152 results about "Inventory optimization" patented technology

The invention discloses an intelligent warehouse optimization management platform based on digital twinning and space-time prediction, which relates to the technical field of intelligent warehouse management and comprises a digital twinning model construction module, a space-time prediction module, an inventory optimization management module and a user interaction module. The digital twinborn model construction module realizes digital mapping of a physical warehousing system by constructing a warehousing space three-dimensional model and associating warehousing sensing data, and the space-time prediction module constructs a prediction model to obtain inventory prediction information in a future time period, and performs inventory risk assessment and early warning according to the inventory prediction information; the inventory optimization management module generates an inventory strategy and an optimized storage position according to the prediction result, the storage cost and the cargo demand, and formulates a warehouse-in and warehouse-out task scheduling scheme; and the user interaction module visually presents the warehouse management data and receives a user interaction operation instruction to realize man-machine collaborative management, and the platform reduces the vacancy rate of the warehouse space and the interruption risk of the supply chain, and reduces the invalid carrying energy consumption.

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 a federated learning-driven cross-domain supply chain elastic inventory optimization system and a method thereof, and aims at realizing inventory data collaboration among same-level enterprises or regional nodes through transverse federated learning and ensuring data security by adopting a self-adaptive differential privacy protection mechanism. The method comprises the steps of constructing a transverse federated learning network, locally performing data preprocessing, adding differential privacy noise, iteratively training a global model based on federated deep reinforcement learning, generating a transverse inventory allocation and replenishment decision, and performing model adaptive adjustment in real time based on key performance indicators. According to the method, multi-target balance is considered, inventory configuration is dynamically optimized through a multi-target reward function, the inventory turnover rate is remarkably increased, the inventory holding cost is reduced, the service level is improved, and the method is suitable for various scenes such as retail chain, manufacturing industry distributed storage and cross-regional logistics distribution; and global optimal inventory configuration is realized on the premise of ensuring data privacy.

The invention provides a retail supply chain optimization system and method based on big data analysis, and relates to the technical field of big data analysis. The system comprises a data acquisition module used for acquiring order data, logistics data and user behavior data and generating structured data; the data processing module is used for preprocessing the structured data and establishing a multi-dimensional association relationship among the data based on a preprocessing result; the inventory prediction module is used for predicting future order demands, calculating regional inventory demands and generating inventory distribution data; the inventory optimization module is used for generating replenishment data and an inventory adjustment strategy based on the inventory distribution data; the behavior analysis module is used for generating commodity recommendation data and promotion priorities based on the user behavior data in combination with the adjusted inventory distribution data; and the logistics allocation module is used for dynamically adjusting the logistics distribution path and the distribution priority. According to the technical scheme, the operation efficiency and flexibility of the retail supply chain can be improved.

The invention provides a multi-warehouse demand management method, device and equipment and a storage medium, and the method comprises the steps: carrying out the exchange rate sensitivity correlation analysis processing of demand fluctuation data in a cross-border e-commerce multi-warehouse network, and obtaining the exchange rate elasticity coefficient early warning information of each warehouse node; performing distributed coordination decision processing on the logistics constraint condition of each warehouse node according to the early warning information to obtain a decision scheme of resource allocation between warehouses; performing block chain trusted measurement processing on inventory distribution information in the multi-warehouse logistics alliance chain according to the decision scheme to obtain a multi-warehouse resource reconfiguration execution instruction based on the smart contract; and performing adaptive exchange rate risk avoidance processing on the inventory configuration strategy of each warehouse node according to the execution instruction to obtain a multi-warehouse collaborative inventory optimization management result. According to the method, the problem of influence of exchange rate fluctuation on multi-warehouse demand management is effectively solved through exchange rate sensitivity analysis and distributed coordination decision.

The application relates to the technical field of supply chain management, in particular to a supply chain management system based on big data, which comprises a data integration module, a demand prediction module, an inventory optimization module, a collaborative scheduling module and a feedback optimization module. The system realizes supply chain global state analysis and demand prediction through multi-source heterogeneous data collection and a deep learning model, combines dynamic inventory adjustment and cross-regional resource scheduling optimization, and improves the supply chain efficiency. The feedback optimization module further generates optimized control instructions by analyzing and intervening in the time delay of historical records. The application can improve the response speed and resource utilization efficiency of the supply chain and reduce the operation cost.

The invention relates to the technical field of warehouse management, and discloses a warehouse management system and method based on dynamic inventory prediction. According to the invention, market, sales, supply chain and warehouse internal data are collected in real time, a dynamic inventory prediction model is constructed in combination with a long short-term memory (LSTM) network, the future inventory demand can be accurately predicted, and the prediction precision is significantly improved; an inventory optimization strategy is generated based on a multi-objective optimization algorithm, so that the inventory holding cost, the stockout cost and the transportation cost are effectively reduced; and intelligent warehouse operation instruction generation and real-time monitoring are realized through the execution control module, and the response capability of an enterprise to market fluctuation and emergencies is enhanced. According to the system and the method, the warehouse management efficiency is improved, the operation risk is reduced, and an efficient and reliable solution is provided for modern logistics and supply chain management.

The invention relates to an AI-based supply chain demand prediction and inventory optimization method and system, and the method comprises the following steps: calling a sales record from a supply chain, recognizing a periodic demand rule of a corresponding commodity, and predicting an estimated demand quantity in a future time period according to the periodic demand rule; carrying out supply and demand difference analysis by combining the current warehouse commodity stock margin, and judging the stock state; if the commodity stock surplus exists, making a corresponding stock digestion strategy; if a shortage quantification result appears, carrying out key degree evaluation and urgency sorting based on a shortage amount in combination with market factors, and generating a key commodity list and an out-of-stock urgency index; and finally, in combination with a preset commodity constraint condition, according to the key commodity list and the urgency index, an optimal replenishment scheme meeting resource and time limit is formulated, and the technical problem of how to realize conversion from passive response to active pre-judgment in a supply chain environment with large demand fluctuation and complex influence factors is solved.

The invention relates to a supply room consumable intelligent inventory management method and device, equipment and a medium. The method comprises the steps that multi-source sensing data of all consumables in a supply room are acquired, multi-source information fusion processing is carried out according to the multi-source sensing data, and inventory state posterior distribution of all the consumables is obtained; performing event correction processing on the inventory state posterior distribution, and performing demand prediction processing according to the inventory state distribution subjected to event correction and the historical consumption data of the consumables to obtain a multi-step demand prediction result of each consumable in the target prediction period; performing inventory optimization calculation processing according to the multi-step demand prediction result and the inventory state distribution subjected to event correction to obtain the replenishment order quantity of each consumable in the target prediction period; and according to the replenishment order quantity and the batch validity period information of each consumable, performing consumable scheduling and risk early-warning processing, and generating an immediate consumable priority use strategy and inventory risk early-warning information. By adopting the method, the consumable stock can be identified, and the replenishment order can be generated.

The invention relates to the technical field of inventory optimization, in particular to an intelligent supply chain demand prediction and inventory optimization system, which comprises a supply chain dynamic demand prediction module for intelligently predicting the demand quantity of a supply chain, and an inventory intelligent optimization module for intelligently optimizing the inventory according to the demand prediction result of the supply chain. The prediction correction and optimization adjustment module is used for dynamically correcting demand prediction and intelligently optimizing and adjusting inventory; according to the invention, through establishment of a three-channel cross validation prediction and anomaly elimination mechanism, single-channel prediction anomaly can be resisted, the stability and accuracy of supply chain demand prediction are improved, and inventory risks caused by prediction errors are reduced; the safe inventory level is dynamically set according to different stages, so that the inventory strategy better conforms to the commodity market change, and the shortage rate and the unsalable inventory are reduced; the correction amplitude is dynamically adjusted through error fluctuation, excessive correction is avoided, and the self-learning and self-evolution ability of a long-term supply chain prediction system is improved.

The invention discloses a tallying method for carrying out inventory optimization storage through automatic ABC classification calculation, and the method comprises the steps: setting the classification attribute of a storage location, carrying out the initialization classification setting of goods, automatically calculating the classification attribute of the goods according to a set period, checking whether the classification attribute of the goods is matched with the classification attribute of the current storage location or not, and carrying out the transfer of the storage if the classification attribute of the goods is not matched with the classification attribute of the current storage location. And after the target storage location is positioned, generating a warehouse moving task from the initial storage location to the target storage location, and putting a plurality of tallying tasks into one tallying task group. The method has the advantages that data calculation is intelligent, data actually generated in a warehouse is analyzed and calculated and then executed according to a configured rule, manual experience is not relied on any more, and the tallying efficiency and accuracy are improved.

The invention relates to the technical field of sales data management, and discloses an electronic component online sales data management and maintenance system, which comprises a data acquisition and processing module, a user demand matching module, a data verification module, an inventory optimization module and the like. The data acquisition and processing module acquires multi-dimensional sales data, and performs natural language processing and dynamic knowledge graph construction, processing and data association; the user demand matching module generates an initial matching result based on a collaborative filtering algorithm and semantic clustering; the data verification module verifies the data by using a Hash algorithm and a block chain network; and the inventory optimization module optimizes the inventory according to the timeliness weight factor and a reinforcement learning algorithm. In addition, the system is further provided with an abnormal transaction detection module, a multi-source data integration module and an interactive query optimization module which are respectively used for detecting abnormal transactions, integrating multi-source data and optimizing query. The system effectively solves the problem of electronic component online sales data management, and improves sales efficiency and user experience.

The invention relates to the technical field of digital management, in particular to a data analysis system based on digital enterprise management. The system comprises a data acquisition module, a model establishment module, an inventory adjustment module and a risk assessment module. According to the invention, competitor data, market promotion data and raw material supply chain data are collected through the data acquisition module, a neural network model for predicting enterprise sales is established by using the model establishment module, and the inventory adjustment module calculates the order quantity of the product by using an economic order quantity model according to the predicted enterprise sales. The maximum inventory level of an enterprise is determined through a reorder point algorithm and safe inventory setting, the risk assessment module comprehensively analyzes various indexes by using an analytic hierarchy process to obtain a comprehensive risk assessment result, an inventory optimization strategy is formulated according to the assessment result, and the risk of the enterprise is optimized. The inventory turnover rate is improved, the customer satisfaction is improved, and the market competitiveness is enhanced.

A metering equipment warehouse division demand prediction and scheduling method based on spatio-temporal feature fusion comprises the steps of firstly obtaining and collecting warehouse division data, then establishing a spatio-temporal feature fusion model based on the warehouse division data, and then establishing a spatio-temporal diagram convolution prediction network, the spatio-temporal diagram convolution comprises a diagram convolution layer and a time convolution layer, through extraction and fusion of the two layered features, dynamic weight fusion is obtained, joint modeling of space-time dynamics is realized, and finally, prediction and allocation decision are implemented based on the dynamic weight fusion. According to the method, the multi-modal graph structure fusing the space-time association and the replacement rule is constructed, and a space-time joint modeling architecture and a dynamic feedback mechanism are designed, so that high-precision demand prediction and global inventory optimization are realized; the method systematically solves the core problems of insufficient spatial correlation modeling, dynamic event response lagging, low efficiency in multi-source data utilization and the like of a traditional method, and provides an efficient solution for electric power material management.

The invention discloses a big data measurement asset supply and demand matching and inventory optimization method, and relates to the technical field of big data processing and supply chain management. The method comprises the following steps: segmenting supply chain asset data through a hash function according to multi-dimensional attributes to generate a data fragment set, and constructing a global index tree according to the data fragment set; and monitoring the load state of the leaf nodes of the global index tree in real time, and migrating and verifying data during unbalance. And querying the global index tree, positioning fragment data associated with the query request from the uniformly distributed leaf nodes, generating a preliminary matching asset set, calculating the matching degree of each asset and the query request, and generating a resource allocation result. And updating the global index tree, and generating the latest data view representation. And adjusting the attribute weight coefficient of each dimension in the hash function, and generating optimized storage layout configuration. Irrelevant data are filtered through a neighbor algorithm, and a final matching result set is generated. Balanced storage, efficient query and accurate matching of asset data are realized, and the overall response speed and the resource utilization rate are improved.

The invention belongs to the technical field of medicine inventory management, and discloses a Western medicine intelligent inventory optimization and automatic replenishment decision-making method and a Western medicine intelligent inventory optimization and automatic replenishment decision-making system. Comprising the steps of collecting environment data, inventory information and historical inventory consumption data of inventory drugs, and integrating the environment data and the inventory information to obtain a health degree index of each drug; based on the drug health degree index and the consumption prediction result, generating a replenishment decision matrix, and outputting replenishment opportunity, replenishment amount and replenishment priority; in combination with an operation audit result and inventory area division, differential inventory management scheme configuration is realized; and after external information is obtained, a replenishment strategy is dynamically adjusted, and a target inventory optimization decision is formed. According to the method, accurate evaluation of the inventory state and intelligent decision of replenishment response are realized, and the safety, efficiency and refinement level of medicine inventory management are improved.

The invention discloses a digital store management method and system, and the method comprises the steps: obtaining multi-source data in real time according to customer behavior data, sales data, inventory data and environment data of a store, and generating a time-synchronized multi-source data set; inputting the multi-source data set into a behavior analysis model, extracting space-time mode characteristics of customer behaviors, and generating a customer behavior analysis report; generating an intelligent replenishment scheme according to the customer behavior analysis report and the inventory data; and inputting the customer behavior analysis report and the intelligent replenishment scheme into a visual platform based on augmented reality, generating a visual chart of store operation, and generating a final digital store management scheme through an interactive operation interface and a multi-dimensional screening function. According to the embodiment of the invention, an efficient, intelligent and flexible store management solution can be provided, and the whole-process intelligentization of data acquisition, behavior analysis, inventory optimization and visual decision is realized.

The invention relates to an inventory management method and system based on demand prediction. The method comprises the following steps: acquiring sales time sequence data, a point consumption curve, commodity shelf life data, external environment factors and other data; feature extraction is carried out on the integral consumption curve, and demand prediction processing is carried out based on sales time sequence data, integral urgency factors, regional integral density and external environment factors; calculating a safety stock threshold value of each warehouse according to the commodity demand prediction result and the integral urgency factor; and generating an optimal allocation path scheme based on the safe inventory threshold value and the current inventory state, generating a regional promotion scheme according to the commodity shelf life data, the meteorological data and the like, and executing the optimal allocation path scheme and the regional promotion scheme. According to the method, through demand prediction, inventory optimization allocation, promotion strategy collaboration and the like, the accuracy of inventory management is improved, complex demand fluctuation can be effectively dealt with, refined management and control of the inventory are realized, and the flexibility and adaptability of inventory management are enhanced.

The invention provides a cold chain supply chain meat inventory optimization regulation and control method and system, and the method comprises the steps: collecting multi-dimensional data to construct an impact factor data set, fusing a long and short-term memory network model, a gradient boosting tree algorithm model and an attention mechanism, and precisely predicting meat demands. Comparing the inventory, the threshold value and the predicted value, and generating an instruction according to a preset rule; and continuously optimizing the model through online learning, error evaluation and rolling prediction. According to the invention, the problem of overstock or stockout caused by inaccurate demand prediction and lack of a scientific regulation and control mechanism in the existing cold chain supply chain meat inventory management is solved.

The invention discloses a multi-target inventory optimization method based on dynamic demand prediction and an intelligent decision-making system, and the method comprises the steps: firstly collecting a cache library inventory state of a cache library system, including considering production order data and AGV transportation capability data, and carrying out the standardization processing of the cache library inventory state; inputting the data into an LSTM neural network model of a dynamic demand prediction module to predict raw and auxiliary material demands in a future time period; the replenishment amount and the replenishment period are solved based on an improved multi-objective optimization algorithm NSGA-II, and the optimization objectives are the lowest total cost, the lowest stockout rate and the lowest inventory fluctuation; issuing a replenishment instruction to the cache library system through the production management system, and scheduling the AGV to execute a transportation task; and the inventory state is monitored in real time in the production process, strategy adjustment or model retraining is triggered according to the fluctuation coefficient, and finally a zero-stockout, low-fluctuation and optimal-cost replenishment strategy is output, so that the method is suitable for a high-dynamic manufacturing environment.

The invention relates to the technical field of inventory optimization, in particular to a multilevel guarantee-based maintenance resource optimization method, which comprises the following steps of: determining a resource allocation quantity based on a guarantee system and a guarantee resource total number, and determining a maintenance function of each base station in combination with spare part historical use data; determining the annual average demand quantity of the superior replacement unit based on the base-level replacement unit, and further determining a superior supply channel mean value and a superior expected stockout number; determining a basic-level supply channel mean value and a basic-level expected stockout number based on the annual average demand quantity, the maintenance function and the superior expected stockout number so as to determine the availability of each basic-level equipment and the total availability, and constructing a resource allocation optimization model based on the total availability and the resource allocation quantity. And performing calculation based on the model to obtain the maximum total availability, the maintenance resource allocation quantity and the inventory configuration. According to the method, dynamic allocation of resources can be guaranteed, and the problem of marginal effect decline existing in single-way improvement is solved, so that the availability level is improved.

The invention discloses an inventory optimization system and method based on multi-source data, and belongs to the technical field of inventory optimization. The production data and the sales data are acquired and fused to obtain the multi-source data, the LSTM neural network is trained by using the multi-source data, and the future market demand is predicted; on the basis of future market demands, in the aspect of space resource consumption and the aspect of time resource consumption, target functions and constraint conditions are set respectively, the corresponding target functions are solved, and the minimum warehouse resource consumption is obtained through calculation; processing the historical sales data by using a moving average method, and predicting the future sales volume of the warehouse products; combining the future sales volume with the existing stock volume and the in-transit volume, and calculating the replenishment volume; obtaining logistics data, determining the replenishment time according to the logistics transportation time, and adjusting the replenishment amount according to the transportation capacity; and based on the replenishment time and the replenishment amount, using a dynamic planning algorithm to obtain an optimal distribution frequency, and matching the optimal distribution frequency with the inventory to obtain an inventory use plan.

The invention relates to the technical field of logistics data processing and artificial intelligence, and discloses an intelligent warehouse management system based on logistics data processing, and the system comprises a multi-source heterogeneous data fusion preprocessing module which carries out the real-time collection of the data of a global distributed storage center, and outputs a time-space standardized multi-dimensional feature data set; the spatio-temporal correlation demand prediction module is used for constructing a spatio-temporal model and carrying out depth prediction by adopting a long-short-term memory network and an attention mechanism; the multi-constraint global inventory optimization module is used for constructing a multi-objective optimization model and solving an inventory configuration scheme through a decomposition coordination method; the reinforcement learning intelligent scheduling module is used for establishing an environment state space and performing strategy optimization by adopting a deep Q network; the digital twinborn visual decision support module is used for realizing real-time synchronization of physical storage and a digital model and outputting decision suggestions and risk early warning; according to the method, multi-source heterogeneous data can be effectively processed, intelligent prediction of cultural perception is realized, and global collaborative optimization is carried out.

The invention provides an intelligent storage demand prediction scheduling method and system combined with an AIAgent, and the method comprises the steps: obtaining a historical storage data set of a target storage scene, carrying out the demand feature extraction processing through the AIAgent, and generating a storage demand fluctuation feature and an inventory dynamic distribution feature; the method comprises the following steps: constructing an inventory-demand matching model based on warehousing demand fluctuation characteristics and preset market trend prediction data, calling a dynamic path optimization algorithm to generate a warehousing operation sequence according to inventory dynamic distribution characteristics and inventory adjustment priorities, and carrying out collaborative scheduling processing on the warehousing operation sequence based on replenishment trigger conditions, and generating an optimized warehousing operation instruction set, and feeding back the warehousing operation instruction set to the warehousing control system to execute inventory optimization operation. The method can effectively reduce the inventory redundancy caused by the demand prediction deviation and the operation cost caused by path repeated planning, and improves the real-time response capability of the warehousing system to the market fluctuation and the resource utilization rate.

The invention provides a method and system for constructing a multi-source time series data fusion prediction model in BI analysis, and relates to the technical field of enterprise-level business intelligent analysis, and the method comprises the steps: obtaining multi-source original time series data, and carrying out the standardization preprocessing; obtaining a multi-dimensional feature set through time sequence feature extraction and business feature extraction; dynamically fusing the time sequence features and the service features by adopting an attention mechanism, and constructing an LSTM and XGBoost combined prediction model for training; performing parameter optimization based on the independent verification set to obtain a final prediction model; the system comprises a data acquisition and preprocessing unit, a multi-dimensional feature extraction unit, a fusion prediction model construction unit and a model verification and optimization unit. According to the method, the problems that multi-source time series data fusion is insufficient, the model is difficult to capture complex spatial-temporal characteristics and the dynamic adaptive ability is lacked are solved, the accuracy and the real-time performance of enterprise-level BI analysis such as sales trend prediction and inventory optimization are improved, and the prediction precision is improved.

The invention provides a hospital equipment department consumable management method based on an AIGC technology. The hospital equipment department consumable management method comprises the following steps of S1, performing multi-modal data acquisition and fusion modeling; s2, a step of constructing a generative demand prediction model; s3, generating a dynamic inventory optimization strategy; s4, performing semantic generation of an intelligent replenishment strategy; s5, a supplier portrait and intelligence matching step is carried out; s6, automation of compliance review is carried out; s7, carrying out three-dimensional digital twin modeling; s8, performing an anomaly detection and self-healing mechanism; s9, performing consumable full life cycle tracing; and S10, performing a cognitive evolution system. According to the method, mathematical modeling and clinical scenes are deeply fused, compared with a traditional SPD mode, leap-type upgrading is achieved in three dimensions of inventory cost, management efficiency and medical safety, and technical support is provided for hospital refined operation under DRG / DIP payment reform.

The invention provides an AI-driven supply chain sales prediction and intelligent inventory optimization system and method, and the system comprises a data preprocessing module which is used for docking with a supply chain order system; the sales prediction module adopts a deep neural network with an adaptive sparseness evolution mechanism; the inventory optimization decision-making module adopts a qualification trace-based streaming reinforcement learning architecture and abandons experience playback cache and a target network structure; the expert model auxiliary module adopts a confidence-based three-level intervention mechanism; and the strategy feedback updating module adopts an incremental parameter updating strategy to realize prediction-optimization-feedback closed-loop iteration. According to the method, a streaming reinforcement learning method is adopted, a traditional experience playback structure is abandoned, and real-time decision making and continuous optimization in a high-frequency order scene are achieved; a sparse sensing network and a confidence regulation mechanism are introduced, and the adaptability of the system to cold start, small samples and jump scenes is enhanced.

The invention discloses a military strength resource scheduling method based on multi-objective optimization, which comprises the following steps: analyzing a strategy selection problem in a confrontation environment through a game theory model according to a resource demand matching degree and an enemy action prediction result, and if an enemy threat mode changes, triggering a self-adaptive adjustment mechanism specified by scheduling, dynamically correcting resource allocation strategy parameters by adopting a reinforcement learning algorithm to obtain an optimal scheduling rule adapted to the current confrontation situation; and calculating a resource consumption rate and supplement demand prediction according to an accurate resource allocation result, predicting a consumption trend of various resources in a future time period through a time sequence analysis algorithm, if the predicted consumption rate exceeds the supplement capability, triggering a resource allocation plan in advance, and determining an optimal resource reserve and supplement strategy by adopting an inventory optimization model. According to the method, the military strength resource scheduling efficiency and the combat effectiveness in a complex confrontation environment are effectively improved.

A system and method for closed-loop tracking of advertising events through offer redemption with artificial intelligence feedback. A session identifier links device, pass, content, and contextual data throughout a content playback session. Advertisements dynamically inserted into the content stream inherit the session identifier. An offer platform generates offers linked to the session identifier and pass identifier, enabling tracking through distinct lifecycle states from push to redemption. A database captures timestamped and geo-referenced data for each event. An artificial intelligence engine uses redemption and avail events as verified ground-truth outcomes for training machine learning models, enabling supervised learning that establishes causal relationships between ad exposure and purchasing activity. An inventory gateway receives merchant product inventory data. An optimization engine employs demand forecasting and reinforcement learning to generate inventory-driven triggers and offer recommendations. A merchant interface presents analytics for inventory optimization.

The invention relates to an intelligent optimization system for flexible supply of electric power materials, and belongs to the technical field of material supply management. The system comprises an intelligent warehouse management unit, a dynamic inventory optimization unit, an intelligent scheduling unit and an emergency response management unit. The intelligent warehouse management unit comprises a regional warehouse management sub-module and a turnover warehouse management sub-module; the dynamic inventory optimization unit comprises a multi-stage inventory linkage module and a loss early warning module; the intelligent scheduling unit comprises a calculation processing module and a transportation resource pool module; the emergency response management unit comprises a grading early warning module and a quick response module. Through inventory position linkage of the regional bins and the turnover bins, the replenishment period is shortened to be within 4 hours, emergency materials can be allocated across regions, the response time is shortened by 33% or above compared with a traditional mode, the supply flexibility is greatly improved, the storage supply rigid requirement for a nearby warehouse of a project is lowered, and supply is more flexible.