965 results about "Systems management" patented technology

The invention relates to the technical field of reservoir earth and rockfill dam leakage abnormity safety monitoring and early warning, in particular to an earth and rockfill dam leakage abnormity real-time monitoring and early warning system based on deep learning and a medium. The system comprises a data sensing transmission module, a data fusion processing and analysis module, an early warning evaluation module, a system management and maintenance module, a database management module and an emergency response command module. Through a well-ground collaborative full-dimensional electrical method and shallow earth surface and full-section distributed optical fiber sensing, the system collects and transmits multi-source data. And multi-mode fusion and a deep learning algorithm are adopted to realize multi-physical field feature extraction and three-dimensional modeling. The system generates graded early warning information based on dynamic threshold and multi-factor coupling, and realizes automatic real-time monitoring, intelligent early warning and efficient management of leakage abnormity of the earth and rockfill dam in combination with a database, management maintenance and emergency response functions. According to the invention, the accuracy of earth and rockfill dam leakage abnormity identification and the intelligent level of early warning are improved.

A value chain network control tower system comprises a processor and memory configured to execute a know your model system that manages the complete lifecycle of Al models in enterprise environments. The know your model system performs model intake and registration actions including model documentation collection, registration procedures, metadata collection, input / output interface standardization, legal and licensing validation checks, and security validation. The system conducts comprehensive model evaluation and risk assessment actions by analyzing foundational properties, task performance, safety and risk management, alignment and compliance characteristics, operational metrics, and tooling transparency capabilities. The know your model system executes model deployment actions through automated environment validation, predeployment approval processes, and controlled production deployment with continuous monitoring.

A computer system for adaptive operation of deep learning networks through hierarchical supervision, meta-level pattern tracking, cross-network signal coordination, and selective activation prioritization. The system operates a layered neural network monitored by a hierarchical supervisory system that collects activation data, identifies operational patterns, implements architectural modifications, detects network sparsity, coordinates pruning decisions, and manages resource redistribution. A meta-supervisory system tracks supervisory behavior, stores successful pruning and modification patterns, and extracts generalizable optimization principles. The system manages signal transmission pathways that enable direct communication between non-adjacent network regions, with signal modification and temporal coordination. A greedy neural system selectively processes activation patterns based on utility metrics and includes a competitive bidding manager to allocate limited computational resources to high-value signals. This architecture enables real-time optimization of network behavior and resource usage while maintaining operational stability and responsiveness across diverse applications.

The invention relates to the technical field of transformer monitoring, and discloses a comprehensive online transformer monitoring system which comprises a sensor layer, an edge computing layer, a cloud platform layer and a communication module. The cloud platform layer comprises a data warehouse, a big data processing engine, a comprehensive diagnosis engine, a model training engine and a system management module, the edge calculation layer is in communication connection with the cloud platform layer through a communication module, the comprehensive diagnosis engine integrates a deep neural network and a knowledge graph inference engine, and cross validation of data driving and knowledge guiding is achieved; a model training engine utilizes historical data and online incremental data to continuously optimize a model, and a dynamic knowledge graph updates a fault rule through a real-time diagnosis result and expert feedback, so that the system can adapt to novel faults and complex working conditions, and the diagnosis accuracy and adaptability are greatly improved.

The invention relates to the technical field of power supply system management, and discloses a centralized management method and management system for a power supply system. The method comprises the following steps: monitoring a current fluctuation sequence and voltage offset data of each node of a power supply network in a continuous operation period, and collecting an equipment state log text and load change time sequence information; identifying a time point of abnormal sudden change of current fluctuation, and extracting a characteristic parameter set of a power supply quality reduction event in combination with a voltage offset data change amplitude; matching a historical abnormal event library, positioning key operation record fragments strongly related to load change in the log, and generating a mapping relation table of equipment operation behaviors and power supply quality fluctuation through semantic analysis and time sequence alignment; and correcting the parameters of the load prediction model, outputting and executing a power supply priority dynamic adjustment strategy, and triggering automatic calibration of equipment operation parameters. According to the method, comprehensive control of the power supply system is realized, abnormity can be identified and processed in time, association between operation and quality is clear, and stability and flexibility of operation of the power supply system are improved.

The invention provides a server management method, device and equipment and a storage medium, and belongs to the technical field of computer system architecture.The server management method comprises the steps that first equipment information of PCIe link equipment and second equipment information of SMBus link equipment are obtained; determining a device supporting a management component transport protocol (MCTP) based on the first device information and the second device information; constructing a heterogeneous equipment topological graph in the server based on the equipment supporting the MCTP protocol; determining a global unique logic identifier (EID) of each device in the heterogeneous device topological graph based on the first device information and the second device information, and constructing a global unique logic identifier mapping table based on the EIDs; and managing the internal equipment of the server according to the global unique logic identifier mapping table. According to the method, the unified management strategy of the heterogeneous bus equipment is integrated in the firmware system of the BMC, additional conversion equipment is not needed, and the design and maintenance cost of hardware is reduced.

The invention relates to the technical field of production process control, in particular to an MES system management and control method and system based on precision mold production and manufacturing, and the method comprises the following steps: obtaining a measurement point cloud and a theoretical model, calculating rigid body transformation matrix registration to generate a registration point cloud, searching a nearest projection point, measuring geometric deviation, and constructing a three-dimensional deviation vector; and calculating the actual residual material thickness, screening and generating margin insufficient risk points, analyzing a numerical control code to extract a coordinate instruction, retrieving a nearest risk point, calculating a compensation distance, and adjusting coordinates to generate a self-adaptive finish machining program. According to the method, the three-dimensional deviation vector is constructed to represent the entity state, the risk area is screened in combination with the machining allowance, the compensation distance is calculated according to the actual residual thickness, and the instruction is dynamically adjusted to generate the self-adaptive program, so that the path is automatically corrected according to the shape of the workpiece, and the overcut risk caused by insufficient allowance is effectively avoided; and accurate matching between the machining process and the workpiece state is ensured.

A multi-energy network double-layer optimization method and system based on privacy-preserving blockchain technology. The method comprises: constructing a microgrid lower-layer optimization model that takes the sum of an electricity purchasing cost and a gas purchasing cost as an optimization objective; collecting information of all microgrids in a cluster, converting an optimization problem of a lower-layer autonomous optimization model, and further establishing a microgrid collaborative optimization upper-layer model; while balancing supply and demand and limiting a transformer capacity within each time period, enabling the microgrids to establish, within remaining time period, an objective function with the objective of minimizing a total cost; and integrating a distributed system manager inside a blockchain system to manage a blockchain network, and finally establishing an interconnected multi-energy microgrid double-layer optimization model based on privacy-preserving blockchain technology. The energy optimization problem is modeled into a double-layer optimization problem based on blockchain technology, so that the decision-making mechanism is simpler, the optimization performance is better, the transaction process is safer, and the transaction cost is lower.

The invention provides a computing resource scheduling method and device, electronic equipment and a storage medium, and the method comprises the steps that a computing task request issued by a user is received, and the computing task request comprises a computing unit mask parameter; a scheduling instruction is generated according to the calculation task request, the scheduling instruction is sent to a command processing module, and the scheduling instruction comprises the calculation unit mask parameters; and analyzing the calculation unit mask parameter in the scheduling instruction by a command processing module, and distributing a calculation task to a target calculation unit set according to an analysis result. According to the method and the device, static binding of masks and task flows / queues in a traditional scheme is replaced by dynamic integration of the mask parameters and the scheduling instructions, the situation that independent task flows / queues are created for each mask combination is avoided, and memory occupation and system management overhead can be remarkably reduced.

Embodiments described herein are directed to systems and methods for modifying issue groups for a graphical user interface of an issue tracking platform. An issue dashboard graphical user interface can include multiple sets of issue tickets, where each set of issue tickets is grouped in accordance with a queue definition. A prompt can be generated and include predetermined prompt text including a recommendation criteria, data extracted from the multiple sets of issue tickets including an issue title and assignee, and data associated with a current user account authenticated with respect to the frontend application. A group modification interface can be displayed and include at least a portion of a generative response. In response to a user input, a new issue ticket group can be created by performing a structured query on issues managed by the issue tracking platform and saving the structured query as a new queue definition.

The invention discloses an automatic debugging and fault diagnosis system, which relates to the field of automatic equipment maintenance and comprises a data acquisition and preprocessing module, a chaotic feature analysis module, a fault mode identification and prediction module, a debugging and diagnosis execution module and a system management and interaction module. The chaos phenomenon in equipment operation data is deeply analyzed through the chaos feature analysis module, whether the equipment operation state is in a chaos state or not and the chaos degree are accurately judged by using chaos feature parameters such as a Lyapunov index, fractal dimension and correlation dimension, and the fault mode identification and prediction module is combined, so that the fault detection accuracy is improved. The system can recognize a potential intermittent fault mode in advance, predict the fault occurrence time and probability and send out an early warning signal, the fault diagnosis method based on the chaos theory remarkably improves the accuracy and timeliness of fault diagnosis, workers are helped to take preventive maintenance measures in time, the non-planned downtime is shortened, and the fault diagnosis efficiency is improved. The production efficiency is improved.

The invention discloses an electric power field operation management system, which relates to the technical field of electric power and comprises the following process modules: S1, planning; s1-1, an operation plan and task management module; s1-2, a power industry core combined scale block; s2, preparing; s2-1, a personnel and qualification management module; s2-2, a tool and material management module; s2-3, a risk assessment and scheme management module; s3, executing; s3-1, a field execution and monitoring module; s3-2, an emergency processing module; s4, checking and accepting; s4-1, an acceptance check and filing module; s4-2, a performance and data analysis module; s4-3, a system management module; and S5, summarizing. According to the electric power field operation management system, through systematicness and integrity of emergency plan management, a process covers full life cycle management of an emergency plan from formulation, auditing, issuing to updating, and meanwhile, links such as emergency response starting, disposal, resource management, information communication and recovery summarization are included; and a complete closed loop of prevention, preparation, response and recovery is formed.

The invention provides a line loss reason analysis and positioning method and system based on artificial intelligence and a medium, and relates to the technical field of power system management.The method comprises the steps that when the bus loss rate of a target station area exceeds a preset line loss threshold value, an excitation sequence is issued to a general meter, a circuit breaker and a user electricity meter of the target station area, respectively obtaining corresponding transient response data, and further constructing a dynamic topological graph of the target transformer area; and a topological structure is optimized by combining a graph neural network and an attention mechanism, and abnormal connection points are detected. And determining downstream associated user electric meters, and calculating the power utilization abnormity score based on the load data. And injecting a steep front traveling wave signal into the electric meter with the highest score to obtain a reflection characteristic parameter. And performing iterative analysis by using a Double DQN reinforcement learning algorithm, determining a fault point, a line loss reason type and confidence, and realizing efficient and accurate line loss reason positioning and analysis. According to the invention, efficient and accurate troubleshooting of line loss reasons is realized.

Techniques are described for onboarding a plurality of devices using a network management system (NMS). The NMS has memory and one or more processors coupled to the memory, the one or more processors configured to obtain onboarding information of a first device of a plurality of devices; assign, based on obtaining onboarding information of the first device, the first device to a group of devices to be managed by the network management system; and based on assigning the first device to the group of devices to be managed by the NMS, automatically assign a second device of the plurality of devices to the group of devices to be managed by the network management system.

The invention provides a power grid standard intelligent recommendation method based on reinforcement learning and post system management. The power grid standard intelligent recommendation method comprises the following steps of 1, collecting and cleaning power grid standard data; step 2, post-standard system knowledge graph construction; 3, designing a reward function for the power grid standard recommendation system, and realizing a personalized recommendation strategy by taking post responsibilities and skill levels as dynamic weight parameters; and step 4, post-driven online incremental training is carried out. According to the method, a reinforcement learning dynamic optimization engine is adopted to learn user behaviors (click rate and task completion score) in real time, and a standard recommendation strategy is adjusted; and when the standard is updated, the post capability weight is automatically adjusted, closed-loop feedback optimization is realized, and the problem of updating delay of a traditional system is solved.

The invention discloses an online house and real estate lease system and method based on a block chain technology. The system comprises a block chain basic service module, a user management module, a house resource management module, an intelligent recommendation module, a lease transaction module, a lease cancellation equipment damage identification module, a data security module and a system management module. According to the invention, non-tampering evidence storage of key data in the whole leasing process is realized through the block chain technology, the multi-node consensus and digital signature are combined, the problems of false house resource information and untrusted transaction data are solved, and the credibility between a tenant and a landlord is improved. The intelligent recommendation module fuses user basic information, preference and behavior features, combines structured and unstructured house source features, and realizes accurate house source matching, reduces user screening cost and improves lease matching efficiency through multi-dimensional score fusion and diversity adjustment.

The invention discloses a neurosurgical operation virtual simulation training system based on multi-modal data fusion, and relates to the technical field of operation simulation. The data acquisition and preprocessing module acquires data such as images and force feedback, and standardized storage is performed after filtering and registration; the multi-modal data fusion module performs weighted fusion on the data by using an attention algorithm to generate a three-dimensional model; the virtual operation scene construction module generates a high-fidelity environment through ray tracing and a physical engine; the surgical operation interaction simulation module realizes accurate interaction through positioning tracking and force feedback; the training evaluation and feedback module quantifies operation performance and provides guidance; and the system management and iteration module guarantees operation and supports data updating and optimization. According to the method, a high-reality scene is constructed through multi-modal fusion, interaction and multi-dimensional evaluation help skill improvement, dynamic difficulty and personalized paths are adaptive to different users, collaborative training and privacy data collaboration are supported, operation training standardization is promoted, and the risk of errors in an operation is reduced.

The invention discloses a two-stage multi-dimensional fusion retrieval enhancement generation method and system, and belongs to the field of large model language processing. Comprising the following steps: dual-stage mixed retrieval: performing BM25 retrieval and vector retrieval in parallel and performing dynamic weighted fusion to realize file-level rough selection and content-level fine selection so as to give consideration to literal matching and semantic understanding; performing intelligent content integrity judgment: performing three-dimensional evaluation on a retrieval result by utilizing a lightweight language model, and triggering supplementary retrieval when information is insufficient; and multi-source information fusion generation: fusing multi-source information by adopting a model based on an attention mechanism, and driving a large language model to generate accurate and traceable answers. The method is also matched with a continuous training optimization method of a small model and an efficient system management architecture based on an asynchronous queue. According to the method, the accuracy and the integrity of the answers are remarkably improved, the retrieval efficiency and the expandability of the system are improved, and the calculation cost is effectively reduced.

A computer system for adaptive neural network architecture implementing sophisticated supervision, pruning, and signal transmission capabilities. The system operates a layered neural network monitored by a hierarchical supervisory system that collects activation data, identifies operation patterns, implements architectural changes, detects network sparsity, coordinates pruning decisions, and manages resource redistribution. A meta-supervisory system tracks supervisory behavior patterns, stores successful modification and pruning patterns, and extracts generalizable principles from these patterns. The system manages signal transmission pathways that enable direct communication between non-adjacent network regions through signal modification and temporal coordination. This multi-level approach enables dynamic network adaptation and efficient resource utilization through pruning while maintaining operational stability. The system's innovative architecture allows neural networks to evolve their processing capabilities during operation while preserving reliable performance through sophisticated supervision and controlled modification.

Devices, systems, methods, and processes for sustainably reallocating resources based on within a plurality of computing nodes of a network, such as a managed network are described herein. Each computing node may be configured to transmit infrastructure data to an infrastructure monitor or ecosystem management tool. Additional sustainability data may also be accessed either internally or externally. The infrastructure data and sustainability data may be utilized to generate one or more scores that can be evaluated against each other. These scores may be configured to reflect various conditions or facts about the computing nodes including the overall sustainability. In order to increase sustainability levels, a variety of different resource configurations can be generated and evaluated against each other and the current configuration. When a more sustainable configuration is located, it may be applied by moving resources from originating computing nodes to destination computing nodes to achieve increased sustainability goals.

The invention relates to a real-time hybrid test control and test system and method based on a Linux-RT system and application, and belongs to the technical field of real-time hybrid tests of structural engineering. Comprising the following steps of: realizing deterministic conversion of states by adopting a real-time system manager of a singleton mode and an atomic state variable; according to the real-time executor based on the SCHEDFIFO scheduling strategy, affinity binding of a high-priority thread and a CPU is set, and periodic task scheduling of microsecond-level precision is achieved; a lambda functional programming data processing pipeline is adopted, and efficient data transmission of a filter chain is achieved through a zero copy mechanism; multi-protocol communication interfaces of UDP (User Datagram Protocol), EtherCAT (Ethernet Control Automation Technology) and WebSocket are supported, and a uniform abstraction layer is provided to realize low-delay data exchange between a numerical value and a physical substructure; and a real-time performance monitoring module adopting an exponential moving average algorithm provides accurate statistics of execution time and jitter.

The invention provides a vehicle-road cooperation roadside signal processing device. The vehicle-road cooperation roadside signal processing device comprises a high-definition camera, a laser radar, a millimeter-wave radar and a signal lamp collector which are arranged on the roadside. The RSU module is connected with the intelligent network connection vehicle and the edge computing node and is used for transmitting the vehicle position, the driving intention, the early warning information and the control instruction to the intelligent network connection vehicle and the edge computing node; the edge computing node comprises a data management module, a fusion sensing module, an application function module and a system management module, and the system management module performs monitoring, log level-to-level management, crash restart strategy and OTA updating functions on equipment states. The invention also provides a vehicle-road cooperation roadside signal processing method, which comprises the steps of designing parameter configuration, collecting data, generating traffic participant data based on a multi-sensor data fusion algorithm, and carrying out ROI partitioning and marking to generate an event detection analysis result and a traffic flow analysis result.

The invention discloses a ventilation system online monitoring method based on the Internet of Things, and relates to the technical field of ventilation system management, and the method comprises the steps: 1, setting a plurality of air environment monitoring units at different heights in a to-be-monitored space region in a vertical direction, and enabling each air environment monitoring unit to synchronously collect environment parameter data at a fixed sampling period; organizing the environmental parameter data according to the spatial position and the acquisition time, and constructing a three-dimensional pollution concentration and flow field snapshot; and 2, analyzing the change trend of the pollutant concentration through a sliding time window. Air quality data are collected and analyzed in real time through the multi-dimensional air environment monitoring units, the multiple air environment monitoring units are arranged in the space, the factors such as the wind speed and the wind direction are combined, and the system can comprehensively sense the vertical distribution and horizontal propagation characteristics of pollutants, so that the air quality data can be analyzed in real time. And accurate data is provided for pollution source positioning and pollution path modeling.

The invention discloses an electromechanical equipment operation remote monitoring system based on Internet of Things, and belongs to the technical field of industrial automation and information, and the system comprises a data collection and preprocessing module which is used for collecting the operation state data of electromechanical equipment, and carrying out the standardization processing of the operation state data, so as to generate a multi-dimensional time series data flow; and the dynamic association graph construction module is used for receiving the multi-dimensional time sequence data flow and constructing a dynamic graph by using a preset time sequence graph neural network model, and the dynamic graph abstracts each electromechanical device into a state node and quantifies a dynamic relationship between the nodes into an edge and a weight. The mutual influence relation between the devices can be visually seen, and a foundation is laid for pre-judgment of system-level risks.

The invention discloses an intelligent design research and development system based on domestic AI and an implementation method thereof, the system adopts a modular loose coupling architecture, and comprises a front end layer, a middle layer, a core layer, a base layer and a data layer; the front-end layer is a Web front-end interface, and the middle layer is an API gateway layer; the core layer comprises a knowledge graph module, a simulation engine module, a decision center module and a digital twin manager module; the basic layer comprises a system management module, the data layer comprises a multi-layer data storage and communication component, and the method comprises the steps of initializing a system manager, constructing a knowledge graph, executing multi-physics field simulation and evaluating simulation confidence; generating a decision strategy based on reinforcement learning and knowledge graph reasoning; according to the method, digital twin bodies are constructed, virtual-real linkage and real-time data interaction are achieved, domestic AI is comprehensively adopted and comprises a MindSpore framework and an ancient big model, the technology is autonomous and controllable, and the method is suitable for the whole process of power equipment design, inspection and detection and supervision and construction.

The invention provides a subject intelligent evaluation informatization system based on medical record home page data. Comprising a data integration and treatment module, a data standardization processing module, a core index intelligent calculation engine, a subject competitiveness multi-dimensional evaluation model, a DRG / DIP analysis application module, a medical quality and safety monitoring module, an intelligent report generation and visualization platform and a system management and configuration center. Through full-process automatic and intelligent analysis, multi-source data can be automatically integrated and strict quality control and standardization processing can be carried out, the accuracy and consistency of evaluation data are guaranteed from the source, complex analysis from basic index operation to comprehensive competitive power score ranking can be rapidly and accurately completed, and the method is suitable for large-scale popularization and application. And the result is visually presented through a visual platform, and even a structured analysis report is automatically generated, so that managers are liberated from tedious data processing and can concentrate on decision analysis, and the management efficiency and the response speed are remarkably improved.

The invention relates to the technical field of fault diagnosis models, and discloses an adaptive AI fault diagnosis model fusing reinforcement learning and an industrial equipment predictive maintenance system. Comprising an industrial equipment data acquisition and preprocessing module, a reinforcement learning-fused adaptive fault diagnosis model module, an equipment health degree evaluation and residual life prediction module, a predictive maintenance decision and execution module and a system management and integration module, and the output end of the industrial equipment data acquisition and preprocessing module is electrically connected with the input end of a self-adaptive fault diagnosis model module fusing reinforcement learning. According to the system, data during equipment operation are effectively collected, the equipment health degree evaluation module is matched to convert the feature deviation degree into 0-100 visual scores, the health trend visualization unit displays changes through a broken line diagram and a thermodynamic diagram, fault risk points are marked, historical backtracking and multi-equipment comparison are supported, non-professional personnel can also rapidly judge the equipment state, and the equipment health degree evaluation module is matched to convert the feature deviation degree into 0-100 visual scores. And the equipment management efficiency is improved by 30%.

The invention, which relates to the technical field of power cable insulation state detection, discloses a power cable insulation state on-line diagnosis system comprising a multi-parameter acquisition module comprising a multi-type sensor group, a signal conditioning unit and a data transmission unit. The data preprocessing module processes abnormal values and noise, unifies parameter dimensions and synchronizes timestamps; the insulation state diagnosis module is used for constructing a hierarchical index system, calculating scores through combination and empowerment and dividing grades; the fault positioning module transmits a narrow pulse signal, receives a reflected signal and calculates a fault distance; the result output module is used for generating a report, visually presenting the report and pushing the report to multiple terminals; and the system management module is used for dividing authority, recording logs, carrying out double backup data and monitoring the equipment state. The comprehensive performance, real-time performance and accuracy of cable insulation diagnosis are improved, faults are accurately positioned, aging trend prediction and personalized maintenance are supported, data safety is guaranteed, operation and maintenance efficiency is improved, the service life of the cable is prolonged, and power supply reliability is enhanced.

The business travel service management method based on the LLM is applied to a service system constructed based on the LLM. Services provided by the service system for users comprise a plurality of service fields; the service system manages a plurality of service models in one-to-one correspondence with a plurality of service fields; comprising the following steps: receiving a user question related to business travel service input by a user; retrieving target structured knowledge from a target sub-knowledge base related to the user questions in a plurality of sub-knowledge bases in one-to-one correspondence with the plurality of service fields, and acquiring historical context information from historical user questions input by the user; generating a cue word based on the user question, the target structured knowledge and the historical context information, and inputting the cue word to an LLM, so that the LLM generates structured intention description information based on the cue word; and analyzing the intention description information, determining a target service field corresponding to the user intention, and distributing the user question to a target service model corresponding to the target service field.

The invention discloses an out-of-band management method and device based on a solid state disk, and relates to the technical field of solid state disks, and the out-of-band management method comprises the steps: obtaining an original signal from a system management bus channel or a PCIe expansion channel in a solid state disk system; determining an original command corresponding to the original signal; analyzing the original command into a target command, and executing the function of the solid state disk system based on the command type of the target command to obtain an execution result; according to the execution result, a response message adapted to the system management bus channel or the PCIe extension channel is generated, and the response message comprises operation data of the solid state disk; and adjusting the priority of the target command by using the operation data, and through the technical scheme disclosed by the invention, the problems of low efficiency and poor flexibility of out-of-band management in related technologies are solved, and the out-of-band management efficiency and flexibility of the solid state disk are improved.