1447 results about "Inventory management" patented technology

The invention provides an inventory optimization method and system based on a deep learning model, and relates to the technical field of deep learning, and the method comprises the steps: obtaining the multi-dimensional data of a commodity, constructing a dynamic convolution kernel through employing a graph neural network, constructing a soft clustering distribution matrix in combination with a multi-layer perceptron, and obtaining a long-term dependence feature fusing a space dimension and a time dimension. And fusing the short-term and long-term features, inputting the fused short-term and long-term features into a bidirectional long-short-term memory network to generate a sales prediction sequence, and calculating a prediction confidence interval to obtain a final sales prediction result. And calculating a safe inventory level based on a sales prediction result, generating a replenishment decision by using a dynamic planning model based on deep reinforcement learning in combination with an inventory cost function, and finally sending the decision to a supply chain execution system. The sales volume is accurately predicted through the deep learning model, the inventory management is optimized, the storage cost, the stockout cost and the overstock cost are reduced, and the supply chain efficiency is effectively improved.

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

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

The invention discloses a distributed intelligent warehouse scheduling system based on artificial intelligence, and belongs to the technical field of warehouse scheduling. Comprising a multi-source environment sensing module, a dynamic inventory management module, a distributed task scheduling module, an intelligent path planning module, a resource dynamic allocation module, an anomaly detection and emergency response module, an energy consumption optimization module, a supply chain collaboration module and a man-machine interaction and visualization module. A warehouse digital twinborn model is constructed, immersive display of a storage state and a scheduling strategy is realized, an AR scene is superposed through a color coding path, a thermodynamic diagram and a particle flow form, a manager can intuitively master inventory distribution, task progress and an abnormal region, eye movement tracking and a gesture recognition technology support an interactive decision, and the workload of the manager is reduced. The AR marking function can mark an abnormal area and synchronize the abnormal area to a decision making system, and through combination of AR and AI, a brand new interaction normal form is provided for intelligence and humanization of warehouse management.

The invention discloses a new energy power generation equipment health management platform based on a large model, and relates to the technical field of power generation equipment health management. The system comprises a multivariate data acquisition and region identification module, a data preprocessing and region adaptation unit, a region perception large model analysis module, an intelligent operation and maintenance decision and scheduling module and a spare part inventory optimization sharing module. The multivariate data acquisition and area identification module deploys multiple types of sensors at key equipment parts of photovoltaic power generation stations in different areas, acquires equipment operation parameters and environment state data, endows the areas with unique codes and feature tags, and ensures that the data are traceable and are associated with area characteristics; according to the invention, the health management efficiency and accuracy of the new energy power generation equipment are significantly improved; the system not only realizes real-time monitoring and fault early warning of the equipment state, but also reduces the maintenance cost and improves the system operation efficiency by optimizing the operation and maintenance resource configuration and intelligent spare part inventory management.

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

The invention discloses a cargo scheduling and distribution management system suitable for a stocker three-dimensional warehouse, and relates to the technical field of logistics warehousing, and the system comprises an intelligent scheduling module which employs a multi-algorithm fusion and dynamic switching mechanism, plans the path of a stocker according to an order and the condition in the warehouse, and distributes tasks; the accurate inventory management module is used for guaranteeing the accuracy of inventory data by applying a multi-sensor fusion and intelligent identification technology and combining environment self-adaption and intelligent auditing; the system integration module is used for realizing multi-system integration through a standardized interface and middleware and has a cluster deployment, fault processing and interface management mechanism; an equipment fault handling module; the energy management module is used for optimizing energy utilization based on an energy-efficiency balance model in combination with hybrid energy and an intelligent switching technology; according to the invention, through close cooperation of a plurality of modules and comprehensive application of innovative technical means, efficient, intelligent and reliable operation of the stacker three-dimensional warehouse cargo scheduling and distribution management system is realized.

The invention provides an e-commerce service intelligent multi-warehouse delivery method and system, and the method comprises the steps: generating a global inventory view according to the user order information of a cross-e-commerce platform and a corresponding logistics performance demand, and carrying out the dynamic adjustment through a preset priority rule, a sorting priority sequence is generated through a dynamically adjusted inventory distribution strategy, spectral feature matching is carried out on mixed goods in all warehouses according to the sorting priority sequence and SKU identification information, goods sorting is completed in combination with target warehouse illumination environment parameters corresponding to inventory geographical distribution, and goods identification information is generated; then generating a cross-warehouse allocation decision by using a distributed inventory sharing model, and finally generating a multi-warehouse collaborative delivery instruction according to the cross-warehouse allocation decision and the cargo identification information; according to the technical scheme provided by the invention, intelligent management of the whole process from order processing to final cargo distribution is realized, the efficiency of inventory management and logistics scheduling is improved, and the accuracy and timeliness of order processing are ensured.

The invention relates to the technical field of logistics management, in particular to a warehouse material storage location distributing and sorting system based on a genetic algorithm, and the system comprises a data access and analysis module which is connected with a main database of a warehouse management system through a security protocol, synchronously updates the information of materials, orders and employees, carries out the establishment of a data index, and transmits the data index to a server; according to the invention, through the design of the sorting system and the combination of simulated annealing and a genetic algorithm, the intelligent configuration of the material storage position is effectively realized, the sorting path is optimized, the material carrying time and the manual error are reduced, the sorting accuracy is improved, the AGV is managed by adopting an asynchronous algorithm, the task scheduling is optimized, and the sorting efficiency is improved. The material processing speed is accelerated, the response efficiency is improved, the inventory configuration is dynamically adjusted, the market demand change is adapted, the high efficiency and the quick response capability of the inventory are kept, the automation and the intelligence of the inventory management are promoted, and the operation efficiency and the service quality of the whole warehouse are greatly improved.

The invention relates to the technical field of e-commerce marketing, in particular to a product marketing service platform system and method, and the system comprises a user behavior tracking module, a consumption intention analysis module, a promotion time optimization module, an inventory monitoring and discount adjustment module, and a precision marketing push module. According to the method, the commodity access frequency and cross browsing data are extracted and analyzed in real time, the consumption trend of the user in a short period is rapidly identified, consumption tendency parameters are generated, potential purchase behaviors are accurately predicted by using browsing behaviors and an interaction mode, the recommendation accuracy is improved, the real-time inventory data are combined, a discount strategy and inventory management are dynamically adjusted, and the recommendation efficiency is improved. According to the method, the promotion activity is ensured to be closely matched with the inventory state, the resource utilization efficiency is improved, precise marketing pushing is performed according to the user equipment preference, the user interaction experience is enhanced, the sales growth is promoted, and the market response speed and the marketing effect are remarkably improved.

The invention relates to the technical field of mine warehouse management, in particular to a self-service warehouse-out system and method, and the system comprises a sensing and interaction module, a data processing and storage module, an automatic carrying and storage module, and a safety and monitoring module. The sensing and interaction module realizes functions such as cargo identification and identification; the data processing and storage module manages inventory, user and task data and analyzes and processes the data; equipment of the automatic carrying and storage module carries goods according to instructions, and the intelligent goods shelf can be automatically adjusted; and the safety and monitoring module ensures accurate cargo quantity, monitors the warehouse and controls personnel to enter and exit. The method comprises the steps that a user initiates an ex-warehouse request, a system checks an inventory and calculates an equipment scheduling priority, then cargo carrying and ex-warehouse operation including equipment scheduling, path planning, carrying detail determination and the like is carried out, and meanwhile weight monitoring, image monitoring and the like are carried out. The warehouse-out efficiency, the inventory management accuracy and the cargo handling safety are improved, and the intelligent level of warehouse management is improved.

The invention discloses a tobacco enterprise composite scheduling method based on multi-agent collaborative optimization, and the method comprises the steps: obtaining the system data of a tobacco enterprise, and obtaining a plurality of corresponding decision-making schemes based on a sales prediction agent, a production plan agent, a purchase stock agent, and an inventory management agent; performing iterative optimization on the decision scheme based on a Nash equilibrium game and a Lupinstein bargaining model, and calculating a system utility value to obtain an optimal decision scheme; according to the optimal decision-making scheme, based on an adaptive mechanism, adaptive optimization is carried out, and an adjusted decision-making scheme of each agent is obtained, so that tobacco enterprise composite scheduling of multi-agent collaborative optimization is realized; the sales prediction agent, the production plan agent, the purchase stock agent and the inventory management agent perform information interaction through an attention mechanism. Interactive cooperation among multiple agents is realized by introducing an attention communication mechanism and a game theory cooperation mechanism, and overall optimization of tobacco enterprise scheduling is promoted.

The invention discloses a multi-warehouse intelligent distribution and scheduling method for raw material inventory. The method comprises the following steps: collecting raw material inventory data of each warehouse, and monitoring the inventory data of the warehouses in real time; based on the real-time inventory data and the historical inventory data, the inventory state of each warehouse is analyzed to determine the inventory balance state of each warehouse, and the warehouses with excess or short inventory are identified; and calculating an optimal inventory distribution scheme by adopting a dynamic adjustment algorithm based on an inventory state analysis result. According to the invention, by collecting the real-time inventory data and the historical inventory data, the inventory state of each warehouse is deeply analyzed, the inventory balance state is determined, and the warehouse with excess or short inventory is identified. Through the real-time data monitoring and analysis, the system can provide accurate inventory early warning, help managers to take measures in time, avoid production interruption and resource waste, and improve the accuracy and predictability of inventory management.

The invention relates to the field of intelligent warehousing, and discloses an intelligent clothing inventory real-time monitoring and automatic checking system based on RFID, which aims at solving the problems of poor environmental adaptability, high dynamic sensing delay, high cross-scene deployment cost and the like in the prior art, and comprises a reconfigurable RFID tag, an integrated spiral-dipole composite antenna and a temperature compensation circuit, dual-band switching of 860 to 960 MHz and self calibration are supported; a heterogeneous read-write network is formed by a fixed reader-writer and AGV mobile nodes, and signal coverage is enhanced through beam forming; the edge calculation module adopts an LSTM trajectory prediction and hybrid filtering algorithm to realize millisecond-level processing; the cloud platform maps the inventory in real time based on the digital twin model and traces the source of the block chain, the dynamic time slot distribution protocol optimizes the cooperation of the reader-writer, and the transfer learning model realizes cross-scene rapid adaptation. According to the invention, the tag reading rate is 99.7%, the inventory efficiency is improved by 300%, the deployment period is shortened by 80%, and the problems of accurate inventory management and dynamic tracking in a complex environment are solved.

The invention provides an operating room high-value consumable management system based on artificial intelligence, and relates to the technical field of artificial intelligence, an intelligent sensing module collects position information, state information and use conditions of high-value consumables in real time through RFID tags and sensors, and identification and tracking of the consumables are achieved. And the artificial intelligence analysis module analyzes the use frequency of the consumables and the relevance between the operation type and the demand based on a deep learning algorithm so as to predict the future consumable demand. And the inventory management module dynamically adjusts the inventory according to the actual use condition and the predicted demand. The operation auxiliary module automatically generates a consumable list according to the operation type and the patient condition, and medical staff is reminded to prepare. And the compliance supervision module records full life cycle data of the consumables, provides a traceability report and ensures compliance. Through the technical means, the operation cost of a hospital is remarkably reduced, the operation safety and the satisfaction degree of a patient are improved, and wide application prospects and important social value are achieved.

The invention provides a medical drug management system and an optimization method, and relates to the technical field of medical information management, the system comprises a medicine management platform, a medicine database and an information technology support module; wherein the medicine management platform adopts a modular design and comprises a medicine inventory management module, a patient information management module, a medicine use process management module and a report statistics module, and information interaction among the modules is realized through interfaces; the medicine database is used for storing medicine information, medicine inventory information and patient medication information; the information technology support module comprises a bar code scanning module, an RFID technology module and a cloud computing module. By integrating the advanced information technology and the medical management concept, the medical drug management system which is comprehensive in function, efficient and safe is provided, the management process is optimized, the drug use safety is improved, the management cost is reduced, and personalized drug use suggestions and decision support are provided.

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.

The invention belongs to the technical field of medicine inventory management, and particularly relates to a medicine expiration early warning method and system in medicine inventory management, and the method comprises the steps: obtaining the basic information of a medicine based on a sensor, and carrying out the data transmission and integration of the obtained basic information of the medicine; obtaining the historical daily sales quantity of medicines, inventory dynamic data and historical storage environment data of the medicines, and obtaining a first predicted expiration early warning threshold value based on the constructed expiration early warning threshold value prediction model; historical drug related data is acquired, and a second prediction expiration early warning threshold value is obtained; comprehensively analyzing the first predicted expiration early warning threshold value and the second predicted expiration early warning threshold value to obtain a comprehensive expiration early warning threshold value; and based on the comprehensive expiration early warning threshold, judging whether the medicine is about to expire. The problems that in the prior art, a prediction model is single, and environmental factors are not considered enough are solved, and the accuracy and reliability of medicine expiration early warning are remarkably improved.

The invention relates to the technical field of inventory management, in particular to an intelligent warehouse management system for main shaft parts based on digital twinning, which comprises a main shaft mapping module for acquiring a main shaft shipping space and an allocation path, comparing shipping space nodes with a moving timestamp difference to generate an identification result, the fluctuation identification module extracts channel nodes to analyze fluctuation threshold deviation and generate a report, the allocation control module generates a scheduling list based on queuing duration and shipping space data distribution weight, and the abnormity positioning module extracts unallocated parts to analyze interface difference and mark abnormal links. And the state early warning module integrates four indexes of abnormal data identification risk paths to generate a management scheme. According to the method, bin position nodes and main shaft movement timestamps are synchronously compared to identify difference nodes, a dynamic weight distribution mechanism is established in combination with channel node fluctuation analysis, an interruption link difference trend and interface delay data are integrated to construct a risk assessment model, and abnormal conduction path positioning and early warning are realized. And scheduling conflicts and resource waste caused by data deviation are reduced.

A system (100) for autonomous inventory optimization and resilient supply chain management in the healthcare and medical device sector, comprising: (a) a data ingestion module configured to collect structured and unstructured data from a variety of sources, including hospital enterprise resource planning (ERP) systems, RFID scanners, electronic health records (EHR), supplier application programming interfaces (APIs), and Internet of Things (IoT) sensors; b) an artificial intelligence / machine learning (AI / ML) module configured to process the ingested data using deep learning and time series prediction models to predict item-level inventory requirements, with the models continuously trained with new data to improve prediction accuracy; c) an optimization module configured to apply operations research algorithms to generate inventory management strategies that minimize inventory costs, reduce expired inventory, and maintain predefined service levels; (d) a supply chain risk module configured to analyze external data sources, including news feeds, weather reports, and supplier performance metrics, to identify potential risks and generate mitigation strategies; and e) a user interface module configured to provide real-time dashboards, alerts and recommendations tailored to clinicians, procurement managers and supply chain analysts.

The invention belongs to the technical field of product inventory management, and discloses an e-commerce product inventory management method based on machine vision, which realizes a full-automatic process of product information from acquisition, recognition to statistics by means of a machine vision technology, acquires images in real time from multiple angles by a high-definition industrial camera, inputs the images into a CNN model to extract features after preprocessing, and realizes product inventory management. Through accurate identification classification and quantity statistics, manual intervention of inventory and data entry is not needed; through the automatic inventory management process, the manpower cost expenditure of manual checking, data processing and the like is greatly reduced, and the personnel employment and training cost is reduced. And on the other hand, historical data is deeply mined by using algorithms such as time sequence analysis, the sales trend and the inventory demand are accurately predicted, the replenishment strategy is optimized, capital and storage space occupation caused by inventory overstock is avoided, meanwhile, order loss caused by stockout is prevented, and in addition, a distributed database storage technology ensures data reliability, and the efficiency is improved. And extra cost caused by data errors is reduced.

The invention discloses a multi-dimensional data statistics intelligent analysis and prediction system and method, and particularly relates to the technical field of data prediction. Historical sales data, consumer behavior data, market environment data and external dynamic data of a target analysis object are collected, an LSTM basic prediction model is constructed, consumer behavior change trend features and market environment dynamic features are further extracted, the features are fused with the LSTM model to generate a multi-modal prediction model, and the multi-modal prediction model is analyzed. Comprehensively analyzing the interaction influence of the multi-dimensional data by using a multi-input structure; according to the method, influence weights and contribution degrees of different characteristics are evaluated through sensitivity analysis and interaction analysis, model parameter weights are dynamically adjusted, an optimized prediction model is utilized to generate a multi-dimensional prediction result in a fixed time period, and inventory management and a promotion resource allocation strategy are optimized accordingly, so that long-term drift of consumption behaviors is effectively coped with, and the consumption performance is improved. The inventory cost and the resource waste are reduced, and the operation efficiency and the business decision accuracy are improved.

The invention discloses an intelligent factory raw material inventory early warning and replenishment system, and belongs to the technical field of inventory management, and the method comprises the steps: building a factory-workshop-production line three-stage digital twinborn model through collecting full-chain production data in real time; a reinforcement learning algorithm is adopted to evaluate the performance ability of the supplier, a credit score and priority list is generated, a replenishment strategy generator is driven to dynamically output a Pareto optimal strategy solution set, and a cost-risk index is quantified through Monte Carlo simulation; a domino effect propagation model is constructed, the cascade influence of raw material delay is predicted, and a double-track replenishment mechanism is formulated; the strategy library optimization model iteratively adjusts replenishment parameters and updates supplier credit scores through real-time data feedback to form closed-loop optimization; according to the invention, active prediction of the supply chain risk, dynamic optimization of the replenishment strategy and differential management of suppliers are realized, the production continuity and the resource utilization efficiency are improved, and the supply chain operation cost and the cascade risk are reduced.

The invention relates to the technical field of distributed system transaction management, and discloses an inventory consistency management method and device based on distributed transactions, and the method comprises the following steps: pre-occupying an inventory, and recording the state information of the inventory to a Redis hash structure; after the inventory is pre-occupied successfully, generating a pre-deducted snapshot, generating a compensation identifier, and writing the pre-deducted snapshot and the compensation identifier into a MySQL transaction log together; verifying the uniqueness of the compensation identifier, and submitting the current database transaction after the uniqueness of the compensation identifier is confirmed; when the submission of the current database transaction fails, executing a compensation operation on the current database transaction; and after the compensation operation is executed, performing data consistency verification on the MySQL transaction log and the Redis hash structure. According to the method and the system, the consistency of inventory management during distributed transaction processing is highly guaranteed.

The embodiment of the invention relates to the technical field of data processing and AI vision, and provides a warehouse inventory intelligent management method and device based on AI vision monitoring and a terminal, and the method comprises the steps: obtaining inventory management basic data and external dynamic influence data of a to-be-monitored warehouse; based on an AI visual technology, according to the sample image data set and the label image data set, performing dynamic optimization processing on current inventory data in the inventory management basic data to obtain dynamically optimized inventory data; according to the inventory management basic data and the dynamic optimization inventory data, carrying out inventory structure evaluation to obtain inventory structure evaluation data; performing real-time dynamic adjustment processing on the inventory structure evaluation data according to the external dynamic influence data to obtain inventory dynamic adjustment data; according to the dynamic optimization inventory data, the inventory structure evaluation data and the inventory dynamic adjustment data, an inventory health evaluation report is generated, and the purpose of more accurate and more comprehensive warehouse inventory intelligent management is achieved.

The invention discloses a material digital centralized management and control system based on the Internet of Things, which relates to the technical field of digital management and control and comprises a supply chain management unit, a loss management unit, a cost accounting unit, an inventory management unit, a dynamic adjustment unit and a user management unit. The system can accurately plan the purchase quantity and time, screen high-quality suppliers, guarantee the quality and stability of material supply from the source, deeply analyze the loss data in transportation, storage and processing, formulate a targeted strategy, reduce material loss, reduce waste, dynamically check the cost in real time, perform apportionment analysis according to different dimensions, and accurately control the cost. Through intelligent warehouse-in and warehouse-out management, regular inventory checking and intelligent inventory prediction, it is ensured that inventory data is accurate, inventory is reasonably planned, an inventory strategy is dynamically adjusted, inventory overstock or stockout is reduced, and inventory cost and service level are balanced.

The invention discloses a material purchasing management system, and the system comprises a historical data summarization and analysis module which summarizes the historical data of material purchasing; the intelligent purchase plan drawing and demand prediction module draws a purchase plan according to historical data and real-time demands, predicts future demands, and constructs a seasonal change demand prediction model in combination with industry characteristics and seasonal factors; the arrival plan making and inventory management module is used for making an arrival plan; the purchase demand adjusting module is used for adjusting the purchase demand; the emergency demand plan processing module judges whether the temporary demand plan is an emergency demand plan or not, adjusts the purchase plan according to the emergency degree, provides an emergency purchase channel, adjusts the purchase plan according to the emergency demand, and feeds back an adjustment result to the seasonal change demand prediction model; and the sub-warehouse distribution plan making and real-time adjusting module is used for making sub-warehouse distribution plans according to different regions and the arrival plans.

The invention, which relates to the technical field of the warehousing system, discloses an intelligent warehousing system comprising a warehouse management information system and an equipment monitoring and scheduling system. The warehouse management information system comprises a goods management module, a warehouse management module, an order management module, an inventory management module, a warehouse-in and warehouse-out management module, an operation management and implementation module and an abnormity early warning module. According to the intelligent warehousing system, real-time tracking and data collection of articles are achieved through RFID, sensors, bar codes and the like, the data accuracy is improved, the warehouse management system integrates the functions of inventory control, order processing, cargo tracking and the like, automatic management of warehousing operation is achieved, inventory layout is optimized through data analysis and a machine learning algorithm, and the demand trend is predicted; and the decision-making efficiency is effectively improved.

The invention relates to the technical field of intelligent warehousing, and provides an inventory management method and system for intelligent warehousing in order to solve the technical problem of how to reduce the risk of inventory article overstock and stockout, and the method comprises the following steps: obtaining the historical data of the shipment amount of a warehouse; according to the shipment amount historical data, shipment change historical data of the warehouse is obtained, and the shipment change historical data comprises the shipment increment of the warehouse in each day in the historical time; a shipment quantity prediction model is trained according to the shipment quantity historical data and the shipment change historical data, and a loss function of the shipment quantity prediction model is defined according to the prediction difference between the shipment quantity historical data and the shipment change historical data; predicting the shipment prediction quantity of the warehouse in the next shipment period through the trained shipment quantity prediction model; obtaining the current stock amount and the safety stock proportion of the warehouse; and carrying out inventory management early warning on the warehouse according to the predicted shipment amount, the current inventory amount and the safe inventory proportion of the warehouse in the next shipment period.

Systems and process for identifying anomalies and changes in supply chain networks, in real-time, are disclosed. The system leverages machine learning models trained on change point detection algorithms and historical supply chain data to detect supply chain network anomalies in real-time. The system can be operatively connected to, and receive data feeds from, supply chain entities and clients. The system can provide the received data feeds to the machine learning models to detect specific points in time at which a change occurs in a supply chain network. In response to identifying anomalies and changes in supply chain networks, the system can generate alerts or modification recommendations for one or more entities or clients in a supply chain network to mitigate against network inefficiencies resulting from the identified anomalies and changes in the supply chain network, or the system can automatically override inventory management configurations in the supply chain network.