772 results about "External data" patented technology

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.

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 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.

The invention relates to a global passenger flow direction real-time tracking method for rail transit station video monitoring, and belongs to the technical field of intelligent traffic, and the method comprises the following steps: S1, collecting video data and business data in a station, and carrying out the time-space synchronization and preprocessing of multi-source heterogeneous data; s2, extracting and fusing fine-grained clothing textures, carried objects and gait features of passengers in real time; s3, compressing a search space based on a station topology and a space-time reachability model, and performing global identity association by adopting a probability graph model; s4, maintaining and correcting the continuity of the track through forward-backward joint reasoning and external data verification, and finally outputting a continuous passenger track with consistent identity; s5, carrying out association and semantic annotation on the continuous passenger tracks and the business data; and S6, performing analysis based on the fused data and generating a regulation and control instruction.

The invention belongs to the technical field of large models and data agents, and particularly relates to a question answering method based on a large model and a data agent, and the method comprises the steps: receiving and preprocessing a user question, and generating a question data set; judging whether external data support is needed or not by utilizing a large model, and if so, dynamically generating an agent calling strategy; real-time data acquisition, domain knowledge analysis, multi-source data fusion and logical reasoning verification intelligent agent cooperative execution tasks are scheduled according to a strategy; fusing the traceability information returned by the intelligent agent and the knowledge of the large model to generate a preliminary answer, and outputting the preliminary answer after interpretable optimization; and finally, optimizing the case library according to user feedback and iterating an agent calling strategy. All data agent processing results are attached with full-link traceability information, data sources and reasoning processes are presented in an explanatory optimization mode, and a user can verify answer basis.

Methods and apparatus are described for enhanced node communication within a wireless node network having nodes and a server. A first subset of nodes transmits first data messages to the server without using an intermediary device where the first data messages include sensed data generated by the nodes of the first subset of the plurality of nodes about or around the nodes of the first subset of the plurality of nodes. A second subset of the plurality of nodes receives external data from the other of the nodes. The second subset of the plurality of nodes are intermediary devices that transmit the received external data to the server, wherein the received external data from the other of the nodes comprises sensed data about or around the other of the nodes.

The invention discloses an external ADC synchronous sampling method and device based on microprocessor timer cascade, and relates to the field of microprocessor external ADC high-speed synchronous sampling. The method comprises the following steps: periodically generating an external ADC sampling trigger signal by configuring a first timer; triggering a second timer update event by using an external ADC data ready signal, automatically sending data to ADC peripherals through an SPI interface DMA sending channel, and automatically carrying ADC sampling data to a memory buffer area through an SPI interface DMA receiving channel; continuous and single conversion modes are supported, and the data integrity is ensured by adopting a double-buffering mechanism. And full-link hardware synchronization from sampling triggering to data storage is realized. By implementing the technical scheme provided by the invention, the technical bottleneck of realizing high-speed synchronous sampling on middle and low-end microprocessor platforms without a DMAMUX function is overcome. The MCU interruption frequency and resource occupation are obviously reduced, time sequence jitter caused by software delay is eliminated, and the sampling synchronization precision and the system real-time performance are greatly improved.

The invention discloses an industrial park comprehensive energy management method and system based on a hierarchical system, and belongs to the technical field of energy management and automatic control. The method comprises the following steps: integrating external data, and predicting load and renewable energy power generation power by using a machine learning algorithm; making a start-stop and output plan of each energy device in the park through an MILP model; and based on short-term prediction and an intra-day scheduling plan, establishing an MPC model, solving a real-time control instruction of each energy device, and controlling operation of various energy production, conversion and storage devices. According to the method, an algorithm combining reinforcement learning and model prediction control is creatively applied, the prediction model and the cost function of MPC are adaptively adjusted on line through reinforcement learning, the equipment power is dynamically adjusted in combination with short-term rolling prediction, uncertainty is effectively dealt with, more accurate second-level power balance is achieved, and the reliability of the system is improved. And the adaptability and the operation economy of the system to high-proportion renewable energy sources are effectively improved through hierarchical collaborative optimization.

The invention relates to the technical field of insurance information, and discloses a personal insurance fraud full-process prevention monitoring system and method, and the system comprises a data collection and fusion module which is used for extracting data in real time or quasi-real time from business systems of insurance buying, underwriting, preservation, claim settlement and the like in an insurance company, external data such as medical records, credit investigation data, judicial information, internet financial behavior data, black and grey lists shared by other insurance companies and the like are integrated through an API interface, and multi-source heterogeneous data are cleaned, standardized and associated; and the multi-modal risk analysis engine comprises a rule engine layer, a machine learning model layer and a correlation graph analysis layer. A prevention and monitoring system covering a personal insurance full-service process is constructed, and anti-fraud monitoring is preposed to insurance and underwriting links and runs through a claim settlement link to form a closed loop. A traditional afterward response mode is changed, and early warning can be given out and intervention measures can be taken at the early stage of risk occurrence.

Described is a system that receives data from a variety of external data repositories and identifies unstructured data within the received content. The unstructured data is processed to generate textual representations. A chat message is displayed in a user interface, prompting the first user to submit a query. Upon receiving the user's query, the system generates a modified version of the query and identifies portions of the textual representations. A content block is then generated from these portions and input into a machine learning model trained to generate responses using content blocks. The system generates a response to the user's query and displays the response within the user interface.

The invention relates to the technical field of financial science and technology and data processing, and discloses a credit customer risk identification method and system based on multi-dimensional financial assets, and the method comprises the following steps: firstly, aligning external unstructured government affair data with internal structured financial data through a unique identifier; carrying out discretization and normalization processing to construct a standardized multi-dimensional feature vector; then, a comprehensive risk score is calculated in parallel based on the metadata rule base and the statistical anomaly detection model; if the score exceeds a threshold value, generating a risk clue, controlling the clue to flow among states of to-be-checked, handling, archived and the like by using a finite-state machine, and executing timeout monitoring; and finally, in response to clue termination, dynamically adjusting a rule weight based on misinformation data, and encrypting and synchronizing a credit decision result to an external data source. According to the invention, through multi-source data fusion, double-track scoring and state machine closed-loop management, the accuracy of credit risk identification, the standardization of process management and control and the adaptive ability of the model are effectively improved.

The invention discloses an instrument equipment operation and maintenance method based on big data, and relates to the technical field of equipment operation and maintenance. The method comprises the following steps: collecting static, dynamic and external data of equipment, integrating the static, dynamic and external data into global operation and maintenance data, and constructing a full-life-cycle file of the equipment; constructing an abnormal behavior recognition model, inputting real-time monitoring data and outputting abnormal event data after full-life-cycle archive training of the equipment; abnormal influence factors are extracted from the full-life-cycle archive of the equipment, a Sharpley additive interpretation method is used for calculating the Sharpley value of each factor, and an abnormal traceability result is obtained; responsibility subject scoring: determining a responsibility weight according to a predefined responsibility rule base, and calculating each responsibility subject score in combination with an abnormal traceability result; and calculating an adaptability score of an existing operation and maintenance strategy and an abnormal traceability result through a strategy comparison and analysis algorithm, and generating a targeted optimization strategy by using a reinforcement learning algorithm. Based on the targeted optimization strategy, the scientificity and accuracy of strategy operation and maintenance management are improved.

The invention belongs to the technical field of big data processing and resource management, and discloses a resource optimization-oriented big data scheduling method and device, equipment and a storage medium. The method comprises the following steps: synchronizing metadata of an off-line production task from an existing data processing system through a preset interface, and carrying out sorting and resource allocation on a task queue by adopting a hybrid scheduling algorithm according to a task priority, a predicted execution time length and a task dependency relationship, so as to generate a task scheduling sequence and a resource allocation scheme, then starting and executing the task, monitoring the task execution state and the resource use condition in real time in the execution process, and dynamically adjusting the task execution sequence to realize bidirectional synchronization of production data and scheduling information without additionally designing an external data acquisition component; the method completely depends on off-line production data acquired by an existing data processing system of a semiconductor enterprise, realizes efficient and reasonable allocation of resources, and remarkably improves the scheduling efficiency and the overall performance of the system.

A system and method for generating computer code are provided. The system receives data indicating a trigger event to initiate code generation and selects a code generation agent based on attributes of the trigger event. The selected agent requests indexed context information from a context indexing component. The context indexing component generates indexed context information including project data associated with a user account, such as existing code, text data, file structure data, open file information, and project documentation. First embeddings are generated from the processed project data and stored in a vector database configured to be queried by the code generation agent. The system can further generate environmental data, hierarchical file structure summarizations, and indexed external data, creating additional embeddings stored in the vector database to provide comprehensive context for code generation.

The invention discloses an enterprise digital management system based on multi-source data fusion, and the system comprises a data collection module which is used for constructing a multi-source data view from internal and external data sources of an enterprise; the embedding mapping module is used for generating a feature domain embedding vector set; the improved extremely deep factorization machine model module is used for feature extraction and explicit high-order interactive modeling and outputting a basic matching score; the triple generation module is used for constructing a positive and negative sample triple and calculating a business importance coefficient and an exposure probability; the Bayesian personalized sorting module is used for constructing weighted sorting loss and updating model parameters; and the online recommendation module is used for calculating matching scores of the candidate schemes and sorting and outputting a recommendation management list. According to the method, accurate and intelligent recommendation and sorting decision of candidate schemes in an enterprise management scene are realized by improving an extremely deep factorization machine model and Bayesian personalized sorting.

The invention discloses an enterprise digital diagnosis method and equipment based on a large model, and belongs to the technical field of large model application and multi-source data processing. The method comprises the steps that multi-source data related to enterprise digital diagnosis are collected, the multi-source data are classified, and the classification result at least comprises internal data and external data; performing data synchronization on the internal data and the external data respectively; constructing an enterprise knowledge graph based on the multi-source data after data synchronization; generating a multi-dimensional enterprise digital diagnosis questionnaire, collecting questionnaire results, analyzing the questionnaire results by using a preset large model, and fusing the analysis results to the enterprise knowledge graph; and in response to a diagnosis request of a user, retrieving the enterprise knowledge graph by using a preset large model, and generating a diagnosis report based on a retrieval result and the policy document. According to the method provided by the invention, through deep fusion of the large model and the multi-source data, intelligentization and dynamics of enterprise digital diagnosis are realized.

The invention discloses an expressway pit pond early warning method and system based on time sequence feature fusion, and relates to the technical field of intelligent traffic monitoring and road maintenance. The method comprises the following steps: S1, collecting multispectral image information of a road surface, synchronously obtaining external information, and respectively generating multispectral and external data sets; s2, correcting and fusing the multispectral data, extracting multidimensional features, and constructing a time sequence optimization feature vector in combination with external data; s3, inverting states of a bonding layer, a base layer and a surface layer in the pavement according to a mapping relation between spectral characteristics and structure parameters, and generating an implicit structure health index; s4, taking the time sequence optimization feature vector, the implicit structure health index and the external data set as input, and utilizing an LSTM model to predict a pit-pond formation probability and a development trend; and S5, calculating a dynamic risk level according to a prediction result. Through fusion analysis of multispectral time sequence characteristics and structural health states, full-period early warning from early damage to forming damage of a pit and a pond is realized, and support is provided for accurate maintenance.

The invention discloses an Internet of Things equipment data analysis method and system supporting multi-protocol custom splicing, and relates to the technical field of communication protocols, and the method comprises the steps: constructing a multi-protocol equipment access link; in response to an original data message reported by the Internet of Things equipment through the multi-protocol equipment access link, determining a product model of the Internet of Things equipment based on the original data message; splitting the original data message into a plurality of functional paragraphs based on the attribute of the product model; analyzing each function paragraph to obtain function paragraph data, and mapping the function paragraph data to a corresponding attribute of the product model; and storing the mapped functional paragraph data into a time sequence database and a relational database. According to the method, the external data interface can be customized and created, the service system can conveniently obtain the equipment data, the operation data and the freezing data, the method has the functions of real-time reading control and the like, the data transmission safety can be guaranteed, equipment access under a large number of protocol concurrency scenes can be effectively handled, and the access efficiency is improved.

The invention discloses a cross-chain middleware system and a communication method thereof, and the system comprises a monitoring layer which is used for carrying out the unified observation and quick feedback of the global operation state of the cross-chain middleware system; the verification layer is used for judging whether the cross-chain transaction is credible or not by using a verifiable process and giving a final conclusion; the transaction layer is used for managing the whole process of entering, executing, confirming and ending the cross-chain transaction; the routing layer is used for providing a schedulable and optimizable transmission path for the cross-chain transaction; the soft bus access layer serves as a connection basis of the system and an external data link network, and provides node resources for the routing layer and the transaction layer through the processes of discovery, screening, access and encryption; and the multi-link access layer is used for facing a plurality of heterogeneous data links and is responsible for unified access and communication bridging of different data links. According to the method, the complexity and the failure rate of cross-chain interaction are reduced, and the stability and the expandability of multi-chain collaboration are improved.

The invention relates to a transaction event interpretation system based on information traceability. The transaction event interpretation system comprises an architecture management module, a vector deployment module and a transaction recognition module, the architecture management module comprises an association construction unit and a type calibration unit; the vector deployment module comprises a transaction construction unit, a transaction evaluation unit, a vector generation unit and a vector deployment unit, and the transaction identification module comprises an information acquisition unit and a transaction index unit. According to the method, two dimensions of static configuration and dynamic retrieval are combined, firstly, static configuration of retrieval vectors is carried out on each monitoring point according to basic information, so that each monitoring point has target performance during transaction event tracing, on the other hand, the incidence relation between the monitoring points is taken into account, information isolation is avoided, and in addition, through dynamic configuration of the probe, the accuracy of transaction event tracing is improved. And according to the actual condition of each monitoring point, external data is rapidly obtained to complement the external event basis required by transaction event tracing.

An apparatus including a graphical user interface (GUI) for displaying and modifying data elements based on user input and location data, the apparatus including at least a processor; and a memory configuring the at least a processor to receive input data objects from external data sources, wherein the input data objects include structured and unstructured data, geospatial coordinates, and metadata, modify the GUI based on the received input data objects, wherein the GUI modification includes reconfiguring the GUI to modify display hierarchy of interactive elements based on the input data objects, and configure a display device, using the reconfigured GUI, to display modified interface components.

Disclosed herein are system, method, and computer program product aspects for generating a prompt for an LLM using a prompt template. A system retrieves an external data reference to context data included in a prompt template. The prompt template is selected from a plurality of prompt templates based on the field type of an input field in an interface. The system then generates a prompt based on the prompt template and the retrieved context data. The system then prompts the LLM accordingly, which generates a non-deterministic output for responding to the user request.

A query associated with an entity is received. The query is transformed into a generic data request that excludes proprietary information. The generic data request is transmitted to at least one external artificial intelligence model. External data responsive to the generic data request is received. The external data is combined with proprietary data associated with the entity to generate a response, and the response is provided to the entity.

Provide a system. 【Solution means】 Means for receiving user information and automatically collecting various event information through an electronic device, Means for analyzing the collected information and automatically setting importance levels, Means for generating an optimal schedule for an event based on the set importance level, Means for automatically changing or adjusting the schedule using external communication means based on the generated schedule, Means for predicting in advance the cancellation or postponement of an event using environmental information obtained from an external data source, Means for automatically notifying the user and enabling final confirmation of the schedule, Means installed in a home support machine for managing the schedule via voice and screen display, A system including.

The utility model discloses a high-density deployment noise monitoring micro-station in the technical field of noise monitoring, which comprises a capacitive sensor, a preamplifier, an ADC (analog to digital converter) circuit, an AGC (automatic gain control) circuit, an MCU (microprogrammed control unit) system and a wireless communication module. The pre-amplifier is used for amplifying an analog signal according to a certain multiple, the ADC circuit is used for the analog-to-digital conversion circuit and converting an analog electric signal into a digital electric signal, the AGC control circuit is used for the automatic gain control circuit, and the MCU system is used for processing, operating and storing received data and controlling an AGC amplification multiple and a communication mode. The wireless communication module is used for transmitting data processed by the micro-station MCU system through a long-distance wireless communication technology or receiving external data, low power consumption and miniaturization of the noise monitoring station are achieved through combination of all aspects, the noise monitoring station can be conveniently installed on any fixable places such as a wall, a trunk and an electric pile, and high-density deployment is achieved.

The invention provides an automatic external connection regression testing method and device, equipment and a storage medium, and belongs to the field of computer software tests.The method comprises the steps that historical external data are extracted from an external connection database in batches based on an external connection regression testing request input by a client interaction page; converting the historical external data into basic test data according to a preset rule based on test demand information input by the client interaction page; updating the basic test data into test sample data matched with the current interface of the external connection component; calling a logic code of the external connection component based on the JSP, analyzing the test sample data and forwarding the test sample data to the service component; and comparing a business result generated by the service component with an expected result, and generating a test report. Through the method provided by the invention, the limitation of a traditional scheme can be overcome, and the efficiency and quality of external connection regression testing are improved in an automatic testing mode.

Disclosed herein are embodiments of systems, methods, and products comprises an analytic server, which automatically manages appointment scheduling. The analytic server receives a customer request to schedule an appointment. The analytic server determines the required data from both customer and service provider for making the appointment. The analytic server retrieves customer data comprising requested service attributes, user preferences, users attributes from internal database and external data source. The analytic server retrieves service providers' data comprising provider service attributes, providers' attributes from internal database and external data sources. The analytic server accesses external data source by web crawling various websites. The analytic server executes an artificial intelligence model to predict user preferences and needs. The analytic server determines potential service providers best matching the customer's input or predicted preferences. The analytic server generates an appointment for each matching service provider and transmits an electronic message comprising the appointments to customer device.

A system is provided for artificial intelligence based predictive analytics of electronic user data. In particular, the system may comprise an artificial intelligence (“AI”) engine that continuously aggregates user data associated with a user and / or a group of users based on internal and external data sources across various different communication channels. The AI engine may then parse and tokenize the user data to generate a complete user snapshot associated with the user and / or the group of users. Based on analyzing the user data, the AI engine may generate a probability score associated with a predicted user action within the network environment. Based on the predicted user action and the probability score associated with the predicted user action, the system may drive decisioning processes within the network environment and / or generate one or more outputs to be presented on one or more user computing devices in and out of the network environment.