3692 results about "Data system" patented technology

The embodiment of the invention provides a network flow anomaly monitoring method and device based on a BiLSTM-Att network, and the method comprises the steps: obtaining network flow time sequence data, system resource use data and service call topological data, constructing a multi-modal time sequence feature matrix through employing a sliding time window method, calculating the importance score of each feature in the traffic feature subset, the resource feature subset and the service feature subset through an information gain algorithm, and screening the most discriminative feature based on a score threshold to construct a fusion feature subset; inputting the fusion feature subset into a set bidirectional long short-term memory-attention network, performing multi-level analysis on target features, performing comprehensive evaluation on a detection result by adopting a confidence-based multi-view decision fusion method, and generating a detection report containing an anomaly level, an anomaly type and an anomaly reason; according to the invention, the efficiency and accuracy of network flow anomaly monitoring based on the BiLSTM-Att network can be effectively improved.

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.

A system to detect, classify and track small objects in an image frame. The system includes an image data system and image analytic system. The image data system receives real-time image data from an image capturing system and inputs the image data to image analytic system. The image analytic system performs the object detection, classification and tracking of objects in an image frame using a combination of techniques.

The invention discloses a multi-modal entity recognition method based on a large language model. The multi-modal entity recognition method comprises the following steps: S1, constructing a multi-modal input data set; s2, determining and applying preprocessing methods of different modal input data through a self-adaptive modal interaction framework; s3, inputting the preprocessed multi-modal input data set into a large language model with a modal perception mechanism; s4, generating an optimized fusion feature representation; s5, performing cross-modal entity recognition based on the fusion feature representation; s6, automatically matching the identified entity with an existing knowledge graph through a dynamic mapping algorithm, and if the entity is a new entity, automatically generating a new node and updating an existing knowledge graph node relationship according to a self-adaptive modal interaction result; and S7, by continuously inputting new multi-modal data, the system automatically adjusts and optimizes the modal perception mechanism and the adaptive modal interaction framework of the large language model, and dynamically expands the knowledge graph. According to the invention, the accuracy of complex entity identification is 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 exhibition hall intelligent control system and method based on the Internet of Things technology, and the method comprises the following steps: collecting environment data in an exhibition hall in real time through a self-adaptive multi-mode sensor network, and dynamically adjusting the sampling frequency of a sensor through a multi-dimensional data sudden change monitoring algorithm by the system, the central processing module is combined with historical and real-time data, time sequence multi-dimensional regression analysis is adopted, an environment change prediction model is generated, then equipment control parameters are generated, the system dynamically adjusts the operation states of temperature equipment, illumination equipment and the like in the exhibition hall according to behavior data of visitors and the environment parameters, the experience of the visitors is optimized, and the experience of the visitors is improved. The intelligent anomaly detection module monitors the operation state of equipment in real time and identifies and isolates fault equipment in time, and the system preferentially guarantees equipment operation and reduces energy consumption when visitors are dense through a long-term energy consumption optimization learning algorithm, so that energy-saving management and long-term self-optimization of the exhibition hall are realized.

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.

The invention provides a wind power plant monitoring image automatic analysis and fault prediction method, system and device and a medium, and the method comprises the following steps: 1, collecting image data, sound data, vibration data, SCADA system data and environment data of a wind power plant, and forming multi-modal data; step 2, carrying out preprocessing on the obtained multi-modal data; 3, extracting features from the preprocessed multi-modal data, and fusing the features to obtain fused features; step 4, performing fan fault diagnosis and prediction based on the fused features to obtain a fault diagnosis and prediction result; according to the invention, end-to-end training of fault type identification and severity evaluation is realized, the accuracy of diagnosis and prediction is improved, and the workload of manual processing is greatly reduced.

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.

The invention provides an enterprise production data micro-service granularity optimization method, a medium and a system, belongs to the technical field of production data management, and establishes a micro-service granularity evaluation equation set by obtaining a dependency relationship and business flow data of micro-services in an enterprise production data system, constructing a coupling degree and cohesion degree matrix and establishing a micro-service granularity evaluation equation set. According to the method, a hybrid optimization algorithm is adopted, the search direction of a grey wolf algorithm and group evolution of a genetic algorithm are combined, and a composite fitness function containing system response time, resource utilization rate, maintenance cost and micro-service granularity balance degree is constructed. And continuously updating the micro-service division scheme through iterative optimization until the preset number of iterations is reached and the composite fitness function meets the optimization target, and finally outputting the optimal micro-service division scheme. The technical problem that the micro-service granularity is difficult to determine in the prior art is solved according to the division scheme of multiple indexes such as the maintenance cost and the granularity balance degree.

A system for generating highly personalized image content with generative artificial intelligence (AI) that incorporates brainwave data collected from a Brain-Computer Interface (BCI) device worn by the user while they are immersed in their experiences. The system can identify a mood (i.e., cognitive, mental, and emotional states) felt by the user during that time. The system develops a prompt that describes the target scene and modulates the weights assigned to each parameter in the prompt based on the mood identified. The generative AI engine can then to apply the mood to the target scene by adjusting image attributes such as hue, saturation, and lighting. The outputted image can offer a visual representation not only of the objects that were in the user's environment, but also the user's general perception and experience of the occasion. The process is passive and requires no interaction from the user to define their emotional affect.

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.

The invention relates to the technical field of computer storage, and discloses a computer memory data processing system, which comprises the following modules: an intelligent cache prefetching module, which is used for predicting a future memory access address based on deep learning and reinforcement learning technologies and loading data into a cache in advance; the heterogeneous storage management module is used for dynamically classifying the data based on the data popularity value and realizing migration of the data among different storage media; and the storage consistency guarantee module is used for managing the data consistency among different storage layers through the improved MES I protocol. Cache prefetching is optimized by introducing deep learning and reinforcement learning technologies, and by predicting a future memory access address in real time and loading data in advance, the system effectively reduces the cache missing rate, improves the cache hit rate, remarkably reduces the memory access delay, and improves the memory access efficiency. And the system can keep more efficient data access performance in a large-scale data processing scene.

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.

Systems and methods for generating communications based on user account activity data are described herein. For example, the system may obtain an input activity log and generate an activity log transformation. The system may input the activity log transformation into a vector encoding model to obtain a first output vector encoding. The system may input the first output vector encoding into a contrastive machine learning model to obtain a first matching vector encoding. The system may access a first plurality of vector encodings from a communication database. Based on comparing each vector encoding in the first plurality of vector encodings with the first matching vector encoding, the system may generate the matching communication.

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.