2966 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.

A generative AI-supported system (100) for real-time demand forecasting and inventory optimization in supply chain management, which includes the following: a data collection and integration module configured to aggregate and preprocess structured and unstructured data from multiple sources, including historical sales records, IoT sensors, external market trends, and environmental factors; a generative AI-based forecasting module configured to process input data, recognize complex patterns, and generate highly accurate real-time demand forecasts using deep learning techniques; an inventory optimization module configured to dynamically calculate and adjust inventory levels based on demand forecasts, replenishment plans, lead times, and safety stock requirements; a decision support and analysis module configured to generate actionable insights, provide simulations of multiple scenarios, and recommend cost-effective strategies for supply chain operations; a real-time warning and notification module configured to monitor supply chain activities and inform stakeholders of potential disruptions, anomalies, or demand spikes; a user interface and integration module configured to provide outputs via a user-friendly dashboard and seamlessly integrate with existing enterprise resource planning (ERP), warehouse management systems (WMS), and logistics platforms.

The invention relates to the technical field of logistics transportation scheduling, in particular to a logistics management system based on intelligent scheduling, which comprises a data acquisition module, a demand analysis module, an intelligent scheduling module, a path planning module, a monitoring and feedback module, an optimization module and the like. The data acquisition module is used for collecting information such as goods, vehicles, storage, customer demands and traffic road conditions; the demand analysis module predicts logistics demands by using a time sequence analysis or machine learning algorithm; the intelligent scheduling module adopts a greedy and dynamic programming mixed algorithm to allocate resources; the path planning module calculates an optimal path according to road conditions and starting and ending points; the monitoring and feedback module monitors logistics parameters in real time and feeds back abnormity; and the optimization module optimizes the scheduling scheme according to the fitness function based on the genetic algorithm. According to the invention, data and resources of all links of logistics are effectively integrated, accurate demand prediction, efficient resource configuration, transportation path optimization, real-time monitoring feedback and overall performance improvement of the system are realized, the logistics cost is significantly reduced, and the transportation efficiency and the service quality are improved.

The invention provides a production scheduling calculation method of a production plan scheduling platform. The method comprises the steps of data collection, demand prediction intelligent analysis, productivity planning and bottleneck analysis, production scheduling plan making, real-time monitoring and dynamic adjustment, plan execution and feedback analysis and report generation and decision support. Through distributed scheduling and real-time dynamic adjustment, the production plan can respond to emergencies such as equipment failure and order change in production in time, the continuity and stability of the production process can be ensured, production stagnation or delay caused by the emergencies is avoided, meanwhile, the production scheduling plan can be rapidly adjusted through real-time data feedback, and the production efficiency is improved. Resource utilization is optimized, waste is reduced, production efficiency is improved, intelligent productivity analysis and bottleneck identification are utilized, an enterprise can comprehensively know the use condition of each production resource, and possible bottleneck problems are found and solved in advance.

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 relates to the technical field of server resource scheduling, in particular to a server resource scheduling system integrating AI and edge computing. The server resource scheduling system integrating the AI and the edge computing comprises a computing node module, a resource calling module, a task traversal module, a demand prediction module, a resource allocation module and a load scheduling module, the computing node module is used for monitoring the real-time running state of a server computing node and performing distributed node architecture fitting, and the load scheduling module is used for scheduling the load of the server computing node; constructing a distributed server node network; the resource calling module is used for acquiring a server running log; performing server resource calling behavior analysis on the server running log, performing resource calling time sequence characteristic evolution, and generating a server resource calling time sequence characteristic; and the task traversal module is used for carrying out unprocessed task traversal identification on the server running log and constructing an unprocessed task state mapping sequence. According to the invention, efficient and load-balanced server resource scheduling is realized.

The invention relates to the technical field of model optimization, in particular to an e-commerce inventory dynamic optimization model system based on real-time big data, which comprises an inventory information sharing unit, an intelligent replenishment algorithm unit and an inventory distribution strategy unit. In combination with edge calculation and block chain technologies, real-time collection, processing and secure sharing of inventory data are realized, an intelligent replenishment algorithm unit obtains data from a block chain platform, a demand prediction model is constructed by using a gating circulation module, and an automatic replenishment module sets a replenishment threshold and adjusts replenishment amount in combination with risk factors. The multi-objective optimization algorithm selects an optimal supplier, and the inventory distribution strategy unit formulates and optimizes an inventory distribution rule by applying a reinforcement learning algorithm based on real-time data, adjusts an inventory distribution proportion and uploads the inventory distribution proportion to the block chain, so that the inventory management efficiency is improved, the cost is reduced, and dynamic inventory optimization is realized.

The invention provides an automatic resource matching method and system based on trust degree dynamic grading, and relates to the technical field of data analysis and decision making, and the method comprises the steps: building a feature vector matrix through obtaining user historical behavior data, generating a trust degree grading model through the training of a deep learning model, and carrying out the trust degree grading of a target user; determining an accessible system resource pool based on the credibility level, collecting real-time service demand information, and generating a target resource configuration scheme through a resource demand prediction model; and the resource use efficiency is monitored in real time, the credibility level is dynamically updated, and resource redistribution is carried out. Accurate matching and dynamic adjustment of system resources are achieved, the resource utilization efficiency is improved, resource waste is reduced, and the safety and stability of system operation are guaranteed.

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.

The invention relates to the technical field of raw material management systems, in particular to a raw material management system for building engineering construction. The management system comprises a raw material information management module, a purchase management module, an inventory management module, a use management module, an optimization allocation module, a raw material quality evaluation module, an environmental impact evaluation and material selection module, a supply chain transparency module, a life cycle management module and a raw material demand prediction and inventory optimization module. An automatic purchase and supplier collaborative optimization module; the system uniformly manages data information of links of raw material purchasing, inventory, use and the like through a centralized data platform, forms a complete material management chain, avoids information dispersion and lag, realizes real-time sharing of data of each link, improves transparency of management and accuracy of decision making, and meanwhile, greatly improves management efficiency. The complexity and error rate of manual operation can be remarkably reduced through automatic purchase order generation, intelligent inventory management and supplier collaboration.

The present invention relates to the technical field of new energy vehicles, and specifically to a load balancing and scheduling method for charging piles of new energy vehicles based on intelligent algorithms, comprising the following steps: S1, data collection: real-time collection of operating data of charging piles; S2, charging demand prediction: prediction of the charging demand of each charging pile within a predetermined time in the future through a graph convolutional network model; S3, charging pile spatial scheduling optimization: spatial optimization of the scheduling of charging piles according to the geographical distribution of charging piles, the flow of surrounding vehicles and the results of charging demand prediction; S4, load scheduling and resource allocation: based on the results of charging demand prediction and charging pile spatial scheduling optimization, combined with the load of the power grid, resource allocation is performed. The present invention improves the utilization efficiency of charging pile resources, optimizes the layout of the overall charging network, and ensures the fairness of charging services and the balance of power grid load.

A computer-automated system performs slotting optimization based on inputs such as any one or more of the following: sales history, Advanced Shipment Notices (ASNs), picking history, current inventory, pick history, demand forecast, location placement, and multiple warehouse configuration parameters (e.g., slotting rules configurable by the user). Based on those inputs, the system detects product affinities, builds a predictive order book, and accounts for re-slotting costs and runs through multiple simulations to generate a slotting plan. The system receives feedback on its outputs and learns based on that feedback, thereby continuously improving the slotting recommendations that it generates.

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.

A real-time inventory management system that includes: a centralized control platform module configured to receive, consolidate, and process inventory data from multiple sources, including warehouses, retail stores, and distribution centers; an IoT and sensor module with RFID tags, barcode scanners, and smart sensors configured to track inventory items, monitor environmental conditions, and update inventory records in real time; a machine learning and demand forecasting module configured to analyze historical inventory data, market trends, and external factors to predict future demand and optimize inventory levels; an automatic replenishment and replenishment module configured to dynamically generate and execute replenishment orders based on real-time inventory levels and forecasted demand; a data integration and synchronization module configured to enable seamless communication and data synchronization with external systems, including Enterprise Resource Planning (ERP) platforms and supplier networks; a user interface and dashboard module configured to display real-time inventory status, actionable insights, and key performance indicators (KPIs) for decision-making.

The invention discloses an intelligent management system for a service consultation platform, and the system comprises the steps: processing data through a data collection and processing module; using a data fusion unit to construct a feature matrix for the cleaned multi-source data; based on a demand prediction module, generating a prediction result of the user consultation demand; user input is recognized through a semantic analysis and question and answer module; the analyzed semantic information is transmitted to a knowledge management module, matched content is retrieved from a knowledge base based on a semantic similarity matching algorithm, and answers are generated after ranking according to priorities; information in the service process is collected through the service quality dynamic evaluation module for scoring, an optimization scheme is made through the service optimization strategy generation module, and the optimization scheme is fed back to the evaluation module to achieve closed-loop management. According to the method, the prediction capability and the service efficiency of the platform are remarkably improved, and more efficient and more personalized consultation experience is provided for the user.

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.