1915 results about "Dynamic management" patented technology

The invention discloses an ancient building risk prediction management and control method and system based on a large model, and relates to the technical field of ancient building protection. An ancient building risk prediction management and control system based on a large model comprises a multi-source data acquisition module, a multi-modal data fusion module, a large model risk prediction module, a risk assessment and early warning module, a digital twinborn dynamic management and control module and a repair decision module. According to the method, the structure state data, the visual image data and the environment data are fused, so that the state of the historic building is comprehensively perceived; the self-supervised learning pre-trained ViT-GPT model is combined with the LSTM time sequence model, so that the time sequence analysis capability and the multi-modal data processing capability of risk prediction are remarkably improved, the future damage probability and the risk trend of the historic building can be accurately predicted, and the defects of the prior art in long-time span risk prediction are overcome.

The invention discloses a virtual power plant peak regulation optimization scheduling method and system, electronic equipment and a medium, and the method comprises the steps: obtaining multi-source data of distributed energy, energy storage equipment and a load side, and carrying out the cleaning and normalization processing; utilizing an LSTM neural network and an XGBoost model to carry out short-term and medium-and-long-term load prediction respectively; based on the prediction result and the resource state, generating a scheduling optimization strategy through a deep Q network (DQN) algorithm; according to an optimization strategy, distributed energy power generation, energy storage charging and discharging and load side response are dynamically managed, and multi-resource cooperative control is achieved; and through a feedback optimization module, DQN model parameters and strategy updating are automatically adjusted according to monitoring data, and the scheduling effect is optimized. The system comprises a data acquisition module, a data preprocessing module, a load prediction module, a scheduling decision module, a resource control module, and a monitoring and feedback optimization module. The method has the beneficial effects that the load prediction precision is improved, the resource scheduling is optimized, the operation cost is reduced, and the method is suitable for scheduling management of a multi-source energy system.

The invention discloses an unmanned aerial vehicle route planning and dynamic management and control method for an urban low-altitude airspace, and relates to the technical field of unmanned aerial vehicle route planning and dynamic management and control, and the method comprises the steps: dividing the low-altitude airspace into a basic flight layer, a buffer anti-collision layer and an emergency guarantee layer in the vertical direction; setting different air route grades and limitation rules in different areas in the horizontal direction; generating a basic airway network skeleton and performing structure optimization; obtaining the operation state and environment updating data of the unmanned aerial vehicle; semantic annotation is carried out on each leg and node in the basic airway network skeleton, and airspace regulations, supervision rules, flight limiting areas, temporary control areas and recommended flight corridor information are embedded into a semantic layer of the airway network; and inputting the basic airway network skeleton, the semantic annotation information and the real-time monitoring data into an urban low-altitude airspace management and decision-making platform. According to the invention, cooperative scheduling and avoidance among multiple unmanned aerial vehicles are realized, and the operation efficiency of an air route network and the cooperative work capability of the unmanned aerial vehicles are improved.

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 provides a cabbage knowledge dynamic expression and interaction method and system based on multi-modal fusion, and relates to the technical field of agricultural planting management, and the method comprises the steps: obtaining multi-modal data of cabbage planting management, carrying out the feature extraction of the multi-modal data, obtaining multi-modal features, and carrying out the feature alignment to obtain final fusion features; constructing a dynamic knowledge graph of cabbage planting management based on the final fusion features; and for the to-be-processed cabbage planting interaction data, calling the dynamic knowledge graph to retrieve the cabbage planting interaction data to obtain a corresponding cabbage planting feedback result. According to the application, accurate dynamic management and decision support in the cabbage planting process are realized by realizing the processes of multi-modal data acquisition and fusion, dynamic knowledge graph construction, intelligent interaction function and the like.

The embodiment of the invention discloses a context-based big language model dialogue processing method and device and electronic equipment, and can solve the problem of how to provide a dynamic management method based on context similarity analysis and intelligent screening to optimize big language model interaction. The method comprises the steps of obtaining current text information input by a user; coding the current text information and a plurality of pieces of pre-stored historical dialogue information to obtain a current text vector and a plurality of historical dialogue vectors; according to the semantic similarity between each historical dialogue vector and the current text vector, screening the plurality of historical dialogue vectors to obtain screened target historical dialogue information; splicing the current text information and the screened target historical dialogue information to obtain a target input text; the target input text is input into a target language model, target output response information is obtained, and the target language model comprises a large language model.

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 an expressway lane control method based on real-time traffic flow. The expressway lane control method comprises the following steps of performing fusion sensing through a distributed optical fiber vibration sensor array and a millimeter wave radar; constructing a space-time diagram convolutional network model, and dynamically mapping the road topological structure into a lane connection diagram with variable weight; a layered reinforcement learning architecture is adopted, and an emergency lane dynamic management mode is triggered; and deploying a digital twin sand table to perform multi-resolution simulation verification, and when the actual traffic flow deviates from a predicted value by more than 15%, starting a causal inference engine to backtrack a decision link and update an agent strategy library. According to the method, global dynamic lane optimization is realized through a multi-source perception fusion and hierarchical reinforcement learning architecture, simulation data shows that the morning peak traffic volume is improved by 29.3%, and the accident scene traffic efficiency is improved by 28.6%; a digital twinning and causal inference engine is adopted, the strategy updating response time is shortened to 30 seconds, and the congestion propagation distance is reduced by 35%.

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 provides an emergency lane dynamic management method and system, and the method comprises the steps: obtaining historical monitoring videos of all monitoring points, and carrying out the video processing of the historical monitoring videos; extracting vehicle information in the processed video, and redefining and calculating traffic flow parameters according to the extracted vehicle information; according to the traffic flow parameters, constructing a traffic flow interruption probability model, and determining a final threshold value of emergency lane opening; based on the traffic flow parameters and the final threshold, training the multiple classification models; training a traffic jam early warning model according to the traffic flow parameters; a monitoring video of a target monitoring point in a target time period is obtained, video processing and data extraction are carried out, based on the calculated real-time traffic flow parameters, a traffic jam early warning model is used for processing, and traffic flow density and an early warning point are predicted; and according to the prediction result, using multiple classification models to decide whether to open the emergency lane, and fusing all decision results. According to the invention, the use of the emergency lane is optimized.

The invention discloses an energy management system data filling method and architecture based on time sequence analysis, and the method achieves the intelligent prediction and filling of a data missing segment in an energy management system through constructing a filling model based on a naive Bayes algorithm, and improves the data integrity and continuity. The system firstly dynamically manages historical data and new data through a sliding window mechanism to ensure that the model can fully consider the change trend of time sequence data in the filling process. The filling model combines the distribution characteristics of historical data and current data during initialization, and endows the data in different time periods with weights, so that the model can adapt to the equipment state and environment change. And after the data missing segment is detected, the system immediately calls the filling model to carry out prediction filling, replaces the original missing data with the result and writes the result into the database, so that the accuracy of subsequent analysis and anomaly detection is ensured.

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 precast beam field production scheduling analysis management method and system based on a large model, and the method comprises the steps: combing precast beam production technological processes, constructing a beam field Internet of Things sensing network, and collecting production information in real time; defining a general engineering data format, establishing a unified standardized beam field production knowledge base, and performing multi-modal data fusion on production elements; analyzing feasibility constraint rule conditions, describing a precast beam production scheduling problem, and establishing a beam field self-organizing scheduling decision mechanism; defining production state characteristics, establishing a beam field scheduling decision process model, and training a large scheduling model based on a PPO algorithm; and developing a beam field scheduling analysis management platform, automatically generating a production scheduling scheme in combination with a real-time production state, and realizing dynamic management of beam field production scheduling. The problems that in the prior art, the resource utilization efficiency is low, scheduling decision making depends on artificial experience, and continuous learning and self-optimization capacity is lacked are solved, and the intelligent level and the management effect of the precast beam field are improved.

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 discloses an unmanned logistics warehouse management method and system based on real-time data, and relates to the technical field of logistics warehouse management, and the method comprises the steps: collecting the real-time data of the cargo inventory, the cargo location state, the equipment working state, the equipment task capability and the environment parameters in a warehouse environment in real time; obtaining cargo circulation data of cargo demand fluctuation, warehouse-in and warehouse-out records and environmental adaptability; constructing a warehouse management network model based on the data, and performing information aggregation and relation modeling on the data by taking goods, goods locations and equipment as nodes; optimizing the goods allocation according to the environmental adaptability and the environmental parameters; the inventory is dynamically adjusted according to warehouse-in and warehouse-out records and demand fluctuation; and scheduling optimization is carried out based on the equipment state. The logistics warehouse management system has the advantages that efficient utilization of inventory, goods allocation and equipment is realized through an intelligent and dynamic management mode, the problem that the goods allocation is difficult to dynamically adjust according to real-time environment change in logistics warehouse management is effectively solved, and unmanned management is 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.

Embodiments of the disclosed systems and methods provide allow for dynamic management of electrical systems using an intelligent energy management system. Consistent with embodiments disclosed herein, an intelligent energy management system may allow for the dynamic connection of electrical sources and / or loads in accordance with applicable operating rules. Operating rules may be articulated and enforced by the intelligent energy management system that provide for event and / or state driven of an electrical system and / or associated sources and / or loads. Among other things, embodiments of the disclosed intelligent energy management system may support relatively flexible connection of electrical sources and loads, bidirectional energy usage to allow for energy to be both consumed from and fed back to a utility grid, improved battery storage and / or management, proactive energy source switching and predictive battery discharge management, and / or the like.

The invention discloses a multi-modal large model adaptive learning method and system, and belongs to the technical field of artificial intelligence and machine learning, and the method comprises the steps: building a dynamic knowledge graph, which is used for capturing and updating a complex relation between multi-modal information in real time; multi-modal information fusion is carried out, and information of different modals is integrated; graph-guided adaptive learning: guiding the model to perform adaptive learning among different tasks by using a knowledge graph structure; and dynamic weight adjustment: dynamically adjusting weights of different modules according to task difficulty and model performance. According to the method, multi-modal information and dynamic management knowledge can be effectively integrated, adaptive learning is realized, and the method is an innovative method with good interpretability, so that the performance and adaptability of an artificial intelligence system in complex practical application are improved.