5392 results about "Data processing system" patented technology

The invention discloses a transformer area intelligent fusion terminal data processing system based on edge calculation, relates to the field of edge intelligence, and solves the problems of poor real-time performance and poor data exception processing accuracy of an existing intelligent fusion terminal data processing system. According to the method, communication between micro-services is efficiently managed through the container arrangement engine and the service grid, the message transmission delay is reduced, and resource allocation is further optimized through a resource scheduling algorithm based on reinforcement learning, so that system resources are used most reasonably; according to the method, anomaly detection is rapidly and accurately carried out through a space-time heterogeneous fusion network model, hierarchical processing of data and on-cloud training of the model are realized through a cloud-edge collaborative architecture, edge nodes participate in model training through a federated learning mechanism, the model performance is optimized, the transmission time of the data in the network is shortened, and the problem positioning speed is increased; the real-time performance and the reliability of an existing system are greatly improved.

A federated distributed AI reasoning and action platform utilizing decentralized, partially observable hierarchical computing for neuro-symbolic reasoning. It features a federated Distributed Computational Graph (DCG) system integrating core components like pipeline orchestration, transformers, and marketplaces. The platform enables privacy-preserving dynamic resource allocation, intelligent task scheduling, and variable information sharing across diverse computing environments. By coordinating with an AI-based operating system and analyzing performance metrics, environmental conditions, and resource availability, the system optimizes efficiency across AI workloads and decision-making processes. This results in an adaptive, power-efficient, and scalable AI-enabled data processing system capable of handling complex tasks while maintaining peak performance under various operating conditions.

The invention discloses a geographic information big data-oriented multi-source data processing system, and relates to the technical field of data acquisition and sensors, and the system comprises a data acquisition module which accesses a remote sensing satellite, an unmanned aerial vehicle, an Internet of Things sensor and social media in real time through a multi-source heterogeneous interface, and carries out the adaptive analysis of a data format and metadata marking; the distributed storage module is used for performing partition storage on the geographic information data based on a space-time database and an object storage architecture, and establishing dynamic space-time index and version control; the data fusion module is used for realizing coordinate system conversion, time sequence calibration and semantic knowledge graph matching based on a multi-source data alignment method of dynamic weight distribution; and an intelligent analysis module and a security control module. According to the method, core pain points such as data splitting, low storage efficiency, extensive analysis and compliance risks in the geographic information field are systematically solved, and a full-stack type technical base is provided for scenes such as smart cities, emergency disaster relief and environment monitoring.

Systems and methods are described for a implementing a streaming data processing system that includes a pool of pre-configured resources and a pool of dedicated resources. The streaming data processing system can implement a processing pipeline using compute resources. The pool of pre-configured resources can support previews of processing pipelines for a plurality of users and the pool of dedicated resources can support full deployments of processing pipelines for a particular user. The streaming data processing system can implement a preview of a processing pipeline using a pre-configured resource of the pool of pre-configured resources. Further, the streaming data processing system can implement the processing pipeline using a dedicated resource of the pool of dedicated resources. The streaming data processing system can provision the dedicated resource and deploy the processing pipeline using the dedicated resource.

A data processing system comprising: inputting a tiled image of a vehicle including four different angle views of the vehicle combined into a single image to a first machine learning model (e.g. CNN), the model trained based on historical image data to predict a first likelihood of total loss vehicle; inputting a multi-fusion of images each into a second set of machine learning models; the multi-fusion of images including a set of separate and distinct images for each of the views input separately into the second set of machine learning models, and extracting features to predict a second likelihood of total loss vehicle; inputting tabular data relating to the vehicle into a third machine learning model to predict a third likelihood of total loss vehicle for the vehicle; and aggregating the first, second and third likelihood of total loss vehicle to determine the overall likelihood of total loss.

The invention discloses an intelligent driving multi-sensor fusion data processing system, which belongs to the technical field of fusion data processing and comprises a data preprocessing unit, a data fusion unit and a data post-processing unit. The data preprocessing unit normalizes the original data of the laser radar, the camera, the millimeter wave radar and the ultrasonic sensor through a standardization, cleaning, noise reduction and calibration synchronization module; the feature level fusion module deeply digs the characteristics of each sensor, and fuses geometric, visual, motion and close-range obstacle features by using a deep neural network; the decision-making level fusion module generates a driving instruction through weighted voting and fuzzy logic reasoning through classification and situation evaluation; and the data post-processing unit combines the vehicle and road condition optimization instruction, evaluates the risk, and stores the data feedback optimization system. The system can improve the sensing precision and decision reliability, enhances the expansion adaptability of the system, and provides guarantee for safe and efficient operation of intelligent driving.

The invention discloses a point cloud data rapid processing system based on 3D vision, and relates to the technical field of point cloud data processing. The binocular acquisition module generates an anti-interference high-precision point cloud; the preprocessing module is used for reducing noise and dimensionality and retaining key geometric features; the hierarchical feature extraction module fuses curvature weight and a non-maximum suppression strategy, and improves the robustness of an ORB algorithm in rotation, scale and noise scenes; the industrial scene segmentation module accurately separates stacked objects through normal vector clustering and a dynamic region growing algorithm, and secondarily verifies boundaries in combination with a lightweight semantic model; the two-stage registration module dynamically adjusts a threshold value based on error feedback to realize pose optimization from coarse registration to fine registration; the coordinate mapping module establishes a space mapping model through multi-attitude calibration and robot multi-axis linkage trajectory optimization. The problem that in the prior art, a 3D machine vision algorithm is high in time delay is solved, the real-time performance and rapidity of 3D model recognition and feature extraction are improved, and the efficiency requirement of industrial production is met.

The invention relates to a remote education data processing system which comprises the following steps: under a remote teaching task, pre-defining a task intention and a data expectation point; a semantic timestamp and a task binding label are printed on each data fragment; mapping the collected confusion data including silence, eye movement drift and prediction into a unified learning semantic vector; a micro-expression + interactive behavior + time sequence decision path ternary modeling mode is introduced, and a potential cognitive intention corresponding to the feature combination is recognized; teaching context information is fused; constructing a cognitive state mapping model; reasoning a current cognitive state label from multi-modal sensing data; searching intervention track VS effect feedback data in a historical database; generating a predicted intervention behavior sequence by using a sequence modeling algorithm; a dynamic combination suggestion chain including light prompt, content reconstruction, personalized practice and tutoring invitation is adopted; and superposing the cognitive state sequences of all students into a group cognitive trajectory map.

The invention relates to the technical field of AI interaction data processing, and discloses an AI interaction data processing system based on multi-modal perception and dynamic decision, comprising the following modules: a multi-modal perception module used for collecting environment data through a multi-source sensor; the data fusion module is used for generating fused feature data; the causal decision-making module is used for generating a decision-making action to cope with the change of the environment; the decision security module is used for identifying potential safety hazards in the high-risk scene and generating alternative decision or early warning information; the sensing calibration module is used for optimizing a sensing strategy in a changing environment; and the adaptive optimization module continuously optimizes the perception and decision strategy. According to the invention, the multi-modal sensing module is combined with a cross-modal consistency learning mechanism and a noise robustness enhancement technology, and the data fusion module introduces a context sensing attention mechanism and a multi-level feature alignment network, so that the sensing ability of the system to complex environment information and the comprehensiveness and accuracy of feature representation are effectively improved.

A federated distributed AI reasoning and action platform utilizing decentralized, partially observable hierarchical computing for neuro-symbolic reasoning. It features a federated Distributed Computational Graph (DCG) system integrating core components like pipeline orchestration, transformers, and marketplaces. The platform enables privacy-preserving dynamic resource allocation, intelligent task scheduling, and variable information sharing across diverse computing environments. By coordinating with an AI-based operating system and analyzing performance metrics, environmental conditions, and resource availability, the system optimizes efficiency across AI workloads and decision-making processes. This results in an adaptive, power-efficient, and scalable AI-enabled data processing system capable of handling complex tasks while maintaining peak performance under various operating conditions.

A data processing system implements receiving a call requesting a generative model to generate a semantic tree for a source content; constructing a first prompt including the source content and instructions to the model to analyze a semantic structure of the source content and to generate a semantic outline and content chunks of the source content, the semantic outline including one or more topics each connected with one or more of the content chunks, to compute one summary for each of the content chunks, to apply indices to reference each topic node of the semantic tree to one of the topics, and to apply indices to reference each leaf node of the semantic tree to one of the content chunks and the respective summary; providing the first prompt to the model and receiving the semantic tree of the source content; and storing the semantic tree in a database.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses an intelligent optimization method, device and equipment for query statements and a medium. The method comprises the steps of recording execution plan information and resource consumption information of an original query statement, generating original execution information, preprocessing the original query statement and the original execution information to generate preprocessing data, extracting feature information from the preprocessing data, generating optimization information through a pre-trained optimization model based on the feature information, and sending the optimization information to a server. And according to the optimization information, adjusting the content or execution mode of the original query statement, and generating an optimization query instruction. According to the method, the feature information is extracted by monitoring the actual query behavior and combining the execution performance, and the optimization strategy is generated by utilizing the training model, so that the automatic optimization of the original query statement is realized, the manual intervention cost is reduced, and the intelligent adaptation capability of the query performance is improved.

A system includes a housing and a first circuit board positioned inside the housing. The housing has a top panel, a bottom panel, a left side panel, a right side panel, a front panel, and a rear panel. The front panel is at an angle relative to the bottom panel in which the angle is in a range from 30 to 150°. The first circuit board has a length, a width, and a thickness, in which the length is at least twice the thickness, the width is at least twice the thickness, and the first circuit board has a first surface defined by the length and the width. The first surface of the first circuit board is at a first angle relative to the bottom panel in which the first angle is in a range from 30 to 150°. The first surface of the first circuit board is substantially parallel to the front panel or at a second angle relative to the front panel in which the second angle is less than 60°. The system includes a first data processing module and a first optical interconnect module both electrically coupled to the first circuit board. The optical interconnect module is configured to receive first optical signals from a first optical link, convert the first optical signals to first electrical signals, and transmit the first electrical signals to the first data processing module.

The described aspects and implementations enable efficient calibration of a sensing system of a vehicle. In one implementation, disclosed is a method and a system to perform the method, the system including the sensing system configured to obtain a plurality of images associated with a corresponding time of a plurality of times. The system further includes a data processing system operatively coupled to the sensing system and configured to generate a plurality of sets of feature tensors (FTs) associated with one or more objects of the environment depicted in a respective image. The data processing system is further to obtain a combined FT and process the combined FT using a neural network to identify one or more tracks characterizing motion of a respective object.

A data processing system for providing a service to extract information from a resource includes: a network interface for communicating over a computer network; a scraper tool to receive user instruction specifying a target resource and to extract content from the specified resource, wherein the user instruction further specifies a desired restructuring of the extracted content; and a prompt generator to structure the extracted content into a prompt for an Artificial Intelligence (AI) model, the prompt further directing the AI model to restructure the extracted content based on the user instruction. The prompt generator is to call the AI model with the generated prompt. The service is to receive restructured content from the AI model and provide the restructured content to a workstation submitting the user instruction, the restructured content presenting the content of the target resource in a form according to the user instruction.

A data processing system implements obtaining build logs that include information associated with a software build problem; analyzing the logs to generate a knowledge graph identifying the relationship between various entities in the logs; extracting a signature of a candidate root cause of the build problem from the knowledge graph representing a subset of nodes and edges of the knowledge graph; providing the signature of the candidate root cause to a graphical language model to obtain a prediction of a category of root cause failure selected from among a plurality of root cause failures; constructing a prompt for a language model to generate a root cause failure analysis that describes the root cause of the build problem, the prompt including the category of root cause; receiving the root cause failure analysis from the language model; and performing one or more actions in response to receiving the root cause failure analysis.

Methods and systems for managing operations of a data processing system are disclosed. To manage operations of the data processing system, a management controller of the data processing system may obtain validated location data for the data processing system via an out-of-band communication channel. The management controller may identify applicable policies based on the validated location data. If the management controller identifies a policy applicable to a geographical location of the data processing system, then the management controller may perform an action set to manage operation of the data processing system based on instructions associated with the applicable policy. The updated operation of the data processing system may increase the security of sensitive data managed by the data processing system.

The invention relates to the technical field of medical data processing systems, in particular to an artificial intelligence-driven medical diagnosis and treatment data processing method and system. The method comprises the steps that a multi-modal medical data acquisition module acquires and processes multi-source heterogeneous medical data of a patient, and a standardized data set is generated; a medical feature depth extraction module performs multi-dimensional feature extraction on the data set, and constructs a dynamic evolution feature matrix; a multi-dimensional health state space construction module constructs a patient health state multi-dimensional space according to the matrix and determines a key medical early warning index set; the real-time medical data fusion module maps real-time data to the space to generate real-time health risk factors; and the medical risk prediction and decision-making module establishes a personalized model, outputs a disease occurrence probability and generates personalized treatment suggestions. The system solves the problems that medical data processing is difficult, diagnosis analysis is not comprehensive, and a treatment scheme lacks personality, and diagnosis accuracy and treatment pertinence are improved.

A data processing system implements receiving, via a user interface of a client device, a query to a large language model (LLM) working in conjunction with an embedding model comprising a corpus of artifacts. The system further implements converting the query into a query embedding, transforming the query embedding into a transformed query embedding using a first transformation function that adds a first residual term to the query embedding, the first residual term being specific for a task associated with the query, measuring a first similarity between the transformed query embedding and corpus element embeddings in an embedding space of the embedding model, determining first top-rated corpus element(s) associated with the corpus element embeddings based on the first similarity, incorporating the first top-rated corpus element(s) into the query as a prompt to the LLM to generate a response to the query, and providing, via the user interface, the response.

Smart surveillance data processing systems support safe uncrewed aircraft system (UAS) or uncrewed surface vessels / vehicles operations where remote pilots are operating their uncrewed aircraft beyond visual line of sight (BVLOS) or near controlled areas, by providing: (i) real-time situational awareness in dense crewed aircraft and uncrewed drone environments while providing automated detection and alerting of intruders to remote pilots in command; (ii) surveillance network performance assessments through radar and ADS-B quantifiable measures; (iii) detection and alerting of any appreciable degradation of surveillance sensor performance, allowing remote pilots to suspend or alter operations to ensure safety; (iv), standardized airspace reporting including traffic patterns; (v) certification of portions of the airspace where the surveillance network is capable of meeting a minimum standard; (vi) assessment and reporting of drone compliance and non-compliance with UAS operating regulations.

A data processing system implements receiving, via a user interface of a client device, images for generating a collage image; generating captions for the images; constructing a first prompt by appending the captions to a first instruction string including instructions to a generative language model to extract a theme from the captions; providing the first prompt to the generative language model and receiving the theme therefrom; constructing a second prompt by appending the theme to a second instruction string including instructions to a text-to-image model to use the theme to create a background image with placeholders; providing the second prompt to the text-to-image model and receiving the background image therefrom; identifying the placeholders in the background image; creating the collage image by fitting the images into the identified placeholders; providing the collage image to the client device; and causing the user interface to display the collage image.

Methods and systems for managing a data processing system based on location data are disclosed. A management controller of the data processing system may provide the location data via an out-of-band communication channel to a geolocation management server tasked with mapping a location of the data processing system over time. The data processing system may identify an occurrence of an event based on the location data (e.g., a change in location of the data processing system), prompting the data processing system to perform an action set to update operation of the data processing system (e.g., to conform to location-based policies). Based on its updated operation, the data processing system may provide computer-implemented services that are more likely to be in compliance with local policies.

A data processing system implements receiving, via a user interface, a first request for first content to be generated by a generative model, the first request including a first prompt describing the first content associated with output value(s) of a statistical test; executing a query on expert knowledge source(s) to obtain statistical test parameters; constructing, based on a portion of the parameters using a prompt construction unit, prompt(s) to query a user for values associated with the parameters; receiving, via the user interface, response(s) to the prompt(s); retrieving historical data associated with the statistical test; providing the parameter value(s) and the historical data to a calculation tool to generate output value(s); constructing a second prompt as an input to the generative model, by appending at least the prompt(s) and the response(s) with an instruction string comprising instructions to generate an interpretation of the output value(s) as the first content.

The invention relates to the field of cloud computing and distributed computing, and discloses a cloud data processing system based on an artificial intelligence algorithm, which comprises a data input module, a task decomposition module, a resource scheduling module, a parallel computing module and a result reconstruction module, the data input module receives high-dimensional data and converts the high-dimensional data into a tensor structure; the task decomposition module decomposes the tensor, generates a core tensor and an orthogonal matrix, and generates a plurality of sub-tasks; the resource scheduling module is used for carrying out resource allocation and scheduling on the sub-tasks, and task execution time and resource consumption are minimized; the parallel computing module distributes subtasks to the computing nodes for execution, and the task execution sequence is guaranteed; and the result reconstruction module combines and verifies the execution results of the subtasks to generate a final calculation result. The cloud data processing efficiency and the resource utilization rate are effectively improved, the accuracy and consistency of calculation results are ensured, and the method is suitable for the fields of artificial intelligence training, large-scale data analysis, scientific calculation and the like.

The invention discloses a multi-modal data processing method and system, computer equipment and a readable storage medium, which can realize deep association and complementarity mining of multi-modal information and improve the accuracy and robustness of multi-modal understanding. The method comprises the following steps: an environment sensing module adjusts an environment sensing strategy according to feedback information transmitted by a self-adaptive decision module, and acquires multi-modal data according to the environment sensing strategy; the multi-modal encoding module encodes the multi-modal data into multi-modal feature vectors of the same dimension; a cross-modal fusion module fuses the multi-modal feature vectors to obtain fusion features; the self-adaptive decision-making module selects a decision-making network matched with the task type from a predefined network library according to the task type of the current decision-making task, inputs the fusion features into the decision-making network, and generates feedback information according to the decision-making process of the decision-making network; and the meta-learning controller evaluates the system performance of the current multi-modal data processing system and adjusts system parameters according to an evaluation result.

Methods and systems for managing update events for data processing systems are disclosed. An update event may indicate that an update configuration is available for a data processing system. A management controller for the data processing system may utilize an out of band communication channel to obtain the update configuration for the data processing system without hardware components of the data processing system being powered. The update configuration may indicate a combination of hardware and / or software components for an application hosted by the data processing system. The update configuration may be assigned by an administrator and may be implemented by an update agent for the data processing system upon powering the hardware resources of the data processing system.

Methods and systems for managing data processing systems are disclosed. To manage a data processing of the data processing systems, the data processing system may be placed in a first operating state. To do so, first log in credentials may be obtained from a first user and may be used to obtain a first user persona from a server. The first user persona may be obtained via an out of band communication channel and may include a first set of configurations for the data processing system. The first set of the configurations may indicate preferences for operation of the data processing system associated with the first user. At least a portion of the first set of the configurations may be implemented by hardware resources of the data processing system and, therefore, computer-implemented services may be more likely to be provided as desired by the first user.

The invention relates to the technical field of environment monitoring, solves the problem that a monitoring system and method cannot meet the requirements for accuracy and real-time performance of animal behavior research and climate environment research in a plateau area at the same time, and particularly discloses a monitoring system and method integrating environment monitoring and animal monitoring recognition. Comprising a sensing system, a data transmission system and a data processing system, the sensing system is used for collecting environment and animal data of a monitoring site, and the data processing system is used for receiving and processing the data and analyzing the change trend and the correlation influence degree of the environment data and the animal data; the monitoring method comprises the following steps: acquiring environment and animal data through the environment monitoring network and the animal behavior sensing network; data is transmitted to the data processing system in real time through the data transmission system; the data processing system analyzes the dynamic change trend of the environment data and the animal data; and generating early warning information and triggering a protection measure execution instruction. The method can be used for monitoring the plateau environment and animals, and is high in accuracy and high in real-time performance.

Methods and systems for managing a data processing system are disclosed. The data processing system may provide computer-implemented services and may include in-band hardware resources that operate as specified by operation data of the data processing system while providing the services. However, if the operation data becomes unavailable to the data processing system, then the data processing system may be unable to provide the desired services. Therefore, to increase the likelihood of providing the desired services, the operation data may be managed using distributed backup and recovery processes. To decrease security risk associated with operation data backup and recovery, the distributed processes may be performed using out-of-band components of the data processing system, bypassing in-band hardware components that may be more vulnerable to being compromised (e.g., by a malicious party). The out-of-band management of the operation data may reduce the likelihood of interruptions in providing the desired services.

Methods and systems for managing a data processing system are disclosed. To manage the data processing system, a management controller may be included in the data processing system. The management controller may perform managerial functions for the data processing system regardless of whether in band components are compromised, depowered, and / or otherwise non-functional. In addition, to communicate with remote entities, the management controller may utilize a network module of the data processing system. The network module may direct network traffic between in band and out of band communication channels and may operate independently from hardware resources of the data processing system.