3184 results about "Configuration system" patented technology

Technology disclosed herein includes a prompt engineering service that integrates artificial intelligence with the programming systems of an industrial automation environment to design a system of the industrial automation environment. The interface service leverages the capabilities of a large language model (LLM) trained on industrial automation workflows to provide accurate and relevant system design information. For example, the interface service receives system configuration data and generates a first prompt requesting a category associated with the system configuration data. The interface service uses the first prompt to generate a response from the LLM. The interface service generates a second prompt requesting a user interface message for offering assistance to configure the system based on the category. The interface service uses the second prompt to generate the user interface message and displays the message in a user interface.

The invention provides a Python-based automatic system configuration and monitoring method and system, and relates to the technical field of automatic system configuration and monitoring, and the method comprises the steps: constructing a multi-level service dependence model and a service node affinity matrix, generating a configuration parameter priority queue, and completing the initial configuration of a system through a distributed transaction coordinator. When the system runs, the monitoring agent program collects system running data, calculates a node health degree index and shares the node health degree index through the net-shaped sharing channel. And the core node performs data association analysis and updates a node load value and a health degree index. And when the node health degree is lower than a preset threshold value, triggering an alarm processing module, selecting an optimal node based on a node health state evaluation report to execute reconfiguration, updating a service registration center and broadcasting a configuration updating message. According to the invention, automatic system configuration and monitoring can be realized, and the system reliability and stability are improved.

The invention provides a modular power supply system configuration method and system for a micro-module machine room, and relates to the technical field of electric power, and the method comprises the steps: collecting power utilization state data and load distribution data of functional equipment in the micro-module machine room, building a power supply network topology model, dividing a power supply system into a first-stage load, a second-stage load and a third-stage load, an independent power supply loop and a data acquisition unit are respectively configured in the first-stage load, the second-stage load and the third-stage load; a power supply network topology model is updated according to the power utilization data acquired by the data acquisition unit, a reinforcement learning algorithm is adopted to generate an adaptive power distribution strategy, and a modular energy storage unit is arranged to provide a standby power supply for a primary load and a secondary load; and constructing a machine learning fault prediction model based on the power consumption data, and dynamically adjusting a power supply path and a power distribution scheme of a power supply loop.

Systems and techniques for generating and animating non-player characters (NPCs) within virtual digital environments are provided. Multimodal input data is received that comprises a plurality of input modalities for interaction with an NPC having a set of body features and a set of facial features. The multimodal input data is processed through one or more neural networks to generate animation sequences for both the body features and facial features of the NPC. Generating such animation sequences includes disentangling the multimodal input data to generate substantially disentangled latent representations, combining these representations with the multimodal input data, and using a large-language model (LLM) to generate speech data for the NPC. Further processing using reverse diffusion generates face vertex displacement data and joint trajectory data based on the combined representation and generated speech data. The face vertex displacement data, joint trajectory data, and speech data are used to produce an animated representation of the NPC, which is then provided to environment-specific adapters to animate the NPC within a virtual digital environment.

Accurate prompting improves the operation, efficiency, and output of computer models, such as language models. The disclosed systems and methods improve interaction with computer models by facilitating the generation of accurate prompts for targeted interactions with computer models. For example, disclosed systems can be configured to store prompts and responses generated with computer models in session memories. The system can use information in session memories to generate combined prompts when receiving prompts that refer to previous prompts or answers. The system improves prompt accuracy by generating combined prompts—formed by combining the context from the stored information (e.g., in context sub-prompts) and instructions in the new prompt (e.g., in instructions sub-prompts). The system can generate responses based on the combined prompts allowing the computer models to leverage the context in previous interactions, without burdensome or complicated prompts, for more meaningful or accurate responses.

A system for automatically self-labeling a digital dataset includes a first sensor for generating a first data stream, a second sensor for collecting information to generate a second data stream, and a causal model manager (CMM). The CMM is configured to determine a first causal event from a first data segment of the first data stream, and a causal relation between the first causal event and a second data segment selected from the second data stream. The system further includes (a) an interactive time model for determining an interaction time between the first and second data segments, and (b) a self-labeling subsystem configured to derive a label from the second data segment, associate the first data segment with the derived label, form a self-labeled data pair from the associated first data segment and the derived label, and automatically annotate the self-labeled data pair with the interaction time.

The invention discloses an automatic configuration method and system for an electric energy quality monitoring terminal supporting multiple protocols, and the method comprises the steps: obtaining a communication data packet through the establishment of communication connection between a terminal and a configuration system, and carrying out the protocol recognition through the combination of statistical characteristics and structural characteristics. According to the method, the hierarchical decision tree is innovatively adopted to identify the protocol characteristics, protocol family identification and specific protocol type determination are realized through the preset protocol mapping template, and a new protocol can be automatically processed. The system comprises a communication module, a feature extraction module, a protocol identification module, a configuration instruction module and an execution module, and all the modules work cooperatively to achieve automatic configuration of the terminal. The technical problems that the power quality monitoring terminal is difficult to configure in a multi-protocol environment, the protocol identification efficiency is low, and the new protocol adaptation is complex are solved.

A method and edge computing system configured to generate or process extended reality (XR) data. Processors in the edge computing system receive, process, and analyze a sensory feed from an optical device to generate analysis results that include a relative position of the optical device from surrounding objects. The processors generate mapper output results (that include virtual coordinates) based on the analysis results, request and receive information (e.g., salient points of interest, etc.), and compare the generated mapper output results to the received information to identify a correlation between a feature included in the processed sensory feed and a feature included in the received information. The processors generate and send augmented information to a renderer and / or send the processed sensory feed to a cloud object recognizer for further processing.

The invention discloses a self-adaptive intelligent symbiotic data ecosystem and a cooperative implementation method thereof. The system comprises an intelligent agent ecosystem module, a self-adaptive domain knowledge graph module, a self-adaptive workflow engine module, a CLID configuration system module, a man-machine cooperation interface module, a data processing pipeline module and an innovation trigger module. Through cooperation of diversified intelligent agents, the dynamic knowledge graph, the self-adaptive workflow and CLID multi-dimensional configuration, high-flexibility and self-adaptive data processing and analysis capacity is achieved. The system can automatically construct a complex workflow, perform multi-modal data fusion, and integrate human judgment and AI capabilities at key points, thereby providing an innovative solution. The method is suitable for complex and dynamic big data processing and analysis scenes, and the data processing efficiency and innovation ability are remarkably improved.

A portable computer includes a display portion comprising a display and a base portion pivotally coupled to the display portion. The base portion may include a bottom case and a top case, formed from a dielectric material, coupled to the bottom case. The top case may include a top member defining a top surface of the base portion and a sidewall integrally formed with the top member and defining a side surface of the base portion. The portable computer may also include a sensing system including a first sensing system configured to determine a location of a touch input applied to the top surface of the base portion and a second sensing system configured to determine a force of the touch input.

The invention provides a micro-module machine room UPS capacity setting method and system based on load prediction, and relates to the technical field of uninterruptible power supplies, and the method comprises the steps: constructing a load prediction model, dynamically adjusting the weight coefficient of each sub-model, carrying out the multi-dimensional comprehensive modeling, predicting the multi-granularity load change, and adaptively correcting the current prediction result. Generating a load prediction result; a dynamic game mechanism and a Monte Carlo simulation risk assessment engine are integrated, a multi-target UPS capacity configuration model is constructed, UPS response time and a capacity scheduling strategy are adaptively adjusted, and a UPS optimal capacity configuration scheme is obtained; the UPS optimal capacity configuration scheme is applied to UPS system configuration of a micro-module machine room, a distributed UPS group meeting different load partition requirements is constructed, load sharing control based on active current sharing is configured, and the operation mode and capacity distribution of the distributed UPS group are dynamically adjusted according to a load prediction result in combination with a real-time operation state.

A computing system for generating enhanced output of a generative model is disclosed. The computing system is configured to receive an input set forth by a user of a client computing device that is in network communication with the computing system. An intent classifier produces, from the input, an output indicative of a user intent. The output of the intent classifier and the input are provided as input into a workflow model. The workflow model generates a prompt based upon the input and the user intent and provides the prompt as input into a generative model, which causes the generative model to generate an output based upon the prompt. The workflow model receives the output of the generative model and identifies supplemental content related to the output. The workflow model then generates an enhanced output based upon the output and the supplemental content for presentation to the user.

Systems and techniques for generating and animating non-player characters (NPCs) within virtual digital environments are provided. Multimodal input data is received that comprises a plurality of input modalities for interaction with an NPC having a set of body features and a set of facial features. The multimodal input data is processed through one or more neural networks to generate animation sequences for both the body features and facial features of the NPC. Generating such animation sequences includes disentangling the multimodal input data to generate substantially disentangled latent representations, combining these representations with the multimodal input data, and using a large-language model (LLM) to generate speech data for the NPC. Further processing using reverse diffusion generates face vertex displacement data and joint trajectory data based on the combined representation and generated speech data. The face vertex displacement data, joint trajectory data, and speech data are used to produce an animated representation of the NPC, which is then provided to environment-specific adapters to animate the NPC within a virtual digital environment.

A robot fleet management platform includes a job configuration system that determines tasks to be performed by robots of a robot fleet based on a job request and a first fleet objective. A proxy service applies fleet configuration services to the tasks to produce a data structure. An intelligence layer activates intelligence services to produce a robot task and associated contextual information that facilitates robot selection and task ordering. A job workflow system generates a workflow defining a performance order of the tasks. A workflow simulation system simulates performance of the job request based on the workflow to recursively redefine the tasks, the data structure, or the workflow until the simulation result satisfies a second fleet objective. In response to the simulation result satisfying the set of fleet objectives, a plan generator generates a job execution plan based on the set of robot tasks, the data structure, and the workflow.

The invention provides a PCIe resource dynamic configuration method and device, and relates to the technical field of computer hardware resource management. The method comprises the following steps of: establishing communication connection with the pluggable equipment through an expansion interface, and reading equipment identification information pre-stored in a characteristic value storage module of the pluggable equipment by utilizing an I2C (Inter-Integrated Circuit) interface; identifying mainboard PCIe resource allocation parameters and the equipment identification information; displaying configurable PCIe resource parameters on a system configuration interface, and providing configuration options which can be operated by a user; according to the device identification information and a configuration option selected by a user, a pre-stored PCIe configuration mapping table is inquired, a corresponding PCIe resource configuration instruction is generated, and the PCIe configuration mapping table comprises the device identification information and the corresponding relation between the configuration option and the PCIe resource configuration instruction; and configuring PCIe resource allocation of the mainboard according to the PCIe resource allocation instruction.

An AI-powered system for optimizing industrial workflows and predictive maintenance, which includes the following: a network of sensors configured to collect real-time data from industrial plants, including temperature, pressure, vibration, and energy consumption metrics; a central control unit configured to process and analyze the collected data using machine learning algorithms that continuously improve by learning from both historical and real-time data to increase prediction accuracy; a predictive maintenance module configured to analyze data to predict potential equipment failures, schedule proactive maintenance tasks, and prioritize maintenance activities based on the predicted impact of the failure and the criticality of the equipment; a workflow optimization module configured to dynamically adjust operating parameters to optimize the process flow, including the detection of inefficiencies and the suggestion of improvements in real time; an integration module configured to connect and interact with existing industrial control systems and ERP (Enterprise Resource Planning) software to ensure a seamless flow of data between the platforms; a user interface configured to visualize data in real time, provide alerts and customizable dashboards, and is accessible via desktop and mobile devices for remote monitoring and management; an alarm and notification module configured to deliver critical alarms and maintenance notifications via email, SMS and in-app notifications to ensure a timely response; a cloud-based architecture configured to facilitate scalable data storage, remote access, and management across multiple industrial sites; a reporting module configured to generate comprehensive reports on plant performance, maintenance history, and workflow efficiency for analysis and decision-making; A security protocol to protect data integrity and ensure secure communication between all system components.

A system for adjusting gains for position control of a header of an agricultural harvester includes a header of an agricultural harvester, an actuator configured to adjust a position of the header relative to a field, and a computing system communicatively coupled to the actuator. The computing system is configured to receive data indicative of a contour of the field forward of the agricultural harvester relative to a forward direction of travel of the agricultural harvester. The computing system is further configured to determine an aggressiveness of the contour of the field based at least in part on the data indicative of the contour of the field. Additionally, the computing system is configured to adjust a gain for controlling the actuator based at least in part on the aggressiveness of the contour of the field.

Systems and methods provide collection of telemetry by an Information Handling System (IHS). A policy decision point (PDP) of a zero-trust computing environment controls access to protected resources and receives an indicator of attack related to the IHS. The PDP identifies a telemetry definition specifying telemetry being collected by the IHS and updates the telemetry definition to specify a subsystem telemetry chain for configuring telemetry by hardware subsystems of the IHS that are related to the indicator of attack. The updated telemetry definition is transmitted to the IHS. Upon identifying the subsystem telemetry chain in the updated telemetry definition, the IHS adjusts telemetry generation by one or more of the hardware subsystems of the IHS based on their position in the subsystem telemetry chain.

A predictive incident management system, consisting of: a sensor input module configured to receive heterogeneous telemetry data streams from mechanical, thermal, electrical and cyber sources; a temporal correlation control unit operationally coupled to the sensor input module, wherein the temporal correlation control unit is configured to normalize received data into a uniform time series envelope that includes identifiers, microsecond-precision timestamps, metric names, values, and context markers, and is further configured to compute sliding window-cross-sensor correlation matrices, event motifs, and lead-lag dependencies across multiple time granularities; a scalable rule processor that is communicatively linked to the control unit for temporal correlation, wherein the rule engine includes an in-memory runtime environment for processing complex events and a domain-specific declarative language, and is configured to apply rules that reference primitive sensor metrics, derived correlation features, and motive-based early warning vectors to classify, escalate, or resolve predicted incidents; A historical repository that is communicatively connected to both the temporal correlation control unit and the rule engine. The repository is configured to store tagged event histories, correlation motif dictionaries, rule versions, and rule origin metadata to ensure the verifiability and explainability of predictions; and An incident response interface is operationally connected to the rule engine. The incident response interface is configured to trigger automated workflows, including the generation of tickets for IT service management, chat ops notifications, the execution of orchestration playbooks, and direct machine control via industrial protocols. the system is configured to perform predictive analyses based on temporal correlations between sensors and to execute context-aware, rule-based incident management in real time.

The invention discloses a patent disclosure auxiliary writing method and system based on multi-agent collaboration. The method comprises the steps that a multi-agent system composed of a collaboration agent, an agent agent and an expert agent is constructed, a knowledge base and a decision module of each agent are configured, and a collaboration architecture of a task scheduler, an arbiter and a shared information space is established; the method comprises the following steps: receiving an original technical scheme of a user, and constructing a structured intermediate representation through multi-agent parallel semantic analysis and feature extraction; based on the representation, a writing task sequence is generated through dynamic task decomposition, intelligent agent division is coordinated through a contract network protocol, and content generation and verification are completed based on a multi-round debate mechanism; performing multi-dimensional consistency detection on the generated draft, and eliminating logic conflicts and expressing defects through iterative revision; and finally, an interest portrait is generated based on the user technical scheme and the optimized draft, interest retrieval is performed, a technical content report is output, and the efficiency and quality of disclosure book writing are improved.

Systems and methods for testing a power plant emulation are disclosed. Software for an energy management system is deployed to a computer system comprising computing devices. A model of a power plant site is deployed to a test framework. Test commands and / or use cases are injected, via the test framework to public energy management system end points. The test commands and / or use cases are used to emulate and test the model of at least the portion of the power plant site and to generate simulated operating parameter values. The generated simulated operating parameter values of the model are compared with expected values. A determination is made as to whether the model passed testing using the comparison. After determining the model passed testing, software, corresponding to at least a portion of the model, is made available for deployment to operate a physical version of the power plant site.

In particular embodiments, a consent notice configuration determination system may be configured to: (1) scan a particular website from a plurality of different locations; (2) identify one or more types of technologies available on the particular website for individuals accessing the particular website from each of the plurality of different locations; (3) determine, for each of the plurality of different locations, based on a global set of databases, legal / regulatory guidance, etc. and the one or more types of technologies, particular legal and industry requirements for each of the plurality of different locations; and (4) automatically configure, for each of the plurality of different locations, a consent interface for the particular website for each of the plurality of different locations based at least in part on the one or more types of technologies and the global set of databases.

In some embodiments, the system includes a virtual reality environment that includes a digital twin of at least a portion of a physical industrial environment. In some embodiments, the system is configured to change the virtual reality environment based on changes in the physical industrial environment. In some embodiments, the changes are received by the system through sensors such as temperature and / or pressure sensors, as non-limiting examples. In some embodiments, the changes in the physical environment are detected through the analysis of images. In some embodiments, the system is configured to use artificial intelligence to detect the changes. In some embodiments, the system is configured to predict the effect of changes in the virtual and / or physical environment on other system components through variable changes in virtual models. In some embodiments, the system is configured to display the effects in the virtual environment before they are implemented in the physical environment.

The invention discloses an unmanned aerial vehicle real-time vegetation classification system and method based on a lightweight AI model, and belongs to the technical field of vegetation monitoring and analysis. On the basis of an existing processor of the unmanned aerial vehicle, vegetation real-time classification is achieved by optimizing the deep learning model, and the flight path is dynamically adjusted according to the classification result. The method comprises the steps of system configuration and initialization, lightweight AI model development and optimization, real-time data acquisition and processing, adaptive flight path adjustment and feedback optimization. The method has the following advantages: (1) real-time classification of vegetation and adaptive flight path adjustment are realized, and the acquisition efficiency is optimized; (2) the sampling density is adaptively adjusted for different value regions, and the precision and coverage efficiency of vegetation classification are improved; (3) the acquisition strategy is optimized in real time in combination with the battery state, and unnecessary flight time and energy consumption are reduced; and (4) the method is suitable for application fields such as agricultural monitoring, forestry management and ecological protection.

An illustrative storage management system is configured to provide persistent data storage to workloads managed by a container orchestration platform deployed in a cluster of nodes. The storage management system is configured to receive a request comprising identifiers indicative of a plurality of layers associated with a virtual machine to be run in the cluster, generate, based on the request, a virtual machine disk image comprising the plurality of layers, the virtual machine disk image configured to be used to run the virtual machine in the cluster, and provide the virtual machine disk image in response to the request. In some implementations, the virtual machine disk image is used by a virtual machine handler associated with the container orchestration platform to run the virtual machine in a virtual machine type pod that is managed by the container orchestration platform.

A system for managing future costs associated with a product includes a future requirement system programmed to estimate an amount of resources required for manufacturing, distributing, and selling the product at a future point in time. The system includes an adverse contingency system configured to identify adverse contingencies and calculate changes in costs associated with obtaining the amount of resources at the future point in time. The system includes a smart contract system programmed to autonomously configure and execute a smart futures contract based on the amount of resources required and on the changes in costs to manage the future costs associated with the product.

The invention discloses a configuration system and method for dynamically adjusting a business process, particularly relates to the technical field of business adjustment, and aims to dynamically group users accurately according to the degree of dependence of the users on different network services so as to enable resource allocation to be more accurate, and dynamically adjust a bucket dividing strategy according to the change of dependence of the users on the services so as to improve the efficiency of business process adjustment. According to the method, changes of markets and user behaviors can be quickly responded, and a network service provider can more accurately adjust priorities of the user bucket groups by comprehensively analyzing the hidden danger degree of abnormal user traffic distribution and network resource demands in peak periods; the preventive analysis and adjustment method not only can effectively predict and relieve the potential service interruption risk, but also optimizes the resource allocation, and through the dynamic and comprehensive adjustment strategy, the network operator can improve the service reliability and the user satisfaction, and reduce the service quality problem caused by improper resource allocation at the same time.

Various aspects of the disclosure relate to enforcing dynamically updated network security policies in real-time (or upon an identified update) from multiple organizations and anonymously analyze computing system configuration information uploaded from third-party computing systems. An analysis engine monitors compliance information and compare the compliance information against the security rules and / or requirements for one or more enterprise networks. A visualization providing a network map with a visual representation of each product system service system may include communication links between internal applications and / or computing systems and drill-down capability to identify issues as they are occurring or are predicted to occur. The security escrow system may include a mechanism to automatically enable / disable access between third party networks and one or more enterprise computing systems in real-time based on identified compliance information.

The invention discloses an enterprise network weak password evaluation method based on a logic attack graph and password evaluation, which comprises the following steps: firstly, collecting a network topology structure, a security policy and system configuration of an enterprise intranet, and combining ATTamp; according to the inference rule mapped by the CK knowledge base, logical inference is carried out by using a MulVAL tool to obtain a logical attack graph; then acquiring system account password information of each host, calculating password entropy and simulating password blasting time evaluation to obtain a password risk value; then, the password risk value is associated and combined with the logic attack graph, and a new weighted attack graph containing the password risk value is generated; identifying an attack starting point and an attack target point in the attack graph with the weight at the same time; traversing all attack paths in the weighted attack graph by using a depth-first search algorithm, and calculating a weak password risk value on each attack path; and finally, taking the attack path with the highest weak password risk value as an optimal attack path, generating and visualizing, and improving the network security defense level of an enterprise.

In general, this disclosure describes techniques for leveraging a containerized routing protocol process to implement virtual private networks using routing protocols. In an example, a system comprises a container orchestration system for a cluster of computing devices, the cluster of computing devices including a computing device, wherein the container orchestration system is configured to: deploy a containerized application to a compute node; and in response to deploying the containerized application to the compute node, configure in the compute node a virtual routing and forwarding (VRF) instance to implement a virtual private network (VPN) for the containerized application.