109 results about "Stockout" patented technology

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 discloses an e-commerce supply chain intelligent scheduling optimization system and method based on big data, and relates to the field of intelligent logistics scheduling, and the system comprises a demand prediction module, a balance optimization module, a path optimization module, a collaborative replenishment module and a feedback driving module. According to the method, the deep learning model and the time sequence decomposition method are combined, and multi-level modeling is performed on the historical order data and the real-time sales data, so that supply chain imbalance caused by prediction deviation is avoided. A dynamic replenishment plan is generated based on the prediction result and the inventory early warning information, real-time matching of the inventory and the demand is achieved, the stockout rate and inventory redundancy are effectively reduced, and the storage resource utilization rate is improved. By establishing the multi-constraint path optimization model and comprehensively considering order distribution, vehicle load and real-time traffic data, the distribution path can be dynamically adjusted, the vehicle utilization rate and the distribution time efficiency are improved, and the energy consumption and the cost caused by empty driving and detour are reduced.

The invention discloses an optimization method and system for aviation equipment storage and a medium, and the method comprises the steps: collecting multi-dimensional dynamic data, carrying out the fusion cleaning and structural feature extraction, and generating a high-dimensional feature set; outputting a dynamic demand prediction value and uncertainty measurement by using a hybrid intelligent prediction model fusing time sequence prediction and ensemble learning; calculating an optimal inventory control parameter through a stochastic optimization model in combination with the aviation material key grade and the stockout loss cost; and generating an inventory operation instruction according to the optimal inventory control parameter and the real-time inventory state, and outputting a strategy report with confidence evaluation and key influence factor analysis. According to the method, multi-dimensional accurate prediction of the aerial material demand and dynamic optimization of the inventory strategy are realized, the inventory control precision and the resource utilization efficiency are remarkably improved, the stockout risk and the overstocked cost are effectively reduced, and the reliability and the economical efficiency of aviation equipment guarantee are enhanced.

The invention discloses a full life cycle management system for an enterprise product supply and demand order contract, relates to the technical field of enterprise supply and demand contract management, and is used for solving the problem of low efficiency of supply and demand performance collaboration. Through multi-source business event collection and unified coding time reference, records dispersed in a business system are integrated into an event stream sorted according to time, a supply and demand event chain and a life cycle unit corresponding to a contract are automatically identified, the performance state is visible and the abnormity is traceable, and the efficiency is improved. The performance terms and the settlement terms are analyzed into computable constraints, cooperative control of a performance task network and resources is driven, resource demand intervals are generated according to uncompleted tasks and constraints, key resource states are compared to identify resource conflicts, and processing control items such as production, delivery, purchase or term adjustment are generated, so that stockout and delay risks are reduced; and the performance stability and the resource utilization efficiency of the enterprise are improved.

The invention relates to the technical field of commodity purchase-sale management, in particular to a commodity purchase-sale-stock real-time management system. The commodity purchase-sell-stock real-time management system is provided. According to the invention, the environment sensing module collects dynamic data during the business period in real time, including the real-time stock of commodities, the remaining time of the shelf life of the commodities, the passenger flow volume of nearly 10 minutes and the like, thereby effectively solving the problem that the data update of a traditional purchase-sales-stock system is lagged, ensuring the real-time accuracy of stock and price information, and improving the real-time performance of the purchase-sales-stock system. Meanwhile, whether the commodities need to be replenished or not is judged in real time through a dynamic analysis module according to a preset safety inventory threshold value, the safety replenishment amount is calculated, the risk of stockout or excessive accumulation is reduced, the inventory turnover rate is increased, a price reduction sales promotion instruction is automatically triggered through the dynamic analysis module according to the real-time risk value of the commodities and the preset threshold value, and the price reduction sales promotion efficiency is improved. The price is quickly refreshed by using the electronic price tag, and the freshness change of the short-term preserved commodity is responded in time, so that the high loss rate is reduced, and the shopping experience of customers is improved.

The invention relates to the technical field of logistics management systems, and particularly discloses an artificial intelligence-based supply chain logistics information management system, which comprises a data acquisition module, a demand analysis module, a logistics decision module, a dynamic sensing module and a supply chain monitoring module. Through multi-source data acquisition and standardized preprocessing, the problem of traditional system data is solved, and a foundation is laid for analysis; on the basis of an improved LSTM model and a dynamic threshold value, demands are accurately predicted, risks are early warned, and inventory and stockout losses are reduced; a hybrid algorithm is adopted to collaboratively optimize paths, assess inventory and dynamically adjust environmental interference, and balance logistics efficiency and cost; a toughness index is generated by means of an Attention-LSTM model, and risk quantitative traceability and rapid disposal are realized in combination with hierarchical response and visualization; the modules are in real-time cooperative closed loop, cooperate with parameter self-optimization and threshold updating, adapt to industry and environment changes, and provide stable and intelligent support for supply chain management.

The invention relates to the technical field of purchase demand prediction, and discloses a part purchase demand prediction method and system based on machine learning, and the method comprises the steps: obtaining modular product basic data, and generating a BOM graph structure; calculating prior probability distribution through a Bayesian inference algorithm; executing an adaptive probability pruning algorithm to solve the problem of combinatorial explosion; quantizing uncertainty by using a Bayesian deep learning network; calculating a differentiated safety inventory coefficient based on the value-at-risk model; executing an importance sampling algorithm to carry out Monte Carlo simulation on high-risk low-frequency configuration, and verifying a demand coverage rate in an extreme scene; an incremental learning mechanism is utilized to update probability distribution and a pruning threshold according to the new order data, and a self-adaptive optimization demand prediction result is output; according to the method, the inventory cost is remarkably reduced, the stockout risk is reduced, and the balance between the calculation efficiency and the prediction accuracy is realized.

A method includes: accessing a first image captured at a first time; deriving a first in-stock condition of the slot at the first time based on product units of a product type occupying a slot, depicted in the first image, at the first time based on features detected in the first image; accessing a second image captured at a second, later time; deriving a second out-of-stock condition of the slot at the second time based on features detected in the second image; accessing a back-of-store inventory status of the product type at the second time; and triggering an increase in quantity of facings of the product type at the slot based on a) the first in-stock condition at the slot at the first time, b) the first out-of-stock condition at the slot at the second time, and c) presence of back-of-store inventory of the product type at the second.

The invention provides a medical material multistage warehouse intelligent management system and method based on demand prediction, and the system comprises an intelligent recognition module which is used for achieving the quick warehouse-in and warehouse-out operation of medical materials; the stockout pre-taking module is used for receiving and recording the request application when the inventory is insufficient; the demand prediction module is used for adopting a prediction model of multi-granularity time decomposition; the dynamic priority adjustment module is used for constructing an evaluation system; the multi-target optimization distribution module is used for carrying out material distribution; the multi-stage warehouse collaborative scheduling module is used for monitoring the inventory difference of the multi-stage warehouses in real time; the real-time monitoring and early warning module is used for monitoring the storehouse in real time; and the intelligent analysis and optimization module is used for generating a visual report. The intelligent level and the operation efficiency of medical material management are comprehensively improved. According to the system, the group fairness and the resource utilization efficiency can be considered while the quick response of the emergency medical demand is ensured, and the stockout risk and the operation cost are remarkably reduced.

The invention discloses an ERP intelligent management system and method based on big data. The method comprises the following steps: firstly, arranging historical sales volume data and historical price data of multiple days according to a time dimension, and then performing local time sequence feature analysis to obtain a sequence of historical sales volume local time sequence feature vectors and a sequence of historical price local time sequence feature vectors; performing fusion processing on each group of corresponding historical sales volume local time sequence feature vectors and historical price local time sequence feature vectors to obtain a sequence of historical sales volume-historical price local time sequence fusion feature vectors; and determining a short-time yield demand prediction value of the to-be-predicted commodity based on historical sales-historical price time sequence fusion features obtained by sequence cascade of the historical sales-historical price local time sequence fusion feature vectors, and judging whether loss will be caused or not. In this way, the situation of commodity inventory excess or stockout can be avoided, and the production efficiency and the customer satisfaction degree are improved.

The application provides a kind of intelligent delivery and replenishment management method and system based on multi-dimensional inventory early warning, belong to goods management technical field, this method includes: obtaining the global demand data and full-link supply data of inventory object, and determining demand fluctuation intensity sequence and supply interruption frequency sequence;The demand fluctuation intensity sequence and supply interruption frequency sequence are cross recursive analysis, determine the mismatch risk evolution path between the goods demand and goods supply;According to the mismatch risk evolution path, the adaptive adjustment coefficient when safety stock is determined, and the safety stock threshold curve that evolves smoothly with time is obtained;When the inventory level of inventory object is lower than safety stock threshold curve, generate out-of-stock risk early warning, when inventory level is higher than safety stock threshold curve, generate backlog risk early warning.The technical scheme provided by the application can realize the adaptive management of delivery and replenishment under the nonlinear coupling effect of demand fluctuation and supply interruption.

The application discloses a virtual commodity intelligent shelving and management system for a financial institution credit mall, relates to the technical field of virtual commodity intelligent shelving and management, and is aimed at the sudden shortage of a single product in a combination product, automatically screens the best substitute based on a dynamically constructed equity relation graph, and performs real-time rule conflict detection through graph weight fusion, so that the substitute scheme can meet the user preference and the cost limit of the financial institution at the same time, compared with the existing scheme that needs manual checking of the inventory and rules, the system can compress the processing time limit from hours to seconds, and completely avoid customer complaints caused by manual errors; meanwhile, a double protection mechanism through an intelligent compliance fuse is introduced, the matching degree of a user risk label and a KYC strategy is verified in the commodity shelving stage, and high-risk exchange is intercepted.

The invention provides a replenishment data processing method and device, electronic equipment and a storage medium. The method comprises the steps of displaying a historical replenishment record query page, and displaying at least one historical replenishment record in the historical replenishment record query page; in response to selection triggering of the target historical replenishment record, displaying a replenishment detail page, and displaying replenishment data of a target out-of-stock commodity corresponding to the target historical replenishment record in the replenishment detail page; receiving a tracing operation of tracing the replenishment data on the replenishment detail page, and displaying a replenishment decision information prompt page corresponding to the replenishment data; and displaying the plurality of first sorting results of the replenishment source storage locations and the available inventory information of each replenishment source storage location in a replenishment decision information prompt page, and displaying the plurality of second sorting results of the replenishment destination storage locations and the to-be-replenished information of each replenishment destination storage location in the replenishment decision information prompt page. According to the embodiment of the invention, the efficiency of tracing the abnormality reason of the replenishment data can be improved.

The application provides a commodity out-of-stock scheduling method and device, equipment and medium, belonging to the technical field of logistics scheduling and retail management, which comprises the following steps: receiving historical sales time series data and real-time inventory data uploaded by edge computing nodes of each vending machine in a target area; for each target vending machine, performing a first target operation to realize a restocking operation of the target vending machine. The commodity out-of-stock scheduling method, device, equipment and medium provided by the application can improve the commodity scheduling efficiency and accuracy of the vending machine.

The application discloses a kind of inventory optimization methods of charging facility spare parts replacement cycle and failure rate analysis, and the core process of this method includes building data layer, multidimensional data is cleaned and optimized, to realize standardized storage;Based on the LSTM algorithm, a multidimensional linkage prediction model is built, the loss effect of design defects and environmental factors is quantified to optimize the model, and the accurate spare parts replacement cycle threshold and equipment failure rate are output;According to the flow properties of spare parts, a differentiated inventory management mode is designed, and the optimal management mode of slow-moving parts is selected;A full-cost quantification model is built to calculate the total cost and service level under each mode;Finally, with the goal of minimizing cost, an intelligent inventory control model is built, which dynamically outputs inventory warning thresholds, replenishment quantities and times;Through the algorithmization and parameter quantification of the whole process, the application realizes fine and intelligent management of inventory, significantly reduces operating costs and stockout risk, and improves the stability of charging facilities.

This invention relates to the field of e-commerce transaction technology, and more particularly to a digitally driven agricultural supply chain demand analysis method and system. The invention includes: collecting agricultural product attribute data from the supply side and pre-setting tags to construct a knowledge graph containing supply nodes; extracting explicit search instructions and implicit descriptive text from the demand side, generating customer demand tags and mapping them to demand nodes; constructing a supply-demand relationship graph based on variety logic, geospatial factors, and logistics timeliness; dynamically allocating explicit and implicit demand weights to identify the optimal supply node and generate matching instructions; calculating semantic deviation values ​​using fuzzy logic based on transaction feedback, and dynamically correcting graph weights through a reverse feedback mechanism. This invention achieves precise alignment under fuzzy sensory preferences and flexible sourcing and allocation during stockouts, constructing a closed-loop optimization mechanism from transaction feedback to attribute correction, significantly improving the accuracy of supply-demand matching, contract fulfillment resilience, and the system's intelligent decision-making level for non-standard agricultural products.

The application provides an artificial intelligence-based supplier food ERP whole-chain inventory early warning system, relates to the technical field of food inventory monitoring, and comprises a data acquisition and processing module, an inventory prediction module, an inventory early warning module and an ERP execution module; the data acquisition and processing module is used for acquiring whole-chain inventory data and pre-processing the whole-chain inventory data; the inventory prediction module is used for predicting future inventory; the inventory early warning module is used for presetting inventory early warning rules and early warning future inventory; and the ERP execution module is used for generating a restocking strategy. The application accurately predicts future inventory changes by means of a time series model, a machine learning model and a fusion strategy, so that an enterprise can plan production and procurement in advance; potential inventory risks are discovered in time by means of multi-dimensional early warning rules and a risk assessment matrix, so as to reduce out-of-stock or overstock losses; and the ERP execution module generates an optimal restocking strategy based on a cost model and a linear programming algorithm, and the restocking strategy is dynamically adjusted in combination with a real-time state of a supply chain, so as to reduce the costs of procurement, transportation and warehousing while ensuring supply.

An object of a system according to an embodiment is to automate inventory management of daily necessities and prevent out-of-stock.SOLUTION: A system according to an embodiment includes a usage tracking unit, a reminding unit, and a purchase option providing unit. The usage tracking unit is configured to track usage of the daily necessities. The reminding part reminds when the stock is reduced based on the data tracked by the use state tracking part. The purchase option providing unit provides an online direct purchase option when reminded by the reminding unit.SELECTED DRAWING: Figure 1

This invention relates to artificial intelligence, and particularly to a supply chain demand forecasting method based on condition-guided temporal diffusion and multimodal fusion. The method includes: acquiring multimodal business samples containing historical sales data with missing masks and graphic descriptions; obtaining cross-modal aligned feature vectors through feature extraction and spatial projection to construct a static multimodal conditional representation; under this guidance, performing inverse denoising sampling using a temporal diffusion network to obtain reconstructed temporal features; generating a measure signal to fill uncertainty through multiple sampling variance calculations, inputting it into a dynamic gating network to adaptively allocate fusion weights, and obtaining global cross-modal attribute features; finally, generating demand forecasting results and performing end-to-end training based on a joint loss function of prediction error and diffusion noise prediction loss. This invention effectively overcomes the problems of supply chain cold start and stockout gaps, significantly improving prediction accuracy and model robustness in extremely sparse scenarios.

The invention discloses a material management method, system and device and a warehouse management system, and relates to the field of intelligent warehousing, and the method comprises the steps: obtaining an available stock, a replenishment period, a current order, and a historical order of each commodity; judging whether the commodity demand of the current order is greater than the available inventory of the commodity of the current order; if yes, marking that the current order is out of stock; according to the available stock of the commodity and the historical order, predicting the stock exhaustion time of the commodity; and the latest replenishment time is prompted according to the replenishment period and the commodity inventory exhaustion time. Commodity inventory is managed, and user query is facilitated. The method not only can predict the commodity inventory depletion time, but also can prompt the latest replenishment time, and avoids the influence of commodity shortage on the warehouse-out time.

The application discloses a supply chain demand prediction method based on deep learning and big data analysis, and aims to solve the problems of demand truncation identification and rectification caused by out-of-stock. The application realizes the technical effects of recovering the real demand distribution, stable point prediction and improving the cross-commodity substitution and complementary modeling capability by the following steps: unified time axis and resampling, construction of saleable quantity and supply constraint mask, establishment of a commodity relationship graph and coding by graph attention Transformer, modeling of demand distribution and truncated probability mass by the main output head and the auxiliary output head of conditional normalization flow, joint truncation likelihood, inventory constraint and loss of consistency of unsold quantity, and introduction of the differentiable inventory cycle unit and the soft minimum value gate at the decoding end.

The invention relates to the technical field of supply chain management, in particular to a supply chain collaboration platform intelligent software interaction system based on a large model, which comprises a dynamic replenishment strategy module for generating a dynamic replenishment strategy model according to historical purchase cycle data of multiple categories of materials, and the model takes an inventory turnover rate and a stockout probability as optimization targets; the collaborative prediction and feedback module takes an output result of the dynamic replenishment strategy model as an input, drives a collaborative prediction engine to perform order fulfillment deviation analysis, and dynamically updates prediction model parameters through a feedback mechanism; the logistics routing adaptive adjustment module is used for constructing a path propagation map based on the time sequence data of the logistics abnormal event, and performing adaptive adjustment on a routing decision model in combination with a real-time transportation state; and the intelligent contract cooperative regulation and control module synchronizes the adjusted routing strategy and the production plan change information to an intelligent contract system, and triggers scheduling elastic regulation and control and cooperative clause automatic updating of each node of the supply chain.

This invention, entitled "A Distributed Hybrid Interleaved Batch Scheduling Method and System for Parallel Production Lines," belongs to the field of production line scheduling. It addresses the problem of minimizing inventory and stockout costs. The method includes: complete delivery based on vehicle model bill of materials; each production line comprises multiple sequentially connected stages, with the same set of dies only allowed on one production line at any given time; during the stamping stage, only one production line can utilize the stamping die to process the corresponding sub-batch; a consistent sub-batch partitioning strategy is adopted for the same batch, ensuring that the quantity and corresponding dimensions of each sub-batch remain unchanged in subsequent processing stages after partitioning; the dimensions between different sub-batches can be determined by the selected sub-batch partitioning rules; alternating processing of sub-batches from different batches is allowed on the same production line; each sub-batch can only be processed by one production line in each stage; the scheduling objective is to minimize the total sum of inventory and stockout costs while achieving complete delivery.

The invention discloses a cargo allocation management method and system of an intelligent warehouse and a storage medium thereof, and relates to the field of intelligent warehouse management, and the method comprises the steps: firstly receiving and synchronizing order data of multiple sales terminals; then, dynamically sorting the orders through a configurable intelligent cargo allocation rule engine, generating a priority processing sequence, and generating an electronic cargo picking list of an optimal cargo picking path based on the locked inventory; in a cargo allocation execution stage, the system provides navigation guidance through a warehouse operation terminal, monitors an operation state in real time, triggers a preset automatic processing flow once detecting out-of-stock, wrong picking or order information change and other abnormalities, and constructs an automatic collaborative notification network running through order, inventory, cargo allocation and financial links at the same time. According to the invention, traditional warehouse operation depending on artificial experience and communication is converted into an intelligent closed-loop management process driven by rules and data, the multi-role cooperation efficiency, the inventory accuracy and the order processing timeliness are significantly improved, and the operation cost and risk are reduced.

The invention relates to the technical field of catering management, and discloses a canteen food material purchase-sale-stock intelligent management system, which comprises a food material data acquisition module, a demand prediction module, a purchase plan generation module, a stock monitoring module, a risk assessment module, an intelligent report module, a data synchronization module, a user interaction module and a performance optimization module. The food material data acquisition module acquires inventory data of canteen food materials in real time; analyzing a historical sales mode and external factors by using a demand prediction module; according to the predicted demand data, combining supplier evaluation to generate purchase plan data; the inventory monitoring module dynamically updates the inventory level; the risk assessment module calculates out-of-stock probability and excess inventory risk; the food material state data, the prediction demand data, the purchase plan data, the inventory early warning signal and the risk level index are integrated, and the intelligent report module generates a visual comprehensive management report and pushes the visual comprehensive management report to the user terminal. The economical efficiency and stability of canteen operation are enhanced.

The invention discloses a retail cabinet replenishment analysis method and system based on a multi-dimensional decision matrix, and the method comprises the steps: collecting the place characteristics of a retail cabinet, the user characteristics of a service object, and local urban hot commodity data, and constructing a multi-dimensional decision matrix; completing commodity compliance screening and recommendation based on the multi-dimensional decision matrix, and determining a candidate commodity set of a retail cabinet adapted to the current scene and the user demand; the shelf display positions of the candidate commodities are adjusted through a dynamic distribution rule in combination with the sales performance and attribute characteristics of the candidate commodities; according to the historical sales frequency of the commodities, commodity categories are divided by adopting a dynamic classification algorithm, and differential replenishment cycles are formulated for different categories of commodities; and predicting a commodity replenishment demand by using the sales prediction model, and planning an optimal delivery path in combination with a path optimization algorithm. The problems that due to static display of a traditional retail cabinet, the space utilization rate is low, stock-out or overstock is caused by rigid replenishment cycle, logistics distribution resources are wasted, and compliance management response is lagged are solved.

The system is an inventory replenishment solution based on artificial intelligence (AI), and aims to optimize the inventory management process of an enterprise through automatic and intelligent technical means. The system integrates a plurality of data sources, utilizes an advanced AI model to carry out prediction and decision making, provides an accurate inventory replenishment strategy for enterprises, ensures efficient inventory management, and reduces risks of stockout and excessive inventory. The system is a key component in modern supply chain management, and aims to automatically adjust the inventory level by means of intelligent analysis of historical sales data, market demand prediction, supply chain efficiency optimization and the like so as to reduce inventory overstock, avoid stockout conditions and improve the overall operation efficiency. According to the system, through intelligent inventory management and replenishment decision, the inventory management efficiency is greatly improved, the inventory cost is reduced, and the customer satisfaction is improved. The method is suitable for enterprises of various scales, and is especially suitable for industries with complex supply chains and large demand fluctuation, such as retail, manufacturing and electronic commerce.

The present disclosure provides a replenishment task generation method and device, electronic equipment and computer readable storage medium, relating to the technical field of warehouse logistics. Among them, the replenishment task generation method comprises: predicting the quantity of goods to be shipped out in a target period based on the category and corresponding quantity of goods in historical shipping-out orders; calculating the replenishment state value of the goods in the picking position, and determining the first group of goods in the replenishment state and the second group of goods in the adjacent replenishment state based on the replenishment grading rule; determining the to-be-associated goods in the second group of goods that enter the replenishment state in the target period based on the quantity of goods to be shipped out; identifying the associated goods having an association relationship with the first group of goods from the to-be-associated goods based on an associated goods identification model; and associating the first group of goods and the associated goods to generate a replenishment task. Through the technical scheme of the present disclosure, the goods shipping-out efficiency can be improved, and the probability of order waiting caused by goods shortage can be reduced.

The invention discloses a production and marketing link system, a storage medium and electronic equipment. The production and marketing link system comprises a supplier end, a store end and a selling system end corresponding to the store end, the method comprises the following steps that a store side generates a purchase request and sends the purchase request to a supplier side, and the purchase request comprises the purchase quantity, the commodity type and the time; the supplier end carries out delivery according to the purchase request sent by the store end and sends a delivery completion request to the store end; the store end calculates the settlement amount according to the receiving amount and the loss deduction proportion and sends the settlement amount to the supplier end; the store end calculates the available stock amount according to the loss deduction proportion and the store arrival amount and sends the available stock amount to the corresponding selling system end, and the selling system end displays the available stock amount; according to the technical scheme, the store end can update the sales data and the inventory information in time through the selling system end, the selling system end can provide the real-time sales data and the inventory state for the store, the store end is helped to better manage the sales and the inventory, the account of the inventory is consistent, and the sales error and the stockout condition are reduced.

The invention discloses a reagent storage linkage warehousing system based on an improved Transform large model. The system comprises the steps of collecting reagent attributes, warehousing real-time operation and use demand data, preprocessing and constructing a feature set, generating a warehousing priority and an adaptive scheme, controlling execution components to perform automatic warehousing, synchronizing data and pre-judging demands, adjusting environmental parameters, and performing bidirectional verification on the data. The system solves the problem that existing warehousing and warehousing demand pre-judgment is disjointed, warehousing and warehousing linkage is achieved, inventory accuracy is guaranteed, and stockout or improper storage is avoided.