1005 results about "Inventory management" patented technology

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 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 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 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.

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 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 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 discloses a laboratory whole-process intelligent management and control system based on AI and Internet of Things fusion, which relates to the technical field of laboratory management and comprises an equipment access unit, a central decision unit and a business application unit. According to the system, a risk model library and a real-time risk map can be constructed through the risk assessment module, accident risks caused by human errors are reduced through the real-time risk map, automatic emergency disposal and personnel behavior compliance monitoring, and when the equipment operation and maintenance module detects that the equipment is abnormal, predictive maintenance can be automatically carried out, so that the downtime is shortened, and the maintenance efficiency is improved. The resource scheduling module can be used for optimizing the inventory and scheduling the equipment, and carrying out global allocation based on an inventory sharing model, so that resource idleness is reduced through cross-courtyard inventory sharing, resource sharing is facilitated, cross-courtyard collaboration is completed, and the situation that data cannot be synchronized due to independent inventory management of each courtyard is avoided; and further, global optimization can be realized.

The invention discloses an AI-based hospital logistics zero inventory intelligent scheduling method and system, and relates to the technical field of intelligent medical treatment and resource optimization, and the method comprises the steps: collecting multi-dimensional data to generate a multi-dimensional data set, carrying out the feature extraction through a GNN, dynamically adjusting the edge weight through an RL, carrying out the updating through a Q-learning formula, and predicting the material demand and carbon emission. Synthesizing the 3D model and the map into a virtual environment, loading a scheduling instruction set and a hospital logistics real-time state data set, generating a digital twin environment, constructing a DRL model to predict arrival time, operating the digital twin environment to update a state vector, adjusting the predicted arrival time, and updating the scheduling instruction set. According to the invention, accurate data is provided for zero inventory, the response speed of scheduling is improved, the adaptability of the system to complex scenes is enhanced, the high efficiency of scheduling execution is ensured, the stability and intelligent level of hospital logistics are improved, and the high-efficiency zero inventory management of hospital logistics is realized.

The invention relates to a warehouse inventory management and optimization method based on artificial intelligence, and the method comprises the steps: constructing a novel mathematical model through comprehensive consideration of the storage position of a commodity, tray partition, order priority, system state and other factors, solving through employing a deep reinforcement learning DQN network, achieving the intelligent decision of a commodity pickup position, and generating an optimal picking path; according to the method provided by the invention, the order processing time can be effectively shortened, aiming at the requirements of small-batch and multi-category orders, the storage efficiency is improved, the movement distance of a stacker and the number of goods taking times are reduced, the operation cost is reduced, and flexible scheduling can be performed according to the priority to adapt to the real-time state change of the system; compared with a traditional method, after the optimization strategy is adopted, the number of times of taking out the trays is remarkably reduced, and the method has the remarkable beneficial effects, can be widely applied to the field of intelligent warehousing, improves the automation, informatization and intelligentization levels of warehousing management and meets the requirements of the modern logistics industry for an efficient warehousing system.

The invention discloses a mobile rescue intelligent management method based on RFID automatic identification and AI artificial intelligence interaction, and belongs to the field of mobile rescue intelligent management. According to the invention, the problem of low efficiency of medicine and material inventory management of the rescue carriage in an inpatient area due to an existing manual inventory mode is solved, batch accurate identification can be carried out by adopting an RFID remote identification technology, batch inventory is realized to replace one-by-one checking, the inventory efficiency is effectively improved, and the inventory management cost is reduced. Manual electrocardiogram data checking is replaced by image visual recognition of electrocardiogram data, secondary recording is not needed, rescue data can be automatically recorded, rescue efficiency is improved, medical advice recognition is assisted by AI voice instead of manual recording of medical advice, the workload of nurses is reduced, the workload of secondary recording is reduced, and rescue efficiency is improved. And through a rescue scene full-process digital AI closed loop, digital tracing can be completed in the rescue process, and the problems that traditional handwritten records and data are not real-time and inaccurate are solved.

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 provides a commodity sales volume prediction method and system based on artificial intelligence, and relates to the field of data processing and intelligent prediction.The method comprises the steps that hot topic data are obtained, keyword tags in the hot topic data are extracted to be matched with commodity class tags of an e-commerce platform, and a hot associated commodity list is generated; and dynamic modeling of a user interest conversion path is combined, a user behavior sequence mode is analyzed, and a commodity popularity value of a real purchase intention is generated. The method comprises the following steps: carrying out time sequence sampling on popularity values, setting differential popularity threshold values based on commodity category features, dynamically identifying potential hot commodities, and calculating a stepped sales prediction value in future time in combination with a historical popularity value change curve and a sales conversion rate. In this way, the real-time performance and accuracy of sales volume prediction can be improved, the e-commerce platform can find potential hot commodities in time, inventory management is optimized, operation efficiency is improved, and prediction requirements in a complex and changeable market environment are met.

The invention relates to a supply room consumable intelligent inventory management method and device, equipment and a medium. The method comprises the steps that multi-source sensing data of all consumables in a supply room are acquired, multi-source information fusion processing is carried out according to the multi-source sensing data, and inventory state posterior distribution of all the consumables is obtained; performing event correction processing on the inventory state posterior distribution, and performing demand prediction processing according to the inventory state distribution subjected to event correction and the historical consumption data of the consumables to obtain a multi-step demand prediction result of each consumable in the target prediction period; performing inventory optimization calculation processing according to the multi-step demand prediction result and the inventory state distribution subjected to event correction to obtain the replenishment order quantity of each consumable in the target prediction period; and according to the replenishment order quantity and the batch validity period information of each consumable, performing consumable scheduling and risk early-warning processing, and generating an immediate consumable priority use strategy and inventory risk early-warning information. By adopting the method, the consumable stock can be identified, and the replenishment order can be generated.

Automated, environmentally monitored and controlled storage units and systems for storing, monitoring, and maintaining a supply of products, particularly temperature sensitive pharmaceutical and / or other high-value products, be they temperature sensitive or not. Such automated storage units contain an array of independently addressable holding locations for containers with product in one or more environmentally monitored (e.g., for temperature, humidity, etc.) and controlled zones fitted with appropriate environmental sensors. In preferred embodiments, the automated storage units also include a reader to track product information and status. Product loading, retrieval, and movement within such automated storage units is performed by a computer-controlled robot. A user interface device, preferably in communication with an application service provider to provide remotely managed inventory management and other services, provides users with inventory- and product-specific information.

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.

A warehouse goods intelligent identification and positioning management system based on an image segmentation algorithm comprises an image acquisition and preprocessing module, an image segmentation algorithm module, a goods positioning and feedback module, an inventory management module, a data communication and integration module and a user interaction and management module. The image segmentation algorithm module is used for segmenting and extracting image information. The cargo identification and positioning module is used for identifying and positioning cargoes. The position feedback and alarm module is used for feeding back cargo positions and giving an alarm. The inventory management module is used for managing and maintaining cargo information. The data communication and integration module is used for transmitting information in real time. According to the warehouse goods intelligent identification and positioning management system based on the image segmentation algorithm, the warehouse image segmentation algorithm based on deep learning is put forward to classify goods, and the goods identification and positioning algorithm based on improved label template matching is put forward to intelligently position the goods.

The invention discloses a multi-dimensional data management method and system based on a spreadsheet, and the method comprises the steps: responding to an editing operation instruction of the spreadsheet displayed by a client, and updating the table structure and table data of the spreadsheet; the editing operation instruction at least comprises one of a dragging instruction for dragging rows and columns of the spreadsheet to carry out free combination, a hiding and screening instruction for hiding and screening the rows and the columns and a sorting instruction for sorting data of the rows and the columns; capturing a timestamp corresponding to the editing operation instruction, taking the timestamp as an index, taking the updated table structure and table data as historical data modification records, and storing a mapping relation between the index and the historical data modification records; and tracing the historical data modification record through the mapping relationship. Therefore, by adopting the embodiment of the invention, multi-dimensional operation can meet the requirements of enterprises on refinement and individuation of inventory management, meanwhile, operators and modification history can be tracked, and the security and reliability of data are improved.

The invention discloses a workshop part declaration management system, and relates to the technical field of production workshop part inventory management. The workshop spare and accessory declaration management system comprises an acquisition and division unit used for analyzing data of a comprehensive production evaluation index of each part in each part type after a preset workshop production cycle is finished; the defect judgment unit is used for judging and screening production defect parts based on the comprehensive production evaluation index and a preset production evaluation interval; the maintenance analysis unit is used for analyzing the maintenance evaluation index; and the maintenance judgment unit is used for dividing maintainable defective parts and scrapped defective parts based on the maintenance evaluation indexes and a preset maintenance evaluation interval and generating a part declaration report. Each part is comprehensively analyzed through the comprehensive production evaluation indexes, so that the production quality of the parts can be accurately evaluated, and the production efficiency of the parts is improved. A comprehensive evaluation result is provided for each part type, and potential problems of the parts can be identified more comprehensively and meticulously.

The invention relates to an e-commerce cold start commodity sales prediction method and device based on big data, equipment and a medium, and the method comprises the steps: constructing a multi-source heterogeneous data set through integrating internal data of an e-commerce platform and competing product data of a third-party platform, and generating standardized commodity attributes and a cross-platform association relationship through cleaning and category mapping; constructing a knowledge graph based on commodity attributes and supply chain features, and extracting vectorization features representing commodity semantic association; fusing the knowledge graph and the cross-platform data to generate a virtual historical sales volume sequence of the target commodity; and the confidence coefficient of the virtual sequence is dynamically corrected, and the multi-modal fusion prediction of the time sequence model and the structured feature model is combined, so that high-precision sales volume estimation can be realized. According to the method, dependence of a traditional method on historical data is broken through, multi-platform heterogeneous information is effectively utilized, prediction robustness in a cold start scene is improved through a dynamic optimization mechanism, and accurate inventory management and marketing decision support is provided for e-commerce enterprises.

The invention relates to the technical field of information, and particularly discloses an online mall platform intelligent management method and system, and the method comprises the following steps: S1, constructing a data collection system, and obtaining platform operation data, user behavior data and external market data in real time; s2, acquiring historical sales data, analyzing the historical sales data by adopting a Prophet time sequence model, predicting a future sales trend and inventory requirements, and generating a dynamic replenishment suggestion; s3, optimizing inventory management through a dynamic programming algorithm, calculating an optimal inventory threshold value in combination with external market data, and performing automatic replenishment triggering; s4, establishing a user portrait according to the user behavior data, and generating a personalized coupon strategy based on a collaborative filtering algorithm; s5, dynamically matching the work order and the customer service according to a multi-attribute decision algorithm; and S6, storing quality assurance information, and ensuring non-tampering and traceability of construction time, material batches and technician ID data. Therefore, the operation efficiency and the user experience of the shopping mall platform are improved.

The invention relates to the technical field of inventory management, in particular to a supermarket intelligent shelf real-time inventory dynamic monitoring system and method based on multi-sensor fusion. The system comprises a data acquisition module, a data processing module, a model construction module, an intelligent feedback module and a layout recommendation module. The data acquisition module is used for acquiring commodity data on a goods shelf in real time, and the commodity data comprises weight change data, visual feature data, radio frequency identification and spatial position information; the data processing module is used for preprocessing the commodity data collected in real time to obtain preprocessed commodity data; the model construction module is used for constructing a real-time three-dimensional virtual model based on the preprocessed commodity data and mapping the position and state of the commodity; and the intelligent feedback module is used for generating commodity replenishment data by using the prediction model based on the pre-stored historical sales data and the preprocessed commodity data, and storing the inventory change time piece hash value.

The embodiment of the invention provides an inventory management method and system based on the demand quantity of medical instruments, and the method comprises the steps: receiving the inventory information and real-time medical event information of the medical instruments in each medical institution, and obtaining the predicted demand quantity of the medical instruments corresponding to each medical institution; obtaining a consumption level coefficient and an advance period of each medical institution, determining a demand standard deviation and a historical daily average demand quantity corresponding to the medical institution, and determining a preset inventory safety value of the current medical institution according to a preset inventory safety calculation rule; and based on the inventory information, determining an inventory safety value corresponding to the medical apparatus in each medical institution, when the inventory safety value is smaller than a preset inventory safety value corresponding to the current medical institution, determining a supplement demand, a supply warehouse and a supplement route, and transporting the medical apparatus to the current medical institution according to the supplement route. According to the method, the defects of lack of real-time medical event information integration, multi-warehouse cooperative allocation and the like are effectively overcome, and the response capability of emergency replenishment is remarkably improved.

The invention relates to the technical field of intelligent inventory management, and discloses an intelligent inventory management method and system for electronic components, and the method comprises the steps: creating block chain identities of the electronic components; performing cryptographic operation on a tag ID of an RFID tag in a built-in encryption chip and the block chain identity identifier to obtain an encryption tag; a challenge code sent by the RFID reader-writer is received, the encryption tag uses a built-in encryption chip to carry out AES encryption operation on the challenge code and a pre-stored secret key, and a secure communication link is established; and reading the price and material number information of the components passing through the secure communication link, and executing consistency verification to obtain a verification result, the security strength and anti-clone capability of the anti-counterfeit label are improved, and efficient verification of large-scale component inventory is realized.

The invention discloses a blockchain-based inventory prediction and management system and method for cross-border e-commerce goods, and belongs to the technical field of inventory management. The method comprises the following steps: splitting alternative links according to the dimensions of a transportation mode and a transportation line, calculating the transportation time of each link, and constructing a transportation time set; based on the transportation time set, constructing an input feature matrix, training a convolutional neural network, generating an adjusted transportation time set, and screening an optimal link; generating a transportation list based on the optimal link; when the transportation list is confirmed to be executed, deducting the stock quantity of the SKU corresponding to the departure place warehouse; when the goods arrive at the destination area warehouse, updating the stock quantity of the SKU corresponding to the destination warehouse; obtaining the maximum daily purchase amount of the manufacturer, calculating the regional inventory demand priority, and formulating a purchase amount distribution rule; matching the optimal link with a purchase quantity distribution rule, adjusting the optimal link, and calculating an inventory turnover rate; and adjusting the daily purchase amount of the manufacturer based on the inventory turnover rate.

The invention relates to the technical field of cargo allocation, and particularly discloses an offline store cargo allocation method and related equipment, and the method comprises the steps: a system receives a cargo allocation order from a store, analyzes the information, number and priority of commodities in the order, and generates a cargo allocation task in combination with real-time inventory data; based on the warehouse layout, the commodity position and the real-time data, intelligent path planning is carried out by using an intelligent algorithm; goods sorting, packaging and carrying are completed through automatic equipment; inventory data is automatically updated after the commodities are delivered, and a replenishment instruction is triggered when the inventory is lower than a threshold value; the system dynamically allocates tasks to the distribution staff through the mobile terminal according to the position and the working state of the distribution staff, and monitors the progress and the abnormal condition in real time; after distribution, the system scans the code to complete handover, and the handheld intelligent distribution terminal equipment automatically uploads a distribution result to the warehouse management system; through a dynamic path planning algorithm, automatic sorting equipment and real-time inventory management, an efficient and accurate cargo allocation process is realized.

The invention discloses an operation management method and system for a big data intelligent warehouse, and relates to the technical field of data processing and intelligent warehouse management, and the method comprises the steps: collecting multi-source heterogeneous data, dividing the multi-source heterogeneous data into grid units, fusing time, space and environment features, generating a spatio-temporal data matrix, inputting the spatio-temporal data matrix into a spatio-temporal diagram convolution network model, and obtaining a spatial-temporal diagram convolution network model; according to the method, the relevance among the grid units is analyzed through the multi-layer space-time convolution, the safety inventory prediction value of each area is obtained, the multi-source heterogeneous data is integrated, the relevance among the data is analyzed through the space-time diagram convolution network model, the safety inventory prediction value of each area can be accurately obtained, and the safety inventory prediction accuracy is improved. The accuracy of inventory management decisions is improved, the generative adversarial network model is utilized to deeply analyze the distribution characteristics of the abnormal candidate regions, and the abnormal regions and the deviation levels thereof are accurately positioned by calculating the deviation degree, so that the false alarm rate is greatly reduced, and the dependence on manual review is reduced.