36 results about "Grid management" patented technology

The invention discloses a standardized centralized DTU multi-level fault diagnosis and self-healing method and system, and relates to the technical field of intelligent power grid fault diagnosis, and the method comprises the steps: building a dynamic topological graph according to a unified coding rule, extracting spatial-temporal characteristics through a graph convolution network, and obtaining a spatial-temporal characteristic power grid topological graph; building a power grid digital twin environment based on a multi-level standardized fault diagnosis result, generating a plurality of sets of self-healing strategy candidate sets by adopting a reinforcement learning algorithm, screening an optimal self-healing strategy, and converting the optimal self-healing strategy into a control instruction conforming to a substation automation standard protocol; the control instruction is sent to the DTU and analyzed, and the intelligent power switch cabinet is controlled to execute self-healing operation. By automatically correcting the abnormal diagnosis result, false alarm and missing alarm are reduced, and the accuracy of fault diagnosis is improved. The intelligent level of power grid management is obviously improved, the maintenance cost is reduced, and the operation efficiency and the service quality of a power system are optimized.

The invention provides a micro-grid management method and system based on source grid load storage, and relates to the technical field of micro-grids, and the method comprises the steps: S1, obtaining renewable energy power generation data in a micro-grid, the state of charge (SOC) of an energy storage device, a load demand, and power grid operation state information; and S2, performing real-time prediction on the load demand by using an intelligent algorithm and generating a load scheduling plan. According to the micro-grid management method and system based on source grid load storage, load prediction is carried out by introducing an intelligent algorithm, power scheduling between renewable energy sources and an energy storage device is optimized by a dynamic coordination mechanism, and resources are allocated by a multi-objective optimization algorithm, so that efficient management of the micro-grid is successfully realized. By acquiring various data in the micro-grid in real time and combining with a dynamic adjustment scheduling strategy, the method can effectively deal with the uncertainty of load fluctuation and renewable energy output, optimize the operation efficiency of the power grid, and ensure the stability and economy of the system.

The invention provides an intelligent service system and device for urban community micro-grid governance, and belongs to the technical field of community intelligent services, the system comprises a community data acquisition module, a community data management module, an intelligent algorithm module, a service function module, a micro-grid task module and an application execution module; deeply mining multi-source data of grid management basic data and community monitoring data; according to the community intelligent management system, community data overall management is realized by combining a database, a computing cluster device and a deep learning framework, multiple functions of resident community life circle, community position space management, community event intelligent processing and personnel service management are realized by utilizing an artificial intelligence model algorithm, and community intelligent management efficiency and service level are improved; the problems that an existing micro-grid intelligent governance framework is not clear, data collection and processing are difficult, task distribution responsibility is not clear, service quality is not high, and the intelligent degree of event processing is low are solved.

A grid management platform is disclosed for predicting and mitigating peak electricity demand events using probabilistic modeling and cost-optimized control. The system includes a likelihood model that evaluates the probability of each remaining day within an evaluation interval being the peak load day. This is achieved through a Monte Carlo simulation engine that generates multiple stochastic realizations of future load trajectories based on historical data, forecast data, and forecast error profiles derived from back testing. The system computes likelihood scores and classifies days using optimized bin thresholds that minimize operational cost, considering demand charges, available distributed energy resources, and forecast uncertainty. Based on the predicted likelihoods, the platform generates actionable insights and transmits control signals to grid-connected assets such as batteries, HVAC systems, and electric vehicle chargers to shift or reduce load during predicted peak periods.

The invention provides an intelligent video security monitoring method and system based on grid management and control, and relates to the technical field of video security monitoring. According to the method, firstly, a monitoring area is divided into a plurality of management grids, and secondly, for each management grid, the activity state of a dynamic target in the management grid is determined according to collected video data; under the condition that a physical communication path exists between at least two management grids, whether a cross-grid flow event exists or not is judged according to the activity state of the corresponding dynamic target; and finally, when a cross-grid flow event exists, generating gridding linkage alarm information. According to the technical scheme provided by the invention, the monitoring area is divided into the grids and the space-time activity association of the dynamic target between the adjacent grids is cooperatively analyzed, so that accurate perception and structured alarm of cross-regional flow behaviors are realized, and the linkage and refinement level of regional safety monitoring are effectively improved.

The present invention proposes a community health service management system based on grid management. The grid-based community health service management system includes: a health management grid division module for establishing a health management grid and managing the health service status of community residents within the grid through the health management grid; a data collection module for collecting resident health information and establishing a resident health information database; a health service function module for establishing a daily health service platform and performing hierarchical and classified management of community residents based on their health status; and an information management and support module for uploading community residents' health data to a health cloud and automatically generating health management reports through the health cloud.

The invention relates to the technical field of electric power marketing digitization, and discloses an electric power marketing digitization grid management platform based on a virtual number technology, and the platform comprises the steps: generating a coverage potential field through entropy index analysis, automatically segmenting a line according to a trace gradient, and forming a corresponding relation between a slice and a responsibility subject; a virtual number pool is configured and the service life is bound in combination with historical call data, so that number source dynamic allocation and synchronous effectiveness are realized; and calling routing is completed through space coordinate mapping. According to the platform, the problems of communication mismatching and service cross-lattice in the mountainous area are effectively solved, and the refinement and intelligence level of marketing management is improved.

1. The name of this design product: Graphical User Interface for the Grid Management Platform of Electronic Devices. 2. The use of this design product: For an electronic device. 3. The design key points of this design product: Lie in the interface content displayed on the screen. 4. The picture or photo that best shows the design key points: The front view. 5. The use of the graphical user interface: The interface of this design product is used for the graphical user interface of the grid management platform of electronic devices. 6. Description of the change states of the graphical user interface: The interface displayed in the front view is the login interface for entering the account number and password of the system; The interface change state diagram 1 is the directory interface displayed after the system login is successful; The interface change state diagram 2 is the interface entered by clicking the corresponding directory bar in the interface change state diagram 1.

The invention provides a community health service management system based on grid management. The grid management-based community health service management system comprises a health management grid division module which is used for establishing a health management grid and managing health service states of community residents in the grid through the health management grid; the data acquisition module is used for acquiring resident health information of community residents and establishing a resident health information base; the health service function module is used for establishing a daily health service platform and performing grading and classification management on community residents according to health conditions; and the information management and support module is used for uploading the health data of the community residents to a health cloud and automatically generating a health management report through the health cloud.

The present invention discloses an artificial intelligence-based construction safety grid management optimization method and system, belonging to the field of construction safety management technology. The method includes grid area division, data preprocessing, environmental risk prediction, accident risk prediction, and construction safety management optimization. The present invention uses a self-attention multi-scale convolutional long short-term memory model for environmental risk prediction, comprehensively capturing information of different scales in environmental-related data and enhancing attention to critical moments, thereby accurately capturing important risk signals; uses a dual-path multimodal deep neural network model for accident risk prediction, comprehensively capturing multiple factors affecting accident risk, enhancing the accuracy of the prediction results, and improving the model's computational efficiency by processing different types of data through dual paths; and uses two steps, environmental risk prediction and accident risk prediction, to assist in grid-based construction safety management optimization, improving the accuracy, response speed, and visualization of construction safety management.

The application belongs to the technical field of city planning, and provides a city planning application method based on grid management, which comprises the following steps: obtaining current city management data, dividing a target management area into multiple basic management grids; presetting a reserved virtual sub-grid that can be dynamically enabled for each basic management grid through modular design, wherein the reserved virtual sub-grid is in an inactivated state in an initial state and has self-configuration and adjustment capabilities; continuously monitoring dynamic operation data of each basic management grid, analyzing a current data mode by using a decision tree model, and generating a grid adjustment trigger signal. Through dynamic monitoring and the design of the elastic reserved virtual sub-grid, the division and management of the grid are more flexible, the city changes can be responded in a timely manner, and the management efficiency and adaptability are improved.

1. The name of the design product: grid marketing operation management graphical user interface for electronic equipment. 2. The use of the design product: for an electronic device. 3. The design points of the design product: in the graphical user interface content. 4. The picture or photo that best indicates the design points: front view. 5. The rear view is the conventional design, and the rear view is omitted; the left view is the conventional design, and the left view is omitted; the right view is the conventional design, and the right view is omitted; the top view is the conventional design, and the top view is omitted; the bottom view is the conventional design, and the bottom view is omitted. 6. The use of the graphical user interface: the graphical interface of the design product is used for the graphical user interface of grid management, wherein the "add grid" button in the upper right corner of the front view is clicked to enter the interface change state diagram 1; the mouse is moved to the map anchor icon in the interface change state diagram 1, and the related anchor information content is displayed in a floating window to enter the interface change state diagram 2; after entering the information in the interface change state diagram 2, any area on the map is clicked to draw a region box to enter the interface change state diagram 3.

The invention belongs to the technical field of city planning, and provides a grid management-based city planning application method, which comprises the following steps of: acquiring current city management data, and dividing a target management area into a plurality of basic management grids; a reserved virtual sub-grid capable of being dynamically started is preset for each basic management grid through modular design, and the reserved virtual sub-grid is in an inactive state in an initial state and has self-configuration and adjustment capacity; and continuously monitoring dynamic operation data of each basic management grid, analyzing a current data mode by adopting a decision tree model, and generating a grid adjustment trigger signal. Through the design of dynamic monitoring and elastic reservation of the virtual sub-grids, grid division and management are more flexible, city changes can be responded in time, and the management efficiency and the adaptive capacity are improved.

The application provides a user-side micro-grid load regulation method, system and equipment based on artificial intelligence, and relates to the technical field of micro-grid management.The method comprises the following steps: analyzing multi-dimensional heterogeneous data to obtain load time series data, and determining the demand weight of each user according to power demand data through an analytic hierarchy process; processing the load time series data through empirical mode decomposition to obtain intrinsic mode function components; dynamically predicting the intrinsic mode function components, and obtaining a multi-modal load prediction result by weighting and fusing the prediction results through an attention mechanism; optimizing the multi-modal load prediction result in combination with the operation target of a micro-grid cluster to generate a basic load regulation strategy of the micro-grid cluster; and generating a final regulation strategy corresponding to each user in combination with the demand weight corresponding to the user according to the basic load regulation strategy, and regulating the final regulation strategy. The application significantly improves the regulation accuracy of the user-side micro-grid load.

The invention discloses a method and a system for grid management of energy consumption of an industrial control link of a multi-agent system. The method comprises the following steps: extracting multi-agent system data, and based on agent feature classification, generating a training matrix by adopting a Pareto optimal solution algorithm; calculating a grid segmentation index of the industrial control link to carry out adaptive grid segmentation; a Minimax-Q algorithm is adopted to obtain an optimal matrix training result set; and acquiring memory and CPU resource data of each sub-link through a PLC program, comparing the memory and CPU resource data with the optimal set, if a condition is met, mapping the data as a Socket service request and sending a control signal to execute optimization, otherwise, continuously monitoring and recording. The system comprises a data aggregation and training matrix generation module, an intelligent industrial control grid segmentation module, a reinforcement learning optimization module and an energy consumption monitoring execution module. The method can achieve the optimal energy consumption of the system, optimizes the resource distribution through the self-adaptive grid segmentation, and improves the quality of decision data.

The invention discloses an open sea unmanned ship cooperative dynamic energy charging method and system based on grid management, and relates to the technical field of unmanned ship dynamic energy charging management, and the method comprises the steps: carrying out the grid division of a target sea area, distributing an unmanned ship for each grid, and carrying out the resident monitoring; current environment information and the electric quantity of the unmanned ship are collected in real time, and when the electric quantity of the unmanned ship meets an energy charging condition, a collaborative energy charging strategy is started; according to the method, a sea area division strategy based on grid management is constructed, so that distributed resident monitoring and ordered scheduling of space tasks of the unmanned surface vehicle are realized; a cooperative energy charging mechanism triggered by environment information and an electric quantity state is combined, autonomous floating charging or butt joint with a maintenance boat for energy complementation is dynamically selected, the energy management flexibility in an open sea complex environment is improved, the closed-loop optimization capability of system operation is constructed, and the energy management efficiency is improved. According to the overall technical scheme, the task continuity, the energy utilization efficiency and the autonomous intelligence level of the offshore multi-unmanned ship system are improved.

A system and method for dynamic energy grid management with centralized intelligence and distributed edge execution is disclosed. An energy grid optimization computer continuously receives operational data from edge units distributed throughout an energy distribution network. The computer monitors the data to detect trigger events such as renewable generation fluctuations or predicted grid congestion, performs system-wide analysis to identify affected edge units, calculates updated parameters accounting for variable fault current contributions from renewable sources, generates lightweight configuration packages, and deploys them to edge units through secure communication networks. Edge units receive configurations, perform validation, execute atomic rolling updates without interrupting operations, continuously sample measurements, autonomously execute protection and control algorithms, coordinate actions using GPS / PTP synchronized timing, and stream filtered operational data back to the central platform. The continuous configuration update methodology enables dynamic adaptation to variable renewable generation and changing grid conditions while maintaining millisecond-level autonomous protection response.

Computer device for grid management with integrated real-time analytics interface

The invention discloses a micro-grid demand side management method and system based on group enhancement strategy optimization, and belongs to the technical field of micro-grid management, and the method comprises the steps: building an environment model based on the charge state of a battery energy storage system, and enabling a state space to be composed of real-time system state data; constructing a self-adaptive reward function including economic rewards and security punishment; training an intelligent agent in the environment model by using a reinforcement learning algorithm based on group enhancement, and obtaining an optimal energy storage scheduling strategy network model based on an adaptive reward function; and the intelligent agent takes real-time system state data of the environment model as input and outputs optimal energy storage charging and discharging actions at the moment, so that intelligent management of a micro-grid demand side is realized. On the premise of not depending on prediction, real-time, self-adaptive and high-precision optimization control over the energy storage system is achieved, and the operation stability, safety and control intelligence level of the system facing renewable energy fluctuation and load change are improved.

The invention discloses a source load prediction method based on cloud side-end collaborative optimization. The method comprises the following steps: processing multi-source data; training a lightweight model; carrying out edge end transplantation and incremental learning; and a cloud-edge-end three-layer collaborative architecture is constructed. According to the method, effective combination of large-scale historical data analysis and real-time increment prediction is realized through a cloud-edge-end three-layer collaborative architecture, the real-time performance, the expandability and the resource utilization efficiency are greatly improved while the prediction accuracy is ensured, and the method is suitable for popularization and application. The method can be widely applied to various scenes such as distributed power system dispatching, micro-grid management and intelligent power distribution area operation and maintenance, and provides powerful technical support for safe, stable and efficient operation of a power system.

The application discloses an engineering construction emergency call grid command management system, and relates to the technical field of data management, comprising: mapping the physical space and organizational responsibility relationship of the engineering project into a digital grid management structure; integrating various risk perception devices to construct a digital twin model; real-time analysis of original stress wave signals to identify potential risk events and perform forward-looking dynamic deduction to predict the chain influence area of the risk; spatial superposition analysis of the chain influence area and the grid management structure to determine the affected target grid, generate predictive emergency instructions, initiate targeted pre-call to the responsibility terminal of the affected target grid, and receive execution feedback information. The application solves the technical problems that the existing technology is difficult to identify the stress mutation inside the key temporary structure and the possible chain risk caused thereby in the engineering construction, and lacks forward-looking and precision in emergency response, and achieves the technical effects of predicting the risk influence range in advance and realizing predictive and targeted emergency response.

The invention discloses an emergency rescue gridding management system, and particularly relates to the field of big data evaluation. Comprising a system central processor module, a system operation database, a user information terminal, a grid region division module, an information acquisition and integration module, a data standardization processing module, a risk monitoring and early warning module, an emergency command and scheduling module, a resource management and allocation module and an interactive feedback module. According to the invention, a detailed and comprehensive resource ledger is established through the resource management and allocation module, all aspects of information of emergency resources are covered, the number of rescue resources can be accurately allocated according to the scientifically calculated number of estimated casualty numbers when a disaster occurs, cross-grid efficient allocation can be realized when the resources are insufficient, and the rescue efficiency is improved. Reasonable utilization and optimal configuration of resources are ensured, the management level and allocation efficiency of emergency resources are effectively improved, and the actual demand of a rescue site is met.

The invention relates to the technical field of micro-grid operation management, and discloses a data-driven micro-grid management optimization scheduling method, system, device and medium, and the method comprises the steps: collecting the predicted power and actual power data of renewable energy in a micro-grid system in real time; based on historical data, a denoising probability diffusion model is adopted to learn probability distribution of prediction errors, and a plurality of uncertainty scenes are generated; quantifying the risk cost in the scheduling process in the optimization model by using a conditional value-at-risk method; and constructing an optimal scheduling model aiming at minimizing economic cost and risk cost, and solving an optimal scheduling decision. According to the method, the wind and light uncertainty is accurately described in a data driving mode, balance optimization of economy and operation risks is achieved, and the reliability and economy of micro-grid dispatching are effectively improved.

The invention, which relates to the technical field of campus spoofing prevention and control, discloses a grid-management campus spoofing intelligent prevention and control Internet-of-Things system comprising an audio acquisition module, an audio data windowing module, a feature extraction determination module, a beam pointing control module, a beam width adjustment module and a depth analysis alarm module. The audio acquisition module is used for performing multi-channel synchronous acquisition on audio signals in the environment through a high-precision microphone array to obtain sound information from different directions and different distances; and the feature extraction and judgment module is used for carrying out feature extraction and machine learning analysis on the audio signals in each time window. According to the invention, through the multi-point distributed high-precision microphone array and the sliding time window analysis, the capturing capability of the short-time crying signal is improved, and the space focusing and noise suppression of the target signal are effectively enhanced in combination with the credibility-based beam pointing and width adaptive adjustment, so that the method is suitable for large-scale popularization and application. And the real-time discovery, accurate early warning and quick response capabilities of the campus spoofing event are remarkably improved.

The application discloses a traffic construction engineering safety production grid management system and relates to the related field of traffic management, which comprises the following steps: a collection module collects multi-dimensional parameters of a construction site and constructs a collection data set; a division module establishes an initial grid unit, performs boundary self-adaptive updating, and establishes a grid division result; an establishment module configures safety management equipment, performs real-time monitoring inside the grid, and establishes a grid monitoring data set; an identification module performs risk identification inside the grid and establishes a grid-level risk index; and an early warning module configures grid correlation, performs risk sharing of the grid-level risk index, utilizes the risk sharing result to perform risk global reconstruction of the grid division result, and performs early warning management. The application solves the technical problems of the existing safety production management, such as incomplete monitoring coverage, lack of flexibility in regional management, and weak cross-regional risk prediction capability, and achieves the technical effects of global monitoring coverage, dynamic regional management, and accurate cross-regional risk prediction.

The invention relates to the technical field of grassroots statistical work and enterprise regulation-lifting and unified management, in particular to an enterprise regulation-lifting and unified dynamic monitoring system based on grid management and department collaboration. Comprising a grid management module, a tax data comparison module, an enterprise monitoring and cultivation module, an efficient regulation-lifting integration module and a leader decision reference module. A grid management mode and a department division cooperation mode are fused, a modern information technology is fully utilized, and multi-source data are gathered for comparison and analysis. The grid management module is used for establishing an enterprise basic information database; the tax data comparison module forms a standard enterprise database and a seed enterprise database; the enterprise monitoring cultivation module is used for a department to carry out dynamic monitoring cultivation; the high-efficiency regulation-lifting and integration module is used for automatically discriminating the enterprises reaching the regulations and realizing high-efficiency regulation-lifting and integration; and the leader decision reference module provides a visual interface for decision reference.

The invention relates to the technical field of campus digital health management, in particular to an intelligent health campus management system and method based on gridding management, and the system comprises a gridding space and personnel organization mapping module which is used for carrying out the gridding of a campus physical space, so as to obtain a multi-level management grid, mapping is carried out according to an administrative organization so as to generate a campus space digital management platform; the multi-source heterogeneous health data acquisition and fusion module is used for acquiring multi-source heterogeneous data in the platform so as to construct a personal health electronic file; the health risk assessment and hospital guide module is used for predicting a health risk prediction result and personalized triage suggestions of each individual according to the personal health electronic archives; and the service touch and intervention module is used for pushing health knowledge or activities and sending personalized triage suggestions to a grid responsible person or a corresponding professional institution. Therefore, the problems of data islands, service passivity and fragmentation and the like in existing college health management are solved.

The invention discloses a heterogeneous transplantation and parallel optimization method for multiphase flow numerical simulation software, and belongs to the technical field of heterogeneous parallel calculation of computational fluid mechanics. According to the method, cross-language adaptation is realized by establishing a cross-language calling interface, unifying an array storage access mode, adapting a function interface format and reconstructing a core calculation function module; computing deployment is completed through efficient management of a storage space of a device end, directional copying and synchronization of data, optimization and definition of a kernel function, thread scheduling optimization, result return and parallel communication. In combination with a grid management scheme of double-layer boundary expansion, grid data interaction efficiency is optimized; efficient parallel scheduling and expansibility optimization are realized by dividing computational domains, allocating processes, cooperatively dividing tasks and formulating a weak expansibility optimization strategy. Software seamless transplantation and efficient interoperation can be achieved, the single-step iteration throughput rate and the speed-up ratio are improved, the communication transmission bottleneck is relieved, and good expansibility and engineering practicability are achieved.

The invention discloses a data-driven home-based care collaborative nursing scheduling method, and particularly relates to the technical field of home-based care collaborative nursing. The method comprises the following steps of: 1, constructing an associated grid database based on a dynamic data fusion and real-time updating mechanism of grid management and multi-source data acquisition and preprocessing; 2, utilizing historical data and real-time information in the associated grid database, combining a deep learning model and an XGBoost algorithm, and performing accurate pre-judgment on material demands and service demands possibly generated by the old; and step 3, dividing the community into a plurality of dynamic neighborhood cooperation units by adopting a DBSCAN density clustering algorithm and comprehensively considering factors of geographic distance and service demand complementarity. According to the home-based care collaborative nursing scheduling method, more accurate and efficient care service can be provided for old people through integration and deep analysis of multi-dimensional data, so that comprehensive upgrading of community and city care service systems is promoted.

The application relates to the technical field of containerization, in particular to a distributed computing method and system based on a service grid, which comprises the following steps: a grid management center is used as a central controller of the whole service grid, and is used for managing and controlling the functions of the service network; a grid agent is used for taking over the request and response of services, and realizes the routing and flow control of the request; the distributed computing method and system based on the service grid have the beneficial effects that through automatic service registration and discovery, the grid management center can monitor the creation and deletion of service instances in real time, and automatically distributes the latest service instance information to the grid agent, and the whole process is transparent and non-perceptual to the service instances.