelligent evaluation are improved.

The invention discloses an electric power spot market electric charge settlement system and method fused with a block chain technology, and the system comprises a core settlement layer which is used for storing the electricity price and user identity data, and executing an electric charge settlement rule through a built-in intelligent contract module; the high-frequency transaction processing layer is used for processing a transaction process through an under-chain caching technology and uploading data in the transaction process to the core settlement module; an external data interface is integrated on one side of the high-frequency transaction processing module and used for acquiring electric quantity and load data in real time, and a cross-chain gateway is arranged on the other side of the high-frequency transaction processing module and used for achieving synchronous settlement of electric charge and capital flow. According to the method, the efficiency and transparency defects of a traditional mode are overcome, the performance and energy consumption bottleneck of the block chain technology in an electric power scene is broken through, high efficiency, safety, expandability and environmental protection benefits are achieved, and a credible, real-time and compliant digital settlement infrastructure is provided for an electric power spot market.

Methods and corresponding systems and apparatuses for configuring user access to data stored in and / or accessed through an external computer system are described. Access permissions can be configured through defining a permission set relative to a proxy entity and assigning the permission set to one or more users. A proxy entity is a local representation of an external data entity. A proxy entity can be a virtual entity in that the proxy entity does not contain underlying data. A proxy entity can, however, include metadata describing its corresponding external data entity. A computer system maintaining a proxy entity can store mapping information linking the proxy entity to an external data entity. The mapping information and the permission set can be used to determine an access permission relative to the external data entity and to communicate this access permission to the external computer system so that access can be provided accordingly.

The embodiment of the invention provides a method for constructing large model cue words in the financial credit field based on the ReAct theory. The method can solve the technical problem of cue word errors caused by semantic understanding deviation in related technologies. The method comprises the following steps: defining a role and a capability boundary of a dynamic cue word generation model, and binding a structured credit knowledge graph; inputting the received original business problem, the scene label and the real-time data into a dynamic prompt word generation model to obtain at least one first keyword; matching entity nodes related to the first keyword in the credit mapping knowledge domain, and generating a keyword entity association result; calling a preset process template according to the scene label, generating a reasoning chain comprising at least one reasoning layer node, and inserting a judgment condition: binding a tool calling instruction at the action layer node, obtaining external data, backfilling the external data to the reasoning chain, obtaining backfilled data, executing the reasoning chain, and outputting a service result; the cue word priority weight is improved according to the service result, and the cue word accuracy is improved.

The invention discloses an intelligent marketing document generation device and method based on multi-source data fusion. The method comprises four processes of user instruction analysis, regular cleaning, model parameter extraction, Neo4j knowledge graph verification and structured parameter output. External data acquisition: calling a Baidu large model to acquire data according to parameters, and screening high-quality data through duplicate removal, semantic enhancement and weighting; internal cases are processed, timed slicing cases are stored in a library, filtering, propagation calculation and multi-dimensional scoring are combined, and a CoT inference chain report is generated; and performing multi-modal output, adapting formats, integrating data by means of a BART model and outputting a document with metadata. The device and the method cooperate with each other, through multi-source fusion, knowledge graph association and multi-mode conversion, high-quality marketing documents adaptive to multiple industries are efficiently generated, support is provided for decision making, and enterprises are assisted to improve market response and output efficiency.

Systems and processes are disclosed for enhancing cybersecurity and optimizing software repositories through integration of web crawling, web scraping, feature engineering, and advanced machine learning algorithms to detect phishing attempts, prevent account takeover fraud, and identify unused code in repositories. The system collects and refines data from various sources, including transaction logs, customer databases, device details, external data sources, and historical fraud data, to build comprehensive datasets. Feature engineering creates new, meaningful features from the refined data, which are used to train and evaluate machine learning models. The best-performing models are deployed in production to monitor incoming communications and transactions in real-time, flagging suspicious activities and optimizing codebases. This processing ensures timely detection and prevention of security threats while maintaining efficient software development processes. Robust protection is provided against evolving cyber threats and enhances software performance and security through continuous learning and adaptation.

The invention discloses a knowledge base knowledge association fusion method based on a knowledge graph, and the method comprises the steps: integrating structured, semi-structured and non-structured data through a cross-modal alignment technology, and constructing a multi-source heterogeneous data association network of a unified semantic space; newly added external data are fused to a multi-source heterogeneous data association network in real time by using a dynamic attention mechanism, and entity conflicts are eliminated by combining rule reasoning and a machine learning model; hidden association among entities in the multi-source heterogeneous data association network is mined based on the graph neural network, and a knowledge graph logic chain is complemented; based on the knowledge graph, intelligent question and answer and risk assessment decision scenes are supported through a hybrid retrieval architecture and an inference engine; and automatically updating and associating the knowledge base of the network extension knowledge graph by adopting an incremental learning technology. Natural language questions and answers are supported, accurate answers are generated through knowledge reasoning, and user experience is remarkably enhanced.

The invention relates to the field of power systems and intelligent power grids, in particular to an intelligent analysis system and method for the influence of multi-scene new energy power generation on a distribution network, and the system comprises a data collection module, a multi-scene modeling module, an influence analysis module, an intelligent evaluation module and an optimization suggestion generation module. And the data acquisition module, the multi-scene modeling module, the influence analysis module, the intelligent evaluation module and the optimization suggestion generation module are in communication connection with an external data bus. The hybrid digital twinborn model is constructed through the multi-scene modeling module, four typical scenes of new energy output fluctuation, extreme weather, AC / DC hybrid access and power grid topology change are dynamically simulated, and dynamic configuration of scene parameters (such as new energy permeability and load growth rate) is supported. According to the model, through fusion modeling (such as a power flow equation, an LSTM time sequence and a graph neural network GNN) driven by a physical mechanism and data, the running state of the distribution network under the multi-scene coupling effect can be reflected more truly.

The invention discloses a power data management method based on a block chain, and relates to the field of block chains, and the method comprises the steps: initiating a data request; randomly selecting a preset number of oracle machine nodes; segmenting a private key of the request task into a plurality of parts by adopting a threshold key segmentation method; acquiring requested power data from an external data source; verifying the obtained power data; a Byzantine fault-tolerant consensus mechanism is adopted to achieve a consensus for the power data; signing the verified power data through a threshold signature method; the oracle machine management contract performs aggregation verification on the received signature data; the oracle machine management contract returns the power data passing the aggregation verification to the intelligent contract of the data requester; the intelligent contract of the data requester obtains the returned power data through an event monitoring mechanism, and executes corresponding business logic and state updating according to the power data; aiming at the low fault-tolerant capability of an oracle machine in power data management in the prior art, the fault-tolerant capability of the system is improved.

The invention relates to the technical field of tunnel engineering automation and intelligent control, and discloses a TBM tunneling parameter intelligent optimization decision-making system based on an LSTM network, and the system comprises a data collection and preprocessing module which obtains external data and outputs a tunneling parameter sequence; the probabilistic tunneling trend prediction module is used for outputting a prediction expected value and prediction uncertainty; the prospective geological precursor sensing module is used for matching and identifying known risks and outputting alarm events; and determining an optimal tunneling mode by the dynamic risk avoidance decision matrix. When the prediction uncertainty is too high, activating the prospective template driven by the uncertainty to excavate a new precursor template and update the template library; meanwhile, the decision-efficiency relevance evaluation and strategy self-optimization engine optimizes the decision rule according to the actual tunneling efficiency. According to the method, decision making is carried out through quantitative risk prediction and fusion of multi-source information, and a double learning closed loop of knowledge discovery and strategy optimization is established, so that the reliability, the adaptability and the long-term efficiency of system decision making are remarkably improved.

The invention discloses an intelligent Internet of Things home energy management system, and relates to the technical field of intelligent homes, the system comprises a multi-dimensional data sensing module which uses a sensor and an intelligent equipment interface to collect human physiological data, obtains environment and external data through an environment sensor and a weather API, and sends the environment and external data to a cloud server; historical physiological records, environmental regulation logs and user feedback data are called, timestamps and space coordinate information are added to all kinds of data at the same time, and the data are converted into data vectors and uploaded to a system database; through the multi-dimensional data sensing module, human body physiological data, environmental data and external data can be comprehensively collected, and a physiological state prediction model and a health risk identification model exclusive to a user are constructed in combination with historical records and user feedback, so that the personalized level of health management is improved, and the health risk of the user is improved. And through a chaos embedding biological rhythm prediction formula and a dynamic Bayesian health risk assessment formula, accurate prediction of the physiological state of the user and identification of the health risk are realized.

The invention discloses an electric power big data automatic reasoning platform and an electric power distribution system, and relates to the technical field of electric power systems, and the platform comprises a knowledge acquisition unit which collects and preprocesses electric power internal and external data, and converts the data into structured data through a knowledge processing unit; organizing the triple data by the dynamic knowledge graph construction unit to form an initial graph; the updating unit monitors a new dynamic state in real time and corrects or adds atlas content; the reasoning unit is based on the dynamic graph, power fault accurate reasoning and future demand trend prediction are achieved through a fault diagnosis and demand prediction algorithm, efficient integration and dynamic evolution of power knowledge are achieved through construction and real-time updating of the dynamic knowledge graph, timeliness and accuracy of the graph are ensured, and reliable data are provided for fault diagnosis; through a closed-loop architecture of data acquisition-inference analysis-scheme making-execution control, an intelligent sensor and an optimization algorithm are combined to generate a distribution scheme adaptive to a scene, and the power distribution adaptability and economy are improved.

Techniques for simulating multi-agent interactions within a virtual world in response to generated hypotheses using dynamically retrieved external data are disclosed herein. A computing system can receive instructions including a user input with a seed phrase and provide the user input to a configuration generation model to generate agent traits associated with agents in a virtual world and / or data-source identifiers associated with external data sources. The system can retrieve external data from the external data sources and provide the agent traits and external data to a hypothesis generation model to generate a hypothesis that includes a constraint associated with the external data. The system can instantiate agents that have the agent traits and execute a simulation session consistent with the constraint (e.g., by causing the agents to generate an output set using the hypothesis and question). The system can generate a representation of the output set for analysis.

A decision support system and a computer-implemented method of enterprise decision support include a data consolidation module, domain-specific machine learning models, an enterprise decision intelligence model, and a user interface layer. The data consolidation module collects data from both internal and external data sources. The domain-specific machine learning models generate decisions based on the collected data. The enterprise decision intelligence model integrates real-time trends and the decisions to provide context-aware recommendations. The enterprise decision intelligence model maintains a decision graph that connects decision variables of the domain-specific machine learning models in a causal relationship, with the domain-specific machine learning models interacting as an interconnected network. The decisions are influenced by affects of decision variables from other domain-specific machine learning models. The user interface layer facilitates interactive decision-making by way of the enterprise decision intelligence model and visualizing the recommendations.

A computer-aided system and associated method for automated building design includes receiving input parameters and generating a three-dimensional parametric computer model of a building frame and populating the three-dimensional computer building model with generic and manufacturer-specific products by accessing internal and external data sources. The populated computer model undergoes real-time simulation to analyze regulatory compliance, verify engineering requirements including load calculations, and evaluate cost considerations. Based on simulation results, the method generates output documents including requests for proposals (RFP documents) for contractors, requests for quotations (RFQs) for vendors, and regulatory compliance documentation. These documents are transmitted to respective stakeholders through a secure data exchange protocol. The method further includes receiving responses to the generated RFP documents / RFQs.

The invention relates to an energy-saving method and an energy-saving system based on electric power big data, and belongs to the technical field of energy-saving optimization data processing. The method comprises the following steps: constructing a data model based on time-space fusion multi-source data through a cloud through an intelligent electric meter and external data access; establishing a dynamic reference of an operation scene based on a data model to perform group division on nodes, and then obtaining a multi-dimensional comprehensive sequence and generating an energy-saving reconstruction node list based on group evaluation index deviation; and completing energy-saving optimization by bidirectionally analyzing the node list, including upwards aggregating the nodes to generate a scheduling strategy and downwards decomposing the load identification equipment to optimize the energy consumption of the user. According to the method, a unified data model is constructed through an intelligent electric meter and multi-source data real-time acquisition and encrypted transmission, and node clustering and feature extraction are carried out by utilizing a machine learning clustering optimization operation reference; node deviation is calculated in real time, an energy-saving potential list is generated, and node resource scheduling and high-energy-consumption equipment optimization are achieved through bidirectional analysis.

The invention relates to the technical field of traffic data processing, and discloses a short-time traffic flow prediction method, system and device based on spatial-temporal characteristic analysis and a storage medium, and the system comprises a data processing module, a spatial-temporal characteristic mining module, a characteristic fusion and model construction module and a prediction and result display module. Through cooperative work of the data processing module, the spatial-temporal feature mining module, the feature fusion and model construction module and the prediction and result display module, the spatial-temporal features of the traffic flow are comprehensively and deeply analyzed, the prediction precision is effectively improved, a more reliable basis is provided for traffic management decisions, and the traffic flow prediction efficiency is improved. Multi-source external data is introduced and an attention mechanism is adopted to perform feature fusion, so that the adaptability of the model to complex traffic influence factors is enhanced, a prediction result is more fit with an actual traffic condition, a visual result output mode is adopted, different users can conveniently and quickly know the future change trend of traffic flow, and reasonable travel and management strategies can be conveniently formulated.

The invention relates to the technical field of data processing, in particular to an FPGA (Field Programmable Gate Array) and CPU (Central Processing Unit) cooperative network data processing system, which comprises a network interface module for receiving and forwarding an external data flow, and a protocol analysis module for distinguishing a service message and a protocol message based on a message header characteristic and a load mode; the service processing module executes protocol format conversion, security encryption and data extraction in the FPGA; the protocol stack interaction module transmits a protocol message to the CPU through the MAC layer communication interface; the protocol stack module activates necessary protocol services in the CPU and generates a resource allocation instruction; and the data integration module receives the service data flow and the protocol processing result, and dynamically adjusts the encryption rule. According to the invention, FPGA business data closed-loop processing and CPU protocol service physical isolation are realized, a double-processing-unit architecture supports financial signature and industrial filtering cascade operation, the MAC layer interface is additionally provided with cyclic redundancy check to guarantee transmission reliability, and equipment processing efficiency and anti-attack capability under a fusion scene are improved.

Disclosed are a method, system, and apparatus of a body-worn camera system with integrated artificial intelligence for real-time field assistance and automated incident reporting. In one embodiment, a body-worn safety device includes a body-worn camera configured to capture a video of an incident from a perspective of a wearer of the body-worn camera. In this embodiment, a microphone is configured to capture audio concurrently with the video. A processing unit includes an artificial intelligence module in this embodiment. The artificial intelligence module is configured to respond to a voice command from the wearer by analyzing the captured audio, the captured video, and / or an external data of the artificial intelligence module. In another embodiment, a method of a wearable safety system provides an audible answer and a guidance in real-time using the natural language queries; and outputting an audio response and an alert to the wearer.

The invention discloses a multi-modal data fusion enterprise benefit policy declaration automatic processing system, which relates to the field of enterprise benefit policy declaration and comprises a data acquisition module, a preprocessing module, a fusion module, a policy matching module, a material generation module, a declaration process automation module, an intelligent feedback and optimization module, a user interaction module, a policy recommendation optimization module and a data quality evaluation module. The method comprises the following steps: acquiring enterprise internal and external data marking reliability in a multi-source manner; cleaning and analyzing normalized data by adopting an innovative algorithm; fusing data by combining CNN, RNN and a self-attention mechanism; generating and auditing materials based on a knowledge graph and deep learning matching policy and a template and NLG; and an immersive interface is created to support various interactions. According to the invention, multi-source data is collected efficiently, enterprise benefit policies are matched accurately, declaration materials are generated automatically, enterprise declaration efficiency and success rate are improved, government auditing process is optimized, data security is enhanced, policy accurate landing is promoted, and government and enterprise win-win is assisted.

The invention relates to the technical field of power system operation and control, and particularly discloses a time-sharing electric quantity prediction method based on a logarithmic load density growth curve, which comprises the following steps of: firstly, performing causal detection and dynamic time-delay optimization on historical load and multivariate external data through convergence cross mapping and mutual information technologies, and constructing a causal time-delay feature set; and the problems of multi-element coupling and time-delay effect quantization are solved. Secondly, fitting a load trend by using time-frequency decomposition in cooperation with a segmented logistic model, extracting dynamic parameters representing a growth rate and a saturation capacity, and endowing the model with a sensing ability for a load evolution stage; then, causal features, growth parameters and load components are deeply fused through cross-domain modulation and a gating mechanism, the nonlinear modulation effect of an external environment on a load mode is explicitly modeled, and finally, a probability interval is generated in combination with quantile regression and residual error correction. According to the scheme, accurate and probabilistic prediction of the time-sharing electric quantity in a complex scene is realized, and the scientificity of an agent electricity purchase decision is improved.

A system and method for collaborative creation and management of intellectual property (IP) projects is disclosed. The system includes a server configured to dynamically assign roles and permissions to participants based on predefined criteria. A graphical user interface (GUI) facilitates real-time collaboration, editing, and version control, while a semantic analysis engine ensures compliance with intellectual property standards and assesses content novelty. Predictive analytics generate metrics, such as patentability scores and inventorship overlap, to guide project development. Contributions are electronically tracked and stored in a secure, non-transitory computer-readable medium alongside generated metrics. The system supports role-specific recommendations, automated notifications, and integration with external data sources for enhanced functionality. Participants can visualize contributions, resolve conflicts, and simulate commercialization outcomes, enabling efficient project management and innovation. This invention improves the collaborative development process, ensuring compliance, enhancing productivity, and maximizing the value of intellectual property assets.