2363 results about "Demand forecasting" patented technology

The invention provides a procurement supply chain collaborative intelligent management method and a procurement supply chain collaborative intelligent management system. Belongs to the technical field of supply chain management. Constructing a purchase demand prediction model; dynamically adjusting the purchasing strategy according to the prediction result; the record and the evaluation result are stored on the block chain; the system automatically sends order information to a supplier; the supplier confirms the order through the block chain platform and updates the production progress and the logistics information; the system adjusts an inventory strategy in real time; the logistics state is monitored in real time; the system analyzes data of each link of the supply chain in real time and identifies potential risks. Through dynamic path planning and real-time transportation optimization, the logistics path can be adjusted in real time according to traffic conditions, environmental factors and emergencies, the transportation time is shortened, the transportation cost is reduced, and therefore the overall efficiency of a supply chain is remarkably improved.

The invention belongs to the technical field of charging station planning, and discloses a charging station planning method and system based on an automobile charging demand, and the method comprises the steps: building a space-time charging demand distribution map through multi-source data collection and fusion processing, carrying out the seasonal change analysis and future demand prediction, and obtaining a dynamic charging demand prediction model; site layout optimization under a multi-constraint condition is performed by combining an urban road network structure and traffic flow data to form a preliminary charging station layout scheme, and power grid load capacity evaluation and renewable energy access analysis are implemented to establish an energy collaborative supply guarantee system. And designing a peak-valley period charging price dynamic adjustment and appointment queuing mechanism to form an intelligent scheduling control strategy, and finally obtaining a diversified charging facility configuration scheme through different charging power requirements and vehicle type suitability evaluation. According to the method, the problems of inaccurate demand prediction, unreasonable layout, uneven resource allocation and the like in traditional planning are solved, and accurate matching of charging resources and user demands is realized.

The invention relates to the technical field of computing power scheduling, and discloses a heterogeneous resource computing power intelligent scheduling method and system. According to the method, real-time state monitoring is conducted on heterogeneous computing resources, and resource state parameters such as the computing unit utilization rate and the memory occupancy rate are obtained; task attributes and user request parameters of the task queue are collected, historical task data are processed based on the genetic algorithm optimization model to execute task demand prediction, and predicted demand parameters are generated. A dependency graph containing resource unit nodes and communication link roadsides is constructed through a resource topology analysis tool, predicted demand parameters are input into a scheduling priority classifier trained by a graph neural network, and an actual scheduling priority is identified. And executing resource conflict prediction based on the priority, inputting task feature vectors into a conflict resolution module of a fuzzy logic decision maker, outputting actual conflict resolution parameters, and finally integrating to generate a scheduling scheme containing a resource allocation sequence and an execution time table.

The invention discloses a cross-regional virtual power plant cooperative scheduling method and device, a medium and a product, and relates to the field of data processing. The method comprises the following steps: acquiring real-time characteristic data such as space-time positions, output / demand prediction and the like of distributed energy resources and loads, and determining dynamic weights of characteristic dimensions based on a global optimization target and data of a current scheduling period; generating a dynamic resource cluster division instruction containing a member list and a coordination constraint condition according to the dynamic weight and the real-time data, and sending an initial cross-regional coordination scheduling instruction containing a net exchange power target value and the like and a compensation price signal to each dynamic resource cluster local agent; after aggregation response boundary information returned by the local agent is received, an instruction and a signal are updated, a target collaborative scheduling instruction is obtained and finally sent to each dynamic resource cluster for execution, and effective control over cross-regional virtual power plant resources is achieved. According to the method, the problem that the adaptability of the cross-regional virtual power plant collaborative scheduling instruction and the actual resource capacity is insufficient can be relieved.

The invention relates to the technical field of new energy vehicle charging, and discloses a new energy vehicle charging station power supply optimization regulation and control system in a power supply peak period, and the system comprises a processor, a charging demand prediction module, a power supply load distribution module, a real-time regulation and control module, and a power supply performance monitoring module. The charging demand prediction module predicts a charging demand based on historical data and user behaviors; the power supply load distribution module distributes power supply priority and power upper limit according to the prediction result and the power grid state; the real-time regulation and control module adjusts power supply parameters according to the real-time data The power supply performance monitoring module records the regulation response time and the deviation value. In addition, the system is further provided with an energy storage coordination module, a user priority evaluation module and a power grid interaction module which are respectively used for optimizing energy storage management, evaluating user priority and coordinating power grid power supply. The system can accurately predict the charging demand, reasonably distribute power supply resources, regulate and control power supply parameters in real time, and improve the power supply efficiency and stability of the charging station.

The invention relates to the technical field of logistics supply chain optimization, and provides an order dynamic distribution optimization method and system based on a user portrait, so as to improve the precision service capability and resource scheduling efficiency in a community retail scene. The method comprises the steps that user behavior data and community merchant inventory data are collected, and the user behavior data comprise an access behavior sequence recorded by intelligent cat eye equipment and a parcel access event triggered by an Internet of Things lock; generating a dynamic demand prediction parameter based on the user behavior data, and constructing a user portrait including consumption cycle characteristics and instant demand intensity; according to the user portrait and the community merchant inventory data, a replenishment order generation operation of a target community merchant is triggered, and a replenishment order comprises a delivery time limit constraint and a category priority identifier; and in combination with the real-time distribution load parameters and the community inventory distribution topology, generating a dynamic distribution path planning result and updating a time sequence control instruction of the distribution carrier.

The invention provides a multi-source heterogeneous distributed computing power fusion scheduling method and system, and the method comprises the steps: carrying out the multi-dimensional collection and preprocessing of heterogeneous scheduling source data, generating a scheduling joint data set, carrying out the key demand prediction processing of a task set based on a preset quantum channel topology, generating a key pre-distribution matrix, and carrying out the key pre-distribution matrix, and generating a real-time task queue according to a task set of the scheduling joint data set, and performing multi-target matching degree calculation processing on the key pre-distribution matrix and the real-time task queue based on a dynamic priority quantification model to generate a distribution decision matrix. Then adaptive key supplement and migration path optimization processing are performed on the allocation decision matrix to obtain a migration task set, and finally hidden node resource prediction and preset quantum channel topology updating are performed on the migration task set, so that the rationality of key allocation is improved, the problem of compatible conflicts of resource scheduling is solved, and the resource scheduling efficiency is improved. Therefore, the task migration success rate is improved.

The invention relates to the field of data backup, in particular to an intelligent data backup method and system based on an AI large model. The method comprises the following steps: acquiring an enterprise global data list, performing intelligent data structure deconstruction and dynamic attribute mapping modeling, and constructing a holographic data semantic perception model; performing real-time transient risk mutation detection on the holographic data semantic perception model, and constructing an intelligent backup triggering mechanism; carrying out storage resource demand prediction based on an intelligent backup trigger mechanism, carrying out multi-storage cloud environment resource dynamic scheduling, and constructing an elastic backup storage resource pool; carrying out incremental backup analysis and self-adaptive compression coding to obtain an incremental backup coding packet; and performing dynamic backup sequence adjustment and intelligent incremental backup decision on the incremental backup coding packet based on the elastic backup storage resource pool, and constructing an intelligent incremental backup execution engine. According to the method, the reliability, the accuracy and the traceability of a backup result are improved through self-adaptive intelligent incremental backup.

A data-driven dynamic energy management system for the adaptive coordination of renewable and conventional energy sources, consisting of: a processing unit configured to perform real-time calculations to optimize the generation, storage, and distribution of electrical energy by continuously analyzing operational data, forecasting future energy demand, and generating control instructions to match available generation resources with forecasted consumption demand; a storage unit connected to the processing unit, configured to store records of historical energy production and consumption, environmental data, operating thresholds and learned model parameters, and to provide said data as input for the forecasting and optimization routines performed by the processing unit; a multitude of IoT-based monitoring units, each comprising at least one sensor configured to measure instantaneous parameters of generation, storage level, consumption rate and environmental conditions, with each monitoring unit being configured to periodically transmit measurement packets to the processing unit via a secure communication network; a forecasting unit implemented in the processing unit, configured to process historical and real-time data to create forecast curves for demand and generation using statistical and probabilistic forecasting techniques, and to dynamically update the weights of the forecasting model in response to observed deviations between forecasted and actual output; an optimization control unit implemented in the processing unit and configured to evaluate the outputs of the forecasting unit together with current operational data to determine a set of optimized control variables representing the target generation contribution of each energy source, and to pass these targets to a lower-level controller for execution; a controller that is communicatively connected to the processing unit and the multiple energy generation sources and is configured to regulate the operation of each source by adjusting the activation state, output level and operating priority based on the control signals received from the processing unit; an energy storage management unit comprising at least one battery array and a power conditioning circuit, configured to receive control instructions from the processing unit, store excess generated energy, release stored energy when forecasted demand exceeds available generation, and report charging and discharging characteristics in real time to the processing unit for continuous recalibration; an alarm and notification control unit connected to the processing unit, configured to continuously compare storage levels and generation reserves with stored operating thresholds, trigger predefined responses when critical or abnormal conditions are detected, and transmit acoustic, visual, and digital remote alerts to designated operators; a user interface terminal connected to the processing unit, configured to display real-time generation statistics, demand forecasts, energy storage status, and system alerts, and to accept operator-defined parameter inputs that are transmitted to the processing unit for recalibration of forecast or optimization parameters; and a secure server interface configured to synchronize operational logs, learning data, and performance indicators with a remote monitoring or analysis server for centralized monitoring, long-term data analysis, and distributed decision support.

The invention relates to the technical field of supply chain management, in particular to a supply chain middle station collaborative management method and system based on an AI intelligent decision engine. Comprising the steps of accessing multi-source data of each participant of a supply chain; utilizing a deep learning model to predict market demands and dynamically adjust the market demands; based on the prediction result, using an optimization algorithm to realize global optimization configuration and scheduling of resources; a machine learning risk assessment model is constructed, various risks are monitored in real time, and early warning is performed in time; a collaborative decision-making platform is established, online communication, negotiation and decision-making of participants are supported, and real-time data sharing is achieved; defining a performance indicator system, evaluating the performance of the supply chain in real time, and automatically adjusting a strategy for continuous improvement. The method can improve the demand prediction accuracy, optimize the resource configuration, enhance the risk response capability, improve the collaborative decision-making efficiency, realize the continuous optimization of the supply chain, and effectively solve the problems of unsmooth data circulation, low collaborative efficiency and the like in the traditional supply chain management.

The invention relates to the technical field of charging pile power dispatching, in particular to a service area new energy charging management method and system based on multi-device data analysis. The method comprises the following steps: acquiring real-time operation monitoring parameters of all charging piles in a service area; multi-dimensional feature perception and charging behavior time popularity distribution analysis are carried out, and a dynamic charging behavior hotspot map is constructed; carrying out multi-time-point power sampling according to the dynamic charging behavior hotspot map, carrying out power cooperation path topology evolution, and constructing a power cooperation path network; performing charging pile power trend prediction and power peak congestion evolution according to the power cooperation path network, and generating a power peak congestion state feature of the charging pile; and obtaining new energy traffic flow data of the service area, carrying out unit time traffic flow average calculation, and carrying out traffic flow prediction to obtain a traffic flow prediction thermodynamic diagram. Charging power scheduling is carried out through demand prediction, and the operation efficiency and stability of charging equipment are improved.

The invention provides a power system optimization control method and device based on power market transaction. The method comprises the following steps: acquiring real-time electricity price fluctuation data, load demand prediction data and dynamic topology connection data between power grid nodes; generating a simplified network structure adaptive to a transaction scene based on the data, and adjusting an equivalent impedance parameter of a key transmission path in real time; a node voltage amplitude and a phase angle are cooperatively regulated and controlled through a multi-stage regulation mechanism of the solid-state transformer, and a power distribution scheme is formed; monitoring operation data of the solid-state transformer in real time by utilizing an edge computing node, and generating a local control instruction by combining the current equivalent impedance parameter; and carrying out space-time mapping matching on the local instruction and the electricity price fluctuation data, generating a combined control strategy through collaborative priority ranking, and dynamically adjusting a power distribution scheme. According to the technical scheme provided by the invention, collaborative optimization of economical operation and physical transmission performance of the power system is realized.

The invention discloses an accurate flow control method and system for a two-phase cold plate cooling data center, and relates to the technical field of two-phase cold plate cooling, and the method comprises the following steps: S1, collecting multi-mode operation monitoring data in real time, and carrying out the data preprocessing; s2, constructing a multivariable short-time-sequence prediction model, predicting the cooling demand, and performing optimization regulation and control on a cooling demand prediction result; s3, the target regulation and control flow of the cooling liquid is predicted, and flow and cooling execution measures are taken according to the target regulation and control flow prediction result; the opening degree of the valve is accurately adjusted and evaluated in real time, and accurate flow control is achieved; s4, integrating multi-mode operation monitoring data, a cooling demand prediction result, a target regulation and control flow prediction result and a valve opening accurate regulation evaluation result, and constructing a parameter optimization and safety fault-tolerant mechanism; the problems of chip safety and energy consumption risks caused by cold plate temperature overshoot and cooling capacity regulation lag under high-load fluctuation of the server are solved.

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 provides a supercomputing resource intelligent allocation method and system based on AI load monitoring, and the method comprises the steps: firstly obtaining a real-time load data set of a plurality of computing nodes in a supercomputing system, including a resource occupancy rate index and a task queue state parameter, then carrying out the load feature extraction processing of the real-time load data set, generating resource occupancy fluctuation characteristics and task queue evolution characteristics, performing resource demand analysis on the load characteristic set by using a pre-constructed resource demand prediction model, predicting resource demands of each computing node in a subsequent time window, determining resource allocation priorities and migration strategy parameters based on prediction results, and performing resource allocation according to the resource allocation priorities and the migration strategy parameters. The resource dynamic allocation instruction is generated and sent to the target computing node, the resource reallocation operation is triggered, intelligent and efficient allocation of super computing system resources is achieved, the resource utilization rate and the task execution efficiency are improved, and the overall energy efficiency ratio and stability of the system are optimized.

The invention provides a digital twinning-based stereoscopic warehouse goods allocation and sorting scheduling method and system, and relates to the technical field of digital twinning, and the method comprises the steps: constructing a three-dimensional digital model of a stereoscopic warehouse; goods allocation is carried out based on a deep reinforcement learning algorithm, the goods access frequency, the associated purchase probability and the seasonal demand prediction are used as input parameters, and a goods allocation instruction is generated by taking the shortest sorting path and the associated goods centralized storage as optimization targets; warehousing operation is executed, and a sorting order is received; constructing a virtual potential field in the model, and when the repulsive force between stackers exceeds a threshold value, determining a task priority based on the order emergency degree and triggering obstacle avoidance; and calculating an obstacle avoidance track and generating a cooperative scheduling instruction to execute picking operation. According to the invention, efficient goods allocation and intelligent multi-stacker collaborative scheduling are realized.

The invention discloses a supply chain intelligent management system based on dynamic collaborative optimization, and relates to the technical field of hotel supply chain management, and the system comprises a supply chain intelligent management platform which is in communication connection with the following modules: a multi-modal data sensing module, a multi-modal data processing module and a multi-modal data processing module. The multi-modal data acquisition module is used for acquiring multi-modal data in a hotel through a LoRaWAN + BLE hybrid sensor network, accessing an external data source and acquiring real-time information through an API (Application Program Interface); and the dynamic demand prediction module captures a time sequence trend through bidirectional LSTM based on an ASTGNN model. According to the method, external dynamic data such as social media public opinions and weather are fused, the multi-modal data analysis technology is combined, the accuracy of demand prediction is remarkably improved, the ASTGNN model is utilized, the time sequence trend and the demand association between the branches are analyzed in combination with bidirectional LSTM and GCN, and the feature weight is dynamically adjusted, so that the demand prediction error rate is greatly reduced, and the demand prediction efficiency is improved. A more reliable demand prediction basis is provided for enterprises, and optimization of inventory management and production plans is facilitated.

The invention belongs to the technical field of medical resource intelligent management, and relates to a hospital comprehensive monitoring analysis method and platform based on artificial intelligence, and the method comprises the following steps: generating a medicine management basic data set; generating a primary prediction signal; adjusting a prediction weight according to the conflict level and outputting a reliable demand prediction instruction; a quantitative evaluation value reflecting the medicine turnover efficiency, the immediate pressure and the cost loss is calculated, and when the quantitative evaluation value exceeds a preset safety alert threshold value, an inventory abnormity alarm signal is generated; outputting an allocation strategy instruction containing the medicine flow direction, the allocation quantity and the time efficiency; and comparing a deviation value between the reliable demand prediction instruction and the allocation strategy instruction, and when the deviation value continuously exceeds a preset deviation threshold value, triggering an update prompt of the drug safety use limit rule base. The problem that demand prediction and clinical rule verification are separated in a traditional mode is solved.

The invention provides an anti-islanding protection method based on cloud edge collaboration, and the method comprises the steps: obtaining historical fault data, carrying out the data processing of the historical fault data, obtaining a fault mode discrimination result, judging whether a micro-grid has an islanding operation risk or not through combining the topological structure information of a power grid, real-time operation data, and load demand prediction data, and obtaining an islanding protection result. If so, acquiring power output fluctuation data; the output power of the power supply and the load demand power are monitored in real time, if the output fluctuation of the power supply exceeds a voltage protection constant value or the load demand power exceeds a frequency protection constant value, the edge computing gateway starts a local anti-islanding control strategy, and meanwhile, the charging and discharging power of the power distribution network is adjusted; in the anti-islanding control strategy execution process, the edge computing gateway continuously collects real-time operation state data of the micro-grid, the power supply duration, the electric energy quality and the standby capacity of the micro-grid under islanding operation are evaluated according to the operation state data, and the multi-target optimization model and the operation constraint condition of the micro-grid are dynamically adjusted.

The invention discloses a flexible load multi-target collaborative scheduling system and method, and relates to the technical field of collaborative optimization of power systems. The method is used for solving the problem of lack of accurate prediction and multi-target coordination of agricultural electricity and water utilization regulation and control. Firstly, based on meteorological data, soil moisture content and crop growth characteristics, an irrigation demand prediction model is constructed, irrigation water demand is predicted, and a water pump load power baseline is generated; then, a dynamic baseline constraint condition is generated in combination with historical behavior data and water pump start-stop logic; establishing a power grid side objective function, a user side objective function and a water affair side objective function, and introducing an underground water and carbon emission punishment mechanism; thirdly, dividing the power distribution network into sub-regions, adopting an alternating direction multiplier method to solve a region regulation and control strategy in parallel, coordinating water resource distribution conflicts through virtual interactive variables, and outputting a global scheduling instruction; and finally, collecting real-time response data and correcting the prediction model on line to realize closed-loop adaptive optimization.

The invention discloses a converged communication system and method for emergency command and dispatch, and the method comprises the following steps: S1, constructing a converged communication network environment, and configuring a self-adaptive network switching module; s2, collecting and analyzing task demand data in an emergency scene, and generating a task demand prediction matrix; s3, generating a task scheduling rule; s4, acquiring current network state data, and generating task scheduling constraint conditions; s5, constructing and training a multi-agent reinforcement learning model; s6, optimizing a communication path decision strategy and a task scheduling rule by adopting a self-adaptive multi-objective optimization algorithm; s7, calculating an optimal communication path, and executing intelligent adaptive network switching; and S8, collecting task execution feedback data, and adjusting training parameters of the multi-agent reinforcement learning model. According to the method, multi-agent reinforcement learning and dynamic entropy regulation and control optimization are combined, emergency communication task scheduling and path optimization are achieved, and the method has the advantages of being high in adaptability, high in communication stability and excellent in task execution efficiency.

The invention provides a virtualized computing resource scheduling method and system based on a power wireless local area network, and the method comprises the steps: firstly obtaining power equipment operation load data of access equipment in the coverage of the power wireless local area network, including real-time current fluctuation and other features, carrying out the load feature extraction of the power equipment operation load data, and carrying out the load feature extraction of the power equipment operation load data; performing dynamic resource demand prediction on the set based on a preset load prediction model to generate a virtual resource demand prediction result including computing resource allocation magnitude and the like; generating a virtualized resource scheduling strategy containing edge computing node resource allocation topology and the like according to a virtualized resource demand prediction result, and finally dynamically adjusting virtualized computing resources based on the virtualized resource scheduling strategy, triggering resource reallocation and updating a global resource state mapping table. Effective scheduling of virtualized computing resources of the power wireless local area network is realized.

The invention discloses an emergency material intelligent scheduling system and method based on an ant colony algorithm. A demand prediction module of the emergency material intelligent scheduling system dynamically predicts material demands by using a time-space diagram neural network, and constructs a hierarchical network model integrating multi-modal transportation of unmanned aerial vehicles, ground vehicles and the like. And the optimization calculation module adopts an improved ant colony algorithm, introduces a demand urgency degree weight factor and a green weight factor to construct a multi-objective fitness function, and optimizes a transportation path in combination with a dynamic pheromone updating mechanism and a multi-ant colony collaborative strategy. The system is equipped with an edge computing driven dynamic adjustment module to realize 30-second fast path re-planning, a psychological assistance priority model is innovatively integrated, and a psychological crisis index optimization scheduling strategy is extracted through sentiment analysis. According to the method, the problems of response lag and insufficient multi-objective optimization of a traditional scheduling system are effectively solved, the transportation efficiency is remarkably improved by 25%-35%, carbon emission is reduced, and high efficiency, fairness and humanity care of emergency scheduling are guaranteed.

The invention discloses a purchase demand prediction method and system based on big data analysis, and relates to the technical field of supply chain management and intelligent prediction, and the method comprises the steps: collecting multi-source heterogeneous data, and carrying out the data fusion and preprocessing; based on a causal inference technology, key influence factors of purchase demand changes are analyzed, and a purchase demand prediction model is constructed; dynamically adjusting the prediction model by using real-time data, and generating a real-time or periodic purchase demand prediction result; carrying out data simulation and model optimization aiming at an extreme scene of a purchase demand; and optimizing a supply and demand matching strategy based on a prediction result, and carrying out iterative improvement on the prediction model through a feedback mechanism. Through multi-source heterogeneous data fusion, causal inference, dynamic adjustment and feedback optimization mechanisms, high-precision purchase demand prediction is realized, a supply and demand matching strategy is optimized, the adaptability of the model to market fluctuation and extreme scenes is enhanced, the purchase cost is effectively reduced, and the supply chain management efficiency and stability are improved.

The invention provides a supply chain collaborative management method and system based on artificial intelligence, relates to the technical field of artificial intelligence, and quantifies collaborative, substitution and complementary relationship strength among commodities by constructing a commodity association graph network; utilizing a graph attention mechanism to identify a commodity group and calculate a demand influence coefficient, and performing time sequence analysis on historical order data to obtain basic demand prediction; meanwhile, a scene recognition model is constructed by adopting reinforcement learning, a basic prediction result is adjusted according to a current market scene, the demand conduction quantity in the commodity group is calculated in combination with a demand influence coefficient, and final demand prediction is obtained; and finally, based on a prediction result, determining a collaborative decision-making scheme of each participant by applying a cooperative game method, and generating an inventory allocation instruction. According to the method, the complex incidence relation between the commodities and the market dynamic change are accurately captured, the supply chain prediction accuracy and the cooperation efficiency are remarkably improved, and inventory optimization and cost reduction are realized.

The invention discloses an intelligent engineering vehicle scheduling method based on artificial intelligence, and the method comprises the steps: constructing a scheduling objective function which at least comprises the engineering vehicle transportation cost, the task completion time cost, the path congestion cost and the energy consumption cost; establishing a multi-dimensional task demand prediction model based on historical task data and real-time traffic data, and outputting a task distribution prediction value and a traffic state prediction value in a future time period; inputting the model into an engineering vehicle dynamic scheduling model established based on a deep reinforcement learning algorithm for iterative optimization; and generating a real-time scheduling instruction based on the optimized dynamic scheduling model, dynamically allocating task paths and resources of the engineering vehicle, and monitoring an execution state in real time to adjust a scheduling strategy. According to the method, efficient, economical and environment-friendly intelligent scheduling of the engineering vehicle is realized through construction of a scheduling objective function, multi-dimensional demand prediction, deep reinforcement learning dynamic optimization and real-time scheduling and monitoring.

The invention discloses a logistics transportation intelligent prediction scheduling method and system based on digital twinning. The method comprises the steps that transportation carrier data and cargo data are acquired in real time through Internet of Things sensing equipment; constructing a logistics transportation digital twinborn model, wherein the logistics transportation digital twinborn model comprises a physical entity layer, a data fusion layer, a virtual model layer and a decision service layer; simulating a logistics transportation process, and deducing and predicting resource demands in a future time window; the method comprises the following steps: dynamically adjusting marshalling configuration and loading and unloading operation time sequence of transportation carriers by constructing a digital twin-driven elastic resource scheduling mechanism; and training and optimizing the logistics transportation digital twin model based on feedback data and scheduling execution deviation data in the logistics transportation process to form a closed-loop optimization system. The method has the advantages that logistics transportation data are simulated through the digital twin technology, logistics carrier scheduling is predicted, resource demand prediction and elastic scheduling are achieved, and the transportation efficiency is optimized. And the logistics process is ensured to efficiently and flexibly cope with uncertain factors.

The invention relates to the technical field of cloud side end computing, and discloses a cloud side end cooperative computing resource dynamic allocation method based on task-aware migration. The method comprises the following steps: acquiring real-time load characteristics and resource demand characteristics of calculation tasks in a cloud side end system, and dividing task priority queues in combination with task type identifiers; extracting historical execution records of tasks at cloud, edges and terminal nodes, constructing a task execution feature library, and generating a resource demand prediction model in combination with real-time load features; analyzing network transmission time delay characteristics of a cloud end and edge nodes, measuring real-time calculation capability fluctuation data of terminal equipment, and establishing an inter-node resource collaboration degree evaluation matrix; generating an initial migration strategy according to the prediction model and the evaluation matrix, monitoring actual resource occupancy deviation of the task, forming a final decision in combination with a node resource state correction strategy, triggering cross-node migration, and synchronously updating the priority queue and the evaluation matrix.

The invention relates to the technical field of electric vehicle charging management, and discloses an electric vehicle charging optimization management system. The system comprises a dynamic charging demand prediction module which constructs a model based on historical data, collects voltage fluctuation, current phase deviation and charging power pulse characteristics of a charging pile in real time, and outputs a theoretical charging load value; the multi-dimensional demand difference analysis module is used for performing three-dimensional comparison of time accumulation deviation, frequency response difference and pulse characteristic similarity on theoretical and actually measured load values to generate a charging pile group level difference characteristic tensor; the hot spot region association positioning module inputs the difference feature tensor into a spatial topology mapping network, generates a fault probability thermal distribution diagram in combination with power grid node capacity and charging station position information, and positions a charging bottleneck region; and the self-adaptive regulation and control strategy generation module is used for starting millisecond-level monitoring on a high fault probability region according to the thermal distribution diagram and applying a power disturbance test on adjacent nodes. The system can improve the accuracy and effectiveness of charging management.

The invention relates to the technical field of valve opening control, and discloses a valve opening control method for user-side hydraulic balance. The method comprises the following steps: carrying out parameter acquisition on the buried pipe cold storage system to obtain dynamic system parameter data; performing cold load fluctuation spectrum analysis and multi-scale decomposition based on the dynamic system parameter data to obtain a user demand prediction model and a hydraulic balance ideal flow distribution proportion; generating a comprehensive system state evaluation index according to the user demand prediction model and the hydraulic balance ideal flow distribution proportion; constructing a global optimization objective function based on the comprehensive system state evaluation index, and solving to obtain a time-phased valve adjustment strategy; and a valve flow characteristic model is constructed according to the time-phased valve adjusting strategy and the valve historical response data, the target opening degree of each adjusting valve is calculated, and a valve control execution instruction is output. The hydraulic balance strategy can be dynamically adjusted according to different working conditions, and flexible coordination of hydraulic balance and cooling requirements is achieved.