295 results about "Inventory data" patented technology

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.

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 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 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.

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 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.

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.

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

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 and a RFID interrogator subsystem comprising at least one RFID interrogators that are each configured to read the unique identifier of the RFID tag associated with each of the plurality of items that are within a defined boundary of at least one scan zone generated by the plurality of antenna that are coupled to the at least one RFID interrogator and to communicate the unique identifier of the each scanned RFID tag identified within each scan zone to the global inventory database subsystem. The management system being selectively configured to effect user desired levels of fidelity and / or resolution with respect to the generated unique identifier of each scanned RFID tag within a defined space of the materials handling facility.

The invention relates to an inventory threshold and purchase quantity calculation processing method and system based on AI prediction, and relates to the field of supply chain management, and the method comprises the steps: collecting historical data, real-time inventory data and external parameter data; preprocessing the historical data to form a training data set and an input data set; using the training data set to train the long-short-term memory network model to obtain a prediction model; inputting the input data set into a prediction model for analysis and prediction to determine a future demand trend; determining a material inventory threshold value according to the future demand trend and preset industry characteristic parameters; obtaining a basic purchase quantity according to the future demand trend, the real-time inventory data and the external parameter data; a material purchase amount is generated based on the external parameter data, the material inventory threshold, and the base purchase amount. The method and the system have the effects of intellectualization and accuracy of purchase decision.

The invention discloses an apple industry chain multi-source heterogeneous data coupling prediction and early warning method and system, and belongs to the field of agricultural information technology and intelligent prediction.The method comprises the steps that price data, weather data, Internet of Things sensor data and inventory data of an apple industry chain are collected, and vectorization is conducted after cleaning and dynamic time warping alignment; calculating a disaster influence factor alpha and a price conduction time lag tau based on the disaster event; inputting the multi-source data vector into a prediction model fused with bidirectional LSTM, multi-head cross attention and disaster sensing gating modules, and outputting a price prediction sequence; the invention aims to solve the problems of single data, lagging disaster response and the like in the prior art.

The invention relates to the technical field of Internet of Things, and particularly discloses an Internet of Things-based engineering construction progress dynamic regulation and control method and system, which automatically collects personnel, machinery and material data of a construction site through Internet of Things sensing equipment, replaces traditional manual recording, solves the problem of data islands, and improves the working efficiency. The method comprises the following steps: calculating a human influence factor based on a personnel state, quantifying configuration rationality and working efficiency, calculating a mechanical influence factor according to mechanical operation efficiency and a safety state, evaluating comprehensive performance of equipment, obtaining a material influence factor through material supply and inventory data, and monitoring supply timeliness and inventory stability. The three are fused to obtain a construction progress comprehensive deviation index, if the construction progress comprehensive deviation index exceeds a preset range, an adjustment instruction is automatically generated, resource allocation is dynamically optimized, real-time diagnosis and intelligent regulation and control of progress management and control are achieved, and the problem of traditional management lag is effectively solved.

The invention relates to the technical field of cold chain management, and discloses a cold chain collaborative management method and system based on cloud edge collaboration, and the method comprises the steps: obtaining the temperature, humidity and inventory data of a multi-node sensor and the global distribution point path information; according to the temperature and inventory data, edge data aggregation, noise filtering and temperature threshold monitoring are carried out, and a local inventory adjustment demand is obtained; according to the local inventory adjustment demand and the global distribution point path information, performing global path optimization analysis, multi-path variation cross set generation and comprehensive evaluation sorting to obtain a global distribution path set; and performing node conflict detection, comprehensive decision and adaptive adjustment on the global distribution path set to obtain final optimal path distribution. According to the method, global efficient collaborative management of cold-chain logistics and dynamic optimization of a distribution path can be realized.

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 and a RFID interrogator subsystem that are configured to read the unique identifier of the RFID tag associated with each of the plurality of items and to communicate the unique identifier of each scanned RFID tag identified within each scan zone of the respective RFID interrogator to the global inventory database subsystem. The management system being selectively configured to effect user desired levels of fidelity and / or resolution with respect to the generated unique identifier of each scanned RFID tag within a defined space of the materials handling facility.

The invention discloses a plastic trade scene-oriented cargo purchase-sales-stock data management system, which belongs to the technical field of cargo purchase-sales-stock management and specifically comprises a database, a purchase management module, a sales management module, a stock management and distribution module and an order tracking module. According to the method, a purchase order-inventory real-time binding mechanism is introduced, original inventory data is updated through in-transit inventory data, a traditional inventory is divided into new inventory concepts of available inventory and in-transit inventory, after sales order data is obtained, an inventory locking engine mechanism is called, a binding relation between a sales order and a new inventory is generated, and the new inventory is obtained. According to the method, the locked inventory is determined through matching and locking of the sales order and the inventory, the inventory data is updated again to cancel after verification of the locked inventory, dynamic monitoring and synchronous association of the inventory data, the purchasing data and the sales data are achieved, the inventory management efficiency and the order performance accuracy are effectively improved, and goods are effectively prevented from being oversold and mistakenly sold.

The present invention relates to a system of hands-free intelligent point of sale. The system (100) includes a computing device(102), a shopkeeper touch interface(108), and a customer touch interface(140), a camera(110), a customer microphone(112), a shopkeeper microphone(142), a first speaker(114), a second speaker(116), a networking device(118), a cloud server computer(120). The system(100) is being used as a point of sale that provides the shopkeeper with data analytics for sales, customers and inventory management. The camera(110) captures customer image and sends to the cloud server computer(120) through the computing device(102). The cloud server processor(124) recognize the customer in real time using machine learning models provides the personalized services, promotions and entertainment options to customer as per previous purchase. Simultaneously, the cloud server processor(124) provides the inventory data to the shopkeeper, so that shopkeeper is able to track inventory of particular items and order items from distributor through voice command using the shopkeeper microphone(142).

The invention relates to the technical field of information management, in particular to a material digital management system and method based on a cloud platform. The method comprises the following steps: acquiring material data; distributing a material storage space by using the material data; receiving an operation message of the storage space, and pushing the operation message to an inventory database; creating a circulation record table and an inventory state table; using the operation message in the inventory database to update a circulation record table, and recording circulation data; automatically updating an inventory state table according to the circulation data so as to determine the material inventory capacity; when the material inventory capacity is lower than or equal to a preset safety inventory threshold value, management early warning information is generated; and when the material inventory capacity is higher than a preset safety inventory threshold value, updating the management safety field of the inventory state table. The material inventory is monitored in real time based on the information management technology, the replenishment information is intelligently generated, inventory dynamic management is achieved, and the material management efficiency is improved.

The application relates to the technical field of information security, and discloses a security control method and system of a cloud inventory management platform. The method comprises the following steps: obtaining cloud interaction data sets; performing abnormal operation identification by using a behavior benchmark template to obtain an abnormal operation set; matching the behavior characteristics in the set with historical risk patterns to generate threat possibility scores; extracting relevant inventory data records from data whose threat possibility scores exceed a preset score threshold to form a data group to be checked; comparing the field content and time sequence of the data group to analyze tampering signs and obtain a tampering possibility level; inputting the level into a risk processing rule library to generate and execute a security control strategy. The method can effectively identify and cope with potential data tampering risks by constructing a full-process security mechanism from user behavior monitoring to data authenticity verification and then to risk closed-loop disposal, thereby improving the security and data reliability of the cloud inventory management platform.

The application provides a feed production line multi-device collaborative scheduling optimization method and system, and particularly relates to the technical field of feed production. The method acquires the target raw material spot price and short-term trend prediction data through a raw material market price real-time acquisition interface, synchronously acquires the device running state, the to-be-processed production batch attribute, the inventory monitoring data and the device collaborative constraint relationship. The price deviation value is calculated based on the difference between the spot price and the preset reference price, the adjustment threshold value is generated in combination with the inventory data, the priority factor set is generated by calling the dynamic scheduling priority rule, and the conflict detection graph is constructed. The price deviation value is converted into the device scheduling influence factor through the influence factor conversion matrix, the dynamic response rule is generated by inputting the conflict resolution type initial scheduling rule library, the multi-device collaborative scheduling sequence is output in combination with the device running state, the control instruction is generated to drive the device to execute, the real-time collaborative optimization of production scheduling and raw material price fluctuation is realized, and the operating cost is reduced.

The present application belongs to the technical field of tobacco logistics management, and discloses a kind of horizontal sorting equipment cigarette automatic inventory management system and method, comprising: data acquisition module is used to collect horizontal flue replenishment data, horizontal flue operation data and vertical tobacco warehouse data;Inventory module is used to collect horizontal flue real-time inventory data and vertical tobacco warehouse real-time inventory data;Wireless communication module is used to transmit the data collected by data acquisition module and inventory module;Processing module is used to receive the data sent by the wireless communication module, and combines inventory data to analyze and judge whether the inventory module is abnormal;Mark and output module, according to the judgment result of the processing module, mark and output horizontal flue real-time inventory data and vertical tobacco warehouse real-time inventory data;Wherein, the inventory module includes the first laser ranging sensor arranged at the rear end of horizontal flue and the second laser ranging sensor arranged at the top end of vertical tobacco warehouse.

To provide an inventory management device, an inventory management method and an inventory management program, for executing journalizing creation related to an inventory difference caused by daily inventory movement.SOLUTION: Stock movement data in which a movement quantity that is a weighing result at the time of movement of the raw material and a stock unit price are set in association with each other is acquired, adjustment movement data in which the difference quantity and the stock unit price are set in association with each other is acquired when the difference quantity between the theoretical stock quantity and the movement quantity of the raw material occurs at the time of movement based on the current stock data, the production performance delivery data, and the stock movement data, and adjustment journal data is created based on the adjustment movement data.SELECTED DRAWING: Figure 3

The invention provides a textile raw material intelligent proportioning method and system based on multi-source data fusion, and the method comprises the steps: constructing a prediction relation between a raw material proportioning scheme and a spun yarn quality index based on historical multi-source data including raw cotton detection data, inventory data, historical cotton assorting records and spun yarn quality data; taking the minimization of the raw material cost as a target, and taking the condition that the spun yarn quality index output by the prediction relation meets the preset quality standard, the inventory availability and the production process requirement as constraint conditions, and constructing a proportion optimization model; the matching optimization model is solved by adopting an LLM-based adaptive reflection evolutionary algorithm, the algorithm distinguishes a batch changing scene and a batch receiving scene, and adaptive evolution of a heuristic strategy is realized by circularly executing the following steps to form an iterative process: strategy generation; performing strategy evaluation; strategy reflection and evolution; and when iteration meets a termination condition, outputting a target raw material proportioning scheme which meets all constraints and is optimal in cost.

The invention provides a drug inventory data prediction system based on machine learning, and the system is characterized in that a system data collection unit collects a first identification code of a first drug to obtain first information data of the first drug; the first information data comprises at least one of a drug name, a manufacturer, an approval number, warehousing time, a period of validity, a batch number, a circulation record and a use record; and the inventory data prediction unit is used for obtaining predicted inventory data of the first medicine according to the first information data and the plurality of first feature values by adopting an inventory prediction model based on machine learning.

The invention provides a data processing method and device, equipment, a medium and a program product, which can be applied to the field of intelligent logistics. The method comprises the following steps: in response to a received target order, carrying out order positioning on the target order according to a cargo state of a to-be-distributed cargo in inventory data in a warehouse to obtain a cargo positioning quantity of each storage location in the warehouse; performing minimum value calculation on the order distribution quantity obtained after distribution of the target order and the cargo positioning quantity of each storage position, and determining a distribution matching result corresponding to each storage position; the distribution matching result of each storage position is correspondingly matched with the goods picking number of each storage position returned after goods picking in the warehouse based on the goods positioning number, and the distribution difference of each storage position is determined; and hedging operation is performed on the plurality of distribution differences to obtain a target difference, and the target difference is used for representing inventory abnormal data generated after the target order is processed.

The application belongs to the technical field of data processing. A supply chain replenishment decision optimization method, system, medium and computer equipment are provided. Multi-source features are associated with hyperparameters of a machine learning model. The hyperparameters are mapped to different dimensions of a hyperparameter search space, and each dimension corresponds to a value range of a hyperparameter. The multi-source data features are input into a pre-trained machine learning model to obtain a demand prediction result of the target product in a future period. A decision result of whether to replenish is generated according to current inventory data and the demand prediction result. The machine learning model adopts a fruit fly optimization algorithm for hyperparameter optimization, and fruit fly individuals search for an optimal solution in the hyperparameter search space as the optimized hyperparameters. The application improves the sensitivity of different hyperparameters to different features and realizes higher-precision decision making.

The application provides a steel cutting production scheduling method and device based on dynamic risk assessment, relates to the field of steel processing pre-production management, and realizes data structured expression by constructing a multi-dimensional numerical matrix through real-time acquisition of order, equipment and inventory data; candidate production scheduling sequences are generated by using a Monte Carlo method, combined with dynamic assessment of offcut loss and delay risk, and iterative optimization is performed by combining simulated annealing and an adaptive threshold mechanism; the optimal production scheduling scheme is determined within an error allowable range through probability similarity screening and regression fitting. The method effectively balances production efficiency and resource utilization, reduces cutting loss and delivery risk, and is suitable for intelligent production scheduling decision-making in complex manufacturing scenarios.