Block chain-based bicycle digital twin anti-theft method and system

Abstract

The invention relates to a bicycle digital twin anti-theft method based on a block chain, and the method comprises the steps: continuously detecting the data of a bicycle sensor and the data of each part chip through a main recognition module disposed on a bicycle body, and judging whether the behavior of a bicycle is abnormal or not through an abnormality detection strategy; when it is judged that the bicycle behavior is abnormal, first-level early warning is triggered, and communication verification is sent to the user side; when the communication verification fails or the communication verification result is abnormal, entering a secondary early warning mode, obtaining the traceability data for reliability verification, and when the reliability verification is passed, writing the traceability data into a one-time writing storage unit arranged in the bicycle body, and triggering the one-time writing storage unit to permanently lock the data; and when the vehicle is sent to an authorized maintenance point, the locking data in the one-time write-in storage unit is read through a special device and is compared with the data in the cloud digital twin platform, and vehicle traceability and illegal modification identification are carried out.

Description

Technical Field

[0001] This invention relates to bicycle anti-theft, and more particularly to a blockchain-based digital twin anti-theft method and system for bicycles. Background Technology

[0002] Electric bicycles, as a convenient and flexible mode of transportation, are widely used in daily commutes and leisure activities. However, theft has long plagued users. Existing bicycle anti-theft technologies have many limitations: traditional mechanical locks are easily cracked by brute force; electronic anti-theft devices mostly rely on single sensor monitoring, resulting in high false alarm rates and a lack of effective traceability; after a vehicle is stolen, parts are easily disassembled and replaced, making it difficult to verify ownership and confirm illegal modifications through identity verification; anti-theft data is mostly stored on a single terminal or centralized platform, making it susceptible to tampering and lacking security; identity verification methods are mostly passwords or simple biometrics, posing a risk of identity theft; furthermore, there is a lack of unified management of the vehicle's entire lifecycle status, making it impossible to track the trajectory after signal loss and predict high-risk areas, resulting in low anti-theft efficiency and a low recovery rate. With the development of IoT and blockchain technologies, there is an urgent need for a new anti-theft solution that combines accurate monitoring, tamper-proof traceability, and full-process verification to address the pain points of existing technologies. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a blockchain-based digital twin anti-theft method and system for bicycles, so as to overcome the above-mentioned defects in the existing technology.

[0004] To achieve the above objectives, the present invention provides the following technical solution: Blockchain-based digital twin anti-theft methods for bicycles include: The monitoring process involves continuously detecting vehicle sensor data and chip data from various components using a main identification module installed on the bicycle frame, and determining whether the vehicle's behavior is abnormal through an anomaly detection strategy. The warning and verification process involves triggering a level one warning when abnormal bicycle behavior is detected, and sending a communication verification message to the user's device. In the data locking process, if communication verification fails or the communication verification result is abnormal, the system enters a level 2 warning mode and obtains traceability data for reliability verification. If the reliability verification is passed, the traceability data is written into a one-time write storage unit set in the bicycle body, and the unit is permanently locked. The traceability and verification process involves using specialized equipment to read the locked data written to the storage unit when the vehicle is sent to an authorized repair shop. This data is then compared with the data in the cloud-based digital twin platform to trace the vehicle's origin and identify any illegal modifications.

[0005] Preferably, the reliability verification includes collecting current environmental and status data from multiple sets of different types of independent sensors or positioning modules as traceability data, analyzing the consistency data of the collected multi-source data through a cross-comparison strategy, calculating the reliability score of the current data based on the consistency analysis results, and setting a reliability threshold. When the reliability score is higher than the reliability threshold, the traceability data is written to a one-time write storage unit.

[0006] Preferably, the cross-comparison strategy includes a spatial consistency verification sub-step and a physical consistency verification sub-step. The spatial consistency verification sub-step includes: obtaining the master positioning coordinates provided by the master positioning module; based on the master positioning coordinates, querying a pre-stored expected signal environment topology map associated with the master positioning coordinates from a local or cloud database; scanning the wireless signals in the current environment in real time to generate a real-time signal environment topology map; performing similarity matching between the real-time signal environment topology map and the expected signal environment topology map; and using the similarity as spatial consistency data. The physical consistency verification sub-step includes: continuously collecting motion trajectory data from the inertial measurement unit and environmental parameter data from the environmental sensor; analyzing whether the changes between the motion trajectory and the environmental parameter data conform to physical causal logic in the time series; if it is detected that the motion trajectory is just a specific action of the vehicle, but the corresponding environmental parameter data does not change in accordance with the expected physical laws, or the change order has logical contradictions, then it is determined that the physical consistency between the sensor data has failed, and physical consistency data is generated.

[0007] Preferably, the reliability verification further includes a spatial consistency verification sub-step. The main identification module sends an encrypted response process to the user terminal, calculates the real-time accurate distance between the user terminal and the vehicle based on the radio round-trip time, instructs the user terminal to synchronously scan the channel state information of its current wireless environment, and receives the CSI fingerprint data reported by the user terminal. At the same time, the main identification module scans the current wireless environment itself, generates local CSI fingerprint data, determines whether the real-time accurate distance is less than a first preset threshold, and performs similarity matching between the user terminal's CSI fingerprint data and the local CSI fingerprint data to determine whether the similarity is higher than a second preset threshold. If and only if the real-time accurate distance is less than the first preset threshold and the similarity is higher than the second preset threshold, it is determined that the vehicle owner is near the vehicle; otherwise, spatial consistency data is generated.

[0008] Preferably, the method also includes a trajectory completion step, which involves acquiring data records of multiple stolen vehicles. These data records include the inertial navigation data of each vehicle during the signal loss period, the last known location before the signal loss, and the final location when the signal is restored or the vehicle is found. For each stolen vehicle, its relative motion trajectory is estimated based on its inertial navigation data, and this relative motion trajectory is matched and fitted with the road network data of the digital map to generate a predicted path from the last known location to the destination location. The predicted paths of all stolen vehicles are aggregated, and spatiotemporal feature points on each predicted path are extracted. A spatiotemporal clustering algorithm is used to analyze the spatiotemporal feature points to identify one or more high-risk areas that are geographically clustered and temporally related.

[0009] Preferably, the anomaly detection strategy includes acquiring historical usage data of bicycle users, constructing a behavioral baseline model representing the user's personalized usage habits based on the historical usage data, collecting real-time vehicle status data, comparing the real-time status data with the behavioral baseline model, obtaining the deviation, and triggering an early warning when the deviation exceeds a preset threshold.

[0010] Preferably, the anomaly detection strategy further includes a component interaction sub-step, which includes deploying a sensor network on key components, continuously detecting and learning the static positional relationships and dynamic motion relationships between components, generating a key component behavior model of the bicycle, acquiring the current data of the sensor network in real time, calculating the current component relationship value, comparing the component relationship value with the baseline relationship value stored in the component behavior model, obtaining a difference score, and when the difference score exceeds a preset threshold, determining that the integrity of the vehicle has been compromised and triggering an early warning.

[0011] A blockchain-based digital twin anti-theft system includes: The main identification module is used to monitor the vehicle status and execute early warning logic. When the sensor data determines that the vehicle behavior is abnormal, it enters the first-level early warning mode and communicates with the user terminal for verification. When the communication verification fails or the verification result is abnormal, it enters the second-level early warning mode. A one-time write-to-storage unit is used to store traceability data that cannot be tampered with when the secondary warning mode is triggered and the reliability verification is passed. A digital twin platform is used to verify the legal identity and status information of vehicles; The main identification module is communicatively connected to the one-time writing unit and the digital twin platform.

[0012] Preferably, the digital twin platform includes an authentication strategy, which comprises: The multimodal authentication process involves the user client initiating an authentication dialogue, which requires continuous and mandatory complete biometric verification, password verification, and geofence-based current location verification. The information collection process guides the user to scan the vehicle's original identification and the identifier of the new component, and collects a status diagram of the new component's installation. The review process involves uploading authentication information, original identity identifier, new component identifier, and installed status diagram to the digital twin platform. The digital twin platform verifies the compliance of component installation in the installed status diagram. Once the verification is successful, a unique vehicle code is generated. The identity update step involves sending the updated vehicle unique code to the vehicle's main identification module for storage.

[0013] Preferably, the digital twin platform maintains an immutable event log associated with the physical vehicle and ordered chronologically. Each event represents an operation instruction that changes the vehicle's state. The current state of the digital twin platform is calculated by sequentially replaying the event log.

[0014] The beneficial effects of this invention are as follows: By constructing a user-personalized behavior baseline model and monitoring the interaction relationship of components, combined with cross-verification from multiple sources of sensors, anomalies are determined from multiple dimensions such as user habits, vehicle integrity, and environmental matching, avoiding misjudgments caused by a single monitoring dimension and improving the accuracy of anti-theft triggering; relying on blockchain technology and one-time write storage units, the traceability data under abnormal conditions is permanently locked to ensure that the data cannot be tampered with throughout the entire process; authorized repair shops can quickly complete vehicle ownership traceability and illegal modification identification by comparing data with the cloud digital twin platform through dedicated equipment, providing solid evidence for vehicle recovery and rights protection; adopting multimodal authentication biometrics, passwords, and geofencing, combined with CSI fingerprint data matching and distance verification, a multi-level identity verification system is constructed to prevent identity theft; the immutable event log maintained by the digital twin platform enables full lifecycle traceability of vehicle status and avoids information fragmentation. Attached Figure Description

[0015] Figure 1 This is an overall flowchart of the present invention; Figure 2 This is a flowchart of the anomaly detection strategy of the present invention; Figure 3 This is a flowchart of the identity update process of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] It should be noted that when a component is described as "fixed to" another component, it can be directly on the other component or may have a component in between. When a component is considered "connected to" another component, it can be directly connected to the other component or may have a component in between. When a component is considered "set on" another component, it can be directly set on the other component or may have a component in between. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0018] 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 invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0019] The embodiments of the present invention will be further described in detail below with reference to the accompanying drawings: like Figure 1-3 As shown, this invention provides a blockchain-based digital twin anti-theft method for bicycles, including: The monitoring process involves continuously detecting vehicle sensor data and chip data from various components using a main identification module installed on the bicycle frame, and determining whether the vehicle's behavior is abnormal through an anomaly detection strategy. There is an anomaly detection strategy, which includes acquiring historical usage data of bicycle users, constructing a behavioral baseline model representing the user's personalized usage habits based on the historical usage data, collecting real-time vehicle status data, comparing the real-time status data with the behavioral baseline model, obtaining the deviation, and triggering an alert when the deviation exceeds a preset threshold. Historical usage data includes time data, spatial data, and operational behavior data. Time data includes the user's regular riding time periods, the distribution of single riding durations, and the pattern of inactivity time after parking. Spatial data includes high-frequency riding routes, frequently used parking locations, the geographical range of riding areas, and environmental characteristics of typical riding scenarios. Operational behavior data includes unlocking method preferences, riding habits, and vehicle function usage patterns. Based on the above historical data, a user-specific behavioral baseline model is constructed using machine learning algorithms. During vehicle use, the main recognition module collects dynamic information corresponding to the dimensions of historical data in real time, such as current time, real-time location, unlocking method, real-time speed, and acceleration. It then calculates the deviation from the baseline model through the following steps. For each feature dimension, it determines whether the real-time data falls within the normal range defined by the baseline model. A weighted algorithm integrates the single-dimensional deviation into a comprehensive deviation. When the real-time calculated comprehensive deviation exceeds a preset threshold, the main recognition module determines that the vehicle behavior is abnormal and triggers an alert.

[0020] The anomaly detection strategy also includes a component interaction sub-step, which involves deploying a sensor network on key components, continuously detecting and learning the static positional relationships and dynamic motion relationships between components, generating a key component behavior model for the bicycle, acquiring the current data of the sensor network in real time, calculating the current component relationship value, comparing the component relationship value with the baseline relationship value stored in the component behavior model, obtaining a difference score, and determining that the integrity of the vehicle has been compromised when the difference score exceeds a preset threshold, thus triggering an early warning. Prioritize components that play a decisive role in the vehicle's uniqueness and functional integrity, including: core structural components: frame, handlebars, wheel axles, bottom bracket; anti-theft related components: locks, battery compartment, seat post; dynamic linkage components: pedals, chain / freewheel, braking system; match sensors based on the static / dynamic attributes of component interactions, for static positional relationship monitoring: deploy miniature displacement sensors and Hall effect position sensors at component connection points to monitor the relative distance, angle, and assembly gap between components; for dynamic motion relationship monitoring: deploy gyroscopes on linkage components, such as angular velocity sensors and torque sensors for handlebars and pedals, torque transmission data for the bottom bracket and chain, and stroke sensors to capture... The system captures the synchronicity and proportionality of component movements; static positional relationships are the foundation of component assembly integrity, referring to the fixed geometric relationships between key components, such as angles, distances, and gaps, in the non-riding state, which need to be established as a benchmark through initial calibration and long-term stable learning; dynamic motion relationships are the core of component functional linkage integrity, referring to the synchronous motion logic between key components in the riding state, which needs to be established as a dynamic benchmark through motion feature extraction and linkage logic modeling; the main identification module continuously collects real-time data from the sensor network to determine whether components are abnormal; a preset comprehensive difference threshold is set, and when the real-time comprehensive difference score exceeds the threshold, it is determined that the vehicle integrity has been compromised, triggering a targeted warning.

[0021] If the handlebars are forcibly removed, the static sensor detects that the angle between the handlebars and the frame suddenly changes from 90° to 60°, with a static difference of 1. Simultaneously, the pressure value of the contact sensor drops sharply, with a connection difference of 1. The overall difference is 1×0.3+0=0.3. If the removal of the handlebars also causes the scooter lock linkage to loosen, the scooter lock pressure difference is 0.8, then the overall difference is 1×0.3+0.8×0.2=0.46, which is close to the threshold. If the handlebars are further removed, the angle becomes 0°, with a difference of 1, and the overall difference is 1×0.3+0.8×0.2=0.46. If the bottom bracket drive is removed at the same time, the difference is 1, and the overall difference is 1×0.3+1×0.3+0.8×0.2=0.76>0.5, triggering a warning.

[0022] The warning and verification process involves several steps. When abnormal bicycle behavior is detected, a Level 1 warning is triggered, and a communication verification request is sent to the user's device. If movement is detected while the bicycle is locked, such as being dragged, the main identification module controls the electronic lock cylinder to enter a forced locking state. Even if the physical lock is damaged, the electronic system will still lock wheel rotation or pedal linkage. If a risk of component disassembly is detected, such as the handlebars being twisted, unnecessary power to critical sensors is cut off to prevent data loss due to malicious damage. If an abnormal unlocking attempt is detected, such as an unfamiliar fingerprint / password, the unlocking interface is temporarily frozen to prevent brute-force attacks. Simultaneously with the Level 1 warning, the main identification module initiates a communication verification request to the user's device via an encrypted communication link, the core purpose of which is to confirm whether the abnormal behavior is indeed performed by the user.

[0023] The data locking process involves a two-level warning mode. If communication verification fails or the result is abnormal, traceability data is retrieved for reliability verification. If the reliability verification passes, the traceability data is written to a one-time write-only storage unit located within the bicycle body, permanently locking the data in that unit. Only when an anomaly is confirmed as a genuine risk, and communication verification fails or an anomaly occurs, is the traceability data's authenticity and validity ensured through multi-dimensional reliability verification before being permanently locked in an immutable one-time write-only storage unit, providing irrefutable evidence for subsequent traceability and rights protection. The one-time write-only storage unit uses a miniature package embedded inside the frame, with no exposed interfaces and a rigid connection to the frame, preventing forced disassembly. It employs a one-time programmable memory chip, supporting single complete data writing. After writing, permanent read-only functionality is achieved through physical melting or logical locking, ensuring that even if the chip is disassembled, the stored data cannot be modified.

[0024] Reliability verification involves collecting current environmental and status data from multiple sets of different types of independent sensors or positioning modules as traceability data. The collected multi-source data is analyzed for consistency through a cross-comparison strategy. Based on the consistency analysis results, a reliability score for the current data is calculated, and a reliability threshold is preset. When the reliability score is higher than the reliability threshold, the traceability data is written to a one-time write storage unit.

[0025] The cross-comparison strategy includes spatial consistency verification sub-steps and physical consistency verification sub-steps. The spatial consistency verification sub-step includes: obtaining the master positioning coordinates provided by the master positioning module; based on the master positioning coordinates, querying a pre-stored expected signal environment topology map associated with the master positioning coordinates from a local or cloud database, wherein the expected signal environment topology map consists of a set of expected wireless access point identifiers and their typical signal strength relationships, and / or a set of expected cellular base station identifiers and their signal strength relationships; scanning the wireless signals in the current environment in real time to generate a real-time signal environment topology map; performing similarity matching between the real-time signal environment topology map and the expected signal environment topology map, and using the similarity as spatial consistency data; verifying whether the positioning data matches the actual wireless environment. Matching prevents GPS signal hijacking and location coordinate spoofing. During normal vehicle use, the main identification module continuously collects wireless signal data from high-frequency activity areas, such as frequently used parking spots and commuting routes, generating a topology map of the expected signal environment. It obtains the unique identifiers and typical signal strength ranges of fixed wireless access points within the area, as well as the identifiers and signal strength relationships of cellular base stations. The topology map of high-frequency areas is cached locally, while the topology map of low-frequency areas is synchronized to a cloud database for easy subsequent querying. When a secondary warning is triggered, the main identification module scans the current environment's wireless signals in real time, collecting the identifiers and real-time signal strength of surrounding WiFi / base stations, and matching them with the expected topology. Figure 1The system generates a real-time signal environment topology map in a consistent format. It then compares the signal strength relationships of devices with the same identifiers in the real-time topology map with those in the expected topology map to generate spatial consistency data. The physical consistency verification sub-step includes: continuously collecting motion trajectory data from the inertial measurement unit and environmental parameter data from environmental sensors; analyzing whether the changes in the motion trajectory and environmental parameter data conform to physical causal logic in the time series; if the motion trajectory is detected as simply a specific action of the vehicle, but the corresponding environmental parameter data does not change in accordance with the expected physical laws, or the order of changes is logically contradictory, then the physical consistency between the sensor data is determined to be invalid, and physical consistency data is generated. This verifies whether the causal logic between the motion trajectory and environmental parameters conforms to physical laws, filtering out false data generated by sensor tampering or deception; continuously collecting motion trajectory data and environmental parameter data from environmental sensors, aligning the two types of data by timestamp to form a three-dimensional data sequence of time-motion-environment; and judging the rationality of data changes based on classical physical laws and common sense about vehicle use. For example, if the vehicle is detected to be lifted, the corresponding environmental sensor should detect changes in light intensity, such as moving from indoors to outdoors or an increase in vibration frequency. If the environmental parameters do not show any expected changes, then physical consistency is determined to be invalid; if the vehicle is detected to be moving at high speed, the corresponding environmental sensor should detect changes in airflow or rapid switching of light. If the environmental parameters remain stable without fluctuation, then the motion data is determined to be false. The physical consistency results are quantified; the higher the physical consistency score, the more it conforms to physical logic.

[0026] The reliability verification also includes a spatial consistency verification sub-step. The main identification module sends an encrypted response process to the user terminal, calculates the real-time accurate distance between the user terminal and the vehicle based on the radio round-trip time, instructs the user terminal to synchronously scan the channel state information of its current wireless environment, and receives the CSI fingerprint data reported by the user terminal. At the same time, the main identification module scans the current wireless environment itself, generates local CSI fingerprint data, determines whether the real-time accurate distance is less than a first preset threshold, and performs similarity matching between the user terminal's CSI fingerprint data and the local CSI fingerprint data to determine whether the similarity is higher than a second preset threshold. If and only if the real-time accurate distance is less than the first preset threshold and the similarity is higher than the second preset threshold, it is determined that the vehicle owner is near the vehicle. Otherwise, spatial consistency data is generated. To verify whether the vehicle owner is nearby and to help determine whether abnormal behavior is performed by the user, further filtering out false positives, the main identification module sends an encrypted response command to the user terminal. The command includes a timestamp and a random checksum, and the user terminal immediately responds upon receiving it. The real-time accurate distance between the vehicle and the user terminal is calculated based on the radio round-trip time. CSI is a unique fingerprint of the wireless environment. The multipath propagation characteristics of wireless signals differ in different spatial locations, and CSI data is unique. The main identification module and the user terminal synchronously scan the current wireless environment, generating a local CSI fingerprint and a user terminal CSI fingerprint. The similarity between the two sets of CSI fingerprints is compared, and two preset thresholds are set: a first preset threshold and a second preset threshold. Only when the real-time accurate distance is less than the first preset threshold and the similarity is higher than the second preset threshold can it be determined that the vehicle is nearby; otherwise, it is determined that the vehicle owner is not nearby, generating spatial consistency data.

[0027] The traceability and verification process involves several steps. When a vehicle is sent to an authorized repair shop, a dedicated device reads the lock data written to the storage unit and compares it with data on a cloud-based digital twin platform to trace the vehicle's origin and identify any illegal modifications. Authorized repair shops must be certified by the brand owner or regulatory agency. Repair personnel first scan the visible markings on the vehicle's surface using a dedicated device. The device uploads this information to the cloud platform to verify if the vehicle is registered and has any theft records, thus preliminarily confirming the vehicle's legality and preventing invalid reads on vehicles without records. The device guides the repair personnel to locate the OTP unit based on the vehicle model and establishes a physical connection through a dedicated interface. During the connection process, the device automatically detects the physical status of the OTP unit; if the unit is damaged, a physical damage marker is recorded. The cloud-based digital twin platform serves as the storage hub for the vehicle's digital image. Its data structure must correspond to the OTP data to ensure comprehensive comparison dimensions. Through multi-dimensional comparison of OTP data and cloud data, the system achieves three main objectives: vehicle identification, theft status determination, and illegal modification identification. After the comparison is completed, the specialized equipment generates a vehicle traceability and modification appraisal report, which includes the following core information: vehicle identity confirmation results, theft status determination, details of illegal modifications, and data reliability rating.

[0028] It also includes a trajectory completion step, which obtains data records of multiple stolen vehicles. The data records include the inertial navigation data of each vehicle during the period of signal loss, the last known position before the signal loss, and the final position when the signal is restored or the vehicle is found. For each stolen vehicle, its relative motion trajectory is estimated based on its inertial navigation data, and the relative motion trajectory is matched and fitted with the road network data of the digital map to generate a predicted path from the last known position to the destination position. The predicted paths of all stolen vehicles are aggregated, and the spatiotemporal feature points on each predicted path are extracted. The spatiotemporal feature points are analyzed using a spatiotemporal clustering algorithm to identify one or more high-risk areas that are geographically clustered and temporally related. To achieve trajectory completion and risk area identification, sufficient and standardized stolen vehicle data must first be collected to ensure data comparability and usability. For each stolen vehicle, the complete path from the origin to the destination is completed based on incomplete data, with the core being a dual logic of relative trajectory estimation and road network matching. Using the last known location before signal loss as the origin, combined with inertial navigation data, the relative motion trajectory of the vehicle is calculated using a dead reckoning algorithm. Road network data between the origin and destination is retrieved, including road topology, road attributes, and non-road areas. The relative motion trajectory is matched with the actual road network, with the core being to exclude impassable paths and closely match real roads. The inferred paths of all stolen vehicles are aggregated, and common features of high-frequency stolen trajectories are mined to extract statistically significant spatiotemporal feature points. Clustering algorithms are used to mine the spatial clustering and temporal correlation of spatiotemporal feature points to accurately identify high-risk areas, with the core being a dual judgment of spatial concentration and temporal correlation. The core information of high-risk areas is output: area boundary coordinates, risk level, high-incidence time period, typical stolen paths, and the cumulative number of associated stolen vehicles, and synchronized to the cloud-based digital twin platform.

[0029] A blockchain-based digital twin anti-theft system includes: The main identification module is used to monitor the vehicle status and execute early warning logic. When the sensor data determines that the vehicle behavior is abnormal, it enters the first-level early warning mode and communicates with the user terminal for verification. When the communication verification fails or the verification result is abnormal, it enters the second-level early warning mode. A one-time write to the storage unit, when the level 2 warning mode is triggered and the reliability verification is passed, the traceability data is stored in the one-time write storage unit in an immutable manner; A digital twin platform is used to verify the legal identity and status information of vehicles; The main identification module communicates with the one-time writing unit and the digital twin platform.

[0030] The digital twin platform has an authentication policy, which includes: The multimodal authentication process involves three mandatory steps: when a user initiates the authentication dialogue, it requires continuous and complete biometric verification, password verification, and geofence-based current location verification. This confirms that the person initiating the authentication is the legitimate vehicle owner. This triple-layered verification system prevents identity theft and remote malicious operations. Biometric verification uses core biometric features stored during user registration, which are collected in real-time and compared with feature templates stored in the cloud to identify legitimate users from a physiological perspective. Password verification requires a 6-12 digit mixed password, supporting dynamic password supplementation. Geofence-based location verification uses a 5-10 meter geofence centered on the vehicle's current real-time location to verify whether the user's device is within the geofence. This verification is only successful when the user is physically near the vehicle, preventing unauthorized authentication initiated remotely.

[0031] The information collection process guides users to scan the vehicle's original identification and the identifier of the new component, and collect an image showing the new component's installed status. Details of changes to the vehicle's physical condition are collected proactively by the user, providing a complete basis for subsequent verification and ensuring the traceability of changes and the verifiability of installation status. Users are guided to scan the vehicle's original identification via the mobile app, including the laser-engraved VIN code on the chassis, the unique QR code on the main identification module, and the chip ID of core components. The purpose of this collection is to confirm that the vehicle whose identity is to be updated is a legitimate vehicle registered on the platform, avoiding the authentication of illegal vehicles. If a user needs to replace a component for repair or upgrade, they must scan the unique identifier on the new component, collecting information including the new component's model, production batch, and unique code to ensure the traceability of the new component's origin. The app provides guidance on taking images of the new component's installation status, requiring users to upload 3-5 clear photos from multiple angles, fully demonstrating the assembly relationship between the new component and the original vehicle, avoiding false declarations based solely on uploaded component photos without actual installation.

[0032] The review process involves uploading authentication information, the original identity identifier, the new component identifier, and the installed status diagram to the digital twin platform. The digital twin platform verifies the compliance of the component installation in the installed status diagram. Upon successful verification, a unique vehicle code is generated. The digital twin platform then verifies the legality of the change and the compliance of the installation through automated verification and manual review. After the review is approved, the platform generates a new unique vehicle code based on the original unique vehicle code, the hash value of the new component identifier, and the review approval timestamp. The identity update process involves sending the updated vehicle's unique code to the vehicle's main identification module for storage. Once approved, the new identity information is synchronized across the entire system, ensuring data consistency between the physical vehicle, the cloud platform, and local hardware.

[0033] The digital twin platform maintains an immutable event log associated with physical vehicles and ordered chronologically. Each event represents an operation command that changes the vehicle's state. The current state of the digital twin platform is calculated by sequentially replaying the event log. The log is designed around three main goals: trustworthiness, traceability, and reproducibility, and possesses the following core characteristics: all events are uniquely sorted by their timestamps, forming a linear event chain with no temporal discrepancies, ensuring the logical consistency of state evolution. Once generated, the log cannot be modified, deleted, or its order rearranged through any operation; even platform administrators have no authority to change it, guaranteeing data credibility. Each log entry is bound to a unique vehicle identifier, recording only the state change operation for the corresponding vehicle, ensuring a one-to-one correspondence between the log and the physical vehicle and avoiding data confusion. Each event is assigned a globally unique event ID, enabling quick location of specific operations via event ID for easy traceability and querying. Events in the log specifically refer to operation commands that change the vehicle's status, covering key behaviors throughout the vehicle's entire lifecycle from registration to scrapping. All events are stored using a standardized structure: the current vehicle status on the digital twin platform is not achieved by storing the latest status in real time, but rather by dynamically calculating it through sequential replay of event logs. The vehicle's initial state is an initial snapshot, and all subsequent state changes are recorded in the event log. During replay, starting from the initial snapshot, the operation commands for each event are executed sequentially in chronological order, and the state change results are accumulated to ultimately obtain the current vehicle status.

[0034] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principle of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A blockchain-based digital twin anti-theft method for bicycles, characterized in that, include: The monitoring process involves continuously detecting vehicle sensor data and chip data from various components using a main identification module installed on the bicycle frame, and determining whether the vehicle's behavior is abnormal through an anomaly detection strategy. The warning and verification process involves triggering a level one warning when abnormal bicycle behavior is detected, and sending a communication verification message to the user's device. In the data locking process, if communication verification fails or the communication verification result is abnormal, the system enters a level 2 warning mode and obtains traceability data for reliability verification. If the reliability verification is passed, the traceability data is written into a one-time write storage unit set in the bicycle body, and the unit is permanently locked. The traceability and verification process involves using specialized equipment to read the locked data written to the storage unit when the vehicle is sent to an authorized repair shop. This data is then compared with the data in the cloud-based digital twin platform to trace the vehicle's origin and identify any illegal modifications.

2. The blockchain-based digital twin anti-theft method according to claim 1, characterized in that, The reliability verification includes collecting current environmental and status data from multiple sets of different types of independent sensors or positioning modules as traceability data. The collected multi-source data is analyzed for consistency through a cross-comparison strategy. Based on the consistency analysis results, a reliability score for the current data is calculated, and a reliability threshold is preset. When the reliability score is higher than the reliability threshold, the traceability data is written to a one-time write storage unit.

3. The blockchain-based digital twin anti-theft method according to claim 2, characterized in that, The cross-comparison strategy includes a spatial consistency verification sub-step and a physical consistency verification sub-step. The spatial consistency verification sub-step includes: obtaining the master positioning coordinates provided by the master positioning module; based on the master positioning coordinates, querying a pre-stored expected signal environment topology map associated with the master positioning coordinates from a local or cloud database; scanning the wireless signals in the current environment in real time to generate a real-time signal environment topology map; performing similarity matching between the real-time signal environment topology map and the expected signal environment topology map; and using the similarity as spatial consistency data. The physical consistency verification sub-step includes: continuously collecting motion trajectory data from the inertial measurement unit and environmental parameter data from the environmental sensor; analyzing whether the changes between the motion trajectory and the environmental parameter data conform to physical causal logic in the time series; if it is detected that the motion trajectory is just a specific action of the vehicle, but the corresponding environmental parameter data does not change in accordance with the expected physical laws, or the change order has logical contradictions, then it is determined that the physical consistency between the sensor data has failed, and physical consistency data is generated.

4. The blockchain-based digital twin anti-theft method according to claim 1, characterized in that, The reliability verification also includes a spatial consistency verification sub-step. The main identification module sends an encrypted response process to the user terminal, calculates the real-time accurate distance between the user terminal and the vehicle based on the radio round-trip time, instructs the user terminal to synchronously scan the channel state information of its current wireless environment, and receives the CSI fingerprint data reported by the user terminal. At the same time, the main identification module scans the current wireless environment itself, generates local CSI fingerprint data, determines whether the real-time accurate distance is less than a first preset threshold, and performs similarity matching between the user terminal's CSI fingerprint data and the local CSI fingerprint data to determine whether the similarity is higher than a second preset threshold. If and only if the real-time accurate distance is less than the first preset threshold and the similarity is higher than the second preset threshold, it is determined that the vehicle owner is near the vehicle; otherwise, spatial consistency data is generated.

5. The blockchain-based digital twin anti-theft method according to claim 1, characterized in that, It also includes a trajectory completion step, which acquires data records of multiple stolen vehicles. The data records include the inertial navigation data of each vehicle during the signal loss period, the last known position before the signal loss, and the final position when the signal is restored or the vehicle is found. For each stolen vehicle, its relative motion trajectory is estimated based on its inertial navigation data, and the relative motion trajectory is matched and fitted with the road network data of the digital map to generate a predicted path from the last known position to the destination position. The predicted paths of all stolen vehicles are aggregated, and the spatiotemporal feature points on each predicted path are extracted. The spatiotemporal feature points are analyzed using a spatiotemporal clustering algorithm to identify one or more high-risk areas that are geographically clustered and temporally related.

6. The blockchain-based digital twin anti-theft method according to claim 1, characterized in that, The anomaly detection strategy includes acquiring historical usage data of bicycle users, constructing a behavioral baseline model representing the user's personalized usage habits based on the historical usage data, collecting real-time vehicle status data, comparing the real-time status data with the behavioral baseline model, obtaining the deviation degree, and triggering an early warning when the deviation degree exceeds a preset threshold.

7. The blockchain-based digital twin anti-theft method according to claim 1, characterized in that, The anomaly detection strategy also includes a component interaction sub-step, which includes deploying a sensor network on key components, continuously detecting and learning the static positional relationships and dynamic motion relationships between components, generating a key component behavior model of the bicycle, acquiring the current data of the sensor network in real time, calculating the current component relationship value, comparing the component relationship value with the baseline relationship value stored in the component behavior model, obtaining a difference score, and when the difference score exceeds a preset threshold, it is determined that the integrity of the vehicle has been compromised and an early warning is triggered.

8. A blockchain-based digital twin anti-theft system, characterized in that, include: The main identification module is used to monitor the vehicle status and execute early warning logic. When the sensor data determines that the vehicle behavior is abnormal, it enters the first-level early warning mode and communicates with the user terminal for verification. When the communication verification fails or the verification result is abnormal, it enters the second-level early warning mode. A one-time write-to-storage unit is used to store traceability data that cannot be tampered with when the secondary warning mode is triggered and the reliability verification is passed. A digital twin platform is used to verify the legal identity and status information of vehicles; The main identification module is communicatively connected to the one-time writing unit and the digital twin platform.

9. The blockchain-based digital twin anti-theft system according to claim 8, characterized in that, The digital twin platform includes an authentication policy, which comprises: The multimodal authentication process involves the user client initiating an authentication dialogue, which requires continuous and mandatory complete biometric verification, password verification, and geofence-based current location verification. The information collection process guides the user to scan the vehicle's original identification and the identifier of the new component, and collects a status diagram of the new component's installation. The review process involves uploading authentication information, original identity identifier, new component identifier, and installed status diagram to the digital twin platform. The digital twin platform verifies the compliance of component installation in the installed status diagram. Once the verification is successful, a unique vehicle code is generated. The identity update step involves sending the updated vehicle unique code to the vehicle's main identification module for storage.

10. The blockchain-based digital twin anti-theft system according to claim 8, characterized in that, The digital twin platform maintains an immutable event log associated with the physical vehicle and ordered chronologically. Each event represents an operation instruction that changes the vehicle's state. The current state of the digital twin platform is calculated by sequentially replaying the event log.

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