1350 results about "Systems management" patented technology

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 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 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 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.

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 provides a full-link electricity consumption monitoring method, system and equipment based on intelligent Internet of Things, and relates to the technical field of power system management. The method comprises the following steps: acquiring operation data acquired by an intelligent sensor group deployed at a power network node in real time, and executing localized carbon flow accounting according to the operation data through an edge computing node; constructing a joint probability model based on Monte Carlo simulation and a deep belief network, and generating a node carbon flow density matrix under multiple scenes; dynamically updating an electricity-carbon conversion coefficient according to real-time energy structure data, and calculating node-level carbon emission: optimizing a power grid topology and an energy storage scheduling strategy by adopting a double-delay depth deterministic strategy gradient algorithm TD3 and taking a carbon flow density matrix as a constraint condition; and generating an intelligent report including a carbon footprint thermodynamic diagram, emission reduction potential evaluation and block chain evidence storage. According to the invention, the capability of multi-target collaborative optimization of the security and economy of the power grid can be improved.

The invention belongs to the field of intelligent information system management, and particularly discloses an intelligent voice telephone robot system and method, voice and image multi-mode data are collected through a microphone and a camera, and after preprocessing, voice, emotion and semantic features are fused through an improved Transform architecture to achieve accurate recognition of user intentions; a double-layer decision network based on reinforcement learning is combined with a dynamic reward function to generate an optimal response strategy; and realizing rapid task migration and parameter optimization of the model by adopting a meta-learning mechanism. The system also has the functions of adaptive noise robustness, multi-language interaction, user portrait dynamic updating, man-machine collaboration and the like. Compared with a traditional scheme, the intention recognition accuracy, the task completion rate and the scene adaptability are remarkably improved, the interaction experience is effectively improved, and the method can be widely applied to the fields of customer service, intelligent marketing and the like.

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 a big data-based finance and accounting data storage management optimization method and system, and relates to the technical field of financial information management, and the method comprises the steps: collecting enterprise financial original data, carrying out the multi-dimensional cleaning and structured preprocessing, recognizing the data type based on a classification model, and generating a metadata label; executing a cold and hot hierarchical storage and load balancing strategy in combination with the access heat and the node resource state; encryption, copying and compression operations are completed before data storage, and a multi-level index is constructed to improve the access efficiency; and finally realizing long-term archiving and system operation and maintenance management. The system comprises a data acquisition module, a preprocessing module, a classification module, a storage optimization module, a data storage module, a data access module and a system management module. According to the invention, intelligent management and efficient storage of massive heterogeneous financial and accounting data are realized, and the data processing capability, security and availability are improved.

The invention discloses an autonomous controllable environment-oriented power business system tuning method and device, and a medium. The method comprises the steps of obtaining a business load fluctuation rate and a resource health index of a power business system; according to the business load fluctuation rate and the resource health index, a target strategy model is determined, and the target strategy model comprises at least one of an offline strategy model, an online strategy model and a preset rule model; and obtaining target feature data of the power business system, and inputting the target feature data into the target strategy model to obtain a target resource scheduling strategy of the power business system. The problems that an existing power business system management mode is insufficient in static performance and flexibility and poor in uncertainty processing capacity are solved, and the scheduling strategy for optimizing power business system resources is achieved. When the power business system oriented to the autonomous controllable technology is in the initial stage of construction, an artificial intelligence method is applied to finally realize interpretable performance adjustment and optimization, and the performance index of the business system after credential adaptation transformation is improved.

An advanced AI system, known as Bayesian Graph-Based Retrieval-Augmented Generation with Synthetic Feedback Loop (BG-RAG-SFL), combines Bayesian evaluation, graph-based retrieval, and synthetic data feedback to create a continuously improving AI platform. The present invention integrates multiple LLMs, optimizing their performance while managing complexities across different models. Key features include a knowledge graph-based RAG system, a Bayesian evaluation network, a secondary ground-truth graph for verification, synthetic data generation for ongoing improvement, and a multi-agent verification system. The system also functions as an AI operating system capable of acting as a virtual user with screen I / O control and managing multiple computers as an intelligent process automation system.

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 intelligent financial lading bill system based on AIAgent and Browserless technology, which belongs to the technical field of financial lading bill management and comprises a data acquisition module, an AI intelligent auditing module, an accounting processing module, a user interaction module and a system management module. According to the method, the Browserless technology is utilized to realize automatic data capture and integration, so that the workload of manual data entry and possible errors are reduced, and the data acquisition time is greatly shortened; the AI intelligent auditing module applies a rule engine and a machine learning model, so that data can be comprehensively and accurately audited, potential risks and abnormities are found, and the accuracy and efficiency of auditing are improved; manual intervention is reduced through an automatic accounting processing flow, and voucher generation and account updating are quicker and more accurate.

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 discloses an industrial data standard detection method based on a large model technology, and the method comprises the steps: 1, carrying out the definition of data standards, including a data content standard, a data format standard, a metadata standard, a data quality standard, a data exchange standard, a data security and privacy standard, and an industry and field specific standard; 2, performing large model knowledge enhancement, including general data standard integration, static standard rule scheme injection, dynamic semantic analysis detection scheme injection, data blood relationship detection scheme injection and composite detection mechanism fusion injection; and step 3, performing data standard detection, including data standard formatting and data standard automatic detection. According to the method disclosed by the invention, remarkable effect improvement is realized in the aspects of coverage rate detection, real-time performance, compliance interpretation capability, domain knowledge deep embedding, system management intelligence and the like, and a new generation of basic technical facilities is provided for industrial internet data management.

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.

The invention discloses a talent recommendation system for matching local productivity based on vocational education, and relates to the technical field of talent recommendation. Comprising a dynamic industry portrait construction module, an expert auditing and correcting module, a personalized path planning module, a talent growth prediction module, a path adjustment module, a talent recommendation module, a data storage module, an interactive interface module and a system management module. The dynamic industry portrait construction module predicts industry trend data by introducing a time sequence hidden Markov model of an attention mechanism, and the dynamic industry portrait construction module and the expert auditing and correcting module integrate undisclosed project information supplemented by Bayesian reasoning based on a knowledge graph completion algorithm. And generating a dynamic industry knowledge graph based on a time decay factor through a graph convolutional network feature fusion technology. According to the invention, the dynamic industry portrait construction module and the expert auditing and correcting module are arranged, so that the problem that the industry information lags behind and the local industry dynamic change cannot be reflected in real time in the existing talent recommendation system can be solved.

A system manager is disclosed for automatically deploying software based on user activity. The system manager receives user-specified parameters to control whether, when, and / or how automatic deployment is performed for software components, without specifying which software components are to be automatically deployed for the user. The system manager stores software component metadata identifying software components that are available for deployment. The system manager analyzes user activity to determine autodeployment utility scores that predict degrees of utility for different software components. Based on determining that an autodeployment utility score for a candidate software component satisfies one or more conditions that are based on the user-specified parameters, the system manager automatically performs deployment operation(s) for the candidate software component in a computing environment of a user.

A system manages conversation topics and uses them in an online conversation. The system generates metadata describing a set of conversation topics based on the conversation. The metadata describes a particular conversation topic comprises a summary of interactions relevant to the particular conversation topic. The system receives a natural language request from the user via the user interface. The system generates one or more prompts comprising the natural language request and metadata describing the set of conversation topics and request the machine learning based language model to generate a reply to the natural language request using a conversation topic relevant to the natural language request. The system provides the prompts to the machine learning based language model for execution and receives responses from the machine learning based language model. The system generates a reply based on the one or more responses.