267 results about "Inventory level" patented technology

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.

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 belongs to the technical field of intelligent supply chain management and optimization, discloses a product supply chain comprehensive optimization system and method based on big data, and aims to solve the problem that a traditional supply chain management system based on historical data and a static model is difficult to quickly adapt to changes. Forming a real-time evaluation vector; integrating the environment data, constructing an environment feature vector, and training to obtain a risk prediction model in combination with a real-time evaluation vector, thereby obtaining a risk probability of each node in the future, and obtaining a risk evaluation vector; based on the risk assessment vector, in combination with the inventory level and the transportation path, performing joint optimization on the inventory configuration and the distribution path to generate an optimization strategy; updating each node of the supply chain according to the optimization strategy, monitoring the state of the updated supply chain in real time, and generating a cost prediction vector when abnormity is monitored; and taking corrective measures on the supply chain according to the cost prediction vector to ensure stable operation of the supply chain.

A healthcare supply management system that uses machine learning for predictive supply chain optimization, comprising: a) a data collection and integration module configured to collect and integrate real-time and historical data from multiple sources, including electronic health records, vendor databases, hospital inventory logs, and external market trends. b) a predictive analytics and demand forecasting module that uses machine learning algorithms to analyze data and generate accurate demand forecasts for medical supplies. c) an inventory optimization and automation module that dynamically adjusts inventory levels based on predictive insights and automates orders. d) a supplier management and logistics module that can be used to evaluate supplier performance, track shipments and predict delivery delays. (e) a risk management and disruption mitigation module that identifies potential risks in the supply chain and proposes emergency strategies. f) a decision support and reporting module that provides real-time insights, AI-driven recommendations, and customizable reports through a user-friendly dashboard. (g) a processing unit and a cloud-based database to store and process supply chain-related information and to facilitate seamless data access and communication between the modules.

The invention discloses a chain drugstore purchasing scheme generation method and system based on big data, and relates to the field of purchasing management. The method comprises the following steps: firstly, collecting historical sales data, real-time inventory data and external environment data, and constructing a multi-dimensional database; performing standardization processing on the data, and constructing a medicine classification matrix through a data mining model; identifying key factors influencing drug requirements by using a causal analysis method, and establishing a requirement influence factor weight matrix; based on the weight matrix, calculating purchase parameters such as a safe inventory level, an economic purchase batch, a purchase trigger point and a purchase period; and finally, in combination with a supplier evaluation system, generating a purchasing scheme including a purchasing medicine list, a purchasing quantity, purchasing time and supplier selection. According to the method, through multi-dimensional data analysis and causal relationship mining, scientization and accuracy of purchasing decision are realized, the purchasing efficiency is improved, and the inventory cost is reduced.

The invention relates to the technical field of supply chain management, in particular to a part supply chain full-process digital collaborative management system and method, and the method comprises the steps: evaluating supplier data through constructing a dynamic scoring model, and obtaining a supplier score; when the supplier score is lower than a predetermined threshold value, generating a multi-source purchase plan, and dynamically adjusting purchase distribution according to the supplier score; constructing a warehouse-in and warehouse-out part intelligent verification model to analyze warehouse-in and warehouse-out parts, generating an acceptance report and block chain record association, and triggering an alarm and freezing an order when a difference is found; a supply chain risk topological graph is constructed through the supplier fulfillment rate, the logistics data and the market demand fluctuation, and high-risk nodes are identified and early warned in real time; initiating emergency purchase to suppliers with high supplier scores based on high-risk nodes, and optimizing inventory scheduling based on geographic proximity; the inventory level is optimized in real time based on the production line state and the in-transit logistics data, and the safe inventory threshold is dynamically adjusted to cope with supply chain interruption.

The invention relates to the technical field of supply chain management, and discloses a commodity supply chain management method based on the Internet of Things. According to the commodity supply chain management method based on the Internet of Things, errors caused by manual data entry are avoided through time sequence analysis and data filtering processing of the cloud analysis management platform, enterprises can accurately predict future market demands, and the inventory turnover rate, the optimal inventory level and the transportation and storage cost are dynamically calculated, so that the inventory management is optimized, and the enterprise management efficiency is improved. In addition, according to the method, by comprehensively evaluating the delivery timeliness rate of suppliers and the quality defect rate of goods, it is ensured that enterprises can select reliable suppliers, the stability and efficiency of a supply chain are improved, the real-time logistics monitoring and dispatching capacity enables goods to be quickly and efficiently delivered to clients, and the efficiency is improved. The customer satisfaction is comprehensively improved, and the competitiveness and market adaptability of enterprises are promoted.

Various embodiments described herein relate to management of industrial process optimization related to batch operations. In this regard, an optimization request to optimize an industrial process that produces an industrial process product is received. In response to the optimization request, product spent characteristics for one or more blending components of a batch operation subprocess are determined. Also in response to the optimization request, demand data for one or more feed products associated with the one or more blending components is updated based on the product spent characteristics and inventory data indicative of an inventory level for the one or more feed products. Furthermore, a control signal configured based on the demand data is transmitted to a controller configured for optimization associated with the industrial process that produces the industrial process product.

A trained model of an online system is used to generate action recommendations by predicting future demands. The online system gathers in-store data by receiving, from a device of a picker and / or a computing system of an in-store physical receptacle, data with information about an inventory of an item. The online system estimates, based on conversion data for the item, a level of inventory for the item. The trained model is then applied to predict, based on the in-store data and the estimated level of inventory, a demand prediction score indicative of a future demand for the item. The online system generates, based on the estimated level of inventory and the demand prediction score, a depletion metric indicative of a time period until the inventory of the item is depleted. Based on the depletion metric, the online system triggers an action in relation to the inventory of the item.

The invention relates to the technical field of project cost management, in particular to a hydraulic engineering construction cost control system and method, and the system comprises a material consumption analysis module, an inventory early warning calculation module, a dynamic inventory adjustment module, a replenishment trigger optimization module, and a consumption stability evaluation module. According to the method, the material consumption rate is dynamically monitored, the construction progress and engineering quantity are combined to measure and calculate data, a consumption trend curve is established, the actual use condition is compared, the consumption rate deviation is accurately quantified, real-time evaluation of material use is achieved, the number of supply days is calculated in combination with inventory data, the replenishment period is matched, and the replenishment time is dynamically adjusted; the inventory level is optimized, fund occupation is reduced, the stability is evaluated according to the consumption fluctuation range, the maximum consumption fluctuation value is calculated, the demand is adjusted in combination with replenishment, the inventory early warning threshold value is dynamically corrected, the supply chain breakage risk is reduced, the adaptability of construction material management is improved, and the cost control accuracy is improved.

The invention discloses an intelligent management system for clinical department storehouse consumables, relates to the technical field of hospital consumable management, and aims to solve the problems that a traditional consumable management mode often depends on manual recording and manual management, data omission and inaccuracy easily occur, the use condition of the consumables is difficult to track, the replenishment opportunity is likely to be inaccurate, and the use cost is low. According to the system, full-life-cycle tracking of consumables is realized through an RFID tag and a block chain recording module, the authenticity and integrity of data are ensured, the management risk caused by information asymmetry is reduced, the consumable inventory condition and consumable demand prediction are monitored in real time, and the medical service efficiency is improved. According to the technical scheme, the relation between the current inventory level and the safety inventory threshold value is automatically evaluated, early warning is given out, medical service efficiency reduction caused by insufficient inventory is avoided, the replenishment order is automatically generated and transmitted to related departments, the requirement for manual intervention is reduced, and the efficiency and accuracy of the whole replenishment process are improved.

A method, computer program product, and computer system for a cloud-based medication tracking system. The system may include a plurality of intelligent injection devices across multiple healthcare facilities, each having: a barrel in fluid communication with a needle, a piston including a plunger, a microprocessor, a wireless communication module a supply chain tracking system configured to: monitor distribution of an injectable medication, verify availability of the injectable medication, track a global medication delivery pattern relating to the injectable medication, a machine learning system trained to: analyze medication usage data across healthcare entities, predict a supply chain event, optimize an inventory level, and improve a distribution datum.

The invention provides a supply chain toughness and efficiency collaborative improvement method and system and electronic equipment, and the method comprises the steps: obtaining various supply chain management variable data of a supply chain, such as an inventory level, a supplier attribute, a customer order, transportation time, cost, a risk impact resistance coefficient, a recovery coefficient and the like; then, on the basis of the supply chain management variable data, various to-be-evaluated supply chain strategies meeting one or more constraint conditions of a preset continuous supply constraint condition, a preset factory production capacity constraint condition and a preset order fulfillment constraint condition are screened out, and a supply chain comprehensive target value is obtained through a calculation formula of the supply chain comprehensive target value. And outputting the optimal supply chain strategy with the maximum supply chain comprehensive target value. According to the embodiment of the invention, the optimal supply chain strategy which can guarantee the efficiency of the supply chain, can resist risk impact and can be quickly recovered can be determined, the comprehensive performance of the supply chain is further improved, and the sustainable development of an enterprise is maintained.

The invention discloses a supply chain material data management method and a data management system, and particularly relates to the technical field of data management. Through collecting and centrally storing material data of each link of a supply chain, an inventory turnover rate and residence time are analyzed, a safe inventory level is evaluated, and a purchase plan and inventory control are optimized. In order to solve the possible problem that inventory data is not updated in time or input errors occur, interference of the inventory data on inventory turnover rate calculation is evaluated through real-time analysis and abnormal input monitoring, and the accuracy of the inventory data is ensured; according to the method, the accuracy of the inventory turnover rate calculation result is divided into high, medium and low accuracy levels, and corresponding processing is carried out, so that purchase, production and supply chain decisions of enterprises are optimized, strategic decision errors are reduced, and the overall efficiency and response capability of a supply chain are improved.

The invention discloses an intelligent logistics supply chain management system, and the system comprises a block chain information sharing platform which is responsible for building an information sharing platform based on a block chain, inviting all participants on the supply chain to join, and storing and transmitting the data of each link of the supply chain through the distributed account book technology of the block chain; the product information tracing system is responsible for distributing a unique block chain ID for each fresh food and recording the circulation process of the fresh food in a supply chain; and the collaborative management platform is responsible for integrating and uniformly managing data of purchasing, warehousing, transportation and sales, predicting future sales trend and inventory demand change by using a random forest according to historical sales data and market trend information, and optimizing an inventory level, a reorder strategy, a sales strategy and a promotion activity plan in combination with a genetic algorithm, so as to achieve the purpose of sales promotion. The inventory cost is minimized, the sales demand is met, and the block chain information sharing platform and the collaborative management platform carry out data synchronization.

An intelligent warehouse management method based on the Internet of Things and artificial intelligence comprises the steps that 1) intelligent inventory management is carried out, and each commodity which is put in storage and put out of storage is automatically identified and tracked by utilizing a cargo container label and an image identification technology; the artificial intelligence technology is combined with big data analysis, future inventory demands are predicted, the inventory level is automatically adjusted, and the inventory cost and the stockout risk are reduced; 2) an automatic operation process: monitoring the equipment state in real time through the Internet of Things technology, ensuring the efficient operation of the equipment, and reducing the downtime; 3) performing real-time data analysis and decision support, and collecting various data generated in the storage process, including inventory data, order data and equipment operation data; deep learning and analysis are performed on storage data by using an artificial intelligence technology, intelligent decisions such as inventory prediction and path planning are realized, and storage management and logistics distribution processes are optimized; and 4) cross-system collaboration and information interconnection of a plurality of systems such as data sharing, storage, transportation, sales and the like are carried out to form end-to-end supply chain collaboration.

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 relates to the technical field of collaborative operation of a production system and a logistics system in an intelligent manufacturing scene, and discloses an order distribution system for multi-bin supply of production raw materials. A data analysis module of the system extracts data from data sources such as a production plan and warehouse information, and generates a production raw material demand order; the scheme solving module is based on a multi-objective optimization model, comprehensively considers factors such as inventory, conveying time and equipment load rate, and calculates an optimal delivery warehouse and quantity; the distribution execution module dispatches the logistics equipment according to an optimization result to complete a warehouse-out task; the monitoring prediction module learns and predicts historical and real-time order data by using a long short-term memory network, and dynamically adjusts an allocation strategy; and the user interaction module provides a human-computer interaction interface. According to the system, efficient and economical production raw material JIT supply is achieved through an intelligent means, the logistics efficiency and the resource utilization rate are remarkably improved, and reliable technical support is provided for production and logistics collaboration in an intelligent manufacturing scene.

The invention discloses a retail cabinet inventory path decision optimization method and equipment, and the method comprises the following steps: (1), building an inventory path optimization model of a retail cabinet, and enabling the inventory path optimization model to take the minimum sum of the inventory cost, the stockout cost and the vehicle path cost at the current planning period as an objective function, taking a replenishment constraint, a maximum inventory level constraint, a vehicle capacity constraint, a retail cabinet flow balance constraint, a vehicle closed-loop constraint and an elimination sub-loop constraint as constraint conditions; (2) obtaining inventory path related information; and (3) solving the inventory path optimization model by adopting a tabu search method according to the inventory path related information to obtain an optimized inventory decision and path decision. According to the invention, the replenishment decision and the path planning decision are considered at the same time during decision making, and the efficiency is higher.

The invention relates to the technical field of farm vegetable distribution, in particular to a farm fresh vegetable precise distribution system and method based on an intelligent algorithm, and the system comprises a data collection module, an intelligent prediction module, a resource optimization configuration module, a precise distribution module and a user interaction module. The data collection module is responsible for collecting data of a plurality of sources; the intelligent prediction module is used for dynamically predicting market demands and analyzing demand changes; the resource optimization configuration module dynamically adjusts a vegetable planting plan, planting area distribution, supply chain network design and a warehouse inventory level; the precise distribution module is responsible for implementing a distribution plan; the user interaction module provides a user interface and allows consumers to check the types and the number of vegetables about to be delivered. According to the invention, the farm production activity is more flexible and efficient, the cost and time can be minimized, and the efficient operation of the supply chain is ensured.

The invention relates to the technical field of logistics digital management, in particular to a logistics digital management system based on an AI large model, and the system comprises a demand prediction module which collects market trend data, seasonal change information and promotion activity records, combines the market trend data, seasonal change information and promotion activity records into a market data set, calculates the current commodity demand probability, and generates a market demand matrix; and adjusting Bayesian network parameters according to the market demand matrix to obtain a demand prediction value of each commodity. According to the method, the demand prediction result is dynamically generated after the market trend data, the seasonal change information and the promotion activity records are integrated, and the fluctuation characteristics of the market environment can be captured, so that the commodity demand change is predicted, and the problem of information loss in traditional single-point prediction is avoided. In the aspect of inventory management, real-time updating of the inventory state can be achieved by means of ideal inventory level calculation and execution of a dynamic inventory adjustment plan, and the risks of resource waste and inventory structure imbalance are reduced.

An intelligent system for interactive processes and experiences in the store, consisting of: a network of IoT sensors, including RFID readers, computer vision cameras, LiDAR sensors and environmental sensors, configured to capture real-time data on customer movements, product interactions and store conditions; an AI-powered analytics engine configured to process real-time and historical sensor data using machine learning models, customer behavior analysis, and forecasting techniques to generate dynamic insights into customer preferences, purchasing patterns, and inventory optimization; a real-time advertising module that is operationally connected to the AI-supported analysis engine, whereby the module dynamically generates and delivers personalized advertising content via digital signage, mobile applications, interactive kiosks or AI-supported smart carts based on individual customer profiles, customer traffic analyses and product demand trends; a subsystem for inventory management that includes RFID-based tracking, computerized shelf monitoring and predictive analytics, and is configured to automate inventory level detection, trigger replenishment alerts and optimize product placement in the store; an AI-driven workforce management module configured to dynamically allocate store staff based on real-time customer density, service demand, and predicted workload distribution, with staff assignments adaptively updated using AI-driven planning techniques; an interactive checkout subsystem that includes RFID-based automatic product identification, mobile payment integration, and AI-powered fraud detection; a cloud-integrated data processing server that communicates with the IoT sensor network and the AI-powered analytics engine, with the server performing distributed computing tasks to refine machine learning models, store historical data, and synchronize operational parameters across multiple retail locations; and a user interface system consisting of mobile applications, AR-enabled shopping assistants and voice-interactive kiosks, configured to provide customers with guided navigation, product recommendations and virtual try-on features based on real-time store interactions and AI-driven engagement models.

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.

The invention provides a supply chain warehouse stock replenishment method, system and device and a readable storage medium. The method comprises the steps of obtaining supply chain data; constructing a deep learning model; inputting historical data of the current period to the model, and outputting a predicted value of the stock in the future N days; calculating a replenishment quantity by acquiring an actual stock amount in real time and combining the stock prediction value and a preset safety stock threshold value; and generating a replenishment order based on the replenishment quantity, creating a replenishment instruction based on the replenishment order, and transmitting the replenishment instruction to a supplier. According to the invention, the problem of inventory overstock or stockout caused by demand fluctuation, supply chain complexity and intense market competition in traditional inventory management is solved.

The invention relates to the technical field of data processing, and discloses a manufacturing industry data intelligent analysis method and system based on deep reinforcement learning. The method comprises the following steps: carrying out time sequence processing on manufacturing industry equipment state, order characteristics, inventory level and quality index data to obtain a four-dimensional production data matrix, carrying out strategy learning through an LSTM-Actor-Critic algorithm to obtain a manufacturing decision strategy network, carrying out classification processing according to a production cycle to obtain a hierarchical data set, constructing an intelligent experience playback buffer area, and carrying out intelligent experience playback. And carrying out collaborative optimization on order scheduling, inventory replenishment and equipment task allocation decisions to obtain a manufacturing industry data intelligent analysis result. The technical problem that an existing manufacturing industry data analysis method lacks adaptive learning ability and cannot process multi-domain collaborative decision optimization is solved.

This system provides a technical solution for managing product sales, inventory, and deliveries through QR codes and predictive analytics. It incorporates algorithms that generate and assign unique QR codes to products based on real-time sales data from farmers. These QR codes provide direct links to product information, facilitating efficient inventory management and customer engagement. The system also leverages predictive analytics to forecast product demand by analyzing historical sales data, adjust inventory levels, and optimize delivery driver assignments based on proximity to farms. The hardware supporting the system includes servers, processors, network components, and scanning devices to ensure reliable operation and data synchronization. By automating QR code generation, integrating sales data, and dynamically assigning drivers, the system improves operational efficiency, minimizes errors, and enhances the customer experience.

Dynamic storage location allocation and strategy adjustment system (100) for SAP Extended Warehouse Management (EWM) environments, comprising: a) a real-time warehouse data aggregation module configured to collect SKU attributes, inventory levels and bin location availability from SAP EWM; (b) a dynamic bin allocation engine that applies artificial intelligence algorithms to allocate optimal bin locations based on SKU characteristics, space utilization and operational parameters; c) an adaptive strategy adjustment module that dynamically modifies storage and retrieval strategies in response to real-time storage conditions; (d) a pattern recognition and forecasting module configured to analyze historical trends and predict inventory and demand fluctuations; (e) a warehouse resource optimisation module that aligns decisions on the allocation of storage space with the availability of labour and equipment; f) an SAP EWM integration module that communicates with SAP EWM via APIs or BAdIs to implement changes in real time; and g) a review and feedback learning module that tracks system decisions and results to continuously improve allocation and strategy logic.

The invention relates to the technical field of intelligent locks, and discloses a bill of material generation method and device of an intelligent lock, computer equipment and a medium, the method comprises the following steps: obtaining service information of the intelligent lock in a preset range of a service system, the service information comprising repair data and installation data of a plurality of lock models; determining an initial bill of materials according to the repair data, and querying historical fault data of each lock model; on the basis of the installation data and the historical fault data, predicting accessory information required by each lock model in a future time period to obtain a predicted bill of materials; and constructing a target bill of material of the service system by using the initial bill of material and the predicted bill of material. According to the invention, the problems that the intelligent lock accessories are violently fluctuated due to the interference of multiple factors, the coordination of reasonable resource allocation and user experience guarantee is difficult to realize, and the requirements of maintenance accessory inventory management on accuracy and high-efficiency technical indexes cannot be met are solved.

The invention discloses an intelligent PCB production line intelligent regulation and control method and system for an unmanned workshop, and particularly relates to the field of intelligent control, and the method comprises the steps: S1, PCB production line information text collection, S2, data standardization processing, S3, PCB production information processing, S4, PCB production line control judgment, and S5, PCB production line control judgment result feedback. According to the method, a mathematical model is constructed through big data analysis and a machine learning algorithm, the real-time health state and the inventory level state of the equipment are accurately quantified, full-period digital management of the PCB production equipment and the inventory level is realized, meanwhile, the production quality of products is predicted in real time through the production quality prediction model, and the product yield and the quality consistency are improved. According to the method, a production progress optimization model is constructed based on discrete event simulation and reinforcement learning algorithms, the relation between PCB production process flow logic and resource competition is accurately simulated, and optimization process sorting, equipment management and inventory management strategies are learned in real time.

The invention relates to the technical field of storage intelligence, and discloses an early warning and allocation system and method for intelligent inventory of inspection and quarantine reagent consumables, and the system comprises a consumption trend extraction module, a demand prediction module, a risk early warning module, an early warning trigger module, a supply feasibility evaluation module and an intelligent allocation module. Performing seasonal decomposition on the historical consumption record to obtain a consumption trend component; periodically superposing the consumption trend component to obtain a demand prediction result; based on the demand prediction result, performing inventory risk research and judgment on the current inventory to obtain an inventory early warning index; when the inventory early warning index exceeds a safety threshold value, inventory early warning information is generated; according to the consumable demand information, carrying out feasibility evaluation on inventory data and logistics constraint conditions of the supply point to obtain a feasible allocation point; generating a deployment scheme according to the safety inventory standard of the feasible deployment point; according to the invention, the perspectiveness and accuracy of early warning and allocation of the intelligent inventory of the inspection and quarantine reagent consumables can be improved.