1541 results about "Dynamic management" patented technology

The invention discloses a smart park full life cycle management system and method based on digital twinning and Internet of Things, and relates to the technical field of smart park management, and the system comprises a sensing edge module, a data governance module, an intelligent analysis module, a life cycle module and a twinning modeling module. According to the invention, multi-protocol access and edge computing capability are supported, and the data transmission efficiency and stability are greatly improved; the intelligent analysis module outputs an accurate analysis result by constructing a multi-class feature matrix and deep multi-task joint modeling mechanism, and provides data support and model guidance for dynamic management and intelligent decision making of the park; the life cycle module integrates a Kepler optimization algorithm and a multi-agent reinforcement learning and simulated annealing algorithm, establishes a collaborative optimization mechanism, realizes combination of global search and local fine tuning of resource scheduling, and effectively optimizes energy consumption, response time, space utilization and safety risks; and the twin modeling module constructs a park three-dimensional model, so that the interactivity and operability of the system are improved.

The invention relates to a building information intelligent management method and system based on a BIM technology, and the method comprises the steps: intelligently analyzing a user demand into a standardized parameter set, and generating an initial BIM model through combining with a rule engine; model parameters are automatically adjusted through parameter matching and an AI rule engine, and a standard compliance BIM model is output; performing structure, energy consumption and space utilization rate simulation analysis on the compliance model to generate a performance report containing a normalized deviation value; taking the deviation value as deep Q network state input, iteratively optimizing parameters to reach the standard through a predefined action space, and outputting a performance optimization BIM model; construction and operation and maintenance data are obtained in real time, digital twin comparison with the optimization model is carried out, deep Q network secondary optimization is triggered, a dynamic update BIM data flow is generated, and all stages of the whole life cycle are synchronized, so that closed-loop management of building whole life cycle dynamic management and control is achieved, and the design efficiency, the engineering compliance and the cross-stage cooperation capability are improved.

The invention relates to the technical field of cluster scheduling, and provides a GPU heterogeneous cluster scheduling method and system oriented to large model training and reasoning, which constructs a set of complete cluster scheduling system by integrating multi-source information such as hardware features, running states and historical task data and applying technologies such as a clustering algorithm, a fuzzy comprehensive evaluation method and reinforcement learning. Comprehensive, intelligent and dynamic management and scheduling of GPU cluster resources are realized, the cluster scheduling system can significantly improve the execution efficiency of GPU heterogeneous clusters in large model training and reasoning tasks, the resource utilization rate is improved, the energy consumption is reduced, and the stability and adaptability of the system are enhanced. And an efficient and reliable solution is provided for large-scale deep learning application.

Methods and systems for the dynamic management of logistics in the collection / distribution of items, materials, and / or other distributables / collectables are disclosed that include determining static route routing information, assigning one or more transport units of a plurality of transport units to the one or more routes, performing one or more transport operations, identifying a change in route management information, and, in response to the change in the route management information being identified, performing rerouting of at least one of the plurality of transport units. In such an embodiment, the route management information comprises at least one of the static route management information or dynamic route management information. Further, the rerouting can include evaluating the change in the route management information and modifying at least one route of the one or more routes based, at least in part, on the change in the route management information.

The invention relates to the technical field of intelligent power grids, in particular to a power distribution network battery digital dynamic management system based on digital twinning. Comprising a data acquisition unit; the digital twinborn modeling unit is used for constructing a battery-power grid-environment multi-dimensional dynamic twinborn body and realizing virtual-real bidirectional mapping and adaptive updating by combining a multi-physics field coupling model and a long-short-term memory network time sequence prediction algorithm; a dynamic optimization unit; and executing the feedback unit. Through a distributed heterogeneous sensing network of a data acquisition unit, multi-dimensional operation data of a battery pack and a key node of a power distribution network are acquired, and a high-fidelity data set containing four-dimensional labels of a battery state, a power grid parameter, time and a position is generated in combination with a spatial-temporal feature extraction technology; the deep fusion of the full life cycle state of the battery and the global operation data of the power distribution network is realized, and the comprehensive data support covering the global is provided for the optimization decision.

An AI orchestration system dynamically manages multiple artificial intelligence (AI) agents within a cloud computing environment to efficiently process user requests. A model orchestration subsystem determines whether a request is handled locally using a domain-specific database or by invoking one or more AI agents. The system maintains AI agents in active and inactive states, provisioning computing resources for inactive agents as needed. Real-time model metrics guide the selection of target AI agents, and if a degrading performance trend is detected, the system preemptively spins up additional AI instances. The system provisions processor cycles, memory, and network bandwidth through a cloud-based resource manager, instantiates containerized execution environments or virtual machines, and performs automated load balancing among AI instances.

The invention discloses a network access permission dynamic management and control method and system based on behavior analysis, particularly relates to the technical field of network security, and is used for solving the problem of systematic service interruption caused by neglecting implicit association between permission and service logic in the existing dynamic permission strategy. The method comprises the following steps: constructing a permission dependency graph by collecting real-time behavior data of a user and equipment in real time, identifying hidden dependency nodes which have indirect data streams with a target interface but are not explicitly called, and generating a hidden path weight coefficient in combination with a historical behavior log; based on the real-time behavior characteristics and the map topological structure, quantizing the outage probability of permission adjustment on dominant and hidden dependency paths, and dynamically generating an alternative access interface and a data transfer interface; when an alternative path abnormal operation is detected, an original path is traced back through a permission dependency graph, and permission configuration is iteratively optimized; the continuity of the business data flow is maintained while the dynamic contraction of the authority is ensured, and the interruption of the associated service caused by local authority change is avoided.

The invention relates to a steel logistics full-process real-time dynamic management method and device, and belongs to the technical field of steel logistics control methods and devices. According to the technical scheme of the invention, data acquisition and monitoring are carried out, and transport vehicle and cargo states are tracked; demand prediction and inventory dynamic adjustment are carried out, and high-frequency cargo response emergency orders are allocated according to shipment frequency; visual management of the supply chain is carried out, shared data of all links of the supply chain is established, a 3D logistics map is constructed, and inventory, transportation states and bottleneck nodes are displayed in real time; and risk control management: quickly starting the alternative scheme when the early warning is triggered. The method has the advantages that efficiency is optimized, limitation of a traditional single transportation mode is reduced, rolling updating and emergency response of demand prediction are achieved, data transparency and credibility are improved, rapid risk response is achieved, and logistics full-life-cycle management is achieved.

The invention relates to the technical field of data security, in particular to a data asset classification and grading and authority dynamic management system, which comprises an asset parameter acquisition module, a classification feature judgment module, a grading weight calculation module and an authority dynamic adaptation module. According to the method, metadata and path parameters are automatically collected through a distributed crawler in combination with a regular expression, naming parameters and content feature parameters are separated to generate a structured parameter set, field naming similarity is quantized through a Levenshtein distance, content character distribution features are verified through chi-square verification, and a naming similarity and distribution feature dual verification mechanism is constructed. According to the method, sensitivity, access frequency and data volume weight are dynamically distributed through an entropy weight method, grading parameters are generated through linear superposition, an RBAC model is combined with a Dijkstra algorithm to verify access path topology legality, unauthorized access is blocked, and the heterogeneous data classification grading and authority control dynamic adaptive capacity is improved.

The invention relates to the field of engineering management, and discloses a wharf steel structure construction progress dynamic management and control and resource allocation system, which comprises a real-time data perception and fusion module for collecting field data and an initial plan and generating a fusion data stream; the dynamic construction digital twinborn construction and evolution module is used for updating the digital twinborn and a dynamic empirical risk layer in real time based on the fusion data flow; the multi-agent self-adaptive decision engine is used for generating a self-adaptive construction scheme based on digital twinborn simulation deduction and feeding back a risk signal; and the man-machine cooperation and closed-loop execution module is used for visualizing the scheme for manual decision making and issuing an instruction to a site. According to the method, the physical field data sensed in real time is fused with the digital model, the dynamic construction digital twin capable of being synchronously updated with the physical construction field state is constructed, and the technical effect that digital information and the physical entity state are kept highly consistent is achieved.

The invention provides a natural resource analysis method and system combined with multi-source data, and the method comprises the steps: firstly obtaining a multi-source data set of remote sensing images, geographical monitoring, environment monitoring and the like of a target region, and then carrying out the spatial-temporal feature extraction processing of the multi-source data set, thereby obtaining a resource spatial distribution feature set and a time change feature set; according to the method, features of resources under different time-space dimensions are accurately described, then, based on a preset dynamic association rule set, dynamic association processing is performed on a resource space distribution feature set and a resource time change feature set, a resource dynamic association relationship set is generated, and interaction and evolution rules among the resources are revealed. According to the resource dynamic association relationship set, a resource state evaluation strategy is generated, a resource management optimization direction is determined, finally, the resource management optimization direction is fed back to a resource management system, resource scheduling operation is triggered, scientific, accurate and dynamic management of natural resources is achieved, and resource management efficiency and sustainability are improved.

The invention relates to the technical field of energy storage battery health state monitoring and dynamic management and control, and discloses an energy storage battery dynamic management and control method and system.The energy storage battery dynamic management and control method comprises the steps that energy storage battery state data and environment interference data are collected through a multi-modal sensor network, and a multi-modal feature set is obtained; processing the preprocessed multi-modal feature set by using a multi-layer time sequence convolutional network, extracting time sequence features of the health state of the battery, and meanwhile, realizing abnormal noise detection and suppression; establishing a mapping relation between environmental interference and prediction reliability, representing a prediction result as a confidence interval, and calculating a reliability index; dynamically adjusting parameters and a structure of the prediction model, and generating a health state prediction result and reliability evaluation thereof; generating an energy storage battery dynamic management and control strategy with safety margin; through a multi-mode signal acquisition and preprocessing technology, the battery state and environment interference information are effectively captured, and a high-quality data basis is provided for health state prediction.

Systems and methods for managing resources across a global and / or cloud network. In particular, systems and methods for mitigating issues related to providing services while resources are offline (or may potentially go offline). For example, the systems and methods may mitigate issues related to providing services while resources are offline (or may potentially go offline) by monitoring network services at an aggregate level.

The invention discloses a battery full life cycle intelligent management method and system based on a large model, and the method comprises the following steps: S1, collecting the operation data of a battery, and carrying out the preprocessing; s2, inputting the pre-processed sample into a pre-trained Transform encoder model, and extracting a time sequence and cross-stage characteristics; s3, executing model reasoning, outputting a state index, and generating a battery state vector; s4, identifying an abnormal category and a position in combination with the working condition information; s5, generating a dynamically optimized battery management strategy based on the battery state index and the abnormity identification result; s6, deploying a lightweight model at the edge device, executing local reasoning and uploading data; s7, the cloud updates the model through self-supervised training and issues the model; and S8, repeatedly executing the steps S1 to S7, and carrying out optimized closed-loop management. According to the invention, large model modeling and an edge cloud cooperation mechanism are fused, and intelligent sensing and dynamic management of the whole life cycle of the battery are realized.

The invention provides an intelligent parking lot charge management and parking space resource intelligent allocation optimization method, and relates to the technical field of intelligent parking lots, and the method comprises the steps: obtaining the real-time occupation state data of parking spaces in a parking lot, carrying out the time window division and sequence similarity analysis through time sequence data mining, and calculating the real-time parking space pressure value of each region; when the pressure value of the first region exceeds a threshold value, selecting a second region for integration based on a prediction result and a region stability integration degree, and forming a dynamic management region; and calculating a real-time rate by adopting deep reinforcement learning, determining a core influence area according to the parking request, and distributing a target parking space based on neighborhood density characteristics. According to the invention, the utilization rate of parking lot resources is improved, and intelligent parking space allocation and dynamic pricing are realized.

The invention provides a BIM-based engineering progress and cost dynamic management and control optimization method and system, and relates to the technical field of engineering management and control optimization, and the method comprises the steps: building a digital twin basic platform with a real-time sensing capability based on a BIM model and multi-source sensor data; a progress plan and cost reference data are imported, a progress-cost correlation model is established, a comprehensive deviation value is calculated, and multiple groups of optimization schemes including resource configuration, process organization and cost control parameters are automatically generated; parallel simulation is carried out on the digital twin platform, progress and cost prediction results are obtained, multi-target evaluation is carried out, and an optimal scheme is selected and issued to a site for execution; and continuously collecting actual execution data, comparing the actual execution data with a simulation result, and when a deviation exceeds a threshold value, updating a progress-cost correlation model in the digital twin platform by using the actual data, thereby realizing adaptive evolution and forming dynamic collaborative management and control of progress and cost. The accuracy and efficiency of progress and cost control can be effectively improved, and the project risk is reduced.

The invention discloses an intelligent land engineering carbon sink regulation and control system and method based on dynamic management, and belongs to the technical field of land engineering carbon sink regulation and control. The multi-source spatio-temporal data comprises vegetation indexes, soil parameters, meteorological data and carbon dioxide flux data; the data processing and analyzing module is used for performing fusion processing on the multi-source spatio-temporal data, inverting and calculating the carbon sink or carbon source state of the target area in real time based on a built-in carbon sink accounting model, and generating a carbon sink spatio-temporal distribution diagram; the intelligent decision-making module is used for diagnosing a carbon sink capacity limiting factor based on the carbon sink space-time distribution diagram and generating land engineering regulation and control strategies for different sub-regions; and the execution and feedback module is used for issuing the regulation and control strategy to an execution terminal or a management platform. The method improves the carbon sequestration capacity and regulation and control efficiency of land engineering.

The invention is suitable for the field of GPU management, and provides an intelligent dynamic management method for GPU computing power and a cloud platform, and the method comprises the following steps: collecting task data and GPU state data, and generating a task queue and a resource allocation strategy in combination with a scheduling plug-in; based on the task queue and the GPU real-time load, dynamically adjusting a resource allocation proportion through reinforcement learning to analyze a task dependency relationship and generate a migration plan; optimizing a communication path and adjusting asynchronous transmission delay according to the task dependency relationship and the GPU communication topology, and outputting a synchronous state mark; and monitoring abnormity in combination with the synchronization state and the GPU hardware state, executing thermal migration according to the migration plan, performing video memory recovery, and updating the resource idle list. According to the invention, through algorithm innovation and hardware collaborative optimization, intelligent, dynamic and efficient resource scheduling in the multi-GPU system is realized.

The invention relates to the technical field of project cost management, and discloses a project cost dynamic management method. The method comprises the following steps: marking a timestamp of a data updating node in a historical cost database, extracting a consumption index of each subentry project in a specified interval before and after the timestamp, and constructing a consumption feature set dynamically associated with the cost; inputting the consumption feature set into an optimized neural network, calculating the deviation degree of the same sub-project in the construction stage and the design stage through time sequence comparative analysis, and generating the difference representation of the actual cost and the plan cost; a material price fluctuation coefficient is used as an adjusting variable, a fuzzy comprehensive evaluation model is adopted to carry out multi-dimensional reasoning on the difference representation, and the influence weight value of each cost component on the cost deviation is obtained; key parameters related to the market supply and demand index and the construction progress deviation rate are mined, the key parameters are input into a pre-trained evaluation model, a screening threshold value is dynamically adjusted according to a model output numerical value, and core parameters with influence weight values higher than the adjusted threshold value are screened out.

The invention relates to the technical field of data analysis, provides a logistics access dynamic management report generation method and system based on an Internet of Things lock, and is used for realizing full-process intelligent generation and optimization of a logistics management report. The method comprises the following steps: acquiring an access event data set of a target logistics area; performing cross-modal feature extraction processing on the access event data set to obtain a dynamic access feature set containing space-time dimension features; based on a preset report type template, inputting the dynamic access feature set into a trained report generation model for multi-task analysis processing, and generating an initial logistics management report set; performing semantic integrity verification and logic conflict detection processing on the initial logistics management report set to generate an optimized target logistics management report; and binding the target logistics management report with an authorized access interface of the user terminal, and performing incremental updating operation on the target logistics management report according to the access event triggered in real time.

The invention discloses a liquid cooling resource intelligent distribution regulation and control method and system, and the method comprises the steps: collecting first operation data of a target computing power cluster, and carrying out the first preprocessing of the first operation data, and obtaining second operation data; establishing a first model, and evaluating the operation state of the target computing power cluster based on the second operation data in combination with the first model; performing regulation and control according to the operation state in combination with a first regulation and control strategy, wherein the first regulation and control strategy comprises a first target function and a constraint condition; and solving the first objective function to obtain an optimal regulation and control scheme. By continuously optimizing parameters such as the flow speed, the flow and the pressure of the cooling liquid and dynamically adjusting the temperature of a cooling liquid outlet of a heat exchanger and the rotating speed of a fan, intelligent and dynamic management of the computing power cluster cooling resources can be achieved, and efficient and safe operation is ensured.

The invention discloses a multi-source data fusion type intelligent data management system, which comprises a data preprocessing module used for accessing a heterogeneous data source and generating a standardized data frame; the cognitive sub-graph construction module is used for generating cognitive sub-graphs and forming a cross-source evolutionary multi-cognitive hypergraph; the event aggregation module is used for aggregating entities and relationships according to event fingerprints and outputting a multi-source observation chain; the causal checking module is used for performing causal comparison and conflict detection and outputting a checking result, a positioning report and a treatment suggestion; the fusion updating module is used for distributing weights and performing weighted fusion, outputting fusion representation and confidence score, recording conflicts and updating the hypergraph; and the memory pool module is used for storing the hypergraph, the check result and the fusion representation, executing weight adjustment, forgetting and elimination, and outputting a service interface. According to the method, intelligent fusion and dynamic management of multi-source heterogeneous data are realized by constructing a cross-source self-evolution multi-cognitive hypergraph and an active evolution type fusion memory pool.

The invention relates to the technical field of underground pipe gallery asset management, and discloses a BIM-driven underground pipe gallery asset dynamic management and emergency decision-making platform, and the platform comprises a dynamic topology mapping module which achieves the dynamic grid density adjustment of a BIM model through monitoring the change rate of environment parameters, and generates a logic topology network synchronized with a physical pipe gallery structure in real time; the asset state reverse derivation unit is used for reversely deducing asset degradation based on environmental data by using a physical rule library dynamically loaded in a time-sharing domain; the logic rule self-triggering engine constructs a multi-level response rule chain containing space-time prediction and optimizes an emergency plan; and the edge-cloud collaborative architecture synchronously realizes real-time decision-making of low bandwidth dependence through intelligent cache and asynchronous increment. When dynamic topological mapping is driven by the change rate of environmental parameters, the real-time state of the underground pipe gallery can be represented more finely, and a high-reliability data basis is provided for subsequent accurate analysis and decision making.

The invention discloses a construction engineering management quality control system and a risk management method, and relates to the technical field of construction engineering management, and the key point of the scheme is that the system comprises a risk analysis module, an engineering environment monitoring module, an engineering quality detection module and a structure specification examination module. According to the method, dynamic management of project quality and risks is realized by analyzing construction project data and monitoring a project site in real time, the risk level of the construction project is accurately quantified through multi-factor risk assessment, and compared with a traditional method, potential risks can be identified in the early stage of the project, prevention measures can be taken, and the risk level of the construction project can be accurately evaluated. The potential risk occurrence rate is remarkably reduced, the transparency of the construction process is improved through integrated analysis of real-time videos and sensor data, project managers can monitor quality control points in real time, construction strategies are adjusted in time through immediate recognition and analysis of concrete surface defects, the construction process is optimized, and the construction efficiency is improved. And the overall safety and success rate of project management are improved.

The invention relates to the technical field of constructional engineering management, and discloses a multi-channel complex constructional engineering dynamic management system and method. The system comprises a multi-source data integration module, a dynamic progress prediction module, a resource optimization distribution module, a risk dynamic evaluation module, a cooperative control module and the like. The multi-source data integration module collects multi-dimensional real-time data to obtain structured engineering data; the dynamic progress prediction module generates a construction progress prediction result in combination with a specific algorithm; the resource optimization distribution module constructs a model to distribute resources; the risk dynamic evaluation module calculates the risk probability; and the cooperative control module adjusts construction process parameters. According to the invention, dynamic management of complex constructional engineering can be realized, the data processing accuracy, the construction progress prediction precision and the resource allocation rationality are improved, the risk is accurately evaluated, the construction process is optimized, the constructional engineering management efficiency and quality are effectively improved, and the engineering is ensured to be carried out smoothly.

The invention relates to an LLM-based power distribution network knowledge graph construction and fault intelligent diagnosis method. The method comprises the steps of constructing a knowledge system, constructing a normal operation state model and an anomaly detection threshold value, performing fault diagnosis through an LSTM-BP cascade network, and performing fault classification. The abnormal parameters and the fault classification result are input into an LLM model, the fault type is judged, corresponding diagnosis information is obtained, and the system generates a scheduling instruction and an emergency decision suggestion according to the diagnosis information; and generating a detailed diagnosis report. According to the system, statistical analysis and deep learning algorithms are adopted for operation parameters such as voltage and current after preprocessing, and automatic detection and fault classification of abnormal parameters are realized in combination with a CNN recognition module; a preset rule base in the knowledge system is combined to accurately determine a fault type, generate a scheduling instruction and an emergency decision suggestion, and assist operation and maintenance personnel in rapid response and dynamic management; finally, a detailed diagnosis report is generated, thereby improving the fault handling efficiency and the guarantee capability of safe and stable operation of a power grid.

The invention relates to the technical field of engineering management, and discloses an intelligent building engineering progress dynamic management and control system, which comprises a data acquisition layer used for acquiring dynamic information of a construction site, including personnel, equipment, materials and environmental data; the data processing layer is used for receiving, cleaning, storing, analyzing and processing the data acquired by the data acquisition layer; and the application layer is used for providing visual display and man-machine interaction functions of dynamic management and control of the construction progress. According to the invention, through the high-precision sensor and the Internet of Things technology, real-time acquisition and updating of construction site data are realized, and the problems of data lag and errors caused by a traditional manual recording mode are solved; meanwhile, a dynamic tracking module of equipment and personnel adopts GPS, Beidou and UWB technologies, centimeter-level positioning precision is achieved, and a material monitoring module is combined with an RFID technology and various sensors, so that whole-course accurate management of inventory and circulation of building materials is achieved.

The invention relates to the technical field of workflow reconstruction management, and particularly discloses a workflow dynamic reconstruction method and system based on multi-source architecture change awareness, and the method comprises the steps: firstly, sensing and fusing multi-source heterogeneous data through an intelligent agent workflow system, and dynamically adjusting a data processing strategy based on fusion process parameters; the consistency and availability of multi-source data are improved; monitoring the fusion data to capture a multi-source architecture change signal, and obtaining a self-adaptive adjustment parameter set of the multi-source architecture change; finally, the parameter set is analyzed, the framework change process is circularly adjusted, a workflow dynamic reconstruction closed loop is achieved, and the process reconstruction time is shortened. According to the method, a'data fusion-architecture perception-dynamic reconstruction 'full-link system is constructed, the response and adaptation capability of an intelligent agent workflow system to multi-source environment changes is improved, the process execution efficiency can be improved, the process blocking risk can be reduced, and technical support is provided for dynamic management of complex services.

The invention relates to the technical field of electronic commerce dynamic management, and particularly discloses an electronic commerce management system and method based on big data, and the method comprises the steps: collecting the data of each commercial operation node of an electronic commerce platform, and carrying out the real-time collection and time serialization processing of the data; the transmission quantity, the flow direction and the interaction frequency of the collected data are analyzed, interaction modes among different operation nodes are monitored, and potential abnormal interaction nodes are identified; based on the abnormal interaction node, calculating a failure node coefficient for judging whether the node has a failure risk; for non-failure nodes, constructing a multi-dimensional risk assessment model, predicting future risks of the nodes by adopting a random forest algorithm, and calculating risk coefficients; through comprehensive analysis of the node abnormal coefficient, the failure node coefficient and the risk coefficient, priority early warning and emergency processing are performed on the high-risk node, so that the operation risk is effectively prevented, and the stability and the response capability of the platform are improved.

The invention provides a material management intelligent warehouse space dynamic management method and system. Specifically, the method comprises the following steps: acquiring information of to-be-stored goods and materials and information of remaining shipping spaces; calculating the matching degree between the to-be-stored material and each remaining bin position, the space utilization rate of each remaining bin position and the operation convenience of each remaining bin position; corresponding weights are set for the matching degree, the space utilization rate and the operation convenience respectively, a shipping space scoring function is constructed, and the shipping space scoring function is the sum of the matching degree, the space utilization rate and the operation convenience; and the remaining bins are scored based on the bin scoring function, and the to-be-stored materials are stored in the bin with the highest score. According to the invention, through intelligent management, in combination with data analysis and dynamic optimization, efficient configuration of storage resources is realized.