Warehouse goods high-precision positioning and automatic checking method, system and equipment

Abstract

The invention discloses a high-precision positioning and automatic checking method, system and equipment for warehouse goods, and relates to the field of warehouse management, and the method comprises the steps: constructing a fingerprint database; the fingerprint database comprises the received signal strength of each reference tag; the reference tag is a passive radio frequency identification tag arranged on a goods shelf of a warehouse; generating scanning points and planning a flight path according to the inventory task and the layout of goods shelves in the warehouse; a machine vision algorithm and a radio frequency identification positioning method are adopted to control the unmanned aerial vehicle to sequentially pass through the scanning points according to the flight path, and a radio frequency identification reader-writer carried by the unmanned aerial vehicle collects the received signal strength of the to-be-tested tag; the to-be-tested tags are passive radio frequency identification tags arranged on goods in a warehouse; and according to the received signal strength of the to-be-tested tag and the fingerprint database, calculating the spatial position of the to-be-tested tag by adopting a radio frequency identification fingerprint algorithm based on iterative Bayesian so as to check the goods in the warehouse. According to the invention, the checking precision and efficiency of the warehouse are improved, and the hardware deployment cost is reduced.

Description

Technical Field

[0001] This application relates to the field of warehouse management, and in particular to a method, system and equipment for high-precision positioning and automatic inventory counting of warehouse goods. Background Technology

[0002] Inventory counting is a crucial part of modern warehouse management. Regularly checking finished goods and raw materials in the warehouse ensures that the goods are in good condition and that the quantities and types match the records, thus guaranteeing the smooth operation of production and the flow of goods. However, with socio-economic development, the volume and throughput of warehouse goods are constantly increasing. Traditional methods based on paper lists and manual inventory counting are time-consuming, inefficient, and prone to omissions, becoming obstacles to production and the flow of goods.

[0003] Drones equipped with sensors can move and explore quickly in warehouses. To optimize warehouse management efficiency, many intelligent inventory management solutions based on drones have emerged. The most important aspect is the choice of positioning technology. Common indoor positioning technologies such as Wireless Fidelity (WIFI), Bluetooth, and Long Term Evolution (LTE) / 5G have low positioning accuracy. Acoustic and ultrasonic technologies require strict control of ambient temperature and humidity. Ultra-wideband (UWB) and Channel State Information (CSI) have special requirements for the performance of transceivers and signal modulation. These solutions either have positioning accuracy that is difficult to meet the needs of warehouse management or require high equipment and environmental modification costs, which restricts the implementation of intelligent solutions. Summary of the Invention

[0004] The purpose of this application is to provide a method, system, and equipment for high-precision positioning and automatic inventory counting of warehouse goods, which can improve positioning accuracy and inventory counting efficiency, and reduce costs.

[0005] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for high-precision positioning and automatic inventory counting of warehouse goods, including: Construct a fingerprint database; the fingerprint database includes the received signal strength of each reference tag; the reference tag is a passive radio frequency identification tag deployed on the shelves of the warehouse; Based on the inventory task and the layout of the shelves in the warehouse, scan points are generated and flight paths are planned; Using machine vision algorithms and radio frequency identification (RFID) positioning methods, the drone is controlled to pass through the scanning points sequentially along the flight path. At the same time, the received signal strength of the tag under test is collected by the RFID reader carried by the drone. The tag under test is a passive RFID tag deployed on the goods in the warehouse. Based on the received signal strength of the tag under test and the fingerprint database, an iterative Bayes-based radio frequency identification fingerprint algorithm is used to calculate the spatial location of the tag under test in order to inventory the goods in the warehouse.

[0006] Secondly, this application provides a high-precision positioning and automatic inventory system for warehouse goods, including: A fingerprint database construction module is used to construct a fingerprint database; the fingerprint database includes the received signal strength of each reference tag; the reference tag is a passive radio frequency identification tag deployed on a warehouse shelf; The path planning module is used to generate scan points based on the inventory task and the layout of the shelves in the warehouse, and to plan the flight path. The drone control module is used to control the drone to pass through the scanning points sequentially according to the flight path using machine vision algorithms and radio frequency identification (RFID) positioning methods; during the drone's flight, the received signal strength of the tag under test is collected by the onboard RFID reader / writer; the tag under test is a passive RFID tag deployed on goods in the warehouse. The location calculation module is used to obtain the received signal strength of the tag to be tested collected by the drone, and to calculate the spatial location of the tag to be tested using an iterative Bayes-based radio frequency identification fingerprint algorithm based on the received signal strength of the tag to be tested and the fingerprint database, so as to carry out inventory of goods in the warehouse.

[0007] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for high-precision positioning and automatic inventory counting of warehouse goods.

[0008] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a method, system, and equipment for high-precision positioning and automatic inventory counting of warehouse goods. It employs VINS-Mono machine vision technology and Radio Frequency Identification (RFID) positioning for drones, improving drone positioning accuracy. It also uses an iterative Bayesian-based RFID fingerprint algorithm for goods positioning, further enhancing goods positioning accuracy and thus improving warehouse inventory counting accuracy. During inventory counting, scanning points are generated based on the inventory task and the layout of the warehouse shelves, and flight paths are planned, improving inventory counting efficiency. The entire solution only requires setting passive RFID tags in the warehouse and equipping the drone with an RFID reader, reducing hardware deployment costs. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a schematic diagram of the overall process of a warehouse cargo high-precision positioning and automatic inventory method provided in an embodiment of this application.

[0011] Figure 2 This is a detailed flowchart illustrating a method for high-precision positioning and automatic inventory counting of warehouse goods, provided as an embodiment of this application.

[0012] Figure 3 This is a schematic diagram of the overall structure of a warehouse cargo high-precision positioning and automatic inventory system provided in an embodiment of this application. Detailed Implementation

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0014] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0015] From the perspective of intelligentization, there are currently three methods for warehouse inventory counting.

[0016] (1) Manual inventory: Manually obtain material information by visual inspection and verify it one by one with the paper material list, and then manually enter it into the system. It has the lowest hardware cost, but is inefficient and prone to errors.

[0017] (2) Manual and semi-automatic equipment inventory: manual operation of semi-automatic equipment such as PDA or handheld terminal to scan barcodes or QR codes on goods, and automatic entry of goods information by the system ensures the accuracy of goods information, but the efficiency is still very low.

[0018] (3) Automated equipment inventory, manual setting of inventory tasks, automated equipment such as drones, autonomous mobile robots or fixed sensors deployed in a range can obtain cargo information in batches by scanning tags or machine vision and automatically enter it into the system, eliminating the process of manual movement, realizing parallel processing of multiple inventory tasks, greatly improving inventory efficiency, but the cost of equipment and environment layout is high.

[0019] Indoor positioning technology can be divided into three main categories based on physical principles: acoustic, optical, and radio wave based.

[0020] (1) Acoustic positioning can be divided into ultrasonic (>20KHz) and audible (1 to 20KHz) positioning. The distance is calculated by the time of arrival algorithm, which is highly accurate but slow (the speed of sound in the air is about 340m / s, which is much less than the speed of light). Moreover, the speed of sound is sensitive to the ambient temperature and humidity, and needs to be strictly controlled. Therefore, the deployment cost is not low.

[0021] (2) Optical positioning can be further divided into infrared, visible light communication, and machine vision. Infrared and visible light communication calculates distance using time of arrival or angle of arrival algorithms, which is highly accurate and resistant to interference. However, the unobstructed line-of-sight characteristic requires dense deployment of sensors, and both the transmitting and receiving ends need to be able to actively transmit signals, resulting in higher costs. Machine vision calculates the camera position through visual feature matching or image recognition positioning. Visual feature matching establishes the geometric relationship between images by changing feature points or feature regions in the image before and after motion. Image recognition positioning, such as AprilTag, establishes the geometric relationship between the camera and the image by the distortion of the image in the image.

[0022] (3) Radio wave-based positioning can be further subdivided into WIFI, Bluetooth, ZigBee, UWB, RFID, LoRa, millimeter-wave radar, etc. The distance between transceivers is calculated by algorithms such as time of arrival, angle of arrival, phase of arrival, and received signal strength. The essential difference lies in the frequency band, signal bandwidth, signal modulation method, and device cooperation mechanism used. Generally speaking, high-frequency radio waves have poor penetration, but high positioning resolution. The wider the signal bandwidth, the more accurate the measurable time or phase, and the higher the positioning accuracy. LoRa has low power consumption and long coverage distance, but the worst accuracy, with a maximum of only meter level. WIFI, Bluetooth, and ZigBee can achieve sub-meter level positioning accuracy, but the frequency bands used are very congested and easily interfered with, requiring special signal modulation. UWB and millimeter-wave radar have large bandwidth and strong anti-interference, making them suitable for high-precision positioning, but the equipment power consumption and cost are high. In addition, the above-mentioned technologies and active RFID generally require the device being positioned to be powered on for a long time and be able to parse the corresponding communication protocol, which results in high costs for large-scale deployment in warehouse cargo positioning scenarios. Passive RFID typically uses passive tags or paper tags, which have low production costs and positioning accuracy down to sub-meter or even centimeter level, making it an ideal high-volume, low-cost indoor positioning technology.

[0023] In view of the advantages and disadvantages of the above technologies, this application adopts passive RFID technology to locate goods and uses a combination of passive RFID, machine vision and inertial navigation technology to locate drones.

[0024] In one exemplary embodiment, such as Figure 1 and Figure 2 As shown, a method for high-precision positioning and automatic inventory counting of warehouse goods is provided. The method is executed by computer equipment and includes the following steps 101 to 104.

[0025] Step 101: Construct a fingerprint database. The fingerprint database includes the received signal strength of each reference tag. The reference tags are passive radio frequency identification (RFID) tags deployed on warehouse shelves.

[0026] In a specific application example, passive RFID tags and virtual readers are deployed on the warehouse shelves according to the number and size of the shelves. The virtual readers collect the received signal strength of each reference tag to construct a fingerprint database. There are multiple reference tags and multiple virtual readers.

[0027] Specifically, based on the number and size of the shelves in the warehouse, spatial intervals are planned, and passive RFID tags are affixed at the intersections of the shelves as reference tags. The spatial distribution and coordinate parameters of the virtual readers are set according to the planned spatial intervals based on the number and size of the shelves. The virtual readers are distributed as evenly as possible on the horizontal plane, roughly covering the area of ​​all reference tags, and extending vertically to 2 to 3 layers. The drone sequentially passes through the designated virtual reader locations, recording the Received Signal Strength Indicator (RSSI) of the received reference tags, forming a fingerprint database. Each reference tag in the fingerprint database corresponds to a unique and distinct fingerprint, i.e., the detection results from multiple virtual readers.

[0028] Step 102: Generate scan points based on the inventory task and the layout of the shelves in the warehouse, and plan the flight path.

[0029] In a specific application example, the inventory task is either a location inventory task or a source inventory task. Users can choose to inventory by location or by source, selecting one or more locations / sources to be inventoried. After determining the task type and quantity, the user submits the inventory task.

[0030] For inventory location tasks, a specified number of scan points are automatically generated around a inventory location, and the overall flight path is planned by combining scan points from other inventory location tasks. The scan points are arranged horizontally around the inventory location at approximately 1 meter intervals (depending on shelf spacing and aisle width), with 5x5 points and 2 or 3 points vertically. Each inventory location is designed with scan points using the above method. If inventory locations are close together, overlapping scan points can be merged. Finally, the planned flight path passes through all scan points sequentially.

[0031] For inventory counting tasks, multiple paths and sparse scanning points are automatically planned across the entire shelving area. After the drone's initial flight obtains the approximate location of each item, the scanning points and flight path are replanned, and the drone takes a second flight to obtain the precise location of the items. The spacing of the sparse scanning points depends on the shelf spacing, aisle width, and the effective detection range of the RFID reader, aiming to cover the entire shelving area with as few scanning points as possible.

[0032] The flight path and scan points are uploaded to the drone, which then performs the corresponding flight mission.

[0033] Step 103: Using machine vision algorithms and RFID positioning methods, the drone is controlled to sequentially pass through the scanning points along the stated flight path. Simultaneously, the received signal strength of the tag under test is collected by the RFID reader carried by the drone. The tag under test is a passive RFID tag deployed on goods in the warehouse.

[0034] Conventional RFID readers are typically mounted on ceilings or walls. When a drone carries an RFID reader, it achieves the same suspended functionality as a conventional RFID reader through fixed-point scanning. The drone hovers at the scanning point for a period of time, and its RFID reader rotates 360° to scan the received signal strength of the tag under test.

[0035] Among these, machine vision algorithms are based on monocular feature tracking and inertial measurement units, such as the VINS-Mono algorithm or VINS-Fusion. RFID positioning methods can also utilize time of arrival, angle of arrival, and phase difference of arrival for multilateral positioning, or employ artificial neural networks and deep learning to match RFID fingerprints. In principle, the VINS-Mono algorithm can already achieve positioning, but inertial measurement is prone to accumulating errors. The original algorithm corrects the accumulated errors through machine vision feature tracking. This application introduces RFID-based positioning results as an additional reference to assist in error correction.

[0036] Step 104: Based on the received signal strength of the tag under test and the fingerprint database, use an iterative Bayes-based radio frequency identification fingerprint algorithm to calculate the spatial location of the tag under test in order to inventory the goods in the warehouse.

[0037] Specifically, the received signal strength of the tag under test is compared with the received signal strength of reference tags in the fingerprint database. The higher the similarity, the closer the tag under test is to the reference tag. Then, the position of the tag under test is determined based on the position of the reference tag whose similarity to the tag under test is greater than a set threshold.

[0038] The most common implementation of RFID fingerprinting algorithms is based on the Location Identification Based on Dynamic Active RFID Calibration (LANDMARC) algorithm, which is a mature existing algorithm. However, LANDMARC can only directly estimate the specific coordinates of the goods to be measured. This application combines an iterative Bayesian estimation algorithm with an RFID fingerprinting algorithm. In addition to the final calculated specific coordinates, it can also obtain the spatial distribution probability of the coordinates, and this spatial distribution probability is continuously updated as the number of scanning points increases during the scanning process.

[0039] In another exemplary embodiment, the warehouse cargo high-precision positioning and automatic inventory method further includes the following step 105.

[0040] Step 105: Determine and display the spatial probability distribution of goods based on the spatial location of the label to be tested. Specifically, the calculated spatial probability distribution of goods is visualized and displayed on the front end.

[0041] In another exemplary embodiment, the warehouse cargo high-precision positioning and automatic inventory method further includes the following step 106.

[0042] Step 106: Based on the spatial location of the tag under test, replan the scanning points and flight path, and control the UAV to pass through the scanning points sequentially according to the flight path. At the same time, collect the received signal strength of the tag under test through the radio frequency identification reader carried by the UAV, and then return to step 103.

[0043] Specifically, for the inventory task, the approximate location of each cargo is calculated based on the data obtained after the drone's first flight, and the scanning points and flight path are replanned to pinpoint the cargo location. After the drone's second flight passes all the set scanning points, it automatically returns to the hangar according to the planned path, collects the RFID tag information of all designated cargo, and completes the cargo location calculation, thus completing the inventory task.

[0044] For the inventory counting task, after passing all the set scanning points, the drone automatically returns to the hangar according to the planned path, collects RFID tag information around all the storage locations, completes the cargo location calculation, matches the cargo location coordinates with the storage location coordinate range, obtains a unique matching relationship between storage location and cargo, and completes the inventory counting task.

[0045] This application employs RFID technology for cargo positioning. RFID technology can simultaneously detect multiple tagged objects at a long distance. Combined with drones, it enables rapid and efficient large-scale cargo inventory checks, avoiding errors and human oversights. Furthermore, passive RFID tags are low-cost to manufacture, rewritable, and reusable, and are simple and convenient to deploy. A fusion solution of the VINS-Mono machine vision algorithm and RFID is used for drone positioning. The camera and inertial measurement unit required by VINS-Mono are standard equipment on most drones, and the only additional sensor required on the drone is a single RFID reader. The entire solution is intelligent, efficient, and cost-effective, facilitating rapid deployment and widespread adoption.

[0046] In one exemplary embodiment, such as Figure 3 As shown, a warehouse cargo high-precision positioning and automatic inventory system is provided, including: fingerprint database construction module 301, path planning module 302, UAV control module 303 and position calculation module 304.

[0047] The fingerprint database construction module 301 is used to construct a fingerprint database. The fingerprint database includes the received signal strength of each reference tag. The reference tags are passive radio frequency identification (RFID) tags deployed on warehouse shelves.

[0048] The path planning module 302 is used to generate scan points based on the inventory task and the layout of the shelves in the warehouse, and to plan the flight path.

[0049] The UAV control module 303 uses machine vision algorithms and radio frequency identification (RFID) positioning methods to control the UAV 305 to sequentially pass through the scanning points along the flight path. During the flight of the UAV 305, the received signal strength of the tag under test is collected by the onboard RFID reader 306. The tag under test is a passive RFID tag 307 deployed on goods in the warehouse.

[0050] The location calculation module 304 is used to obtain the received signal strength of the tag to be tested collected by the UAV, and according to the received signal strength of the tag to be tested and the fingerprint database, it uses an iterative Bayes-based radio frequency identification fingerprint algorithm to calculate the spatial location of the tag to be tested in order to inventory the goods in the warehouse.

[0051] The fingerprint database construction module 301, path planning module 302, UAV control module 303 and position calculation module 304 mentioned above are all computer programs that execute on a computer device. Among them, the UAV control module 303 and the position calculation module 304 are connected to the external UAV 305 and interact with the UAV 305.

[0052] In addition, the system will update and visualize the cargo positioning process in real time, and display the distribution probability changes of the predicted cargo location synchronously at the front end. As the number of detection points passed by the UAV 305 increases, the predicted cargo distribution range gradually shrinks to the vicinity of the actual location.

[0053] In another exemplary embodiment, the warehouse cargo high-precision positioning and automatic inventory system also includes a drone 305, an RFID reader / writer 306, and passive RFID tags 307. That is, in addition to the computer program (fingerprint database construction module 301, path planning module 302, drone control module 303, and location calculation module 304) executed on the computer device, the warehouse cargo high-precision positioning and automatic inventory system can also include the drone 305 connected to the drone control module 303 and the location calculation module 304, the RFID reader / writer 306 carried by the drone 305, the passive RFID tags 307 deployed on the warehouse shelves, and the passive RFID tags 307 deployed on the goods. The drone 305 transmits the received signal strength to the location calculation module 304 in real time via WIFI.

[0054] In one exemplary embodiment, a computer apparatus is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0055] In summary, compared with the prior art, the beneficial effects of this application include the following points.

[0056] (1) Improved positioning accuracy. The UAV positioning achieves sub-meter or even centimeter-level positioning accuracy through VINS-Mono machine vision technology and RFID positioning method, and acts as a fixed-point reader in the subsequent cargo positioning process. Cargo positioning achieves sub-meter-level positioning accuracy through RFID fingerprint algorithm based on iterative Bayes, which meets the accuracy requirements of general warehouse cargo positioning and achieves 100% accurate warehouse inventory positioning.

[0057] (2) Improved inventory efficiency. During inventory, the drone needs to scan a certain number of scanning points around the target location / source of goods, but it can scan multiple locations / sources of goods at the same time. Considering the number of locations / sources of goods within the scanning point array, as well as the hovering scanning time and the movement time between scans, the efficiency of drone inventory can reach an average of 30 items per minute under ideal conditions.

[0058] (3) The cost is low and easy to deploy. The positioning scheme adopts VINS-Mono machine vision and passive RFID technology. The required drone sensors are a camera, an inertial measurement unit, and an RFID reader. In addition, a number of passive RFID tags are required.

[0059] (4) Real-time visualization of cargo positioning process and iterative prediction results. The iterative prediction results adopt RFID fingerprinting method based on iterative Bayes estimation. As the number of observation points increases, the prediction result range shrinks and is displayed synchronously on the front end.

[0060] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0061] In this application, all actions to acquire signals, information, or data are carried out in compliance with the relevant data protection laws and policies of the country where the location is situated, and with the authorization granted by the owner of the relevant device.

[0062] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0063] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for high-precision positioning and automatic inventory counting of warehouse goods, characterized in that, The method includes: Construct a fingerprint database; the fingerprint database includes the received signal strength of each reference tag; the reference tag is a passive radio frequency identification tag deployed on the shelves of the warehouse; Based on the inventory task and the layout of the shelves in the warehouse, scan points are generated and flight paths are planned; Using machine vision algorithms and radio frequency identification (RFID) positioning methods, the drone is controlled to pass through the scanning points sequentially along the flight path. At the same time, the received signal strength of the tag under test is collected by the RFID reader carried by the drone. The tag under test is a passive RFID tag deployed on the goods in the warehouse. Based on the received signal strength of the tag under test and the fingerprint database, an iterative Bayes-based radio frequency identification fingerprint algorithm is used to calculate the spatial location of the tag under test in order to inventory the goods in the warehouse.

2. The method for high-precision positioning and automatic inventory counting of warehouse goods according to claim 1, characterized in that, Building a fingerprint database specifically includes: Based on the number and size of the shelves in the warehouse, passive radio frequency identification tags and virtual readers are deployed on the shelves. The received signal strength of each reference tag is collected by a virtual reader / writer to construct a fingerprint database.

3. The method for high-precision positioning and automatic inventory counting of warehouse goods according to claim 2, characterized in that, There are multiple reference tags and multiple virtual readers / writers.

4. The method for high-precision positioning and automatic inventory counting of warehouse goods according to claim 1, characterized in that, The inventory task refers to either a location inventory task or a source inventory task.

5. The method for high-precision positioning and automatic inventory counting of warehouse goods according to claim 1, characterized in that, The method further includes: The spatial probability distribution of goods is determined and displayed based on the spatial location of the label to be tested.

6. The method for high-precision positioning and automatic inventory counting of warehouse goods according to claim 1, characterized in that, The method further includes: The scanning points and flight path are replanned based on the spatial location of the tag under test, and the drone is controlled to pass through the scanning points sequentially according to the flight path. At the same time, the received signal strength of the tag under test is collected by the radio frequency identification reader carried by the drone.

7. The method for high-precision positioning and automatic inventory counting of warehouse goods according to claim 1, characterized in that, The machine vision algorithm is the VINS-Mono algorithm.

8. A high-precision positioning and automatic inventory system for warehouse goods, characterized in that, The system is applied to the warehouse cargo high-precision positioning and automatic inventory method according to any one of claims 1-7, and the system comprises: A fingerprint database construction module is used to construct a fingerprint database; the fingerprint database includes the received signal strength of each reference tag; the reference tag is a passive radio frequency identification tag deployed on a warehouse shelf; The path planning module is used to generate scan points based on the inventory task and the layout of the shelves in the warehouse, and to plan the flight path. The drone control module is used to control the drone to pass through the scanning points sequentially according to the flight path using machine vision algorithms and radio frequency identification (RFID) positioning methods; during the drone's flight, the received signal strength of the tag under test is collected by the onboard RFID reader / writer; the tag under test is a passive RFID tag deployed on goods in the warehouse. The location calculation module is used to obtain the received signal strength of the tag to be tested collected by the drone, and to calculate the spatial location of the tag to be tested using an iterative Bayes-based radio frequency identification fingerprint algorithm based on the received signal strength of the tag to be tested and the fingerprint database, so as to carry out inventory of goods in the warehouse.

9. The warehouse cargo high-precision positioning and automatic inventory system according to claim 8, characterized in that, The system also includes: the drone, the RFID reader / writer, and the passive RFID tag.

10. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the warehouse goods high-precision positioning and automatic inventory method according to any one of claims 1-7.

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