429 results about "Inventory data" patented technology

The invention relates to the technical field of dispatching and emergency response, in particular to a supply chain multi-node real-time collaborative dispatching and emergency response system and method, and the system comprises a distributed data collection module which is used for obtaining the inventory data, logistics state and equipment operation parameters of each node in real time; the digital twin modeling engine is used for constructing a dynamic virtual mapping model of the supply chain network; the collaborative decision center generates a multi-objective optimization scheduling scheme based on a reinforcement learning algorithm; the emergency response trigger is used for automatically starting a graded emergency plan through abnormal mode recognition; according to the method, second-level response is realized through millisecond-level data synchronization and edge calculation, so that decision timeliness is improved, cross-node cooperation efficiency is improved by adopting multi-agent game and federated learning, and the punctuality rate of orders and the toughness index of the network are improved through multi-target Pareto optimization on the premise of controllable cost.

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

The invention relates to the field of electric power material warehouse management and control, in particular to an electric power material digital intelligent professional warehouse management and control method, which comprises the following steps: acquiring electromagnetic spectrum data, identifying a central frequency point energy distribution diagram and an electromagnetic interference intensity level, constructing a three-dimensional electromagnetic field intensity distribution model of a professional warehouse, and dynamically adjusting the working mode of an RFID reader-writer; the method comprises the following steps: deploying a multi-modal check node in a storage area of a professional warehouse, and generating a composite check result through an infrared contour matching method and a UWB coordinate verification method when the confidence coefficient of electric power material data read by an RFID reader-writer is lower than a preset confidence coefficient threshold value; updating an electric power material inventory database according to a composite verification result, and performing electric power material inventory state analysis in real time through an edge computing node; the problems that inventory data distortion and material positioning deviation are caused by reduction of the accuracy rate of RFID in a strong electromagnetic environment, and inventory accuracy management of an electric power material warehouse is seriously restricted are solved.

The invention discloses a commodity receiving method and system based on digital twinning, belongs to the technical field of industrial internet and supply chain management, and aims to solve the technical problems of non-transparent information, tedious process, low efficiency, difficult inventory management and lack of effective monitoring in traditional commodity receiving. According to the technical scheme, data collection and twinborn modeling are carried out, specifically, digital and intelligent management of commodity receiving is achieved by building real-time mapping of a physical warehouse and a virtual twinborn body, multi-source data of material information, inventory data and position information of the physical warehouse are collected through Internet of Things equipment, and after cleaning processing is carried out, the real-time mapping of the physical warehouse and the virtual twinborn body is carried out; a three-dimensional modeling technology is adopted to construct a virtual twinborn body, and the consistency of the virtual twinborn body and a physical warehouse is ensured through a dynamic updating mechanism; real-time data processing and synchronization; intelligent receiving is realized; inventory monitoring and prediction; and performing user management and authority control.

The invention provides a logistics supply chain management method and system based on a block chain technology, and relates to the technical field of data processing, and the method comprises the steps that a storage center scans a traceability code and verifies goods information when goods are warehoused, and after verification is passed, the goods are encrypted and stored in a block chain to obtain warehouse-in data; a dynamic inventory database is established according to the goods category and the storage location number through the warehousing data, and an inventory state early warning index is generated. According to the logistics supply chain management method and system based on the block chain technology provided by the invention, dynamic and accurate management of inventory, real-time optimization of logistics paths and credible traceability of full-chain information are realized through distributed storage and non-tampering characteristics of the block chain in combination with an intelligent contract and the Internet of Things technology, the supply chain collaboration efficiency is improved, and the logistics supply chain management efficiency is improved. And the operation risk is reduced.

Certain aspects of the disclosure provide artificial intelligence (AI) methods and systems for generating personalized social media content with trend integration. A method generally includes retrieving data from data sources that includes customer interactions with a business, and inventory data of the business, determining trending-product pairs that increase engagement of the customers with products recorded in the inventory data of the business based on the retrieved data. A generative artificial intelligence (AI) model is used to generate one or more of a caption, a hashtag, and a promotional image that are personalized to each of the customers in response to receiving prompts that contain information about the customers, information about trending-product pairs, and social media platforms of the customers. The method sends one or more of the captions, the hashtags, and the promotional images that are personalized to the customers to social media platforms of the customers.

The invention discloses a business data quality treatment method supporting system hidden danger identification, and relates to the technical field of computer information processing, and the method comprises the steps: S1, collecting a real-time batch tracking data stream from an enterprise inventory system through a multi-source data interface, and obtaining an initial data set containing inconsistent formats and missing batch numbers; s2, scanning a data stream according to the initial data set by applying a preset automatic detection rule, judging positions and types of abnormal points by utilizing a rule matching mode and a threshold comparison mechanism to obtain an abnormal point set, and activating a real-time stream analyzer to process field complete verification to generate an abnormal log record; according to the business data quality treatment method supporting system hidden danger identification, real-time and accurate anomaly detection and repair are realized, the consistency and reliability of inventory data are improved, and the enterprise inventory management efficiency is remarkably optimized.

The invention relates to the technical field of sales management, and discloses a mineral water sales inventory management method based on deep learning, and the method comprises the steps: 1, collecting a data source affecting the sales volume of mineral water, carrying out the format standardization, time alignment and feature extraction of the data source, and carrying out the feature extraction of the data source; generating a group of multi-dimensional input feature vectors for describing external sales driving factors; and 2, inputting the multi-dimensional input feature vector and historical sales volume data into a deep learning model, training to obtain a mineral water future sales volume predicted value, and carrying out inventory state analysis in combination with current inventory data, shelf life parameters and replenishment cycle parameters. According to the method, the technical scheme of feature fusion of fusing external sales driving factors and multi-source structured behavior data and unified input tensor construction is adopted, and the technical effects of dynamic modeling and real-time response of external factors such as weather, holidays and festivals, regional activities and sales promotion states in sales prediction are achieved.

The invention provides an unmanned forklift path planning method, system and device and a readable storage medium. The method comprises the steps that a storage reference base map and a road network topological structure are constructed; establishing mapping association between the nodes and the storage locations and storing the mapping association; real-time inventory data is maintained through a distributed cluster, and the storage location is updated synchronously; marking obstacle nodes based on the inventory information and the mapping relation; planning a path by adopting a path search algorithm and avoiding obstacles; and when the preset path section is completed during driving, the inventory updating obstacle is obtained again, the path is regenerated and compared, and an updating instruction is issued when a difference exists. According to the method and the device, the technical problem that the driving path is easy to fail due to the change of the obstacle node because the traditional unmanned forklift path planning cannot respond to the dynamic change of the warehouse inventory in real time is solved.

The invention discloses an inventory real-time monitoring and abnormity repairing system based on multi-node redundancy check. According to the system, fragmentation and multi-replica redundancy guarantee of inventory data are achieved through a multi-node redundancy storage module, Hash fingerprints are generated through a real-time check monitoring module, and abnormity is found through quick comparison; the fault reason and the influence range are analyzed through the anomaly detection and positioning module, copy reconstruction and transaction rollback are completed through the automatic repair and rollback module, and the repair progress and the push alarm are displayed in real time through the visual monitoring and alarm module. According to the inventory real-time monitoring and abnormity repairing system based on the multi-node redundancy check, consistency maintenance and automatic repairing of inventory data are achieved, and continuous and stable operation of businesses is guaranteed. The system is characterized by comprising a multi-node redundancy storage module, a real-time verification monitoring module, an anomaly detection and positioning module, an automatic repair and rollback module and a visual monitoring and alarm module.

The invention relates to an inventory data synchronization and consistency verification system supporting network disconnection fault tolerance, and discloses an inventory data synchronization and consistency verification system which realizes continuous recording of inventory operation during a network disconnection period through network state detection and local cache, performs batch transmission after network recovery through a synchronous uploading and verification mechanism, and ensures data integrity. According to the inventory data synchronization and consistency verification system supporting network disconnection fault tolerance, data conflicts are automatically solved through a conflict detection and strategy processing module, a difference report is generated through consistency verification, and secondary synchronization is executed, so that safe synchronization and final consistency guarantee of inventory data are achieved, and it is ensured that the service process is continuous and stable. The system is characterized by comprising a network state detection module, a local cache module, a transaction log management module, a synchronous uploading module, a conflict detection module, a conflict resolution strategy module, a consistency verification module and an abnormity alarm and manual intervention interface module.

The invention discloses an engineering material intelligent allocation system and method, and the system comprises a multi-source data collection module which is used for collecting engineering material stock data, transportation equipment state data, construction site environment data and construction progress data in real time through Internet of Things equipment; the intelligent analysis module is used for multi-dimensional data fusion analysis and intelligent algorithm driving to carry out dynamic optimization management of the whole process of the engineering materials; and the dynamic scheduling decision module is used for integrating a multi-objective optimization algorithm and a real-time feedback mechanism, and outputting a scheduling instruction set including a transportation scheme, a storage scheme and an emergency scheme, and through implementation of the system, dynamic optimization management and credible traceability of the whole process of engineering materials are realized. The system deeply integrates the Internet of Things, digital twinning, block chain and artificial intelligence technologies, significantly improves the material allocation efficiency, the cost control capability and the emergency response level, guarantees the data security and the multi-party cooperation transparency, and has certain use value and popularization value.

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.

The invention relates to the technical field of retail management, in particular to a retail end online management method and system based on concurrency control, and the method comprises the steps: constructing a retail knowledge graph based on historical transaction logs through employing a space-time interaction entropy weighting algorithm; calculating a local topology edge density increment by applying a streaming dense subgraph increment mining algorithm, and determining a topology aggregation state of a target node; if the state is the mixed concurrent state, constructing an inventory time slice mirror image containing a reference inventory domain and a dynamic increment domain; and writing the request into the lock-free annular buffer during checking, and merging data by adopting a time sequence alignment algorithm after checking to carry out atomized updating. According to the method, static inventory and dynamic sales are physically isolated by constructing the inventory time slice mirroring and lock-free buffer mechanism, the read-write conflict in the scene of inventory while sales is solved, and zero-blocking hybrid concurrence is realized while the final consistency of inventory data is guaranteed.

According to the intelligent inventory management and automatic replenishment e-commerce operation method, sales data, inventory data, return data, commodity attributes, supply chain data and market environment data (such as social media, weather data, economic indicators and the like) of an e-commerce platform are integrated, and a data preprocessing method is adopted for cleaning and denoising, so that the quality and integrity of the data are ensured. A multi-dimensional data fusion and deep learning algorithm is adopted, multiple factors such as commodity historical sales data, seasonal fluctuation, market dynamics, user behaviors and the like can be fully considered, and therefore compared with a traditional single prediction model, the demand prediction accuracy can be greatly improved, the inventory overstock or stockout risk caused by prediction errors can be avoided, and the user experience can be improved. By designing a mechanism for monitoring and analyzing external events in real time, emergency situations (such as emergency promotion, market demand fluctuation, weather change and the like) can be quickly responded, a replenishment strategy can be timely adjusted, and inventory crisis caused by the fact that the system fails to quickly respond to external changes is avoided.

The invention discloses an intelligent supply and demand matching dynamic optimization method and device and a medium, and relates to the technical field. The method comprises the steps that demand side data and supply side data are collected, the demand side data comprise user interaction logs and real-time positions, and the supply side data comprise inventory data, logistics track data and production line load data; fusing demand side data and supply side data through a knowledge graph, inputting the demand side data and the supply side data into a graph neural network, encoding the demand side data and the supply side data into high-dimensional vectors, and generating a supply-demand matching preliminary screening candidate set; expanding a preliminary screening candidate set through a message passing mechanism of the graph neural network; and searching a non-dominated solution set by adopting a multi-objective optimization algorithm, and distributing index weights in real time so as to sort the expanded preliminary screening candidate sets. According to the method, real-time fusion of multi-source data is realized, a multi-target optimal solution is generated by using deep learning and operational research algorithms, and the strategy is continuously adjusted and optimized in combination with reinforcement learning.

The invention discloses a wire warehouse storage intelligent identification system and method. The method comprises the following steps: S1, collecting a multi-angle image and identifying wire features; s2, mapping an identification result to a warehouse space coordinate to generate a warehouse state diagram; s3, reading the electronic tag and binding the electronic tag with the identification target to confirm the identity; s4, constructing a dynamic inventory database and updating the state of the wire rod in real time; s5, receiving a warehouse-out request, planning a path and generating a carrying instruction; s6, controlling the automatic equipment to execute carrying and monitoring path deviation; and S7, after carrying is completed, the inventory state is updated, and an ex-warehouse log is recorded. According to the invention, full-process automatic identification and accurate warehouse-in and warehouse-out control of wire storage are realized, and the storage efficiency and the management intelligence level are remarkably improved.

The invention relates to the technical field of information security, and discloses a security control method and system for a cloud inventory management platform. The method comprises the following steps: acquiring a cloud interaction data set; performing abnormal operation identification by adopting the behavior reference template to obtain an abnormal operation set; the behavior characteristics in the set are matched with historical risk modes, and threat possibility scores are generated; related inventory data records are extracted from the data of which the threat possibility scores exceed a preset score threshold, and a to-be-checked data set is formed; performing field content and time sequence comparison on the data set, and analyzing a tampering sign to obtain a tampering possibility level; and inputting the level into a risk processing rule base, and generating and executing a safety control strategy. According to the method, a whole-process security mechanism from user behavior monitoring to data authenticity verification to risk closed-loop disposal is constructed, so that potential data tampering risks can be effectively identified and coped with, and the security and data reliability of a cloud inventory management platform are improved.

The invention discloses a logistics warehouse cargo storage method and system and a storage medium, relates to the field of logistics warehouse cargo storage, realizes intelligent warehouse management through multi-module cooperation, remarkably improves inventory monitoring and cargo scheduling efficiency, and improves the cargo scheduling efficiency based on video image recognition and contour matching technologies in combination with database information. Accurate inventory data is generated in real time, unregistered goods are effectively identified, manual inventory errors are reduced, goods flow indexes are quantified through a dynamic time decay model, and goods turnover rules are accurately reflected by combining weighted analysis of carrying proportion and quantity, so that data support is provided for scheduling decision making. Multi-dimensional factors such as environmental parameters, transportation distance and storage capacity are integrated, a callout-storage staged optimization model is established, environmental adaptability, storage safety and scheduling rationality are organically combined, manual intervention is reduced, the warehouse turnover efficiency is improved, the storage cost is reduced, and a full-process closed-loop intelligent warehouse management system is formed.

The invention relates to an intelligent green building material scheduling method and system based on reinforcement learning. The method comprises the following steps: acquiring building material characteristic data; according to the building material characteristic data and preset inventory data, arranging the building materials according to the production emergency degree to obtain a basic production scheduling scheme; the basic production scheduling scheme is used for representing a production sequence of the building materials, and the production sequence is determined based on the building material characteristic data and the inventory data of the building materials; according to a preset experience database, adjusting the arrangement sequence of the building materials in the basic production scheduling scheme to obtain an optimized production scheduling scheme; the experience database comprises the basic production scheduling scheme of the historical scheduling process; the optimized production scheduling scheme is a new building material production sequence obtained by scheduling the basic production scheduling scheme according to the basic production scheduling scheme in the historical scheduling process. By adopting the method, the production scheduling decision quality can be improved.

The invention discloses an intelligent stereoscopic warehouse inventory data synchronization method and device and a computer program product, and the method comprises the steps: collecting multi-source data of an intelligent stereoscopic warehouse, and constructing a multivariate data set; embedding the multivariate data set into a nonlinear mapping space, and constructing an embedded state vector for expressing inventory behaviors; a scheduling weight evaluation function is constructed based on the embedded state vector and historical error feedback, the scheduling weight of the inventory operation is calculated through the scheduling weight evaluation function, a decision is made on the synchronization mode of the inventory operation according to the scheduling weight, and the synchronization mode comprises instant synchronization, delay synchronization or merging synchronization; and based on the scheduling weight and historical error feedback, dynamically correcting the response tension coefficient of each dimension in the embedded state vector, and applying the corrected response tension coefficient to subsequent embedded state vector construction to form a feedback closed loop. According to the invention, a powerful guarantee is provided for efficient, accurate and stable operation of the intelligent warehousing system.

The invention relates to the technical field of intelligent warehouse logistics, and particularly discloses a warehouse logistics AI path planning system and method based on multi-modal dynamic optimization. The system comprises a multi-source data layer which aligns inventory data, traffic flow data and meteorological data of a warehouse management system (WMS) in real time through a sliding window synchronization mechanism, and ensures time-space consistency of multi-modal input; the dynamic fusion engine responds to a traffic interruption event within 3 seconds based on a space-time alternative path algorithm and generates an anti-interference optimized path scheme; according to the path decision core, a two-stage reinforcement learning architecture formed by a graph convolutional network (GCN) and a deep Q network (DQN) is adopted, static topology analysis and dynamic real-time characteristics are fused, and a final transportation path is output; the decision auditing module is used for recording Q value distribution of decision nodes, constructing a traceable path decision tree and automatically detecting regional distribution deviation through a quantization formula; and the zero-code compatible layer is used for presetting a standardized API template library and a rule compiler so as to realize plug-and-play integration with an enterprise resource plan (ERP) or a warehouse management system (WMS). Through multi-source data collaborative optimization, dynamic anti-interference decision and whole-process audit tracing, the real-time performance, robustness and transparency of warehouse logistics path planning are remarkably improved.

The present invention relates to the field of project management technology, specifically to an intelligent management method and system for civil engineering projects, comprising the following steps: acquiring personnel distribution, equipment status, and material inventory data, extracting key task nodes and execution status, identifying tasks with delayed progress, screening key path nodes and analyzing time differences, marking progress trends, identifying equipment impact sections, judging resource allocation imbalances, and generating progress monitoring indicators. In the present invention, through multi-source data collection of personnel distribution, equipment status, and material inventory, the difference identification between task status and planned nodes is enhanced, the efficiency of progress delay warning is improved, the dynamic classification and trend identification of progress fluctuations are achieved, and the optimization of construction rhythm is facilitated. Through the overlap analysis of equipment operation and task fluctuation areas, the bottleneck of construction efficiency is identified, the scheduling accuracy is improved, and then through the comparison of input and cycle differences, fine progress monitoring is achieved, and the synergy between progress control capabilities and on-site resource allocation is comprehensively enhanced.

The invention relates to a material replenishment method and system based on dynamic inventory prediction. The method comprises the following steps: firstly, acquiring a historical inventory data set of multiple types of materials comprehensively covering warehouse-in, warehouse-out and inventory allowance records; secondly, in the inventory feature extraction link, material consumption rules and storage state features are innovatively extracted from time and space dimensions respectively; moreover, the dynamic inventory prediction model carries out combined processing on the two types of features, the limitation of a traditional prediction method is broken through, the complex association of materials in time and space can be fully considered, and the accuracy and reliability of inventory change prediction in a target time period are greatly improved. And finally, a supply trigger condition and a supply amount adjustment strategy are generated based on an accurate prediction result, and are fed back to a supply execution system, so that dynamic and accurate supply operation is realized, the inventory overstock or stockout phenomenon is effectively avoided, the inventory management efficiency is remarkably improved, the inventory cost is reduced, and the whole material inventory management process is comprehensively optimized.

The invention relates to the technical field of product oil management, and discloses a product oil purchase-sale-stock multi-modal data analysis method and system. The method comprises the following steps: acquiring real-time inventory data, transportation state data and sales terminal data in a product oil supply chain to form a multi-modal data set; performing data cleaning on the data, and extracting key inventory characteristics to generate a cleaned data subset; obtaining associated historical purchase data, regional demand prediction data and market price fluctuation data based on the gas station identification information to form a multi-dimensional auxiliary data set; inputting the features into an inventory feature extraction model, generating a multi-feature vector set, and fusing the multi-feature vector set; inputting the fused feature vectors into an inventory state evaluation model set, and outputting inventory early warning level and abnormal type information; and finally, a dynamic allocation scheme is generated by combining real-time inventory data and the like. According to the method, multi-modal data are integrated, so that the comprehensiveness and accuracy of data analysis are improved, and the purchase-sales-stock management of the product oil is assisted to be more suitable for market dynamics.

The invention relates to a dynamic purchase plan generation system and method based on performance prediction. The system comprises a data collection module which is used for collecting and integrating historical sales data, inventory data, market demand data and supplier delivery data; the performance prediction module comprises a prediction model and is used for carrying out demand prediction based on historical data and selected features; the purchase plan generation module is used for making a purchase plan according to the prediction value generated by the performance prediction module, the current inventory condition and a preset purchase strategy, wherein the purchase strategy comprises the minimum order quantity, the maximum inventory quantity, the supplier delivery date and the like; the dynamic adjustment module is used for monitoring actual sales and inventory conditions in real time; the user interface module is used for displaying the prediction data and the purchase plan; and the report generation module is used for generating a prediction result. According to the system, complex data can be effectively integrated and managed, the accuracy of demand prediction is improved, and a purchase plan is optimized, so that efficient supply chain operation is realized.

The invention relates to the field of warehouse logistics, and discloses a warehouse intelligent inventory sorting system which comprises a data acquisition module, an analysis and judgment module, a classification and division module and a summary analysis module. The data acquisition module is used for acquiring basic information of inventory commodities in real time and capturing real-time state data of the commodities; the analysis and judgment module analyzes whether the inventory of the commodities is in a reasonable interval or not based on the information acquired by the data acquisition module, and judges the priority and the optimal path of a sorting task in combination with order data and commodity storage positions; the classification and division module is used for decomposing the whole sorting work into a plurality of sub-tasks and distributing the sub-tasks according to the load capacity of personnel or equipment; and the summary analysis module is used for combining the inventory data and the sorting records. The method has the following advantages and effects that manual judgment errors are reduced, the processing flow of abnormal commodities is standardized, the risk of warehouse management is reduced, the sorting efficiency is greatly improved, and invalid travel and time cost are reduced.

The invention discloses an induction cooker production line productivity prediction system using big data analysis, and relates to the technical field of industrial Internet of Things, the induction cooker production line productivity prediction system comprises a production line management center, the production line management center is in communication connection with the following modules: a multi-source data acquisition module used for acquiring full-link data from each link of an induction cooker production line, carrying out preprocessing to form a full-link data sequence table; and the digital twinning virtual mapping engine is used for constructing a virtual model of the induction cooker production line in combination with a digital twinning technology. The real state of the production line can be reflected in real time by collecting the equipment operation state, the production progress and the material inventory data of the induction cooker production line in real time and combining with the virtual model constructed by the digital twin technology, so that the system can capture abnormities in the production process in time, accurate productivity prediction is carried out, and the production efficiency is improved. The problem that productivity adjustment is not timely due to information lag is effectively avoided, and the accuracy and flexibility of a production plan are remarkably improved.

The invention relates to the technical field of data fusion, and discloses an inventory prediction system based on multi-source data fusion. An inventory data acquisition module of the system is responsible for acquiring multi-dimensional dynamic data including a real-time sales fluctuation sequence, a storage turnover period record, a logistics transportation delay index and the like in a supply chain link; the feature fusion processing module extracts sales trend, storage efficiency and logistics stability features according to the multi-dimensional dynamic data, and generates a fusion feature matrix according to time axis alignment; the inventory state modeling module constructs a dynamic inventory state model capable of reflecting the incidence relation between the inventory consumption rate and the replenishment delay by using the fusion feature matrix; the anomaly detection module carries out deviation degree analysis on an inventory change curve output by the dynamic inventory state model, identifies abnormal fluctuation nodes and marks corresponding supply chain links; and the prediction result generation module outputs an inventory surplus early warning value and stockout risk probability distribution in a future period based on the corrected dynamic inventory state model.

An adaptive inventory management system for use in a materials handling facility storing a plurality of items that are each associated with a Radio Frequency Identification (RFID) tag. The management system including a global inventory database subsystem, a RFID interrogator subsystem and a multi-frequency interrogator subsystem for scanning RFID tags within a defined internal volume of a vehicle. The management system being selectively configured to include an occupancy detection system.