1762 results about "Systems management" patented technology

The invention discloses an integrated energy system operation management method based on load prediction, and belongs to the technical field of energy system management, and the method specifically comprises the steps: collecting operation parameters of energy use equipment, the parameters comprising current waveform characteristics, surface temperature distribution and medium flow change; analyzing a time sequence change rule of the operation parameters, and extracting characteristic indexes related to equipment aging; establishing an energy consumption prediction correction model according to the characteristic indexes, and dynamically adjusting a theoretical energy consumption calculation value of the energy use equipment; inputting the corrected theoretical energy consumption calculation value into an energy distribution optimization model to generate a load distribution instruction of the energy network; after executing the load distribution instruction, comparing the deviation between the actual energy consumption and the correction theoretical value, and updating the parameter weight of the energy consumption prediction correction model; according to the invention, continuous and stable operation and optimal management of the integrated energy system in the equipment aging process are realized.

The invention, which relates to the technical field of community safety supervision, discloses an artificial intelligence-based community safety environment supervision system comprising a data acquisition module, a data processing and analysis module, an intelligent decision module, an early warning response module and a system management module. The data acquisition module is used as a sensing layer of the system; the data processing and analysis module specifically comprises a feature extraction unit and a behavior recognition unit; the intelligent decision module is used for receiving the risk assessment result output by the data processing and analysis module; and the early warning response module generates the scheme according to the intelligent decision module. According to the community safety environment supervision system based on artificial intelligence, intelligent supervision of a community safety environment is realized through a complete closed loop of data acquisition, data processing, intelligent decision making, early warning response and system management; all the modules are in close cooperation, full-process automation from data collection to emergency response is ensured, and the efficiency and accuracy of community safety management are greatly improved.

The invention provides a large model cue word design method, system and device in an industrial scene and a medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: constructing a private cue word template library, and forming a domain special knowledge unit set through multi-source industrial data collection, intelligent semantic analysis and classified storage; customizing a four-layer structured template, sequentially establishing a task overview layer, a step disassembly layer, an instruction refining layer and an output specification layer, and constructing a layered mapping model from task definition to output execution; template library management and iterative optimization are carried out, and dynamic updating and performance improvement of a cue word system are realized through centralized system management, multi-source feedback acquisition and data-driven optimization. According to the method, a cue word design and optimization system based on a hierarchical structure is constructed for complex task requirements in an industrial scene, and the task processing performance of a large model in scenes of industrial production, quality detection, equipment operation and maintenance and the like is improved.

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.

The invention discloses a distributed power supply storage and charging integrated system, and belongs to the technical field of power system management. The system comprises a clean energy access module, an energy storage control scheduling module, an intelligent charging control module, an energy management scheduling module, a communication network interconnection module and an equipment state monitoring module, the battery state is dynamically evaluated from multiple dimensions, and a matching strategy is called through a real-time environment sensing mechanism; the charging and discharging path and rate are more in line with the actual load demand and the energy supply and demand state of the system, the battery circulation loss is reduced, the energy utilization rate is maximized, the non-critical load reduction power of the user is dynamically guided, and the specific charging demand of the user in the future time period is dynamically predicted. Therefore, the system has the capability of allocating energy resources in advance, centralized coordination is carried out on the multiple charging piles, imbalance of the energy resources in spatial distribution is avoided, the scheduling flexibility of the whole system is improved, and the self-adaptive capability of the system to complex power consumption behaviors is enhanced.

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.

The invention discloses a memory fault repairing method and device, equipment, a medium and a computer program product, and relates to the technical field of computers.The memory fault repairing method includes the steps that memory error information sent by a memory controller is obtained, a fault target memory page can be positioned, and a standby memory page is obtained from a standby memory pool; the memory address mapping table is updated, the physical address of the target memory page with the fault is mapped to the physical address of the standby memory page, the repairing process does not depend on triggering of a system management interrupt mechanism, memory repairing in the system running stage is achieved, the system downtime caused by memory fault repairing is shortened, and therefore the memory repairing efficiency is improved. The problem of high delay in memory fault processing can be solved, and the technical effect of improving the memory fault repairing efficiency is achieved.

The invention discloses a charging pile system management method based on dynamic load balancing, and particularly relates to the technical field of charging pile management, and the method comprises the steps: collecting time domain scheduling data of a charging pile side, a power grid side and a distributed renewable energy output device side, carrying out the fusion filtering, and obtaining a station-level state vector; generating a station-level prediction sequence of the charging pile in the next control period based on the station-level state vector and a rolling prediction model; an inner-layer controller reads the station-level state vector and the station-level prediction sequence, establishes a power prediction optimization model and solves the model, and outputs an expected station-level power trajectory and station-level power redundancy; a result output by the inner-layer controller is received, and a station-level power envelope curve and an energy storage charging and discharging set value are comprehensively solved and transmitted; the slope upper limit of the station-level power envelope curve is contracted based on the oscillation amplitude, the new slope upper limit is substituted into the next solution, the subsequent power change rate is limited, and the problem of unstable power exchange between the charging station and the superior power grid is effectively solved.

The invention discloses a non-intrusive load monitoring method based on hybrid federated learning and cross Transform, belongs to the technical field of power system management and energy efficiency, and solves the problems of insufficient accuracy, expansibility and privacy protection in the prior art. The method comprises the following steps of: combining a mixed federated learning framework of centralized federated learning and decentralized federated learning; a communication topology of decentralized federated learning is constructed through a graph neural network, and deep graph structure information is encoded into embedded representation of a client by using the graph neural network; the privacy protection of federal learning is enhanced through a layer sensitivity pruning and robust aggregation method; an input bus power sequence is processed through a one-dimensional convolutional layer and a shared Transform encoder, and prediction of a plurality of load working modes is realized by adopting a plurality of load branch structures; and in the decentralized federated learning training, judging whether the model is converged or not by adopting a voting mechanism. The method is suitable for monitoring the energy consumption of various load devices in a monitoring environment.

Dynamic user interfaces for storage system management using generative artificial intelligence (AI), including: receiving a natural language request via a natural language interface of a graphical user interface (GUI) for a storage system; generating, by a generative AI model and based on the natural language request, one or more GUI components for presenting information associated with the natural language request; and presenting, via the GUI for the storage system, the one or more GUI components.

The invention belongs to the field of early warning and monitoring of power equipment, and particularly discloses an early warning system and method for inclination and collapse of a tower. The system comprises a sensor module, a data acquisition and preprocessing module, an edge calculation module, a data transmission and synchronization module, a central processing and intelligent analysis module, a visualization and early warning module, a power management module, a model training and optimization module and a system management and maintenance module. The system is high in data acquisition precision, strong in system robustness and high in early warning efficiency, can efficiently and accurately carry out real-time monitoring and inclination collapse early warning on the state of the tower of the transformer substation, and remarkably improves the operation safety and management efficiency of the transformer substation.

The invention relates to the technical field of power system management, in particular to a Beidou-based power grid emergency wireless command system, which comprises a Beidou positioning communication terminal, a multi-source data fusion terminal, an intelligent decision-making terminal, a heterogeneous communication terminal and a visual command terminal. The Beidou positioning communication module accurately obtains the position of the power equipment and transmits an encryption instruction; the multi-source data fusion module integrates power, weather and geographic information, and provides support for decision making through data cleaning and space-time correlation analysis; the intelligent decision-making module generates a multi-target optimization decision by applying an improved multi-target genetic algorithm based on a digital twinborn dynamic plan library and a deep reinforcement learning algorithm; the heterogeneous communication module integrates various communication units, realizes mode dynamic switching and bandwidth aggregation, and ensures stable communication. And the visual command terminal displays the power grid situation and efficiently issues an instruction by means of augmented reality and multi-level authority management. The power grid emergency response and command efficiency is greatly improved, and power supply stability is powerfully guaranteed.

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 provides a fault inference analysis method and system based on a large language model, and the method comprises the steps: firstly obtaining a multi-source operation and maintenance data set of a to-be-diagnosed system, including a structured performance index, a semi-structured service log and unstructured text description data, carrying out the semantic enhancement of the multi-source operation and maintenance data set, and carrying out the semantic enhancement of the multi-source operation and maintenance data set; obtaining a semantic data unit containing entity, relation and attribute semantic tags, constructing a semantic association network based on the semantic data unit, calling a pre-trained large language model to carry out root cause reasoning analysis on the network, generating a candidate root cause set sorted according to confidence, and carrying out root cause reasoning analysis on the candidate root cause set; and finally, according to the candidate root cause set, generating an operation and maintenance decision instruction containing an entity operation sequence and a priority sequence, and sending the instruction to a system management terminal to trigger an automatic repair process. Therefore, the operation and maintenance efficiency and reliability of the system can be improved.

The invention provides a fault rapid positioning method and device in an MGX system, and belongs to the field of server system management and fault diagnosis. The method comprises the following steps: determining an idle universal serial bus interface on a mainboard, and selecting a target USB interface and a fixed IP network segment configuration corresponding to the target USB interface from the idle USB interface; the BMC establishes a physical connection with a device where the HMC is located through the target USB interface, and constructs a virtual local area network channel; in the virtual local area network channel, performing out-of-band communication between the BMC and the HMC; the BMC obtains list information of each component in the MGX system from the HMC, and classifies the list information according to component types; the HMC pre-collects and stores the state information of each category according to the classification result; and the BMC periodically reads the state information based on the Redfish protocol, and identifies the fault in the MGX system according to the state information. According to the method and the device provided by the invention, the fault can be quickly and accurately positioned.

The disclosure relates to the technical field of power system management, and in particular to a large-scale electric vehicle cluster coordinated charging optimization method based on greedy repair genetic algorithm. the method includes the following steps: Si, establishing a task model: establishing a task model of electric vehicle cluster coordinated charging in large charging stations; S2, constructing constraint conditions: constructing constraint conditions for the coordinated charging optimization problem; S3, constructing a constrained optimization problem: designing an objective function with the minimum charging cost, performing linear mathematical transformation on non-linear constraint conditions, and constructing a 0 / 1 integer linear programming problem; and S4, solving the constrained optimization problem with a custom genetic algorithm based method: carrying out genetic coding on a decision model of the charging station, and specifically designing a genetic algorithm with greedy repair operators to solve the constructed constrained optimization problem.

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 discloses a smart park facility predictive maintenance system based on a digital twinborn technology, and relates to the field of smart park facility maintenance. Comprising a data acquisition module, a preprocessing module, a digital twin model construction module, a fault prediction module, a maintenance decision module, a maintenance resource management module, a user interaction module, a system management module, a spatio-temporal data analysis and prediction module and a social-technical system fusion module. The method comprises the following steps: collecting and fusing a risk-dependent frequency modulation rate of quantum sensing, improving speed and precision by means of quantum calculation, fusing a digital twin model into a meta-universe concept and an intelligent agent, predicting a fault by combining quantum machine learning and causal inference, optimizing a maintenance decision based on a game theory and reinforcement learning, and managing resources by using a block chain-Internet of Things fusion technology. The invention discloses a brain-computer interface and holographic projection interaction and quantum encryption dual-protection management system. The system is accurate in data acquisition, vivid in model construction, accurate in fault prediction, scientific in maintenance decision, efficient in resource management, immersive in interactive experience and safe and stable in system, and ensures stable operation of park facilities.

The invention discloses a virtual power plant optimal scheduling method based on artificial intelligence, and further reduces the power operation cost through an integrated intelligent management and scheduling strategy. The system is composed of a power generation end, an energy storage end, a power utilization end, a system management platform, a power grid, a power market and other key components. The core lies in that a depth deterministic strategy gradient (DDPG) algorithm is utilized to construct a simulation environment model, and a reward function is designed to evaluate the economic benefit of the strategy. By initializing Actor and Critic neural networks, the system can generate and evaluate a scheduling strategy, and continuously iteratively updates the network through an experience playback mechanism to optimize economic benefits. The invention provides an innovative virtual power plant optimal scheduling method based on artificial intelligence, and maximization of the benefits of the virtual power plant is realized by reducing the operation cost.

The invention discloses a network security supervision system based on a network technology, and particularly relates to the technical field of data security, the network security supervision system comprises an intelligent detection terminal, an edge cooperation terminal, a cloud platform, a strategy management terminal, a management control terminal and a system database, the intelligent detection terminal is used for collecting network security data in real time and performing data preprocessing; the edge collaboration terminal is used for carrying out preliminary analysis on the integrated data and identifying common security threats; the cloud platform is used for carrying out multi-source data fusion, carrying out deep analysis and evaluating a network security situation; the strategy management terminal is used for formulating differentiated security strategies and dynamically adjusting the security strategies; the management control terminal is used for providing a visual operation interface for system management and decision making; and the system database is used for storing data for a long time and supporting a real-time retrieval function. By adopting a multi-layer distributed architecture and multi-source data fusion, the accuracy of security assessment is enhanced, and the efficiency of threat monitoring is improved.

The invention provides a power system network space precision linkage defense control method and device, and belongs to the field of power system management. The method comprises the following steps: generating a network topology structure and a key asset distribution diagram based on real-time dynamic mapping of a power system network space by a space mapping technology, extracting operation state data and potential risk information of network nodes, integrating data sources of different levels of a power system, and performing association analysis by using a deep learning algorithm, so as to obtain a key asset distribution diagram; obtaining a network threat classification result and generating a dynamic defense strategy by integrating the network operation state; wherein the strategy comprises defense path selection, response node scheduling and risk isolation measures; and executing the generated dynamic defense strategy, evaluating the effectiveness of defense measures according to a feedback mechanism, collecting historical defense records, threat features and response effects, and updating a threat knowledge base and a defense rule base based on a data analysis technology. According to the invention, the accuracy and efficiency of network space defense can be improved.

The invention discloses a multi-target dynamic optimization hydrogen energy unmanned aerial vehicle energy management method and system under an MPC framework, belongs to the technical field of energy management, comprehensively considers factors such as energy consumption, battery degradation and output fluctuation, and realizes safe and efficient operation of a hybrid power system based on a predictive control idea. The management system comprises a sensing subsystem, a control subsystem, an interaction subsystem and a power supply subsystem. The management method comprises the following specific steps: adjusting a predictive control time domain according to a historical demand power sequence; updating the aging parameter of the hydrogen fuel cell, and fixing the weight coefficient of the cost function; through a multi-target online optimization algorithm, obtaining an optimal SOC change curve under a prediction time domain; and outputting the reference power of the fuel cell through linear MPC control according to the reference change curve.

The invention provides a full-automatic air compressor system integrating intelligent monitoring and fault diagnosis, and relates to the technical field of air compressor diagnosis. The management main system comprises an AI intelligent interaction module, an AR visual presentation module, a multi-source data fusion module, an intelligent diagnosis decision module, a fault prediction and prevention module, a user feedback learning module, an equipment linkage coordination module and a system core regulation and control unit. Operating parameters and microstructure change data of the air compressor are collected, and a high-precision comprehensive data vector is generated in cooperation with an algorithm based on tensor decomposition and generative adversarial network fusion. Compared with a traditional remote monitoring mode which depends on a single traditional sensor, the system can sense micro fault characteristics of a molecular level, and in cluster operation and maintenance of the air compressors in a factory, micro cracks of the air inlet valve are detected in advance, so that the problem of insufficient air pressure caused by delayed sensing is avoided.

The invention relates to the technical field of educational administration intelligent management scheme design, in particular to an educational administration intelligent management method and system and electronic equipment. Comprising a data acquisition and preprocessing module, an intelligent analysis and decision-making module, a system integration and management module and the like. Data collection is comprehensive, preprocessing is fine, and multiple algorithm mining data values are covered. Intelligent analysis and decision relate to accurate recommendation of student courses, efficient scheduling of courses and classrooms, scientific monitoring of teaching quality and the like. The system can integrate an existing education system and is convenient to manage. According to the scheme, through a series of advanced technologies and innovative designs, many problems of traditional educational administration are effectively solved, and the intelligent level and the comprehensive quality of educational administration are comprehensively improved. In addition, the real-time performance and the accuracy are both considered, the scheme can be adjusted according to real-time data, and it is guaranteed that the management decision always fits the reality. According to the scheme, the educational administration management efficiency and quality are comprehensively improved, and great convenience is brought to educational management.

The invention discloses a hydropower station unit fire alarm auxiliary decision-making management system, which relates to the technical field of power equipment safety and comprises a fire data acquisition module, a fire data analysis module, a fire alarm module, a linkage control module and a system management module. And the fire data acquisition module is used for acquiring fire related parameter data in a hydropower station unit area in real time, wherein the fire related parameter data comprises smoke concentration, temperature change rate, flame characteristics and carbon monoxide concentration. By adopting multiple types of sensor units, a smoke sensor, a temperature sensor, a flame sensor, a carbon monoxide sensor and the like are covered for combined acquisition, compared with the condition that the types of sensors of an existing system are limited, multi-dimensional fire related data can be comprehensively and accurately acquired, deep analysis is performed on the acquired data by means of an intelligent algorithm, and the accuracy of the system is improved. The historical data and the real-time data are compared, the false alarm probability is reduced, and the alarm accuracy is improved.

Techniques are described for a system document management comprising one or more processors having access to a memory. The system is configured to open an electronic document that is to be electronically signed, the electronic document having an active component connected to an external data source. The system is further configured to, while the electronic document is open, execute the active component to retrieve data from the external data source. The system is further configured to insert the data retrieved from the external data source into a field of the electronic document. The system is further configured to output, for display, a graphical user interface that presents the content of the electronic document to a user device.

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.

A system manager is implemented on one or more computing devices operating according to special-purpose instructions to detect anomalies and iteratively collect feedback for model re-evaluation. The system manager trains a machine learning model to detect anomalies and determines an accuracy score of the trained model at detecting anomalies in a set of data items. The system manager also determines a contiguous anomaly score value region that includes a threshold portion of incorrectly labeled outputs. The trained model is used to make predictions on production data, and the system manager determines an accuracy of the predictions by selecting a subset of the production data within the contiguous anomaly score value region, clustering the subset into clusters, and selectively sampling data items from the clusters. The accuracy of the predictions on the production data is combined with the accuracy score of the trained model to determine an updated accuracy score. The system manager determines whether to retrain the model based on the updated accuracy score.

The invention relates to the technical field of energy system management, and discloses an energy storage scheduling method and device of a combined cooling heating and power system, and the method comprises the following steps: S1, collecting and building a physical model and data of each device of the combined cooling heating and power system; s2, load prediction is carried out by using a long short-term memory (LSTM) neural network; s3, based on a load prediction result, a scheduling optimization objective function is established, and the objective function defines an optimization direction and an evaluation standard; s4, scheduling the charging and discharging processes of the energy storage device by adopting a deep reinforcement learning algorithm; s5, performing real-time scheduling adjustment by using model predictive control; and S6, using rolling horizon optimization to continuously optimize the scheduling strategy on each time step. According to the method, the future load is accurately predicted by adopting the long short-term memory neural network LSTM, and real-time scheduling is realized by combining model prediction control MPC and rolling horizon optimization.