2591 results about "Data system" patented technology

Systems and methods for automatically generating and applying selected conditions to process data are provided. A system receives an application request is received, which includes one or more fields and one or more corresponding data items. The system also receives a configuration file from a configuration repository and processes the same to identify a plurality of rules. For each selected rule of the plurality of rules applicable to selected fields the one or more fields, the system evaluates the selected rules against selected data items associated with the selected fields. Based on the evaluating, the system determines a result of the application request and transmits the result for display.

Systems and methods for processing input data using a generic large language model (LLM) and a secondary LLM, whereby the secondary LLM is configured to process structured data. An application is provided, including a semantic kernel, a manager module, and a plurality of workers. An input is received via the semantic kernel. The manager module invokes the plurality of workers comprising a first worker and a second worker. The first worker invokes the generic LLM and the second worker invokes the secondary LLM.

The invention belongs to the technical field of virtual person generation, and particularly relates to a digital person video generation method based on a multi-modal large model, and the method comprises the following steps: 1, constructing a multi-modal data system; 2, multi-modal large model training and adaptation are carried out; 3, constructing a digital human three-dimensional model; step 4, performing semantic analysis and modal mapping; 5, generating a time sequence action and a mouth shape; step 6, building and rendering a virtual scene; step 7, audio and video synchronous rendering and synthesis; step 8, quality optimization and defect repair; and step 9, performing user interaction and iterative optimization. Through technical innovation and engineering, the core pain point in digital human video generation is solved, efficient, vivid and customizable content production capacity is provided for virtual anchors, intelligent customer service, enterprise training and other scenes, and the AI digital human technology is promoted to be applied to large-scale business from experiments.

Systems and methods for generating predicted end-to-end cyber-security attack characteristics via bifurcated machine learning-based processing of multi-modal data are disclosed. The system accesses multi-modal data indicating a set of security information related to a computing system. The system then generates a set of extracted characteristics indicating a cyber-security attack on the computing system, via a supervised machine learning model, using the multi-modal data. Using this information, the system generates a revised set of extracted characteristics indicating the cyber-security attack, via an unsupervised machine learning model, using the extracted set of characteristics indicating the cyber-security attack, where the revised set of characteristics includes at least one new characteristic that was not included in the extracted set of characteristics indicating the cyber-security attack on the computing system. The system then generates for display a graphical representation of the revised set of extracted characteristics.

The invention discloses a charging station operation maintenance method based on a multi-dimensional data system, and relates to the field of intelligent optimization, and the method comprises the steps: collecting the operation, environment, user and power grid data of a charging pile, preprocessing the data into JSON, carrying out the real-time analysis and classification through Kafka-Flume, storing the data into a distributed database, building a composite index, and carrying out the retrieval through Presto; after cleaning, combining GraphQL and a graph query language to construct a space-time atlas, and extracting a multi-dimensional feature vector; a digital twinborn model is generated, multi-source data are fused through virtual-real mapping, and residual correction and reinforcement learning optimization are combined; fusing multi-modal data by using a hypergraph neural network, and optimizing topology in combination with an entropy regularization multi-target strategy and meta-learning; power is dynamically distributed, LSTM-GAN predicts and positions faults, and PID regulates and controls output; and the block chain contract automatically triggers equipment freezing and power limiting, and on-chain data feedback optimizes scheduling and load balancing. According to the invention, through multi-dimensional data fusion and a dynamic optimization mechanism, the operation and maintenance accuracy and response efficiency of the charging station are significantly improved.

Systems, devices, apparatuses, and methods for more effectively protecting a user's private data and information regarding their browsing and other on-line activities. Embodiments remove constraints on an application developer by not requiring them to specify an authentication service provider as part of their application, and instead permit a user to make that choice in a protected and anonymous manner.

The invention relates to a coal mine safety risk intelligent management and control method, device and equipment and a medium. The method comprises the following steps: constructing a multi-source heterogeneous coal mine safety data system according to received data of a target coal mine park; the multi-source heterogeneous coal mine safety data system comprises static structure data, dynamic environment data, personnel behavior data and management data; constructing a coal mine three-dimensional space model according to the static structure data and the dynamic environment data; risk indexes in the dynamic environment data are extracted based on multi-algorithm fusion for evaluation, and a risk level is obtained; and if the risk level reaches a preset threshold value, triggering a corresponding linkage response mechanism, and forming a visual result in the coal mine three-dimensional space model. By the adoption of the method, closed-loop logic from sensing, evaluation to linkage treatment can be achieved, and the real-time performance, predictability and controllability of coal mine safety management are effectively improved through algorithm support of each stage and fine design of implementation details.

Systems and methods for translating natural language to SQL on a custom enterprise data warehouse powered by Generative AI. With an embodiment of the present invention, a natural language question may be converted to a meaningful and accurate database query, e.g., SQL query, relevant to tables existing in an enterprise data warehouse. An embodiment of the present invention is directed to a comprehensive approach of transforming a natural language query to a focused SQL query using domain specific data models across firmwide metadata systems and data systems. In response to a user query, an embodiment of the present invention performs metadata analysis, targeted data retrieval and then SQL generation. An embodiment of the present invention may apply data warehousing standards and guidelines followed in the enterprise and provide a plug-and-play type architecture and solution that is scalable to large warehousing and other systems.

Techniques for using a model to generate a response to a user input, where the response is associated with a personality determined to be relevant to the user input, are described. The system receives a user input and context data associated with the user input. Using the user input data and / or the context data, the system determines a personality (e.g., including a personality type and / or personality characteristics) relevant to the user input. The system generates a prompt instructing a model to generate a response to the user input that corresponds to the personality. The model processes the prompt to generate a response to the user input that corresponds to the personality. In some embodiments, the model generates a request for another component of the system to generate information responsive to the user input. The model may transform the responsive information into the personality-associated response.

In embodiments, systems and methods for configuring and deploying artificial intelligence driven transacting agents that are permitted to autonomously execute transactions on behalf of the individual or organization and configuring the transacting agent based on the agent configuration instructions and a set of predefined system prompts. The method further includes granting the transacting agent access to a digital wallet associated with the individual or organization and deploying the transacting agent to a public network, such that the transacting agent executes transactions on behalf of the individual or organization via one or more digital marketplaces using the digital wallet to which the transacting agent was granted access. In some embodiments, the transacting agent is granted access to the digital wallet using a consent token. In some embodiments, the method also includes hyper-personalizing and / or fine-tuning the agent.

The invention discloses an energy management multi-agent collaborative optimization method and system based on a large model agent architecture. The system comprises a multi-agent architecture module, a model processing module, a data acquisition and management module, a system integration and interaction module and a collaborative optimization control module. The model processing module comprises a large model and a plurality of small models and is used for realizing task processing and analysis; the data acquisition and management module acquires and processes multi-source data; the system integration and interaction module realizes multi-platform integration and visual interaction; and the collaborative optimization control module ensures collaborative work and system optimization of the intelligent agents. The system has the advantages of autonomy and decentralized control, isomerism and modularization, intelligent collaboration and the like, instrument faults can be accurately diagnosed, energy management is optimized, production is intelligently controlled, enterprise energy utilization efficiency, instrument operation stability and the overall operation management level are improved, enterprises are assisted to achieve green and low-carbon transformation, and the system is suitable for popularization and application. The method is suitable for energy management and energy equipment maintenance scenes of various industrial production enterprises.

An adaptive data system (ADS) for cognitive data processing is disclosed. The ADS includes an adaptive semantic preprocessor, a trigger detector, a temporal batching engine, a symbolic encoder, and a dynamic cognitive transformer engine. The adaptive semantic preprocessor is configured to receive input data from one or more databases and identify cognitive data attributes comprising one or more contextual, semantic, and temporal attributes from the received input data. The trigger detector is configured to identify semantic divergence of the identified cognitive data attributes and provide a standardized data. The temporal batching engine is configured to provide a high-dimensional cognitive data from the standardized data. The symbolic encoder compresses the high-dimensional cognitive data. The dynamic cognitive transformer engine is configured to determine decision making rules, analyze the compressed high-dimensional cognitive data based on the decision making rules and provide recommendations based on an outcome of the analysis to a user.

The invention relates to the technical field of data analysis, and discloses a data analysis system for multi-joint exercise rehabilitation monitoring. The system collects three-dimensional motion data, myoelectricity activity signals and spatial position data of multiple joints in real time through a multi-mode sensor array. The system comprises a data acquisition module, a preprocessing module, a space-time analysis module and a result output module. The preprocessing module is used for carrying out noise removal, normalization and feature extraction on the original data; the space-time analysis module constructs a monitoring model based on a graph space-time network, and analyzes space-time characteristics of multi-modal data through dynamic joint relation modeling by adopting an intelligent chip. And the result output module generates a visual report including exercise coordination evaluation, injury risk prediction and rehabilitation progress judgment, and feeds back the visual report to the rehabilitation equipment in real time. Besides, the system also has distributed storage and real-time feedback functions, the accuracy and efficiency of rehabilitation monitoring are improved, and the problems of single data processing and inaccurate spatial-temporal feature capture in the prior art are solved.

The present invention discloses a coal mine disaster simulation prediction system based on multi-dimensional data collection, which relates to the field of coal mine safety technology. A variety of monitoring equipment such as geological radar and microseismic monitoring are integrated to build a real-time monitoring network covering key areas of coal mines, and comprehensively collect multi-dimensional data. The system includes modules such as data preprocessing, model construction and training, prediction and decision-making, early warning and emergency response, and user interaction interface. Data is processed through data cleaning, filtering, standardization and fusion technology, and disaster prediction is performed using a CNN and LSTM hybrid model to output disaster probability and risk areas, and automatically trigger early warning and emergency plans. The user interaction interface displays real-time monitoring, prediction results and early warning information, providing a comprehensive and intelligent solution for coal mine disaster prevention and control.

Systems and methods are disclosed relating to distributed machine learning model resource allocation for building management systems. For example, a system can include at least one of an orchestrator or an optimizer to receive data regarding a workload to be performed using one or more machine learning models. The system can process the data to select one or more computing resources to deploy in order to perform the workload. The system can evaluate, using one or more trigger conditions, one or more characteristics of at least one of the data or the workload to determine to perform the workload.

A computerized system receives an original prompt that a querying user sends to a Large Language Model (LLM) that is operably connected to organizational data sources of an organization. Instead of executing the original prompt by the LLM, the system obtains user-related organizational context that pertains to characteristics of the querying user, obtains data-related organizational context that pertains to data from which the LLM is expected to obtain information for responding to the original query, and obtains pre-defined organizational policy rules, that indicate which type of users are authorized to access which type of organizational data. Based on the obtained data, the system modifies the original prompt into an adapted prompt. The system sends the adapted prompt, and not the original prompt, to the LLM for processing. The system obtains LLM-generated output from the LLM in response to the adapted prompt, and provides that LLM-generated output to the querying user.

The invention relates to the technical field of unmanned aerial vehicle application, and discloses a multi-mode electromagnetic field distribution surveying and mapping unmanned aerial vehicle system and an application method thereof. The system comprises a dynamic adjustment module, a multi-band acquisition module, a hybrid positioning module, a three-dimensional modeling module and a multi-modal fusion module. The dynamic adjusting module adjusts the distance between the electromagnetic sensor and the rotor wing according to the rotor wing electromagnetic interference suppression rule; the multi-band acquisition module acquires and processes electromagnetic signals; the hybrid positioning module integrates TDOA and AOA algorithms to position the radiation source; the three-dimensional modeling module generates a three-dimensional electromagnetic field intensity distribution diagram; the multi-modal fusion module fuses the visible light image and the electromagnetic data. The system can also evaluate the risk of the radiation source and plan a flight path. The application method comprises the steps of distance adjustment, data acquisition and processing, radiation source positioning, model construction and the like. According to the method, rotor interference is effectively suppressed, the positioning precision is improved, multi-modal data fusion is realized, and the method has a wide application prospect in the fields of electric power inspection, urban electromagnetic environment evaluation and the like.

Systems, methods, and computer-readable storage media for data protection. One system includes a data processing system including memory and one or more processors configured to determine one or more protection algorithms corresponding to one or more workstream elements of a workstream of one or more data systems. The processors are further configured to model one or more cybersecurity threats of the one or more data systems. The processors are further configured to map the one or more workstream elements of the workstream into the one or more cybersecurity threats based on one or more correlations between the one or more protection algorithms and the one or more cybersecurity threats. The processors are further configured to update a workstream element validation status of the one or more workstream elements based on the mapping of the one or more workstream elements.

The invention discloses an RFID anti-tracking bidirectional authentication method and system based on a lightweight hash chain, and relates to the technical field of wireless radio frequency identification security. The system comprises a label initialization module, a bidirectional authentication module, an anti-tracking mechanism module, a hash chain updating module, a secure communication module and a system feedback optimization module. The label initialization module generates initial hash chain data; the bidirectional authentication module adopts a bidirectional challenge-response mechanism to generate a dynamic authentication certificate; the anti-tracking mechanism module generates an untraceable session key through Hash chain value dynamic rotation and randomization mask technology; the Hash chain updating module iteratively updates the Hash chain value; the secure communication module adopts a lightweight encryption algorithm to protect communication data; and the system feedback optimization module dynamically adjusts the hash chain length and the key strength. The real-time performance and the anti-attack capability of the RFID system are remarkably improved, and a high-safety and low-power-consumption bidirectional authentication solution is provided for Internet of Things equipment.

The invention discloses a hydraulic system throttling control method based on a hydraulic control one-way valve, which relates to the technical field of throttling control, and comprises the following steps of: in an initialization stage of starting a hydraulic system, setting an optimal initial throttling opening for a throttling valve based on flow data of current oil; the method comprises the following steps: performing high-frequency real-time acquisition on flow state data of oil through a sensor deployed at the upstream of a throttling area, and establishing a set of flow state data system based on a time sequence; continuously collected flow state data are divided according to time windows, and an analysis set of oil flow data is constructed. According to the method, the dynamic flowing state of oil is monitored and deeply analyzed in real time, the load state is intelligently predicted in combination with a support vector machine model, and the opening degree of the throttling valve is adjusted in real time; the problems that in the prior art, response of a throttling control strategy to a high-load working condition is delayed, and oil temperature rise and viscosity abnormal reduction are caused by serious local pressure loss of a throttling area are effectively solved.

The invention belongs to the technical field of geological information processing, and discloses a geological mineral exploration data extraction system which comprises the following steps: constructing an air-ground-hole three-in-one acquisition network, and respectively acquiring earth surface mineral spectrum data, seismic longitudinal wave and transverse wave reflection data and rock core main quantity and trace element composition data; processing the collected data to form a five-dimensional data cube containing space coordinates, spectrum parameters, physical property parameters, element parameters and time indexes, performing physical layer attribute extraction on the five-dimensional data cube, performing analysis to obtain a wave impedance value and a lithology category probability array, performing semantic layer attribute extraction on the five-dimensional data cube, and performing semantic layer attribute extraction on the five-dimensional data cube; an alteration type probability array is obtained through analysis, an alteration zoning graph layer is generated, the mineralization potential index and a five-dimensional data cube are fused, a four-dimensional dynamic database is constructed, a mineralization potential prediction graph layer and a drilling priority graph are generated based on the four-dimensional dynamic database, and therefore the decision-making level of ore body drilling deployment is improved.

The invention relates to the technical field of new user data recommendation, in particular to an e-commerce personalized recommendation data system fused with deep learning, which comprises a data acquisition and processing unit, a dynamic monitoring unit, a GAN data enhancement unit and a recommendation service unit. The multi-modal data of users and commodities is comprehensively collected, features are accurately extracted, the dynamic monitoring unit monitors and predicts data changes in real time and generates adjustment signals by applying quantum state space and causal inference network technologies, and the GAN data enhancement unit generates high-quality simulation data based on a deep convolutional generative adversarial network by means of meta-learning, knowledge distillation and other algorithms. And the recommendation service unit is used for training a model according to the enhanced data, so that accurate recommendation is realized, the exposure degree of new commodities is improved, and the accuracy and efficiency of recommendation are improved.

The invention relates to the field of Web application, in particular to a tree structure data dynamic management method based on node hierarchical attributes and recursive updating. Comprising the following steps: S1, constructing a tree structure data system, and endowing each node with a hierarchical attribute which is used for identifying position information of the node in a tree structure; s2, receiving an operation instruction of a user for the tree structure data, wherein the operation instruction comprises node adding, node batch deleting, node moving, data exporting and importing, node querying and node quantity counting; and S3, according to different operation instructions, executing corresponding processing logic: based on node hierarchy attributes, applying a recursive update mechanism to realize dynamic management of tree structure data, and ensuring data consistency and accuracy.

Systems and methods for generating simulation data based on real-world environments are provided. A method includes obtaining multi-modal sensor data indicative of a dynamic object within an environment of a robotic platform. The multi-modal sensor data is associated with a plurality of timesteps including a first timestep and a second timestep. The method includes providing the multi-modal sensor data indicative of the dynamic object within the environment as an input to a machine-learned dynamic object removal model. And, the method includes receiving as an output of the machine-learned dynamic object removal model, in response to receipt of the multi-modal sensor data, a scene representation indicative of at least a portion of the environment including a reconstructed region based at least in part on removal of the dynamic object and multiple levels of granularity. The scene representation is used as a template for generating different simulations within the depicted environment.

The invention relates to the technical field of transformer substation intelligent patrol, and provides a transformer substation three-dimensional fusion patrol method and system based on digital twinborn and autonomous identification. According to the method, a fused three-dimensional model is constructed through multi-source data acquisition and a three-dimensional Gaussian splash algorithm, and in combination with deep learning-based point cloud semantic segmentation and clustering, an equipment-patrol means coverage relationship is generated. Creating a virtual inspection proxy object based on a three-dimensional virtual environment, and controlling terminals such as an unmanned aerial vehicle to collect real-time video image data; the system carries out automatic identification on pictures, automatically completes equipment level alignment and standard point location identification, generates fine control of camera zooming, horizontal rotation, pitching and the like, and realizes standardized view finding and acquisition. By combining an enhanced recognition algorithm, traditional image processing and a deep learning model are fused, model self-evolution is realized through incremental learning, flexible expansion and collaboration of various patrol terminals are supported through a unified interface, and refined, real-time and intelligent patrol operation and maintenance requirements of an intelligent substation are met.

A secure digital network environment is provided by integrating OTP keys as part of quantum-safe data systems solutions (QPN Solutions), including the use of one-time-pad (OTP) keys to encrypt data, support multi-factor authentication and secure all communications between devices in the secure digital network environment. The OTP keys are “pre-loaded” to endpoint (EP) devices to render them quantum-safe (QS) when connected into the secure digital network environment, or are otherwise provided through removable media to be loaded into user supplied appliances, devices and accessories to render them QS when connected into the secure digital network environment. The application of QPN Solutions refers to the application of QPN enabled technologies to provide a secure digital network environment includes risk assessment and management solutions for establishing and managing cyber security insurable risks and policies.

The invention belongs to archives, and particularly relates to an investment and financing project archive evidence intelligent management platform based on big data, which comprises the following steps: step 1, a system uploads and preprocesses archives through an upload and storage module, and converts project files in different formats into structured data; 2, a data processing and intelligent analysis module automatically extracts key information and performs archive classification, data matching and compliance review by using natural language processing and a machine learning algorithm on the basis of data of the uploading and storage module; 3, the system carries out automatic examination based on the compliance model, an evidence chain meeting legal requirements is generated, real-time risk assessment is carried out, and when potential risks are recognized, the system triggers early warning and provides suggestions; step 4, in combination with risk early warning and legal examination data, a system workflow and task scheduling module dynamically schedules a workflow according to a task priority and a dependency relationship; and 5, providing decision support by the system through a user interface, and helping a manager to make a precise decision.

Systems and methods for player modeling using captured multimedia data. A system can include one or more processors to receive, via an interface, digital content including at least one captured media segment corresponding with at least one player. The one or more processors can apply the digital content as input to at least one artificial intelligence (AI) model to generate a player profile including a sport-specific textual representation of the player corresponding to a plurality of performance parameters, and the AI model can be configured based at least on sport vernacular and historical labeled digital content of a sport of the player. The one or more processors can generate a structured data package including the player profile based on organizing at least a portion of the sport-specific textual representation and provide the structured data package to a digital application or a scouting computing system.

The invention provides a 5G portable WiFi fault diagnosis and recovery method and system. The method comprises the following steps: collecting network connection data, system information and operation logs from a communication terminal connected to 5G portable WiFi equipment; analyzing the data, and generating a fault diagnosis report; selecting and executing corresponding recovery operation from a recovery scheme library according to the report; monitoring equipment state and network connection in real time, and judging whether recovery is successful or not; if the fault is not solved, an advanced solution is provided, and equipment information, program attributes, scheme library information, network connection data, system information and operation logs are sent to an edge server; the edge server obtains fault data and diagnosis reports of a plurality of similar devices as references, potential fault laws are mined through big data analysis, and reference analysis data are generated; and optimizing an intelligent diagnosis program and a recovery scheme library according to the reference analysis data and other information. The fault type can be accurately identified, the fault recovery time is remarkably shortened, and the user experience is improved.

The invention belongs to the technical field of network security, and particularly relates to a network security detection method based on artificial intelligence, which comprises the following steps of: acquiring network flow data, system log data, user behavior data and external threat intelligence data, generating a multi-source heterogeneous data set, preprocessing the multi-source heterogeneous data set, and acquiring a multi-source heterogeneous data set; comprising the steps of data normalization, missing value filling and noise filtering, feature extraction is conducted on preprocessed data through a multi-modal fusion model, extracted multi-modal features are input into a mixed detection engine, the detection rate of zero-day attacks can be increased to a high level through multi-modal data fusion, and compared with the prior art, the detection rate of zero-day attacks is increased. The detection rate of a traditional method has obvious advantages in the aspect of coping with novel threats, and meanwhile, the false alarm rate can be reduced below a normal value in the aspect of false alarm rate control, so that the burden of safety personnel for handling invalid alarms is greatly reduced, and the situation that a large number of false alarms cause waste of manpower and material resources and possibly cause the fact that real safety threats are neglected is avoided.