678 results about "Inventory management" patented technology

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 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 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 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 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.

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 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 belongs to the technical field of medicine inventory management, and discloses a Western medicine intelligent inventory optimization and automatic replenishment decision-making method and a Western medicine intelligent inventory optimization and automatic replenishment decision-making system. Comprising the steps of collecting environment data, inventory information and historical inventory consumption data of inventory drugs, and integrating the environment data and the inventory information to obtain a health degree index of each drug; based on the drug health degree index and the consumption prediction result, generating a replenishment decision matrix, and outputting replenishment opportunity, replenishment amount and replenishment priority; in combination with an operation audit result and inventory area division, differential inventory management scheme configuration is realized; and after external information is obtained, a replenishment strategy is dynamically adjusted, and a target inventory optimization decision is formed. According to the method, accurate evaluation of the inventory state and intelligent decision of replenishment response are realized, and the safety, efficiency and refinement level of medicine inventory management are improved.

The invention relates to the technical field of battery management, and discloses an automatic alarm system and method based on battery production and transportation inventory management, and the system comprises a data collection module which is used for deploying a multi-source sensor in the whole links of battery production, storage and transportation, and collecting battery state data, environment parameter data and transportation process data in real time; the data fusion module is used for carrying out fusion processing on the collected multi-source data based on a physical information neural network and a multi-modal fusion algorithm of adaptive feature weighting, and extracting target feature parameters; the risk prediction module is used for inputting the target characteristic parameters into a space-time adaptive risk prediction model, performing probability prediction on the battery safety risk, and dividing the risk into four grades according to the prediction risk probability; the intelligent alarm decision module is used for automatically triggering corresponding alarm strategies and disposal suggestions according to the risk grades and the links where the batteries are located; according to the invention, the pertinence and practicability of alarm are improved.

The invention relates to the technical field of casting sand production, and discloses an artificial intelligence-based casting waste sand recycling intelligent manufacturing management system, which comprises a data acquisition module for acquiring production data in a casting waste sand treatment process; the data processing and analyzing module is used for receiving the data transmitted by the data acquisition module and processing and analyzing the data; the intelligent control module is used for adjusting operation parameters of crushing, grinding, sorting and roasting equipment; the production scheduling and management module is used for receiving the control information, making a production plan and performing inventory management; and the quality detection and tracing module is used for establishing a quality tracing system and ensuring the traceability of the data. By constructing a data processing and modeling module based on artificial intelligence, dynamic identification and adjustment optimization of a key process state in a foundry waste sand treatment process are realized, so that the adaptability of the system to complex working conditions is improved.

The invention belongs to the technical field of commercial inventory management, and discloses a store inventory commodity management and search system based on RFID induction, and the system comprises the steps: collecting a phase change time sequence of candidate RFID tags at high frequency when a user executes a moving operation; before matching, analyzing actual motion characteristics of a user according to instantaneous frequency of a sequence, and performing dynamic normalization processing on a time axis; and when a plurality of targets exist in a matching result, further performing coded modulation on the transmitting power of the reader and analyzing the relevance of echo response to finish the final uniqueness arbitration. A traditional mode of probabilistic judgment depending on signal instantaneous quantity is converted into a deterministic inventory management process based on dynamic process feature confirmation, so that the problem of uncertainty of inventory searching and checking caused by commodity density and signal instability in a commercial environment is solved.

A VCN process may receive information associated with a value chain network. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models is trained to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of the value chain network and at least one member of the set of AI-based learning models is trained on the training data set to determine, upon receiving the classification of the at least one of: the operating state, the fault condition, the operating flow, or the behavior, a task to be completed for the value chain network. A VCN process may configure a robotic process automation system to execute the task to facilitate an improvement in the value chain network.

A communication system for inventory management via wireless monitoring comprises radio frequency identification (RFID) tags configured to operate in a frequency band that is unlicensed in a first geographic region and is licensed in a second geographic region. The communication system also comprises a RFID reader designed according to operational parameters of the first geographic region. The RFID reader is configured to read, in the second geographic region, data from the RFID tags via radio frequency (RF) signal transmissions in the frequency band at a power level that exceeds the operational parameters of the first geographic region. The RFID reader comprises a RF circuit, an antenna, and a controller. The controller is configured to program the RF circuit with second operational parameters, which cause the RFID reader to operate outside of requirements for operation in the unlicensed frequency spectrum in the first geographic region as defined by the operational parameters.

The embodiment of the invention relates to the technical field of artificial intelligence, and provides a supply chain management method and system based on artificial intelligence. The method comprises the following steps: collecting multi-dimensional supply chain data in real time; and carrying out dynamic quality evaluation on the data, automatically correcting data deviation by adopting an isolated forest abnormal value restoration model in combination with an evaluation result, and obtaining corrected data. And based on plasticizing industry chain platform business scenes, identifying transaction, inventory, logistics and quality detection subjects matched with the scenes in the correction data, aggregating the data according to the scenes and the corresponding subjects, forming a supply chain data set of each scene, and establishing and dynamically updating a supply chain data lake. And extracting supply chain operation characteristics of each scene from the data lake through a space-time diagram convolution model, and predicting a demand change trend of each scene according to the supply chain operation characteristics. The trend input graph attention generative adversarial network is used to predict multi-level inventory distribution, then each scene inventory management scheme is generated, dynamic supply chain management of each business scene is realized, and the supply chain data quality and management degree are improved.

The invention discloses a clinical laboratory inventory management optimization method, and particularly relates to the technical field of inventory management. The method comprises the following steps: respectively establishing a reagent inventory dynamic demand fluctuation analysis model and a reagent efficiency nonlinear attenuation prediction model by collecting real-time demand data of a clinical laboratory reagent inventory and efficiency attenuation monitoring data after a reagent is unsealed, and outputting dynamic demand fluctuation analysis data and efficiency nonlinear attenuation prediction data; predicting the demand change trend of reagent inventory peak and valley according to the dynamic demand fluctuation analysis data; determining the actual efficiency real-time attenuation state of each type of reagent inventory according to the efficiency nonlinear attenuation prediction data; according to the reagent inventory dynamic demand trend data and the reagent efficiency real-time state data, the influence degree of the reagent efficiency attenuation state on the inventory demand response capability is evaluated, reagent inventory efficiency demand coupling evaluation data is generated, and a reagent inventory purchase and turnover strategy is adjusted; according to the invention, collaborative management between reagent efficiency evolution and inventory dynamic demands is realized.

A platform simplifies the process of identifying and cataloging collectibles, specifically collectible cards (e.g., trading cards, sports cards, etc.). The platform can construct a catalog of various cards and use this catalog to identify and classify a card based on an image of the card. The platform includes capabilities to build-out / iterate the catalog when previously unknown cards are provided to the platform. A user's collection of cards can be managed and viewed within the platform. For dealers, the platform will allow the synchronization of inventory across multiple platforms, allowing dealers to list a larger portion of their collection for sale with minimal user involvement. Additionally, the platform includes a buy back button for “breakers” that allows for the participants in a case or pack break to immediately monetize the cards they receive in the break. Related apparatus, systems, techniques, and articles are also described.

A system for, and method of, remotely operating one or more vehicles in one or more inventory management facilities including one or more assets. The system and method include vehicles configured to move one or more assets in one or more inventory management facilities from a first location to a second location and a control station in communication with the one or more vehicles. The control station is remote from the one or more vehicles and communicates at least one operational command to the one or more vehicles to operate the one or more vehicles to perform one or more assigned tasks and / or operational workflows for the one or more vehicles.

The invention discloses a switching-in circuit with a self-adaptive voltage class and a double check logic. The switching-in circuit comprises a high-precision voltage division module, a double-redundancy sampling framework and a double sampling judgment logic executed by an MCU (Microprogrammed Control Unit), the high-precision voltage dividing module adopts a resistance network matched with a temperature coefficient, and can accurately divide input voltage in a wide temperature range; the double-set redundancy sampling architecture performs parallel sampling on the same path of input signals through two sets of completely independent signal conditioning loops, ADCs, digital isolation chips and isolation power supplies to form a physically isolated redundancy channel; the dual sampling discrimination logic executed by the MCU verifies a dual-channel sampling result based on a logic voting mechanism, and finally outputs a reliable input state or an abnormal alarm; according to the invention, through adaptive voltage design and redundancy check, high reliability is ensured, meanwhile, the requirements for spare parts of equipment of different voltage grades are remarkably reduced, and the inventory management cost is effectively reduced.

Systems and methods are directed to automatic listing generation and inventory management based on machine-learning analysis. The system trains a time series-based machine learning (ML) model that forecasts sales. During inference time, the system determines one or more potential categories for a user based on custom preferences and previous analytic queries of the user. High demand items in the one or more potential categories are then applied to the ML model, which outputs probabilities of predicted sales for the high demand items. The system then determines items having a potential inventory gap by cross-checking current inventory with items having a probability outputted by the ML model that satisfies a probability threshold. For each item that satisfies the probability threshold, competitor sales data, predicted sales data derived from the ML model, and editable fields displaying automatic listing thresholds that trigger the automatic generation of a corresponding listing are presented in a user interface.

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 the technical field of inventory management, in particular to a wine sample library management method and system. According to the wine sample library management method, RFID tags are used for identifying warehousing samples with one object and one code, and rule attributes are marked for each sample; defining a rule priority through a preset business rule, and distributing a storage location for the storage sample; a warehouse checking table is generated, and the samples in the warehouse are scanned and checked; when the borrowed samples in the library correspond to the samples which are returned and put in storage, identification and examination and approval are firstly carried out, and the state of each sample is determined; if the sample is damaged or overdue, destroying the sample, directly carrying out warehouse-out operation, and not carrying out return treatment; and if the corresponding replacement materials exist, the replacement materials are put into storage according to new products to be processed. According to the wine sample library management method, real-time sample positioning and tracking and automatic, intelligent and efficient warehouse management are realized, the management cost is reduced, the management efficiency is improved, and the wine sample library management method is suitable for popularization and application.

The invention discloses a production material accurate scheduling method and system under cooperation of WMS and MES, and relates to the technical field of material scheduling. Comprising a data acquisition and processing module which acquires data and preprocesses the data; the material demand planning module is used for constructing a material demand prediction model by using a time sequence analysis method, and decomposing a high-level production plan into specific material demand lists step by step according to a process route and process requirements; and the inventory management module is used for managing the materials, and dynamically distributing and adjusting the storage locations by adopting a clustering algorithm according to the characteristics of the materials and the layout of the warehouse. According to the invention, based on linkage of the WMS and the MES, full-chain closed-loop management from planning to execution is realized, and the cross-system cooperation efficiency is improved; a dynamic demand prediction system is constructed by adopting time sequence analysis, a regression model and a machine learning algorithm, a historical rule and market changes are considered, and the prediction precision is improved.

The invention discloses an intelligent group meal inventory management and replenishment method, and belongs to the technical field of inventory management, and the method comprises the steps: carrying out the data training through employing a machine learning algorithm based on the preprocessed historical multi-source data, and constructing a group meal demand prediction model; then collecting real-time multi-source data related to group meal inventory, and analyzing the real-time multi-source data by adopting a group meal demand prediction model to determine the group meal demand; dish information in the group meal inventory is acquired, and the replenishment amount of the group meal inventory is identified according to the dish information and the group meal demand amount; and finally, making a replenishment plan of dishes to be replenished by adopting an intelligent optimization algorithm on the basis of the group meal inventory replenishment amount, and sending the replenishment plan to a purchasing department or supplier for automatic purchasing and delivery, so that inventory consumption prediction can be realized, food extrusion or shortage is avoided, and the purchase efficiency is improved. And the inventory management and replenishment efficiency is improved.

In large domes and stadiums, this system integrates advanced information systems, user interfaces, and real-time data to ensure that visitors are properly guided to their destinations (seats, restaurants, shops, etc.), can understand congestion levels, receive information on products and events tailored to their individual needs, and, in the face of the increasing adoption of unmanned stores, enable efficient inventory management and payment processing. [Solution] A system including a digital display device and means for the user to input a desired destination; a central control unit and means for receiving destination information from the digital display device and calculating the optimal route; means for providing the user with optimal route guidance using a geographic information system including real-time congestion information; and means for communication with the central control unit.

The invention provides a self-adaptive inventory management method and system based on real-time anomaly detection driving in the technical field of internet product inventory management and artificial intelligence crossing, and the method comprises the steps: S1, obtaining a large amount of historical multi-dimensional heterogeneous data to construct a supply chain data set, training and deploying the created multi-dimensional anomaly detection model and the self-adaptive inventory decision optimization model; s2, the server obtains real-time multi-dimensional heterogeneous data of each supply node of the supply chain and inputs the real-time multi-dimensional heterogeneous data into the multi-dimensional anomaly detection model to obtain a real-time anomaly detection report; and S3, inputting the real-time anomaly detection report, the real-time multi-dimensional heterogeneous data and the historical strategy execution record into the adaptive inventory decision optimization model to obtain a real-time inventory optimization strategy, and executing an adaptive inventory management operation based on the real-time inventory optimization strategy. The method has the advantages that the timeliness, the accuracy, the reliability and the robustness of inventory management are greatly improved.

A control system is provided for inventory management facility with a plurality of containers. The control system includes a server, and a container handler operable within the inventory management facility. The container handler tractor comprises onboard sensors configured to generate sensor data of at least one container of the plurality of containers, a geolocation device configured to generate a geolocation value for the at least one container, a wireless transceiver, and a controller coupled to the onboard sensors, the geolocation device, and the wireless transceiver. The controller is configured to transmit the sensor data and the geolocation value for the at least one container to the server. The server is in communication with the container handler and is configured to generate a database associated with the sensor data, the database comprising, for each container, a container logo image and the geolocation value. The geolocation value includes a latitude value, a longitude value, and an altitude value.

The invention relates to the technical field of inventory management and artificial intelligence, and discloses an intelligent warehouse inventory accurate prediction system, and the system comprises a steady-state quantitative prediction engine which generates reference prediction based on historical structured data; the event-driven causal cognitive engine is used for analyzing an external unstructured data stream in real time and quantifying the impact of an external event into a disturbance vector by utilizing a dynamic causal knowledge graph; the self-adaptive prediction fusion module is used for fusing the reference prediction and the disturbance vector to obtain final prediction; and the reverse error attribution and causal chain calibration module is used for reversely adjusting the causal weight in the dynamic causal knowledge graph according to the error between the final prediction and the actual sales volume. According to the method, the influence of external emergencies can be quantified and fused into the prediction model, and adaptive calibration is continuously performed through a feedback closed-loop mechanism, so that the accuracy and reliability of a prediction result in a dynamic market environment are improved.