564 results about "Stock-taking" patented technology

The invention discloses an unmanned aerial vehicle intelligent dynamic inventory algorithm, and relates to the technical field of warehouse management, the unmanned aerial vehicle flight inventory route is planned according to warehouse layout, shelf distribution and material storage area information, and non-inventory areas are marked; the unmanned aerial vehicle flies to obtain radio frequency data and material image visual data on warehouse materials; the collected radio frequency data and the material image visual data are preprocessed, feature extraction is carried out, and radio frequency features and visual features are obtained; according to the unmanned aerial vehicle intelligent dynamic inventory algorithm, through cooperative application of radio frequency and visual technology, the inventory accuracy is enhanced, radio frequency identification can accurately read material label information and obtain key data of material numbers and category codes, and a visual identification system can be used for capturing material appearance characteristics from multiple angles and multiple distances and assisting in confirming material categories and states; large-area areas in a warehouse can be efficiently checked in a short time, and smooth operation of the warehouse is guaranteed.

The invention discloses a storage checking method based on a multi-modal sensing technology, a robot and a storage system, and belongs to the technical field of storage management and intelligent sensing fusion. Multi-modal data such as a visual image, space depth, radio frequency sensing and infrared temperature are collected, and an image feature vector, a three-dimensional point cloud model, a radio frequency response matrix and a temperature map are constructed; generating a fusion recognition vector through a multi-channel fusion network based on an attention mechanism, and dynamically adjusting a modal weight; constructing an article space distribution map, and marking a perception missing region; automatically complementing low-confidence region data based on a priority scheduling algorithm; performing joint verification on the original fusion result and the completion result to form a final inventory list; if the confidence coefficient of a certain article is lower than an early warning threshold continuously for multiple times, triggering an abnormal alarm and generating a traceable sensing sequence; the method is suitable for a high-precision inventory task in a complex storage scene, and has the advantages of high recognition robustness, intelligent completion mechanism, traceable abnormity and the like.

The invention discloses a cold chain warehouse unmanned aerial vehicle obstacle avoidance and path planning checking method and system based on AI intelligence, and belongs to the field of intelligent obstacle avoidance, and the method comprises the steps: arranging different types of sensors used for sensing the environment change of a cold chain warehouse in the cold chain warehouse, the edge gateway integrates different types of sensors, preprocesses the collected data, and sends the processed data to a cloud database; constructing a digital twin model of the target cold chain warehouse and an intelligent inventory path planning algorithm in the cloud database; the cloud sends a path scheme output by the intelligent inventory path planning algorithm to the unmanned aerial vehicle terminal and the digital twin model at the same time; the unmanned aerial vehicle terminal completes a checking task according to the path scheme, and the digital twin model is updated in real time according to the path scheme and data fed back by the unmanned aerial vehicle terminal. According to the invention, it can be ensured that the unmanned aerial vehicle efficiently and safely performs inventory tasks in a complex cold chain warehouse environment.

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 discloses a storage intelligent monitoring and checking integrated system and method based on multi-technology fusion, and particularly relates to the technical field of storage monitoring, the system obtains environmental parameters and cargo data in real time through a multi-source data acquisition module, carries out cargo identification and segmentation by using YOLOv7 and Mask R-CNN, and carries out storage monitoring and checking on the cargo. A three-dimensional model is constructed in combination with a Poisson surface reconstruction algorithm to calculate volume weight, an environment anomaly analysis module generates an early warning signal through dynamic threshold and influence level evaluation, and an intelligent decision module fuses data to realize quantity anomaly alarm and multi-level response and outputs a three-dimensional visual inventory report. According to the method, the problems of multiple blind areas, low checking efficiency, slow abnormal response and the like of traditional storage monitoring are solved, full coverage of environment monitoring, high accuracy of goods identification, low error of volume calculation and intelligence of abnormal handling are realized, and the automation and digitization levels of storage management are remarkably improved.

The invention provides an intelligent inventory robot path optimization method based on environment modeling and collaborative optimization, and relates to the technical field of intelligent warehouse management, and the method comprises the steps: scanning a warehouse region through an unmanned plane platform, and generating three-dimensional warehouse point cloud data; after point cloud denoising, plane fitting, shelf boundary extraction and channel detection processing, a two-dimensional storage plane graph is generated and transmitted to a robot data base; generating an initial inventory planning path by adopting an A * algorithm, optimizing the initial path through a simulated annealing algorithm, and generating a target inventory planning path; based on the dynamic obstacle influence factors, the RFID signal intensity and the robot residual electric quantity are combined, and a target path is corrected through a mixed algorithm of multi-target optimization and PSO dynamic weight; and the operation end checks the checking progress through the AR equipment and manually adjusts the target path, so that the checking path of the intelligent checking robot can be optimized in real time in a dynamic environment, and the checking efficiency, the coverage rate and the cruising ability of the robot are improved.

The invention discloses an unmanned warehouse shelf-oriented automatic cargo checking method and system, and the method is characterized in that the method comprises the following steps: employing a target detection algorithm to extract a cargo boundary frame from captured continuous cargo images; for each cargo bounding box, calculating shape features, color features, texture features, size features and local features of the cargo in the cargo bounding box, and splicing feature vectors of the cargo according to the features; pre-defining cargo templates of different types of cargos, matching the cargo templates with the feature vectors of the cargos so as to obtain a category label of each cargo bounding box, and iteratively optimizing the target detection algorithm through a loss function; and tracking the goods extracted by the target detection algorithm by using a Kalman filtering target tracking algorithm, and obtaining the position and quantity information of the goods from the continuous goods images.

The invention relates to the technical field of warehouse management, in particular to an unmanned warehouse management method and system with a real-time inventory function, and the method comprises the steps: building a signal strength-probability statistical model through return signal data generated in a historical inventory process, and carrying out the stability verification of each RFID tag return signal in a current inventory period, and under the condition that the stability is confirmed, carrying out preliminary positioning based on the time difference of arrival and determining the probability of the storage unit. And finally, the preliminary positioning is verified in combination with the probability of the storage unit and the checking information of the last checking period, so that the situation that whether the position of the RFID tag is changed really cannot be confirmed due to the fact that verification cannot be carried out when the positioning jumps is avoided. According to the invention, low-cost and high-reliability material positioning can be realized by using an existing RFID system, so that real-time and accurate checking is realized, and an information basis is provided for management of unmanned warehouses.

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 relates to a stereoscopic warehouse checking method and system based on an unmanned aerial vehicle, and the method comprises the steps: receiving a cargo checking task, and obtaining the layout information of a stereoscopic warehouse; determining the position of a to-be-checked goods location in the goods checking task in the stereoscopic warehouse; according to the layout information of the stereoscopic warehouse and the position of the to-be-checked goods location in the stereoscopic warehouse, determining a safe flight area and a hovering point of the unmanned aerial vehicle based on a path planning algorithm, and generating a flight task; issuing a flight task to the unmanned aerial vehicle, and calculating pose information of the unmanned aerial vehicle during execution of the flight task in real time through an inertial measurement unit; based on the observation image of the unmanned aerial vehicle visual system, correcting the accumulated error of the pose information through a Kalman filtering algorithm; rGB color images and depth images collected by the unmanned aerial vehicle at the to-be-checked goods locations are obtained in real time; determining whether the box stack is in an abnormal condition according to the depth image; and for the box stack which is not in the abnormal working condition, the number of goods in the box stack is determined according to the RGB color image.

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 real-time laser coal checking system and method, and relates to the technical field of material management and inventory checking, and the system comprises the following components: a laser detection module, a data processing module, a communication module, a monitoring module and a server module. The laser detection module is adopted to perform high-frequency scanning on the coal pile to obtain the three-dimensional point cloud data of the coal pile, the crown block displacement data measured by the laser range finder and the material and density information of the material pile are combined, the volume and weight of the coal pile are calculated through an algorithm, the outer contour of the coal pile can be determined more accurately, and the accuracy of the coal pile is improved. According to the shape characteristics and the density distribution condition of the coal pile, the coal pile is divided into a plurality of small geometries for volume calculation, and compared with traditional manual measurement or simple geometric shape approximation, the method has the advantages that the measurement precision of the volume and the weight of the coal pile can be remarkably improved; and more accurate data support is provided for material management and inventory checking of enterprises.

The invention relates to the technical field of intelligent warehouse management and Internet of Things perception, in particular to a warehouse checking method and a warehouse system. The method comprises the following steps: performing inventory demand path analysis on storage to obtain an inventory path scheme; performing inventory task issuing on the robot according to the inventory path scheme to obtain a carrying task instruction; carrying out inventory article grabbing processing according to the carrying task instruction to obtain static contact characteristic data; performing dynamic interaction and multi-mode signal acquisition based on the static contact characteristic data to obtain interactive sensing data; performing sensing signal separation on the interactive sensing data to obtain a mechanical parameter characteristic table and an acoustic characteristic spectrum; and performing article type identification matching on the mechanical parameter characteristic table and the acoustic characteristic spectrum to obtain an article type judgment result. According to the invention, the inventory function is seamlessly integrated into the daily operation process, so that the dimensionality and accuracy of the inventory information are improved, and the overall efficiency of warehousing operation is greatly improved.

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 relates to the technical field of warehouse management, and discloses an in-warehouse goods checking method, system and device and computer equipment, and the method comprises the steps: obtaining a tray code on a target checking goods location and the information of a material box on a corresponding tray; identifying the material box information by adopting a deep learning identification algorithm, and determining material specifications; based on the material box information, a preset stacking rule corresponding to the material specification and an AI algorithm, the number of the material boxes on the tray is calculated; and binding the material specifications, the number of the material boxes and the tray codes, and comparing with the upper inventory data based on the tray codes to obtain a cargo inventory result. According to the invention, the tray code and the material box information on the target inventory goods allocation can be directly obtained without continuously moving out the goods at the target inventory goods allocation, the difference between the inventory data and the actual inventory data can be found in time, and the problems of low inventory efficiency and increased inventory time caused by continuously moving out the goods at the target inventory goods allocation are solved.

The invention belongs to the technical field of logistics storage, and particularly discloses a logistics storage management system which comprises a cargo distribution module, a storage configuration module, a storage operation module, a material distribution module and a query statistics module. The cargo distribution module is used for optimizing warehouse space utilization, improving the cargo storage and taking speed, reducing manual errors and losses and conducting accurate inventory management and prediction. The warehouse configuration module is used for configuring warehouse entity parameters to realize identification and management of warehouse resources; the warehousing operation module is used for warehousing self-management, and mainly comprises goods receiving management, goods delivery management, goods checking and goods scrapping; the material distribution module is used by a transportation management department according to the number of packages of clients, the delivery address of the clients, the transportation mode and the transportation capacity of the transportation management department; and the query statistics module is used for providing comprehensive query of business information related to the storage business and outputting a report.

The invention relates to a multi-label identification method and device adapted to a capture effect, equipment and a medium, a checking cycle is started by sending a Query command, the state of a current time slot is judged according to response information of a label end, a corresponding command is sent to the corresponding label end, and when the current time slot is judged to be an effective time slot, the current time slot is identified to be the effective time slot. If yes, a corresponding ACK command is sent to the label end to complete identification, the label end exits the current inventory cycle, whether the current inventory cycle is terminated or not is judged according to a first termination condition, and if not, response information is continuously obtained, and a corresponding command is sent to the label end; if yes, whether current checking is terminated or not is judged according to a second termination condition, if not, the next checking cycle is started, and if yes, the current checking is terminated to complete multi-label identification. By adopting the method, the problem that the weak signal tag cannot be identified in time or cannot be identified in the current inventory cycle due to the capture effect can be solved.

The invention discloses an unmanned retail cabinet commodity expiration date management method based on a machine learning model, and the method comprises the following steps: S1, data preparation: collecting historical shelf commodity inventory data and related features from a database, and carrying out the data cleaning and feature engineering; s2, using the cleaned data to train a machine learning model; and S3, inputting the current feature information of the goods on the shelf into the model trained in the step S2, performing reasoning to identify high-immediate-risk goods, issuing an immediate checking list according to a reasoning result, executing immediate checking, and storing a checking result into a database for next model training. According to data characteristics such as historical sales volume, historical stock, commodity shelf life and replenishment cycle, the immediate risk probability of goods on a shelf is estimated, and immediate risk monitoring indexes are quantified; according to the on-time risk probability, a shelf commodity list needing to be checked is formulated, shelf commodities with high on-time risks are checked accurately, and the checking efficiency is improved.

The invention discloses an inventory device and a replenishment device. The inventory checking device is used for checking the number of goods stored in the storage goods channel, the storage goods channel is provided with a goods channel inlet, the inventory checking device comprises an extending structure and an inventory checking structure, the extending structure is movably arranged, and the inventory checking structure is arranged at the end of the extending structure; the checking structure can enter the storage goods channel through the goods channel inlet so as to check goods in the storage goods channel. According to the inventory checking device, the inventory checking structure capable of extending into the goods storage channel is arranged, the inventory checking of goods in the goods storage channel can be achieved, the inventory checking of the goods is efficient and accurate, and accurate goods replenishment can be achieved.

The invention discloses a physical asset space management method based on three-dimensional point cloud coordinate mapping, and relates to the field of three-dimensional coordinate reconstruction and warehouse management. The method comprises the following steps: performing three-dimensional point cloud modeling on a warehouse space, and constructing a unified coordinate system; collecting three-dimensional point cloud data of goods, generating a unique identifier and binding the unique identifier with a digital model; the digital model of the goods is anchored into a storage coordinate system through a point cloud registration algorithm, and the attitude angle and the spatial position of the goods are calculated; analyzing the geometrical relationship between the goods and the storage positions, and identifying space states such as border crossing, stacking and posture abnormity; and calculating a space utilization rate based on the cargo volume and the storage location volume, and outputting an optimization suggestion. According to the invention, ontology-level coupling management of the goods and the storage space is realized, space deduction type account checking and checking and anomaly detection are supported, and the method is suitable for complex storage scenes such as special-shaped objects and high-density stacking.

The invention discloses a warehouse goods accurate inventory method based on RFID and unmanned aerial vehicle cooperation, and the method comprises the steps: S1, pasting an RFID tag on a warehouse-in goods, binding the warehouse-in goods, and putting the warehouse-in goods on a shelf, and storing the goods inventory information in a warehouse management system; s2, establishing a three-dimensional model and a space coordinate system of the warehouse, wherein each storage location corresponds to a unique three-dimensional space coordinate; s3, the unmanned aerial vehicle executes an inventory task according to a preset route; s4, the unmanned aerial vehicle binds the RFID tag with the strongest signal when hovering in front of each storage location, so that the storage locations are in one-to-one correspondence with the goods; s5, comparing the RFID signal value acquired when the unmanned aerial vehicle flies to the next storage location with the RFID signal value acquired when the unmanned aerial vehicle flies to the previous storage location, and binding the tag with the strongest signal to the current storage location; and S6, after the checking task is finished and the unmanned aerial vehicle returns, the system automatically generates a checking report, compares the RFID real-time data with the WMS inventory record and marks a difference item. According to the invention, efficient and accurate unmanned inventory checking can be realized, and the inventory quantity and inventory position accuracy of inventory checking can be ensured.

A system for asset inventory and resupply for supplying individuals in remote locations is disclosed. The individuals may be people and / or machines. The system and method involve providing multiple individuals in the field with sensors that automatically collect data on the expenditure of their supplies. In some cases, wearable sensors are provided to people in the field. The data is sent to a central depository which tracks their inventory of supplies and anticipates needed supplies, compiles and packages additional supplies so that the additional supplies can be delivered to the individuals in the field. A precision supply delivery system is provided for delivering the supplies.

The invention discloses a warehouse cargo management method and device, equipment, a medium and a program product. The method comprises the following steps: generating a checking path according to the position of a to-be-checked target shelf in a warehouse checking task; aGV equipment is controlled to run to the position of the target goods shelf according to the checking path; identifying the label information of the first label of the target goods shelf through an RFID checking device, and querying the layer height of each goods shelf layer plate of the target goods shelf according to a preset mapping relationship between the label information and the goods shelf information; a lifting control instruction is generated according to the layer height of the goods shelf layer plates, so that a lifting device on the AGV equipment is controlled to lift the RFID checking device to each goods shelf layer plate; and identifying the label information of the second label corresponding to the goods on the goods shelf laminate through the RFID checking device. According to the invention, the method can meet the requirements of goods checking of multiple layers of goods shelves in a warehouse, and improves the storage and logistics management efficiency.

The invention provides an intelligent archive management method and system, and relates to the technical field of archive management, and the method comprises the steps: building an archive location information map, initializing an archive location database, and configuring an equipment communication protocol; after the operation end passes through the warehousing face entrance guard, the to-be-shelved archives are placed in a warehousing temporary storage area; matching a racking position according to the RFID tag and generating an instruction; scheduling a file taking and returning robot to execute a shelving task; the operation end inputs a file taking query instruction through the intelligent interaction terminal and then determines a file to be taken off, the file taking-off position is matched according to the RFID tag, and an instruction is generated; scheduling the file taking and returning robot to execute the off-shelf task; after the operation end passes through the warehouse-out face entrance guard, the files are taken away from the warehouse-out handover area; and according to the archive checking instruction, the robot is scheduled to carry out scanning and identification, a storage position record is generated, a database is updated, and a checking report is generated, so that the archive access efficiency is remarkably improved, and the accuracy, safety and intelligent level of archive management are ensured.

The invention relates to the technical field of warehouse management, in particular to a warehouse management system for a logistics supply chain and a management method thereof, and the system comprises a batch checking adjustment module, a goods shelf space monitoring module, an order circulation discrimination module, a load space matching module and a vehicle scheduling sorting module. According to the invention, through continuous dynamic analysis of validity period and batch data, real-time optimization and adjustment of an inventory cycle are realized, shelf spaces and order flows are cooperatively monitored, inventory circulation bottlenecks are rapidly positioned, and in combination with order timeliness and warehouse partition circulation data, potential abnormal shelves are automatically identified and labeled, and transportation resources and cargo characteristics are overlapped for matching and allocation. Intelligent sorting is carried out between vehicle in-place and task assignment, multi-link self-adaptive linkage is achieved, the response speed and task connection efficiency of the whole process are improved, the multi-scene management adaptability of data driving is enhanced, and the concurrence situation of high-frequency fluctuation and multiple types of tasks is effectively dealt with.

The present invention discloses an intelligent electronic tag management device, which relates to the field of intelligent electronic tag management technology. Nowadays, in the storage environment, there are many types of goods, including large metal products, ordinary packaged goods, and small precision goods. Traditional identification systems mostly use a single frequency or a simple antenna layout. For large metal products, due to their shielding and interference with radio frequency signals, it is difficult for the signal to effectively penetrate, resulting in difficulty in identifying goods or failure to read information, and it is easy to misread or miss reading, affecting the reliability of inventory management. The present invention reasonably allocates radio frequency signal resources to avoid signal conflicts and interference. In terms of antenna layout, it provides all-round and multi-level signal coverage for different types of goods, greatly improving the success rate and accuracy of goods identification. Whether it is in the warehousing of goods, inventory counting or outbound links, it can quickly and accurately obtain goods information, significantly improving the overall efficiency of warehousing operations.

The invention provides an elevated warehouse cargo visual identification checking method and related equipment, and the method comprises the steps: obtaining a cargo box region image, carrying out the gray transformation and noise reduction, obtaining a label local information image sequence, recognizing the focus blurring and light refraction caused by projection angle deviation and distance error, and generating a cargo box label image set; determining a texture feature set according to the texture details, analyzing character strokes and pattern distribution according to the texture feature set, and forming a label local feature description set; performing feature comparison on the label local feature description set and a label template database to obtain a feature matching degree, and if the feature matching degree exceeds a preset threshold value, performing feature weight adjustment according to projection light refraction to determine a candidate label matching list; generating a final label candidate set according to the candidate label matching list, and determining complete contents of labels; a mapping relation is established according to the complete content of the label, an inventory checking result is determined, and the inventory identification accuracy is improved; the method can be applied to the technical field of information.

The invention relates to an RFID-based fixed asset physical management method and system, and the method comprises the steps: firstly obtaining the signal intensity data and positioning data of an RFID tag corresponding to a fixed asset in a use state at a plurality of historical inventory time points, and then carrying out the data sample screening through the signal intensity data and positioning data at the plurality of historical inventory time points; and signal abnormal time points caused by external signal interference are removed. Then, the inertial dynamic feature of each RFID is constructed through accurate positioning, so that whether each RFID tag has the inertial dynamic feature or not is found out, and the inertial dynamic features are extracted; meanwhile, the synchronous use features among different RFID tags are found by utilizing accurate positioning, so that the synchronous inertia atlas is constructed. And performing early warning management and inventory management on the dynamic characteristics of the RFID tags under various conditions by using the inertia dynamic characteristics and the synchronous inertia atlas. The method is more flexible and fits the use habit of the fixed assets in the use state.

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.

The invention relates to the technical field of inventory optimization, and discloses a dynamic inventory optimization method, which is used for dynamically determining an industrial optimal inventory. Through real-time data collection and preprocessing, accurate demand prediction and a dynamic replenishment strategy, an enterprise can quickly respond to market changes, and the risks of stock overstock and stockout are effectively avoided. Meanwhile, the inventory cost is further reduced through the dynamic safe inventory strategy and economic order quantity optimization calculation, and the fund utilization efficiency is improved. In addition, the method further pays attention to inventory minimization and cost optimization, and effective control of the total cost is achieved by optimizing a supply chain and formulating an inventory minimization strategy. And the accuracy of inventory data is ensured through periodic inventory and difference analysis, and the transparency and credibility of inventory management are improved.