Unmanned aerial vehicle rental method and device based on RFID tag, equipment and medium

Abstract

The application relates to an unmanned aerial vehicle (UAV) rental method and device based on RFID tags, equipment and a medium. The method comprises the following steps: configuring RFID tags for a to-be-rented UAV and accessories of the to-be-rented UAV respectively, and writing equipment identity information and initial state information of the to-be-rented UAV into the RFID tags; receiving a rental request initiated by a user through a terminal device; performing batch identification on the RFID tags based on UAV identity identification, obtaining a UAV accessory list, and performing integrity verification based on the UAV accessory list; if the integrity verification is passed, writing current rental information into the RFID tags of the rented UAV and the rented accessories, and storing a change record of ownership to a blockchain database; and sending an unlocking instruction to a corresponding UAV equipment warehouse based on the identity identification of the rented UAV, so as to complete the UAV rental process. The application improves the efficiency of UAV rental.

Description

Technical Field

[0001] This application relates to the field of drone rental management technology, and in particular to a drone rental method, apparatus, equipment and medium based on RFID tags. Background Technology

[0002] In recent years, with the maturity of drone technology and the decline in manufacturing costs, drones have been increasingly widely used in fields such as agricultural plant protection, geographic surveying, logistics delivery, aerial filming, and security inspection. This has led to a diversified market demand for drone usage models, with a growing trend shifting from ownership to on-demand leasing. Drone leasing services can significantly reduce the barriers to entry and costs for users, improve equipment utilization, and have broad market prospects.

[0003] However, existing drone rental management models, especially offline self-service rental lockers or rental sheds, still face numerous technical challenges that hinder their large-scale and automated development. First, verifying the integrity of drone components is inefficient and prone to errors. A complete drone system typically includes several key components such as the aircraft body, battery, remote controller, and spare propellers. Traditional methods rely mainly on manual counting or simple weight sensing. The former is inefficient and prone to human error, while the latter cannot accurately identify the specific type of missing component (e.g., it cannot distinguish whether a battery or a set of propellers is missing), easily leading to asset loss and financial disputes. Furthermore, the rental process lacks automation. From initiating a request to finally picking up the device, users often need to interact and confirm multiple times, making the process cumbersome. Especially in the component identification stage, the inability to achieve rapid, batch, and automated verification has become a bottleneck for improving user experience and operational efficiency. Summary of the Invention

[0004] The purpose of this application is to provide a method, apparatus, equipment and medium for renting drones based on RFID tags, so as to improve the rental efficiency of drones.

[0005] To address the aforementioned technical problems, this application provides a drone rental method based on RFID tags, comprising:

[0006] RFID tags are configured for the drone to be rented and its accessories, and the device identification information and initial status information of the drone to be rented are written into the RFID tags;

[0007] Receive rental requests initiated by users through terminal devices, wherein the rental request includes at least a user identity identifier and a drone identity identifier;

[0008] Based on the drone's identification identifier, batch identification is performed from the RFID tags to obtain a drone parts list, and the integrity of the drone parts list is verified. The drone parts list includes rented drones and rented accessories.

[0009] If the integrity verification passes, the current rental information is written into the RFID tags of the rented drone and the rented accessories, and the ownership change record is stored in the blockchain database. The current rental information includes at least the user identity identifier, rental timestamp, and permission identifier.

[0010] The system sends an unlock command to the corresponding drone equipment compartment based on the identification identifier of the rented drone to complete the drone rental process.

[0011] To address the aforementioned technical problems, this application provides an RFID tag-based drone rental device, comprising:

[0012] The tag configuration module is used to configure RFID tags for the drone to be rented and its accessories, and to write the device identity information and initial status information of the drone to be rented into the RFID tags.

[0013] The rental request receiving module is used to receive rental requests initiated by users through terminal devices, wherein the rental request includes at least a user identity identifier and a drone identity identifier;

[0014] An integrity verification module is used to perform batch identification from the RFID tags based on the drone's identity identifier to obtain a drone accessory list, and to perform integrity verification based on the drone accessory list, wherein the drone accessory list includes rented drones and rented accessories;

[0015] The information writing module is used to write the current rental information into the RFID tags of the rented drone and the rented accessories if the integrity verification is passed, and to store the ownership change record in the blockchain database. The current rental information includes at least the user identity identifier, rental timestamp, and permission identifier.

[0016] The unlock command sending module is used to send an unlock command to the corresponding drone equipment compartment based on the identity identifier of the rented drone to complete the drone rental process.

[0017] To solve the above-mentioned technical problems, one technical solution adopted by the present invention is to provide an electronic device, including one or more processors; and a memory for storing one or more programs, such that the one or more processors implement the RFID tag-based drone rental method described above.

[0018] To solve the above-mentioned technical problems, one technical solution adopted by the present invention is: a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the RFID tag-based drone rental method described above.

[0019] This invention provides a method, apparatus, device, and medium for drone rental based on RFID tags. The method includes: configuring RFID tags on the drone to be rented and its accessories, and writing the device identity information and initial status information of the drone to be rented into the RFID tags; receiving a rental request initiated by a user through a terminal device, wherein the rental request includes at least a user identity identifier and a drone identity identifier; performing batch identification from the RFID tags based on the drone identity identifier to obtain a drone accessory list, and performing integrity verification on the drone accessory list, wherein the drone accessory list includes the drone to be rented and the accessories to be rented; if the integrity verification passes, writing the current rental information into the RFID tags of the drone and the accessories to be rented, and storing the ownership change record in a blockchain database, wherein the current rental information includes at least the user identity identifier, a rental timestamp, and an authorization identifier; and sending an unlock command to the corresponding drone equipment compartment based on the identity identifier corresponding to the drone to complete the drone rental process. This invention enables batch identification and integrity verification by equipping drones and accessories with RFID tags, and combines blockchain technology to record ownership changes, which helps improve the efficiency of drone rental. Attached Figure Description

[0020] To more clearly illustrate the solutions in this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating the implementation of the drone rental method based on RFID tags provided in this application embodiment;

[0022] Figure 2 This is a flowchart illustrating the implementation of the first sub-process in the RFID tag-based drone rental method provided in this application embodiment;

[0023] Figure 3 This is a flowchart illustrating the implementation of the second sub-process in the RFID tag-based drone rental method provided in this application embodiment;

[0024] Figure 4 This is a flowchart illustrating the implementation of the third sub-process in the RFID tag-based drone rental method provided in this application embodiment;

[0025] Figure 5 This is a flowchart illustrating the implementation of the fourth sub-process in the RFID tag-based drone rental method provided in this application embodiment;

[0026] Figure 6 This is a flowchart illustrating the implementation of the fifth sub-process in the RFID tag-based drone rental method provided in this application embodiment;

[0027] Figure 7 This is a flowchart illustrating the implementation of the sixth sub-process in the RFID tag-based drone rental method provided in this application embodiment;

[0028] Figure 8 This is a schematic diagram of an RFID tag-based drone rental device provided in an embodiment of this application;

[0029] Figure 9 This is a schematic diagram of the electronic device provided in the embodiments of this application. Detailed Implementation

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.

[0031] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0032] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0033] Current drone rental services face challenges such as inefficiency, difficulty in locating equipment, unclear ownership, and cumbersome inventory management. Traditional rental processes rely on manual verification of equipment models, serial numbers, and accessory lists, with each rental taking 15-20 minutes and causing long queues during peak periods. Manual inventory checks are prone to omissions or misjudgments, compromising equipment integrity. Paper-based registrations or local databases of ownership changes are susceptible to tampering, and the equipment transfer process lacks a reliable traceability mechanism. Warehouse inventory checks require individual unit inspections, consuming significant manpower and resulting in low efficiency.

[0034] To address the aforementioned issues, the traditional manual verification process suffers from efficiency bottlenecks, prompting an attempt to introduce automated identification technology. For the challenge of verifying accessory integrity, electronic tags are considered for rapid batch identification. To resolve the issue of ownership record reliability, the feasibility of blockchain technology is explored. The final technical solution is as follows: RFID tags are used to establish digital identities for equipment; batch identification algorithms are combined to achieve automatic accessory verification; and blockchain is used to store ownership change records, constructing a complete automated rental system. Therefore, this application proposes a drone rental method based on RFID tags, including: configuring RFID tags for the drone and accessories to be rented and writing equipment identity information and initial status information; receiving rental requests containing user and drone identity identifiers; generating an accessories list based on batch identification of RFID tags using drone identity identifiers and performing integrity verification; updating the current rental information in the tags after successful verification and storing ownership change records in a blockchain database; and sending an unlock command to the equipment warehouse based on the identity identifier to complete the rental process.

[0035] Specifically, when a user initiates a rental request, the system retrieves a pre-stored list of accessories based on the drone's identification. An RFID reader scans the target tag cluster in batches according to dynamically allocated time slots, prioritizing the reading of key accessory tags. The actual tag IDs read are automatically compared with the expected list; if all necessary tags are identified, the integrity verification is considered successful. After successful verification, the current user's identity, rental time, and operating permissions are written into the RFID tag, and a blockchain generates an ownership change record containing the device serial number, user ID, and timestamp. Finally, the system sends an encrypted unlock command to the designated equipment bay, and the drone's door automatically opens to complete the rental. This application achieves full automation of the drone rental process, transforming the traditional manual verification process into automatic electronic tag identification, significantly shortening the rental operation time. The accuracy of accessory integrity verification is significantly improved, avoiding omissions or misjudgments caused by manual inventory. The reliable storage of ownership change records effectively prevents equipment ownership disputes and provides reliable data support for subsequent traceability. The automated unlocking mechanism based on identity recognition reduces human intervention and improves the response speed of the rental service.

[0036] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0037] Please see Figure 1 , Figure 1 This paper illustrates a specific implementation of a drone rental method based on RFID tags.

[0038] It should be noted that if substantially the same result is obtained, the method of this invention is not based on... Figure 1 Limited to the order of the processes shown, this method includes the following steps:

[0039] S1: Configure RFID tags for the drone to be rented and its accessories, and write the device identity information and initial status information of the drone to be rented into the RFID tags.

[0040] Specifically, RFID tags refer to electronic tags installed on the main body of the drone and its accessories such as batteries and propellers. These tags can be read in batches over long distances using the UHF Gen2V2 protocol. Writing device identification information involves generating a unique digital ID card containing a serial number and production information, with data uniqueness ensured through a hash algorithm.

[0041] Please see Figure 2 , Figure 2 A specific implementation of step S1 is shown below:

[0042] S11: Configure the RFID tag with UHF Gen2V2 protocol on the drone to be rented. S12: Configure accessory RFID tags on the battery, propellers, and remote control accessories respectively, forming a tag cluster for the drone to be rented. S13: Generate a unique digital identity identifier by calculating the device serial number, manufacturer code, production date, and random number of the drone to be rented using a hash algorithm. S14: Write the unique digital identity identifier into the storage area of ​​the RFID tag.

[0043] Specifically, by selecting the UHF Gen2V2 protocol as the tag technology standard and leveraging its support for batch reading, the simultaneous identification of the drone and its accessories can be quickly completed during equipment rental. Key accessories such as batteries and propellers are individually tagged with RFID tags to form tag clusters, giving each accessory an independent and identifiable digital identity, avoiding the problem of missing accessories caused by traditional manual inventory. A hash algorithm is used to fuse inherent attributes such as the device serial number and manufacturer code with random numbers to generate a unique digital identity identifier, ensuring the immutability of the identifier while enhancing anti-collision capabilities through the introduction of random numbers. The operation of writing the generated digital identity identifier into the RFID storage area ensures the reliable storage of device identity information on the physical carrier, providing a data foundation for subsequent batch identification. This application achieves rapid batch identification of the drone and its accessories, effectively shortening the rental process time; ensures the authenticity and unforgeability of device identity information through unique digital ID cards; and avoids the risk of missing or replaced accessories through the tag cluster structure design, improving the reliability of equipment integrity verification.

[0044] The UHF Gen2V2 protocol refers to the UHF Gen2V2 communication protocol, which can be implemented using RFID technology with a working frequency of 860-960MHz. This protocol supports batch reading of multiple tags and long-distance identification, effectively improving tag reading efficiency. A tag cluster refers to a collection of multiple independent RFID tags, which can be achieved by configuring each accessory with an independently identifiable RFID tag, ensuring that the accessory and the drone form a verifiable and complete device combination. The hash algorithm is a one-way function that converts input data into a fixed-length output. Specifically, the SHA-256 algorithm can be used to perform mixed calculations on the device serial number, manufacturer code, production date, and random number to generate an irreversible and unique digital identity, preventing data tampering. The storage area refers to the physical area in the RFID tag used to store data, which can be implemented using the tag's EPC memory or user memory, ensuring that the written digital identity information is permanently stored on the physical carrier.

[0045] S2: Receive a rental request initiated by a user through a terminal device, wherein the rental request includes at least a user identity identifier and a drone identity identifier.

[0046] Specifically, if a user needs to rent a drone, the rental request is initiated through a terminal device. The rental request includes at least the user's identity identifier and the drone's identity identifier.

[0047] S3: Based on the drone identification, perform batch identification from the RFID tags to obtain a drone accessory list, and perform integrity verification based on the drone accessory list, wherein the drone accessory list includes rented drones and rented accessories.

[0048] Specifically, the batch identification process employs a dynamic time slot allocation and priority reading strategy, adjusting reading parameters based on the number of tags and environmental interference. Integrity verification ensures the presence of all necessary components by comparing the actual read tags with the expected list.

[0049] Please see Figure 3 , Figure 3 A specific implementation of step S3 is shown below:

[0050] S31: Retrieve the drone accessory list from the RFID tag's storage area based on the drone's identification identifier, generating a cluster of desired tags to be identified. S32: Dynamically allocate reading time slots based on the number of tags in the desired tag cluster and the current environmental interference level. S33: Calculate the reading priority based on the importance weight of each tag in the desired tag cluster, real-time signal strength, and historical reading success rate, and control the RFID reader to perform batch readings according to the reading priority order to obtain batch tag IDs. S34: Compare the batch tag IDs with the desired tag cluster to verify whether all necessary tags have been successfully read.

[0051] Specifically, when a user initiates a rental request, the system extracts a pre-stored accessory list based on the drone's identification to generate a desired tag cluster containing the main unit and accessories. Based on the electromagnetic interference detection results of the current shelf area, such as the presence of Wi-Fi signal interference, the standard time slot length is adjusted from 200ms to 300ms. For remote control tags in the tag cluster to be read, if their historical read success rate is below 80% and the current signal strength is -65dBm, their priority is elevated to the highest level. The reader performs batch scanning according to priority order, and when a propeller tag is detected as unread, a three-retry mechanism is automatically triggered. Finally, the set of actually read tag IDs is completely matched with the desired tag cluster for verification; if any required tag is missing, the integrity verification is deemed to have failed.

[0052] The target tag cluster to be identified refers to a pre-determined set of RFID tags for drones and their accessories. This cluster is generated by retrieving an accessory list from the tag storage area using the drone's identification identifier, and is used to limit the target scanning range and avoid invalid reads. Dynamic allocation of read slots refers to a strategy for adjusting the time window allocation of radio frequency signal transmission based on the number of tags and the current environmental interference level. For example, a slot extension mechanism is used in warehouse environments with metal shelf interference. Reading priority refers to a ranking index constructed based on tag importance weights, real-time signal strength, and historical read success rates. For example, the weight coefficient for battery tags is set to 1.5 times that of propeller tags, and tags with signal strength below -60dBm are automatically given higher read priority.

[0053] S4: If the integrity verification passes, the current rental information is written into the RFID tags of the rented drone and the rented accessories, and the ownership change record is stored in the blockchain database. The current rental information includes at least the user identity identifier, rental timestamp, and permission identifier.

[0054] Specifically, the blockchain database storage uses distributed ledger technology, and each change of ownership generates an immutable timestamp record.

[0055] S5: Send an unlock command to the corresponding drone equipment compartment based on the identity identifier of the rented drone to complete the drone rental process.

[0056] Specifically, the unlock command is sent via an IoT protocol to communicate with the equipment storage control system, triggering the physical lock to open.

[0057] Please see Figure 4 , Figure 4 A specific implementation method following step S5 is shown below:

[0058] S501: Based on the signal strength of the RFID tag of the rented drone at multiple base stations, the area where the rented drone is located is calculated using triangulation to obtain an initial area. S502: Based on the time difference and phase difference of the RFID tag signal arriving at multiple base stations, the three-dimensional coordinates of the rented drone are calculated. S503: Based on the initial area, the three-dimensional coordinates, and current environmental markers, a weighted fusion calculation is performed to obtain the target location. S504: A navigation path is generated based on the user's current location and the target location. S505: A real-time heatmap is generated based on the signal strength read by a handheld reader to guide the user in finding the rented drone.

[0059] Specifically, after the user completes the rental process, the system first collects signal strength data of the target device through multiple RFID base stations deployed in the warehouse. A positioning array composed of three base stations calculates the device's location based on a signal attenuation model, narrowing the search range to a 20-meter diameter area. Then, time-of-arrival (TOA) positioning technology is used, measuring the nanosecond-level time difference of signal arrival at the four base stations and compensating for multipath effects by combining phase difference measurements to calculate the device's three-dimensional coordinates. In outdoor scenarios, the weight of satellite positioning data is automatically increased to 70%, while in indoor scenarios, the weight of RFID positioning is increased to 80%, and multi-source positioning data is fused using a Kalman filter algorithm. The system calls an electronic map interface to generate the shortest path from the user's mobile phone location to the target coordinates, and simultaneously generates a real-time updated signal strength heatmap on the mobile terminal, with colors transitioning from red to green to indicate increasing device distance.

[0060] Among these methods, triangulation refers to estimating spatial location by utilizing the differences in RFID signal strength received from three or more base stations. Specifically, it can be implemented using a positioning algorithm based on received signal strength indication, used to quickly delineate the area where the device is located. Time difference of arrival (TDOA) and phase difference (PDD) measure the time delay and carrier phase difference of the radio frequency signal propagating to different base stations. This can be implemented using ultra-wideband ranging technology, used to improve three-dimensional spatial positioning accuracy. Weighted fusion calculation is a data fusion method that dynamically assigns different weights to positioning data based on environmental type. This can be implemented using fuzzy logic algorithms, used to optimize the accuracy of positioning results in indoor and outdoor scenarios. Real-time heatmaps are two-dimensional visualizations that map signal strength values ​​to color gradients. This can be implemented using heatmap generation algorithms, used to visually display changes in the relative distance between the device and the user.

[0061] This application further proposes a method for obtaining the target location by weighted fusion calculation based on the initial region, three-dimensional coordinates and current environmental identifiers. This method includes obtaining the current environmental identifiers, assigning higher fusion weights to RFID positioning results if it is an indoor environment, and assigning higher fusion weights to GPS / BDS positioning results if it is an outdoor environment, and outputting the target location after weighted fusion of RFID positioning results and GPS / BDS positioning results.

[0062] The current environment identifier refers to classification parameters used to characterize the spatial type of the drone. Specifically, it can be implemented using light intensity collected by environmental sensors, satellite signal strength, or preset geofencing parameters. This parameter serves as the core basis for determining indoor and outdoor scenarios. Weighted fusion calculation refers to a data integration method that dynamically assigns weights to different positioning technologies based on the environmental type. Specifically, it can be implemented using Kalman filtering algorithms or adaptive weighted averaging algorithms, optimizing the combination of positioning results by adjusting the weight coefficients. RFID positioning results refer to spatial coordinate data calculated based on radio frequency signal strength and time difference of arrival. Specifically, it can be implemented using a combination of received signal strength indication algorithms and triangulation methods. This data has high reliability in enclosed spaces. GPS / BDS positioning results refer to latitude and longitude coordinate data provided by satellite navigation systems. Specifically, it can be implemented using multi-band signal reception and differential correction technology. This data has positioning advantages in open areas.

[0063] Specifically, in indoor environments, due to the obstruction of satellite signals by building structures, the system automatically increases the weighting coefficient of RFID positioning results to the range of 0.7-0.9, while decreasing the weighting coefficient of GPS / BDS positioning results to the range of 0.1-0.3, compensating for satellite positioning errors through weighted calculation. In outdoor environments, the weighting is adjusted in the opposite way, increasing the weight of GPS / BDS positioning results to above 0.8, utilizing their wide-area coverage characteristics to ensure positioning continuity. During the fusion process, a sliding window mechanism is used to smooth historical positioning data, eliminating single-point positioning jump errors, and finally outputting target location coordinates that conform to the characteristics of the current environment.

[0064] Please see Figure 5 , Figure 5 A specific implementation method following step S5 is shown below:

[0065] S511: When an operation request for the rented drone is received, the current permission information in the RFID tag of the rented drone is read. S512: Based on the current permission information, the identity and operation permissions of the user initiating the operation request are verified. S513: If the user identity and operation permissions are verified as valid, the operation time, usage location, and usage mode of the rented drone are monitored in real time to obtain monitoring data. S514: An anomaly score is calculated based on the monitoring data using an anomaly scoring model. If the anomaly score exceeds a threshold, a security alarm is generated.

[0066] Specifically, when a user initiates an operation request, the system verifies whether the user's identity matches the rental record by reading the encrypted permission information stored in the RFID tag, and confirms whether the operation type is within the authorized scope. After successful verification, the system continuously collects the drone's operation time distribution, location coordinates within the geofence, and flight mode parameters, forming a multi-dimensional monitoring data stream. The anomaly scoring model calculates in real time features such as the overlap rate between the operation time and the preset rental period, the location offset distance, and the flight altitude deviation. When the comprehensive score exceeds a dynamically adjusted threshold, a tiered alarm mechanism is triggered. This application solves the problem of illegal operations caused by permission management vulnerabilities during drone rental, and significantly reduces the false alarm rate and missed alarm rate through multi-dimensional behavior monitoring and intelligent scoring mechanisms. For example, if a user exceeds the agreed usage area but does not trigger a dangerous operation, the system can issue a warning based on the comprehensive score instead of directly locking the device, ensuring both security and improving the user experience.

[0067] The current access information refers to the user's identification, operation permission scope, and timeliness data stored in the RFID tag. This can be achieved by encrypting the access information using an asymmetric encryption algorithm to prevent unauthorized tampering or forgery. The anomaly scoring model is a multi-dimensional behavior analysis model built on machine learning algorithms. Specifically, it can be implemented using a random forest algorithm to classify and train historical normal and abnormal operation data, generating anomaly scores by quantitatively assessing the degree of deviation from the operational behavior.

[0068] Please see Figure 6 , Figure 6 A specific implementation method following step S5 is shown below:

[0069] S521: Based on the current known locations of all drones and the warehouse layout, calculate the optimal inventory path using a traveling salesman problem optimization algorithm. S522: Move an RFID reader along the optimal inventory path and scan all RFID tags within the path area in batches. S523: Compare the scanned equipment list with the expected list in the database in real time to identify missing, misplaced, or abnormally positioned equipment, thus identifying the target abnormal equipment. S524: Generate an inventory result report based on the target abnormal equipment, wherein the inventory result report includes a list of abnormal equipment and the last known location of each equipment in the abnormal equipment list.

[0070] Specifically, during the inventory phase, the system first obtains the last recorded location of all equipment in the warehouse and constructs a three-dimensional spatial model based on the shelving layout. A path optimization algorithm calculates the shortest movement path covering all areas to be inventoried, avoiding efficiency losses caused by repeated routes. As the automated guided vehicle (AGV) equipped with RFID readers moves along the planned path, it transmits radio frequency signals in real time to capture electronic tag information within a five-meter radius. The read equipment identification and component data are automatically compared with the original records on the blockchain, and missing or misaligned equipment is identified through hash value verification. Abnormal equipment information is associated with its most recent location data to generate a visual report, providing warehouse managers with precise guidance for handling anomalies. This application realizes intelligent inventory management for drone rental warehouses. The system automatically plans the optimal inspection path, eliminating the uncertainty of manual inspection routes. Mobile RFID devices complete batch data collection during movement, avoiding deployment blind spots of traditional fixed readers. The blockchain database provides a reliable data source for equipment status verification, ensuring the accuracy and timeliness of inventory comparison. The location tracing function for abnormal equipment helps managers quickly locate problematic equipment, effectively solving the problems of low efficiency and high error rate in traditional inventory methods.

[0071] Among these, the Traveling Salesman Problem optimization algorithm refers to a computational method that uses mathematical modeling to find the shortest closed path traversing multiple target points. Specifically, it can be implemented using genetic algorithms or ant colony algorithms to generate the optimal movement route given the known equipment locations and warehouse 3D coordinates. RFID reader batch scanning refers to the non-contact group reading of electronic tags within the path's coverage area using radio frequency identification technology. This can be achieved using multi-antenna arrays and dynamic power adjustment technology to ensure continuous tag data capture during movement. The expectation list in the blockchain database refers to a set of device registration information stored in a distributed ledger. Specifically, smart contracts maintain the correspondence between device models, serial numbers, and standard accessory lists, forming an immutable baseline data source.

[0072] Please see Figure 7 , Figure 7 A specific implementation method following step S5 is shown below:

[0073] S531: Obtain the RFID tags for the returned drone and accessories to obtain the RFID tag for the returned equipment, and identify the equipment identity corresponding to the returned drone. S532: Reset the rental status information of the RFID tag for the returned equipment and clear the user identity identifier from the RFID tag. S533: Record the return timestamp and equipment status update record in the blockchain database to complete the drone return process.

[0074] Specifically, when a user returns the device, the reader scans the RFID tags on the drone body and accessories in batches, verifying the device's identity by decoding the device serial number stored in the tag. The system automatically triggers a status reset command, clearing the user ID field stored in the tag and changing the rental status bit from "1" to "0". Simultaneously, the blockchain node generates a transaction record containing the return time, device serial number, and operator ID, which is verified through the consensus mechanism and written into a new block. During this process, accessory integrity verification is automatically completed by comparing the number of tags obtained from the scan with the original configuration list, avoiding errors from manual counting. The application achieves real-time synchronous updates of device status upon drone return, ensuring the accuracy of the rental system's inventory data; it also establishes a reliable chain of evidence for ownership changes, effectively resolving the issue of liability determination in the event of device loss.

[0075] The returned equipment RFID tag refers to a radio frequency identification tag attached to the drone and its accessories. Specifically, it can be implemented using passive UHF tags conforming to the ISO 18000-63 standard. These tags are uniquely encoded and bound to the physical device for rapid identification of the equipment and its components. Resetting the rental status information refers to changing the rental status bit in the tag storage area from "rented" to "unrented." This is achieved by sending a status update command through a reader, ensuring the device can re-enter the rental pool. The blockchain database is a storage system based on distributed ledger technology, specifically built using the Hyperledger Fabric framework. It automatically records device status change events through smart contracts, forming an immutable chain of ownership changes.

[0076] This application further proposes predictive maintenance steps, including collecting historical usage data and sensor data of the UAV, modeling the degradation process of the equipment based on the Wiener process model to predict the remaining service life and failure probability, and generating maintenance recommendations or scheduling instructions based on the prediction results.

[0077] Specifically, the predictive maintenance system collects drone flight control unit logs and sensor data streams in real time via an IoT gateway, performing data cleaning and feature extraction at edge computing nodes. The Wieber distribution is used to fit the degradation paths of critical equipment components, combined with a Kalman filter algorithm to suppress measurement noise. When the model's predicted remaining service life falls below a safe threshold, the maintenance scheduling module automatically triggers a parts replacement work order and removes the equipment from the rentable queue. The system synchronously updates the maintenance status identifier in the RFID tag to ensure equipment availability verification during the rental process.

[0078] The Wiener process model is a mathematical model used to describe the stochastic degradation of equipment performance over time. Specifically, it can be implemented using stochastic differential equations with drift and diffusion coefficients, and the model parameters are calibrated using maximum likelihood estimation. This model can capture the stochastic fluctuations in the equipment degradation trajectory, providing a probability distribution function for remaining life prediction. Historical usage data refers to the cumulative flight time, number of takeoffs and landings, and load condition records of the UAV during the rental period, which can be extracted from equipment usage logs in a blockchain database. Sensor data includes real-time monitoring indicators such as motor temperature, battery internal resistance, and vibration spectrum, collected through a multimodal sensor array installed on the UAV. Data fusion provides multi-dimensional state feature inputs for degradation modeling.

[0079] This application further proposes to use an elliptic curve cryptography-based encryption algorithm and a digital signature algorithm to encrypt and authenticate communication data during the communication process between the RFID reader and the tag, thereby ensuring the security and integrity of data transmission.

[0080] Specifically, during the drone rental process, when the reader initiates a data interaction request, the tag first generates a random number containing a timestamp as a session key, and uses an elliptic curve cryptography algorithm to encrypt and transmit the user identity and permission identifiers in the rental instruction. After decryption, the reader verifies the legality of the instruction. If successful, it uses the ECDSA algorithm to digitally sign the returned drone status data. The tag verifies the signature validity using a pre-set reader public key before allowing the status information update operation. This process, through a dual encryption and signature mechanism, ensures that the data has tamper-proof characteristics at every stage of the transmission link.

[0081] Elliptic curve cryptography (ECC) refers to an asymmetric encryption system built using the mathematical properties of the discrete logarithm problem of elliptic curves. Specifically, it can generate key pairs using the secp256k1 curve parameters and achieve encryption by performing scalar multiplication of plaintext data with the coordinates of elliptic curve points. This algorithm, under the condition of limited RFID tag storage space, can achieve the same security strength as RSA-3072 with a shorter key length. Digital signature algorithms refer to data integrity verification mechanisms based on elliptic curve digital signature algorithms. Specifically, they can be implemented using the ECDSA algorithm. The hash value of the communication data is signed using the tag's private key, and the reader uses the corresponding public key to verify the signature's validity. This mechanism can prevent man-in-the-middle tampering of communication content and provide verifiable original data credentials for blockchain evidence storage.

[0082] In this embodiment, RFID tags are configured for both the drone to be rented and its accessories, and the device identity information and initial status information of the drone to be rented are written into the RFID tags. A rental request initiated by a user through a terminal device is received, wherein the rental request includes at least a user identity identifier and a drone identity identifier. Based on the drone identity identifier, batch identification is performed from the RFID tags to obtain a drone accessory list, and integrity verification is performed based on the drone accessory list, wherein the drone accessory list includes the rented drone and the rented accessories. If the integrity verification passes, the current rental information is written into the RFID tags of the rented drone and the rented accessories, and the ownership change record is stored in a blockchain database, wherein the current rental information includes at least the user identity identifier, rental timestamp, and permission identifier. An unlocking command is sent to the corresponding drone equipment warehouse based on the identity identifier corresponding to the rented drone to complete the drone rental process. This embodiment of the invention achieves batch identification and integrity verification by configuring RFID tags for drones and accessories, and combines blockchain technology to record ownership changes, which helps improve the efficiency of drone rental.

[0083] Please refer to Figure 8 As a response to the above Figure 1 The implementation of the method shown in this application provides an embodiment of an RFID tag-based drone rental device, which is similar to... Figure 1 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0084] like Figure 8 As shown, the RFID tag-based drone rental device in this embodiment includes: a tag configuration module 61, a rental request receiving module 62, an integrity verification module 63, an information writing module 64, and an unlocking command sending module 65, wherein:

[0085] The tag configuration module 61 is used to configure RFID tags for the drone to be rented and its accessories, and to write the device identity information and initial status information of the drone to be rented into the RFID tags.

[0086] The rental request receiving module 62 is used to receive rental requests initiated by users through terminal devices, wherein the rental request includes at least a user identity identifier and a drone identity identifier;

[0087] The integrity verification module 63 is used to perform batch identification from the RFID tags based on the drone identification to obtain a drone parts list, and to perform integrity verification based on the drone parts list, wherein the drone parts list includes rented drones and rented accessories;

[0088] The information writing module 64 is used to write the current rental information into the RFID tags of the rented drone and the rented accessories if the integrity verification is passed, and to store the ownership change record in the blockchain database. The current rental information includes at least the user identity identifier, rental timestamp, and permission identifier.

[0089] The unlock command sending module 65 is used to send an unlock command to the corresponding drone equipment compartment based on the identity identifier of the rented drone to complete the drone rental process.

[0090] Furthermore, the label configuration module 61 includes:

[0091] A drone tag configuration unit is used to configure the RFID tag with the UHF Gen2V2 protocol for the drone to be rented.

[0092] The accessory tag configuration unit is used to configure accessory RFID tags for batteries, propellers, and remote control accessories, which together form the tag cluster of the drone to be rented.

[0093] The identity generation module is used to generate a unique digital identity by calculating the device serial number, manufacturer code, production date and random number of the drone to be rented using a hash algorithm;

[0094] The identity writing module is used to write the unique digital identity into the storage area of ​​the RFID tag.

[0095] Furthermore, the integrity verification module 63 includes:

[0096] The expected tag cluster generation unit is used to obtain the list of drone accessories from the storage area of ​​the RFID tag according to the drone identification and generate the expected tag cluster to be identified;

[0097] The read time slot allocation unit is used to dynamically allocate read time slots based on the number of tags in the expected tag cluster to be identified and the current environmental interference level;

[0098] The read priority calculation unit is used to calculate the read priority based on the importance weight of each tag in the expected tag cluster to be identified, the real-time signal strength and the historical read success rate, and control the RFID reader to perform batch reads according to the read priority order to obtain batch tag IDs;

[0099] The comparison unit is used to compare the batch tag ID with the expected tag cluster to be identified in order to verify whether all required tags have been successfully read.

[0100] Furthermore, the unlock command sending module 65 also includes:

[0101] An initial area identification unit is used to calculate the area where the rental drone is located by using triangulation based on the signal strength of the RFID tag of the rental drone at multiple base stations, and obtain the initial area.

[0102] A three-dimensional coordinate calculation unit is used to calculate the three-dimensional coordinates of the rented drone based on the arrival time difference and phase difference of the RFID tag signal of the rented drone to multiple base stations.

[0103] The target location generation unit is used to perform a weighted fusion calculation based on the initial region, the three-dimensional coordinates, and the current environment identifier to obtain the target location;

[0104] A navigation path generation unit is used to generate a navigation path based on the user's current location and the target location;

[0105] The heatmap generation unit is used to generate a real-time heatmap based on the signal strength read by the handheld reader, so as to guide the user to find the rented drone.

[0106] Furthermore, the unlock command sending module 65 also includes:

[0107] The current permission information reading unit is used to read the current permission information in the RFID tag of the rented drone when an operation request for the rented drone is received;

[0108] The legitimacy determination unit is used to verify, based on the current permission information, whether the identity and operation permissions of the user initiating the operation request are legitimate;

[0109] The monitoring unit is used to monitor the operation time, usage location and usage mode of the rented drone in real time and obtain monitoring data if the user's identity and operation permission are verified as legitimate.

[0110] An anomaly score calculation unit is used to calculate an anomaly score based on the monitoring data using an anomaly scoring model. If the anomaly score exceeds a threshold, a security alarm is generated.

[0111] Furthermore, the unlock command sending module 65 also includes:

[0112] The target optimal inventory path calculation unit is used to calculate the target optimal inventory path based on the current known locations of all drones and the warehouse layout, using a traveling salesman problem optimization algorithm.

[0113] A batch scanning unit is used to move along the optimal inventory path of the target using an RFID reader and scan all the RFID tags within the path range in batches.

[0114] The target abnormal device identification unit is used to compare the device list obtained by scanning with the expected list in the database in real time, identify the missing, misplaced or abnormal devices, and obtain the target abnormal devices.

[0115] The inventory result report generation unit is used to generate an inventory result report based on the target abnormal equipment, wherein the inventory result report includes a list of abnormal equipment and the last known position of each equipment in the list of abnormal equipment.

[0116] Furthermore, the unlock command sending module 65 also includes:

[0117] The device identification unit is used to acquire the RFID tags of the returned drone and returned accessories, obtain the RFID tag of the returned device, and identify the device identity corresponding to the returned drone.

[0118] The user identity identifier clearing unit is used to reset the rental status information of the RFID tag of the returned device and clear the user identity identifier in the RFID tag of the returned device.

[0119] The return timestamp recording unit is used to record the return timestamp and device status update records in the blockchain database to complete the drone return process.

[0120] To address the aforementioned technical problems, embodiments of this application also provide an electronic device. Please refer to [link / reference needed] for details. Figure 9 , Figure 9 This is a basic structural block diagram of the electronic device in this embodiment.

[0121] Electronic device 7 includes a memory 71, a processor 72, and a network interface 73 that are interconnected via a system bus. It should be noted that... Figure 9 Only an electronic device 7 with three components—memory 71, processor 72, and network interface 73—is shown. However, it should be understood that implementing all shown components is not required; more or fewer components can be implemented alternatively. Those skilled in the art will understand that the electronic device described herein is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0122] Electronic devices can be computing devices such as desktop computers, laptops, PDAs, and cloud servers. Electronic devices can interact with users through methods such as keyboards, mice, remote controls, touchpads, or voice-activated devices.

[0123] The memory 71 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 71 may be an internal storage unit of the electronic device 7, such as the hard disk or memory of the electronic device 7. In other embodiments, the memory 71 may also be an external storage device of the electronic device 7, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the electronic device 7. Of course, the memory 71 may also include both internal storage units and external storage devices of the electronic device 7. In this embodiment, the memory 71 is typically used to store the operating system and various application software installed on the electronic device 7, such as the program code of a drone rental method based on RFID tags. In addition, the memory 71 may also be used to temporarily store various types of data that have been output or will be output.

[0124] In some embodiments, processor 72 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. Processor 72 is typically used to control the overall operation of electronic device 7. In this embodiment, processor 72 is used to run program code stored in memory 71 or process data, for example, to run the program code of the above-described RFID tag-based drone rental method to implement various embodiments of the RFID tag-based drone rental method.

[0125] The network interface 73 may include a wireless network interface or a wired network interface, which is typically used to establish a communication connection between the electronic device 7 and other electronic devices.

[0126] This application also provides another embodiment, namely, a computer-readable storage medium storing a computer program that can be executed by at least one processor to cause the at least one processor to perform the steps of the above-described RFID tag-based drone rental method.

[0127] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods of the various embodiments of this application.

[0128] Obviously, the embodiments described above are merely some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the scope of this application. This application can be implemented in many different forms; rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of protection of this application.

Claims

1. A method of drone rental based on an RFID tag, characterized by, The method comprises the following steps: configuring RFID tags for the to-be-rented drones and accessories of the to-be-rented drones respectively, and writing device identity information and initial state information of the to-be-rented drones into the RFID tags; receiving a rent request initiated by a user through a terminal device, wherein the rent request at least includes a user identity and a drone identity; based on the drone identity, performing batch identification from the RFID tags to obtain a drone accessory list, and performing integrity verification based on the drone accessory list, wherein the drone accessory list includes a rented drone and rented accessories; if the integrity verification is passed, writing current rent information into the RFID tags of the rented drone and the rented accessories, and storing a change record of ownership to a blockchain database, wherein the current rent information at least includes the user identity, a rent timestamp, and a permission identity; based on the identity corresponding to the rented drone, sending an unlocking instruction to a corresponding drone device warehouse to complete a drone rent process; the batch identification from the RFID tags based on the drone identity to obtain a drone accessory list, and the integrity verification based on the drone accessory list, comprises: obtaining the drone accessory list from a storage area of the RFID tag according to the drone identity, to generate a to-be-identified expected tag cluster; dynamically allocating a reading time slot according to the number of tags in the to-be-identified expected tag cluster and a current environmental interference level; calculating a reading priority according to the importance weight, real-time signal strength, and historical reading success rate of each tag in the to-be-identified expected tag cluster, and controlling the RFID reader to perform batch reading in the order of the reading priority to obtain a batch of tag IDs; comparing the batch of tag IDs with the to-be-identified expected tag cluster to check whether all necessary tags are successfully read. 2.The RFID tag-based drone lending method of claim 1, wherein, The method for configuring an identity for the to-be-rented drone and accessories of the to-be-rented drone respectively, and writing the identity, device identity information, and initial state information of the to-be-rented drone into the RFID tag, comprises: configuring the RFID tag of the UHF Gen2V2 protocol for the to-be-rented drone; configuring accessory RFID tags for the battery, propeller, and remote control accessories respectively, which together form a tag cluster of the to-be-rented drone; generating a unique digital identity by hashing the device serial number, manufacturer code, production date, and random number of the to-be-rented drone; writing the unique digital identity into a storage area of the RFID tag. 3.The RFID tag-based drone rental method of claim 1, wherein, After the method based on the identity corresponding to the rented drone sends an unlocking instruction to the corresponding drone device warehouse to complete the drone rent process, the method further comprises: calculating the area where the rented drone is located by using a triangulation method according to the signal strength of the RFID tag of the rented drone at multiple base stations to obtain an initial area; According to the time difference and phase difference of the RFID tag signal of the rented drone reaching multiple base stations, the three-dimensional coordinates of the rented drone are calculated; Based on the initial area, the three-dimensional coordinates and the current environment identification, a weighted fusion calculation is performed to obtain a target position; According to the current position of the user and the target position, a navigation path is generated; Based on the signal strength read by the handheld reader / writer, a real-time heat map is generated to guide the user to find the rented drone. 4.The RFID tag-based drone rental method of claim 1, wherein, After the method based on the identity of the rented drone corresponding to the corresponding unmanned device warehouse sends an unlocking instruction to complete the unmanned vehicle rental process, the method further comprises: When receiving an operation request for the rented drone, read the current permission information in the RFID tag of the rented drone; Based on the current permission information, verify whether the user identity and operation permission initiating the operation request are legal; If the user identity and the operation permission are verified to be legal, the operation time, use location and use mode of the rented drone are monitored in real time to obtain monitoring data; Through an abnormal score model, an abnormal score is calculated based on the monitoring data, and if the abnormal score exceeds a threshold, a security alarm is generated. 5.The RFID tag based drone rental method according to any one of claims 1 to 4, wherein, After the method based on the identity of the rented drone corresponding to the corresponding unmanned device warehouse sends an unlocking instruction to complete the unmanned vehicle rental process, the method further comprises: According to the current known positions of all unmanned vehicles and the warehouse layout, a traveling salesman problem optimization algorithm is used to calculate a target optimal inventory path; By moving along the target optimal inventory path through the RFID reader / writer, all RFID tags within the path range are batch scanned; The scanned device list is compared in real time with the expected list in the database to identify missing, misplaced or state abnormal devices to obtain target abnormal devices; Based on the target abnormal devices, an inventory result report is generated, wherein the inventory result report includes an abnormal device list and the last known position of each device in the abnormal device list. 6.The RFID tag based drone rental method according to any one of claims 1 to 4, wherein, After the method based on the identity of the rented drone corresponding to the corresponding unmanned device warehouse sends an unlocking instruction to complete the unmanned vehicle rental process, the method further comprises: Obtain the RFID tags of returned unmanned vehicles and returned accessories to obtain returned device RFID tags and identify the device identity corresponding to the returned unmanned vehicle; Reset the rental state information of the returned device RFID tags and clear the user identity identification in the returned device RFID tags; Record the return timestamp and device state update record in the blockchain database to complete the unmanned vehicle return process. 7.A drone rental apparatus based on an RFID tag, characterized by, Comprise: A tag configuration module for configuring RFID tags for a to-be-rented drone and accessories of the to-be-rented drone, and writing device identity information and initial state information of the to-be-rented drone into the RFID tags; A rental request receiving module for receiving a rental request initiated by a user through a terminal device, wherein the rental request at least includes a user identity and a drone identity; An integrity verification module is configured to perform batch identification from the RFID tags based on the UAV identity, obtain a UAV accessory list, and perform integrity verification based on the UAV accessory list, wherein the UAV accessory list includes a rented UAV and rented accessories; An information writing module is configured to write current rental information into the RFID tags of the rented UAV and the rented accessories if the integrity verification is passed, and store a change record of ownership to a blockchain database, wherein the current rental information at least includes the user identity, a rental timestamp, and a permission identity; An unlocking instruction sending module is configured to send an unlocking instruction to a corresponding UAV device warehouse based on the identity of the rented UAV, so as to complete a UAV rental process. The integrity verification module includes: An expected tag cluster generation unit is configured to obtain the UAV accessory list from the storage area of the RFID tags based on the UAV identity, and generate an expected tag cluster to be identified; A read time slot allocation unit is configured to dynamically allocate read time slots based on the number of tags in the expected tag cluster to be identified and a current environmental interference level; A read priority calculation unit is configured to calculate read priorities based on the importance weight, real-time signal strength, and historical read success rate of each tag in the expected tag cluster to be identified, and control the RFID reader to perform batch reading in the order of the read priorities, so as to obtain batch tag IDs; A comparison unit is configured to compare the batch tag IDs with the expected tag cluster to be identified, so as to verify whether all necessary tags are successfully read.

8. An electronic device, comprising: A memory and a processor are included, the memory stores a computer program, and the processor implements the RFID tag-based UAV rental method according to any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium, characterized in that, A computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the RFID tag-based UAV rental method according to any one of claims 1 to 6.

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