Bicycle GPS anti-theft and automatic alarm method and related equipment

Abstract

The invention relates to the field of bicycle anti-theft and alarm, in particular to a bicycle GPS anti-theft and automatic alarm method and related equipment. Comprising the steps of receiving a user instruction to configure working parameters, collecting and storing geographical position and physical state data of a bicycle in real time, carrying out data labeling and abnormity judgment, triggering automatic alarm and pushing information to a user if abnormity occurs, and responding to a control request to open permission. Meanwhile, the invention also relates to functions of sensor parameter configuration, remote control of an electronic lock and sound-light alarm, cluster early warning, escape route prediction, positioning precision improvement, authority management and the like, and corresponding systems, devices and storage media. The technical effects that effective theft prevention and automatic alarm are carried out on the bicycle, real-time information and control authority are provided for the user, the anomaly detection sensitivity is improved, data support is provided for regional anti-theft police deployment, and the user is assisted in tracing a safety risk source are achieved.

Description

Technical Field

[0001] This application relates to the field of vehicle anti-theft and alarm systems, and in particular to a method and related equipment for bicycle GPS anti-theft and automatic alarm systems. Background Technology

[0002] In outdoor environments, mid-to-high-end bicycles often face a higher risk of theft. With continuous technological advancements, bicycle anti-theft technology has become increasingly diversified and evolving. More and more high-tech anti-theft methods are being applied to bicycle security to combat the rampant theft. These high-tech anti-theft measures not only effectively counter the destructive actions of professional thieves but also provide users with a more convenient and secure riding experience.

[0003] Traditional mechanical anti-theft tools such as locks and chain locks are still widely used, but their security no longer fully meets the needs of modern users. To address this issue, various high-tech anti-theft products have emerged on the market, such as GPS positioning systems, smart alarm devices, and biometric technology. Some bicycle manufacturers have also innovated in design, developing bicycles with built-in anti-theft functions. Some bicycles use special materials and structural designs for their frames and wheels, making them difficult for thieves to break in a short time. Other bicycles are equipped with automatic locking systems that activate automatically when the user leaves the vehicle, further enhancing anti-theft performance. Currently, some GPS-based bicycle anti-theft systems exist on the market. These systems can track the location of bicycles using GPS technology. Current bicycle GPS anti-theft systems mainly use a mobile app, in conjunction with a GPS positioning device installed on the bicycle, to achieve vehicle location tracking.

[0004] Existing GPS-based bicycle anti-theft systems lack intelligent automatic alarm functions, and their accuracy and real-time performance in location tracking still need improvement. These systems cannot immediately trigger an alarm when a bicycle is stolen; users can only check the vehicle's status periodically via an app, making it difficult to promptly detect theft and potentially leading to delayed responses, thus increasing the difficulty of recovering the bicycle. Summary of the Invention

[0005] The purpose of this application is to overcome the above-mentioned technical problems and provide a bicycle GPS anti-theft and automatic alarm method and related equipment.

[0006] The purpose of this application is to provide a GPS-based anti-theft and automatic alarm method for bicycles, including: Receive system deployment instructions sent by users through terminal devices, and configure the operating parameters of the GPS positioning module and status detection sensor on the bicycle, including positioning frequency, data acquisition cycle and transmission protocol; The system receives real-time bicycle location information from the GPS positioning module and physical status data from the status detection sensor via a wireless communication interface. The received bicycle location information and physical status data are stored in a cloud database, and the data is labeled according to the preset data classification rules; The system calls a preset alarm judgment algorithm, combines preset alarm thresholds and allowed movement ranges, analyzes the labeled data, and determines whether the bicycle has engaged in illegal movement or vandalism. If an anomaly is detected, an automatic alarm mechanism is immediately triggered, sending an alarm message containing the anomaly type and real-time geographic location information to the user terminal via SMS, telephone, or application push. It receives control requests sent by users through terminal devices, responds to the requests and grants control permissions to users, allowing them to view the bicycle's real-time geographical location information, historical trajectory and abnormal details, and executes user-initiated instructions to remotely start or stop the automatic alarm mechanism.

[0007] By adopting the above technical solutions, the adaptability and efficiency of data collection can be ensured through precise configuration of the working parameters of the GPS positioning module and status detection sensor. Real-time transmission of bicycle geographical location information and physical status data is achieved through the wireless communication interface. The storage and classification labeling functions of the cloud database ensure the standardization of data management. Furthermore, the combination of alarm judgment algorithms with preset thresholds and allowed movement ranges significantly improves the accuracy of judging illegal movement or destructive behavior, effectively reducing false alarms and missed alarms. When an anomaly is detected, the multi-channel automatic alarm mechanism allows users to receive alarm prompts containing the anomaly type and real-time geographical location information immediately, avoiding increased difficulty in vehicle retrieval due to information delays. Opening remote control permissions allows users to conveniently view vehicle-related information in real time and flexibly operate the automatic alarm mechanism. Overall, this achieves intelligent, precise, and convenient bicycle anti-theft alarms, significantly improving the reliability of anti-theft protection, reducing the risk of vehicle theft, and providing users with a more secure and worry-free user experience.

[0008] In one possible implementation, this application includes the following steps: Configure the operating parameters of the state detection sensor group, which includes a vibration sensor, a pressure sensor, and a tilt sensor, and set the vibration amplitude threshold, the lock pressure threshold, and the vehicle body tilt angle threshold, respectively; It receives vibration amplitude data collected by vibration sensors, lock pressure data collected by pressure sensors, and vehicle tilt angle data collected by tilt sensors in real time. The received multi-dimensional sensor data is fused with the bicycle's geographical location information collected by the GPS positioning module to generate a comprehensive analysis dataset; The integrated analysis dataset is compared with the historical anomaly data sample library stored in the cloud database. The preset thresholds of each sensor in the state detection sensor group are dynamically optimized through machine learning algorithms to achieve adaptive adjustment of the thresholds. Each data point in the comprehensive analysis dataset is compared with its corresponding preset threshold. When any data point exceeds the preset threshold, it is determined to be in an abnormal state.

[0009] By adopting the above technical solution, and configuring a state detection sensor group including vibration sensors, pressure sensors, and tilt sensors, and setting targeted thresholds for each, the system can capture changes in the physical state of the bicycle, such as vibration, lock pressure, and vehicle tilt, from multiple dimensions and in all directions, avoiding the limitations of single-sensor monitoring. By fusing multi-dimensional sensor data with the bicycle's geographical location information collected by the GPS positioning module, the system can achieve correlation analysis between physical state and spatial location, making anomaly detection more evidence-based. By comparing the data with a historical anomaly data sample library in the cloud database and dynamically optimizing the preset thresholds of each sensor using machine learning algorithms, the thresholds can be adapted to different usage scenarios and environmental changes, effectively reducing false alarms and missed alarms caused by fixed thresholds. Finally, through precise comparison of various data with corresponding preset thresholds, the system can quickly and accurately determine abnormal states, further improving the sensitivity and reliability of the bicycle anti-theft alarm and providing more comprehensive and accurate protection for vehicle safety.

[0010] In one possible implementation, this application includes the following steps: Receive remote control commands sent by the user through a terminal device, the commands including the linkage control requirements of the bicycle's built-in electronic lock; Verify the identity and permissions of remote control commands to confirm their legitimacy; An electromagnetic locking control signal is sent to the bicycle's built-in electronic lock. Upon receiving the signal, the bicycle's built-in electronic lock activates the electromagnetic locking mechanism, restricting wheel rotation and unlocking the lock. While sending the electromagnetic lock control signal, the built-in sound and light alarm of the bicycle is triggered to emit a high-frequency warning sound and flashing light. The alarm frequency of the built-in sound and light alarm is positively correlated with the real-time moving speed of the stolen bicycle. The faster the speed, the higher the warning intensity. Record detailed information about remote control operations, including operation time, operation content, user identification, and the activation status of the bicycle's built-in audible and visual alarm. Synchronize the operation records to the blockchain node to form an immutable operation log.

[0011] By adopting the above technical solution, and by receiving remote control commands from the user terminal that include the linkage control requirements of the bicycle's built-in electronic lock, and ensuring the legitimacy of the commands through identity verification and authorization checks, malicious manipulation of the vehicle by unauthorized commands can be effectively prevented, ensuring the security of control operations. Sending an electromagnetic locking control signal to the bicycle's built-in electronic lock can directly restrict wheel rotation and lock unlocking by activating the electromagnetic locking structure, physically blocking the thief's intention to move the vehicle and improving the immediate effectiveness of anti-theft measures. Simultaneously triggered, the bicycle's built-in audible and visual alarm emits a warning sound and flashes at a frequency positively correlated with the real-time movement speed of the stolen vehicle. The faster the lights move, the stronger the warning intensity, which can not only strongly deter thieves and force them to abandon their crimes, but also quickly attract the attention of people in the vicinity and seek on-site assistance. Meanwhile, the detailed information of remote control operations is synchronized to the blockchain node to form an immutable operation log, which can completely preserve the operation traces and provide a reliable basis for subsequent dispute resolution and liability determination. Overall, it realizes the security of remote control, the linkage of anti-theft measures, the targeted warning effect, and the traceability of operation records, which further enhances the comprehensiveness and reliability of bicycle anti-theft protection, minimizes the risk of bicycle theft, and provides assistance in the recovery of bicycles.

[0012] In one possible implementation, this application includes the following steps: Receive automatic alarm trigger signals and bicycle geographical location information from multiple bicycle terminals within the same management area, and count the number of alarms per unit time. Determine whether the number of alarms counted exceeds the preset cluster warning threshold. If it does, mark the area as a high-risk area. Send cluster warning information to the administrator terminal of the high-risk area, and push risk warnings to the bicycle user terminals within 500 meters of the high-risk area that have not triggered the automatic alarm mechanism; Real-time alarm data and bicycle distribution density data of high-risk areas are synchronized to the urban public safety management platform to provide data support for the deployment of anti-theft police forces in the area. At the same time, the intelligent monitoring equipment around the high-risk area is linked to automatically focus on the area. The alarm threshold for bicycles that have not triggered the automatic alarm mechanism in the vicinity of high-risk areas is automatically tightened, thereby improving the sensitivity of anomaly detection and continuously monitoring the status of bicycles in the area.

[0013] By adopting the above technical solution, and by receiving automatic alarm trigger signals and geographical location information from multiple bicycle terminals within the same management area, the number of alarms per unit time is counted and high-risk areas are marked accordingly. This upgrades individual alarms to regional risk warnings, enabling accurate identification of high-incidence theft areas. Sending cluster warning information to administrators of high-risk areas and pushing risk alerts to nearby users who have not yet alarmed can prompt administrators to intervene in prevention and control in a timely manner and remind users to strengthen their precautions, forming a multi-party linkage early warning and response system. Synchronizing real-time data of high-risk areas to the city's public safety management platform and linking surrounding intelligent monitoring equipment to focus on the area can provide data support for police deployment, improve the targeting of on-site monitoring, and strengthen the efficient allocation of public safety resources. Automatically tightening the alarm threshold of nearby bicycles that have not yet alarmed to improve the sensitivity of anomaly detection can enhance the foresight and initiative of overall protection within the area, effectively preventing subsequent theft. Overall, through individual data aggregation, regional risk linkage, public resource collaboration, and dynamic adjustment of protection strategies, a full-chain regional anti-theft system is constructed, from risk identification to multi-party response to proactive prevention and control. This significantly reduces the overall risk of bicycle theft within the area and improves the systematicness and efficiency of anti-theft management.

[0014] In one possible implementation, this application includes the following steps: After the automatic alarm mechanism is triggered, the city's traffic network database and historical theft trajectory database are accessed to obtain relevant reference data; By using big data analysis algorithms, the real-time geographical location information, speed of movement, and reference data from urban traffic network databases and historical theft trajectory databases of stolen bicycles are comprehensively calculated to predict possible escape routes. Select IoT devices such as shared charging stations and smart stations along the escape route as temporary tracking nodes, update the timestamps of the stolen bicycles passing through IoT device nodes in real time, and dynamically correct the tracking path. Filter the monitoring point information along the escape route, combine real-time traffic data and dynamically corrected tracking path, and mark the optimal tracking path; The predicted escape route, information on monitoring points along the way, temporary tracking node data, and the optimal tracking path are synchronized to the user's terminal to provide tracking guidance for the user.

[0015] By adopting the above technical solutions, after triggering the automatic alarm mechanism, reference data is obtained by calling the urban traffic network database and historical stolen trajectory database, providing a rich foundation for predicting escape routes. Combined with big data analysis algorithms to comprehensively calculate the real-time geographical location information and movement speed of the stolen bicycle, the scientific accuracy of escape route prediction can be significantly improved, avoiding blind tracking by users. Using IoT devices such as shared charging piles and smart stations as temporary tracking nodes and updating timestamps in real time enables dynamic correction of the tracking path, effectively compensating for potential deviations in single positioning methods and ensuring the accuracy of the tracking direction. Filtering monitoring point information along the route and combining it with real-time traffic conditions to mark the optimal tracking path provides users with clear and feasible action guidance, reducing the difficulty and time cost of tracking. Finally, various key information is synchronized to the user's terminal, allowing users to fully grasp the dynamics of the stolen vehicle and quickly take targeted actions, significantly improving the efficiency and success rate of recovering stolen bicycles and providing strong protection for users to reduce property losses.

[0016] In one possible implementation, this application includes the following steps: Real-time monitoring of satellite signal strength of GPS positioning module to determine whether signal strength meets preset accuracy requirements; If the satellite signal strength of the GPS positioning module is lower than the preset value, the Beidou positioning module will be automatically activated to receive the positioning data collected by the Beidou positioning module and perform dual-mode fusion with the positioning data collected by the GPS positioning module. If dual-mode positioning still cannot meet the accuracy requirements, a signal relay request is sent to nearby connected IoT devices to receive position calibration data fed back by the IoT devices. By fusing and calculating the dual-mode positioning data from the GPS positioning module and the Beidou positioning module, as well as the location calibration data fed back by the IoT device, the positioning accuracy is improved to within 5 meters, and the precise geographical location information of the bicycle is updated. The updated precise geographical location information of the bicycle is synchronized to the shared bicycle control system around the stolen bicycle, triggering the shared bicycle control system to perform a temporary locking function on the shared bicycles it manages, forming a local enclosure area without affecting public passage and slowing down the movement speed of the stolen bicycle.

[0017] By adopting the above technical solution, and by monitoring the satellite signal strength of the GPS positioning module in real time and dynamically activating the Beidou positioning module for dual-mode fusion, the positioning failure problem caused by the weak signal of a single positioning module is effectively avoided. Combined with the signal relay of nearby IoT devices and the fusion calculation of location calibration data, the positioning accuracy has been successfully and stably improved to within 5 meters. This ensures the continuous acquisition of accurate geographical location information of bicycles in complex environments and completely solves the pain point of insufficient accuracy of traditional positioning technology in scenarios such as obstruction and signal blind spots. At the same time, the accurate geographical location information is synchronized to the surrounding shared bicycle control system, and the temporary locking function of shared bicycles is triggered to form a local enclosure area. Without affecting public traffic, the movement speed of stolen bicycles is effectively slowed down, which buys valuable time for users to track and for the police to intervene. This significantly improves the probability of recovering stolen vehicles. Overall, it achieves multiple improvements in positioning reliability, environmental adaptability and anti-theft interception effect, making anti-theft protection more practical and effective.

[0018] In one possible implementation, this application includes the following steps: Receive user-defined permission management rules, including temporary authorization objects, authorization duration, and operation permission scope; When a temporarily authorized object initiates a device access request, the identity information and authorization validity period of the temporarily authorized object are verified, and the corresponding operation permissions are granted after confirming that it meets the authorization rules. Real-time monitoring of the operation behavior of temporarily authorized objects; if an operation exceeding the authorized scope is detected, the access rights of the temporarily authorized object are immediately terminated and an abnormal operation alert is sent to the original user. When generating permission usage reports, embed correlation data analysis of abnormal operations of temporary authorized objects, including the geographical location of the operation, device terminal information, and cross-validation with the abnormal status records of bicycles, to help users trace the source of security risks; When the authorization period expires, the authorization permissions of the temporary authorized object will be automatically revoked, and the permission usage report containing the cross-validation results will be synchronized to the original user terminal.

[0019] By adopting the above technical solution, and receiving user-defined permission management rules that include temporary authorized objects, authorization duration, and operation permission scope, precise and refined control over device access permissions can be achieved, avoiding security risks caused by excessive permission granting. Strict verification of the identity information and authorization validity period of temporary authorized objects ensures the legitimacy of access requests, blocking unauthorized access at the source. Real-time monitoring of operational behavior and timely termination of operations exceeding the authorization scope, along with reminders sent to the original user, enables rapid response to security risks and reduces potential losses. Embedding abnormal operation correlation data analysis in the permission usage report and cross-validating it with bicycle abnormal status records provides users with clear evidence to trace the source of security risks, assisting in subsequent optimization of permission management strategies. Automatic revocation of permissions upon expiration, along with a report containing cross-validation results, allows users to have full control over permission usage without manual intervention. This improves the convenience of permission management and further strengthens the overall security of the anti-theft system, ensuring users' absolute control over bicycle anti-theft devices and preventing anti-theft function failure or vehicle security risks due to oversights in permission management.

[0020] The second objective of this application is to provide a bicycle GPS anti-theft and automatic alarm system, which includes: Command receiving and parameter configuration module: used to receive system deployment commands sent by users through terminal devices, and configure the working parameters of the GPS positioning module and status detection sensor on the bicycle. The working parameters include positioning frequency, data acquisition cycle and transmission protocol. Data acquisition and transmission module: used to receive bicycle geographical location information collected by GPS positioning module and physical status data collected by status detection sensor in real time through wireless communication interface; Data storage and labeling module: This module stores the received bicycle geographic location information and physical status data in a cloud database and labels the data according to preset data classification rules. Anomaly Detection Module: Used to call the preset alarm detection algorithm, combine the preset alarm threshold and allowable movement range, analyze the labeled data, and determine whether the bicycle has engaged in illegal movement or vandalism. Automatic alarm and information push module: If an anomaly is detected, the automatic alarm mechanism will be triggered immediately, and alarm information containing the anomaly type and real-time geographical location information will be sent to the user terminal via SMS, telephone or application push. Remote control response module: This module receives control requests sent by users through terminal devices, responds to the requests, and grants control permissions to users, allowing them to view the bicycle's real-time geographical location information, historical trajectory, and anomaly details. It also executes user-initiated commands to remotely start or stop the automatic alarm mechanism.

[0021] By adopting the above technical solutions, the adaptability and efficiency of data collection can be ensured through precise configuration of the working parameters of the GPS positioning module and status detection sensor. Real-time transmission of bicycle geographical location information and physical status data is achieved through the wireless communication interface. The storage and classification labeling functions of the cloud database ensure the standardization of data management. Furthermore, the combination of alarm judgment algorithms with preset thresholds and allowed movement ranges significantly improves the accuracy of judging illegal movement or destructive behavior, effectively reducing false alarms and missed alarms. When an anomaly is detected, the multi-channel automatic alarm mechanism allows users to receive alarm prompts containing the anomaly type and real-time geographical location information immediately, avoiding increased difficulty in vehicle retrieval due to information delays. Opening remote control permissions allows users to conveniently view vehicle-related information in real time and flexibly operate the automatic alarm mechanism. Overall, this achieves intelligent, precise, and convenient bicycle anti-theft alarms, significantly improving the reliability of anti-theft protection, reducing the risk of vehicle theft, and providing users with a more secure and worry-free user experience.

[0022] The third objective of this application is to provide a bicycle GPS anti-theft and automatic alarm device, which includes: The system includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed to perform the aforementioned bicycle GPS anti-theft and automatic alarm method.

[0023] The fourth objective of this application is to provide a storage medium.

[0024] The fourth objective of this application is achieved through the following technical solution: A storage medium storing a computer program capable of being loaded by a processor and executing the aforementioned bicycle GPS anti-theft and automatic alarm method.

[0025] In summary, this application includes at least one of the following beneficial technical effects: 1. By precisely configuring the operating parameters of the GPS positioning module and status detection sensors, the system ensures the adaptability and efficiency of data collection. Relying on a wireless communication interface, it achieves real-time transmission of bicycle geographical location information and physical status data. Combined with cloud database storage and classification labeling functions, it ensures the standardization of data management. Furthermore, by combining alarm judgment algorithms with preset thresholds and allowed movement ranges, it significantly improves the accuracy of judging illegal movement or destructive behavior, effectively reducing false alarms and missed alarms. When an anomaly is detected, a multi-channel automatic alarm mechanism allows users to receive alarm prompts containing the anomaly type and real-time geographical location information immediately, avoiding increased difficulty in vehicle retrieval due to information delays. Opening remote control permissions allows users to conveniently view vehicle-related information in real time and flexibly operate the automatic alarm mechanism. Overall, it realizes intelligent, precise, and convenient bicycle anti-theft alarms, significantly improving the reliability of anti-theft protection, reducing the risk of vehicle theft, and bringing users a more secure and worry-free user experience.

[0026] 2. By receiving automatic alarm trigger signals and geographical location information from multiple bicycle terminals within the same management area, the system counts the number of alarms per unit time and marks high-risk areas accordingly. This upgrades individual alarms to regional risk warnings, enabling accurate identification of high-incidence theft areas. Sending cluster warning information to administrators in high-risk areas and pushing risk alerts to nearby users who have not yet reported an alarm can prompt administrators to intervene in prevention and control in a timely manner and remind users to strengthen their precautions, forming a multi-party collaborative early warning and response system. Synchronizing real-time data from high-risk areas to the city's public safety management platform and linking surrounding intelligent monitoring equipment to focus on the area can provide data support for police deployment, improve the targeting of on-site monitoring, and enhance the efficient allocation of public safety resources. Automatically tightening the alarm threshold of nearby bicycles that have not yet reported an alarm can improve the sensitivity of anomaly detection, enhancing the foresight and initiative of overall protection within the area and effectively preventing subsequent theft. Overall, through individual data aggregation, regional risk linkage, public resource collaboration, and dynamic adjustment of protection strategies, a full-chain regional anti-theft system is constructed, from risk identification to multi-party response to proactive prevention and control. This significantly reduces the overall risk of bicycle theft within the area and improves the systematicness and efficiency of anti-theft management. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating a bicycle GPS anti-theft and automatic alarm method provided in an embodiment of this application; Figure 2 This is a virtual structural diagram of a bicycle GPS anti-theft and automatic alarm system provided in an embodiment of this application. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0029] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0030] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0031] This application provides a bicycle GPS anti-theft and automatic alarm method, referring to... Figure 1 The main process of the method is described as follows: S1: Receive system deployment instructions sent by the user through the terminal device, and configure the working parameters of the GPS positioning module and status detection sensor on the bicycle. The working parameters include positioning frequency, data acquisition cycle and transmission protocol. The user terminal device is a smartphone, tablet, or other mobile device with a corresponding control APP installed. After the user fills in or selects preset deployment parameters through the APP interface, they send a system deployment command. The command is transmitted to the cloud server via the mobile network (4G / 5G), and then the cloud server sends it to the main control module on the bicycle via a wireless communication link. The GPS positioning module uses the Ublox NEO-7M high-performance positioning module, which supports GPS / GLONASS dual-mode positioning. The status detection sensor uses an integrated microelectromechanical system (MEMS) sensor, which can simultaneously collect physical state data such as vibration and pressure. The specific working parameters are configured as follows: the positioning frequency is set to 1 time / second by default (the user can adjust it within the range of 0.5-5 times / second through the APP), the data acquisition period is linked to the positioning frequency and set to 1000ms (corresponding to a positioning frequency of 1 time / second), and the transmission protocol adopts the TCP / IP protocol to ensure the reliability of data transmission. After receiving the command, the main control module writes the working parameters into the configuration registers of the GPS positioning module and the status detection sensor through the I2C communication interface to complete the device initialization.

[0032] S2: Receives real-time bicycle location information from the GPS positioning module and physical status data from the status detection sensor via the wireless communication interface; The wireless communication interface uses the SIM800C 4G communication module, which supports full network access and enables real-time bidirectional data transmission with a communication rate of no less than 1Mbps, ensuring data transmission latency is controlled within 500ms. The GPS positioning module receives satellite signals in real time according to the positioning frequency configured in S1, calculates the bicycle's latitude and longitude coordinates (accuracy ±3 meters), positioning timestamp, and other geographical location information, and transmits it to the main control module via the UART interface. The status detection sensor collects the bicycle's physical status data according to the data acquisition cycle, including the pressure value of the lock (unit: N) and the overall vibration amplitude of the bicycle (unit: m / s²), and transmits it to the main control module via the I2C interface. The main control module performs preliminary verification on the two types of data (verifying the integrity of the data format and the rationality of the values), removes invalid data, and temporarily stores it in the local cache (cache capacity no less than 16KB), waiting for batch transmission or real-time push.

[0033] S3: Store the received bicycle location information and physical status data in the cloud database, and complete the data labeling according to the preset data classification rules; The cloud-based database uses Alibaba Cloud RDS MySQL relational database, supporting high-concurrency read / write and persistent data storage. Data transmission is encrypted using the AES-256 encryption algorithm to prevent theft or tampering. Pre-defined data classification rules are based on data type and collection dimensions: bicycle geographic location information is labeled with "Geographic Data - Device ID - Collection Timestamp" (e.g., "Geographic Data - DEVICE - 2025XXXX-XXXXXX"), and physical status data is labeled with "Physical Data - Sensor Type - Collection Timestamp" (e.g., "Physical Data - Pressure Sensor - 2025XXXX-XXXXXX"). The device ID in the label is the unique identifier for the bicycle anti-theft system, and the collection timestamp is accurate to the second. The main control module uploads the tagged data to the cloud database in batches via a wireless communication interface. Each batch of data does not exceed 1KB. An automatic retransmission mechanism is triggered in case of upload failure, with a maximum of three retransmissions to ensure no data loss.

[0034] S4: Call the preset alarm judgment algorithm, combine the preset alarm threshold and allowable movement range, analyze the marked data, and determine whether the bicycle has engaged in illegal movement or vandalism. The preset alarm judgment algorithm is an embedded algorithm based on logical threshold comparison, stored in the Flash memory of the main control module. When invoked, the main control module retrieves labeled historical and real-time data from the cloud database for analysis. The preset alarm thresholds are set by the user through the APP or use the system default values, specifically including: vibration amplitude threshold of 5m / s² (exceeding this value indicates possible destructive behavior) and lock pressure threshold of 100N (exceeding this value indicates possible lock picking). The allowed movement range is defined by the user on the electronic map through the APP, with the default setting being a circular area with a radius of 5 meters centered on the parking location. The user can adjust the size of the range (1-20 meters) according to the actual parking scenario. The algorithm analysis process is as follows: First, the real-time geographical location information is extracted and compared with the initial geographical location information at the time of parking to determine whether it exceeds the allowed movement range; then, the real-time physical state data is extracted and compared with the preset alarm thresholds to determine whether there is abnormal data exceeding the threshold; if either the "exceeding the allowed movement range" or "physical state data exceeding the alarm threshold" condition is met, the bicycle is determined to have engaged in illegal movement or destructive behavior; otherwise, it is determined to be in a normal state.

[0035] S5: If an anomaly is detected, an automatic alarm mechanism is immediately triggered, and an alarm message containing the anomaly type and real-time geographical location information is sent to the user terminal via SMS, telephone, or application push. The automatic alarm mechanism is triggered by the main control module, which simultaneously activates three alarm information push channels to ensure timely receipt by the user. SMS push is implemented through the GSM function of the 4G communication module, calling the operator's SMS service interface to send an SMS message with the format: "[Bicycle Anti-theft Alarm] Your bicycle (Device ID: XXX) has experienced an anomaly at YYYY-MM-DDHH:MM:SS, anomaly type: XXX (illegal movement / lock damage / vehicle vibration), real-time location: XX City XX District XX Road (latitude and longitude: XXX.XXXX, XXX.XXXX), please handle it promptly!" Telephone push is implemented through the VoIP function of the 4G communication module, automatically dialing the user's preset emergency contact numbers (up to 3 can be set), and playing a voice alarm prompt upon connection (the voice content is consistent with the core information of the SMS). Application push is implemented through the JPush service, sending a pop-up notification to the user's terminal APP. Clicking the notification directly jumps to the APP's real-time monitoring interface to view detailed information. All three push channels are triggered simultaneously. If a push fails on one channel, the main control module will re-trigger that channel after 1 minute to ensure the user receives the alarm information.

[0036] S6: Receives control requests sent by users through terminal devices, responds to the requests and grants control permissions to users, allowing users to view the bicycle's real-time geographical location information, historical trajectory and abnormal details, and executes user-initiated instructions to remotely start or stop the automatic alarm mechanism.

[0037] Users send control requests through the control interface of their terminal device's app. Request types include information viewing requests and function control requests. These requests are transmitted to the cloud server via the mobile network and then forwarded to the main control module. Upon receiving a request, the main control module first verifies the user's identity (verifying the app login account's binding relationship with the device). After successful verification, the module grants the user the corresponding control permissions: Information viewing permissions allow users to access the bicycle's real-time geographical location information (updated at the same frequency as the location tracking), historical riding trajectory for the past 30 days (supporting playback on an electronic map), and anomaly details (including the time, type, and physical status and geographical location data at the time of the anomaly). Function control permissions allow users to initiate commands to remotely activate or deactivate the automatic alarm mechanism. After the command is issued, the main control module immediately executes the corresponding operation and sends feedback to the user's terminal app via the wireless communication interface ("Alarm mechanism activated" or "Alarm mechanism deactivated"). Control permissions are available for 24 hours after the user initiates the request; after this period, permissions are automatically deactivated, requiring a new request to regain them, ensuring device security.

[0038] Specifically, in some possible embodiments, the following steps are included: Configure the operating parameters of the state detection sensor group, which includes a vibration sensor, a pressure sensor, and a tilt sensor, and set the vibration amplitude threshold, the lock pressure threshold, and the vehicle body tilt angle threshold, respectively; It receives vibration amplitude data collected by vibration sensors, lock pressure data collected by pressure sensors, and vehicle tilt angle data collected by tilt sensors in real time. The received multi-dimensional sensor data is fused with the bicycle's geographical location information collected by the GPS positioning module to generate a comprehensive analysis dataset; The integrated analysis dataset is compared with the historical anomaly data sample library stored in the cloud database. The preset thresholds of each sensor in the state detection sensor group are dynamically optimized through machine learning algorithms to achieve adaptive adjustment of the thresholds. Each data point in the comprehensive analysis dataset is compared with its corresponding preset threshold. When any data point exceeds the preset threshold, it is determined to be in an abnormal state.

[0039] When configuring the operating parameters of the status detection sensor group, the status detection sensor group uses an integrated sensing module (model: ADXL345 vibration sensor + MPX5700 pressure sensor + MPU6050 tilt sensor). Users can configure the parameters via a terminal APP or the system default settings. The vibration amplitude threshold is set to 3-8 m / s² by default (customizable range 1-15 m / s² is supported), the lock pressure threshold is set to 80-150 N by default (adjustable range 50-300 N is supported), and the vehicle body tilt angle threshold is set to 30°-60° by default (calibrated range 15°-90° is supported). The main control module communicates via the SPI interface. Threshold parameters are written to the configuration registers of each sensor to solidify the parameters. During the real-time data reception phase, each sensor synchronously collects data according to the data acquisition cycle set in weight 1 (default 1000ms). The vibration sensor outputs analog vibration amplitude data (accuracy ±0.01m / s²), the pressure sensor collects pressure data at the lock cylinder (accuracy ±1N), and the tilt sensor outputs the vehicle body X / Y axis tilt angle data (accuracy ±0.1°). The data is transmitted to the main control module in real time via the I2C bus, with CRC verification used during transmission to ensure data integrity. During data fusion processing, the main control module first timestamps the multi-dimensional sensor data (based on GPS positioning). Using the timestamp of the positioning module as a reference, the vibration amplitude data, lock pressure data, and vehicle tilt angle data are then concatenated with the bicycle's geographical location information, such as latitude and longitude and positioning timestamp collected by the GPS positioning module, in the format of "timestamp-geographic data-sensor data" to generate a structured comprehensive analysis dataset (each dataset is no more than 256 bytes in size). In the dynamic threshold optimization stage, the main control module extracts similar scenario data from the historical abnormal data sample library in the cloud database (containing data on theft, false alarms, and normal use scenarios from the past year, with a sample size of no less than 100,000) through the wireless communication interface. The gradient descent machine learning algorithm is then used to compare the current comprehensive analysis dataset with the historical data. The sample performs feature comparison and calculates the optimal adaptation value of each sensor threshold. For example, in construction areas with frequent vibration, the vibration amplitude threshold is automatically increased by 20%, and in densely populated commercial areas, the lock pressure threshold is automatically decreased by 15%, achieving adaptive adjustment of the threshold. In the anomaly judgment stage, the main control module compares the vibration amplitude, lock pressure, and vehicle tilt angle data in the comprehensive analysis dataset with the corresponding dynamically optimized preset thresholds one by one. If any data exceeds the threshold range (such as vibration amplitude reaching 9m / s², lock pressure reaching 180N, or vehicle tilt angle reaching 65°), it is directly judged as an abnormal state, and an abnormal trigger signal is output to the anomaly judgment module.

[0040] In the data fusion processing stage, a Kalman filter algorithm is introduced to reduce noise in multi-dimensional sensor data and bicycle geographic location information, eliminating invalid data caused by environmental interference (such as wind and minor collisions), thereby improving the signal-to-noise ratio of the comprehensive analysis dataset by more than 30%. The historical anomaly data sample library can be classified by scene (such as outdoor parking lots, residential garages, commercial streets, etc.). When the machine learning algorithm dynamically optimizes the threshold, it prioritizes matching the historical sample data of the current bicycle scene, improving the adaptability of threshold adjustment and effectively reducing the false alarm rate under different scenes. In the anomaly judgment stage, a data duration judgment logic is added. For example, if the vibration amplitude exceeds the threshold, it must last for more than 2 seconds, and if the bicycle tilt angle exceeds the threshold, it must last for more than 3 seconds before it is judged as an anomaly, avoiding false triggers caused by instantaneous interference and further improving the accuracy of anomaly judgment. At the same time, the threshold adjustment records of each sensor and the fusion process data of the comprehensive analysis dataset are synchronously stored in the cloud database, forming a full-link traceability log of threshold optimization, providing data support for subsequent algorithm iteration and scene adaptation, enabling the system to continuously optimize the anomaly judgment accuracy during long-term use, adapt to more complex usage scenarios, and significantly improve stability and intelligence.

[0041] Specifically, in some possible embodiments, the following steps are included: Receive remote control commands sent by the user through a terminal device, the commands including the linkage control requirements of the bicycle's built-in electronic lock; Verify the identity and permissions of remote control commands to confirm their legitimacy; An electromagnetic locking control signal is sent to the bicycle's built-in electronic lock. Upon receiving the signal, the bicycle's built-in electronic lock activates the electromagnetic locking mechanism, restricting wheel rotation and unlocking the lock. While sending the electromagnetic lock control signal, the built-in sound and light alarm of the bicycle is triggered to emit a high-frequency warning sound and flashing light. The alarm frequency of the built-in sound and light alarm is positively correlated with the real-time moving speed of the stolen bicycle. The faster the speed, the higher the warning intensity. Record detailed information about remote control operations, including operation time, operation content, user identification, and the activation status of the bicycle's built-in audible and visual alarm. Synchronize the operation records to the blockchain node to form an immutable operation log.

[0042] When receiving remote control commands sent by users through their terminal devices, the user terminal is a smartphone with an encrypted control app installed. The commands are transmitted to the cloud control center via HTTPS encryption. The command data packet includes the command type (e.g., "emergency lock" or "unlock"), the bicycle's unique device ID, the user account identifier, and the bicycle's built-in electronic lock linkage control requirements (e.g., "lock wheels + activate alarm"). During the identity verification and permission check phase, the cloud control center first verifies the binding relationship between the user account and the device ID (by comparing it against a preset binding list), and then completes secondary identity verification through a dynamic verification code (sent to the user's pre-registered mobile phone number) or biometrics (e.g., fingerprint or facial recognition, collected by the terminal app and transmitted in encryption). Simultaneously, it verifies whether the user has remote control permissions (e.g., whether they are the device's main user or have obtained temporary control authorization). After successful verification, a command validity identifier (16-bit encrypted verification code) is generated and sent to the bicycle's main control module. When sending an electromagnetic lock control signal to the bicycle's built-in electronic lock, the main control module transmits a 12V DC control signal to the electronic lock (model: EML-200 electromagnetic lock, response time ≤0.5s) via the CAN bus. When the electromagnetic coil is energized, it generates magnetic force, driving the latch to pop out and engage with the gap between the wheel spokes (limiting the wheel rotation angle to ≤5°). Simultaneously, the electromagnetic pin inside the lock cylinder locks the mechanical structure of the lock, preventing both the physical key and electronic unlocking commands from triggering unlocking. When the bicycle's built-in audible and visual alarm is triggered, the alarm (including a 120dB high-frequency buzzer and a 100lm LED strobe light) receives the PWM control signal from the main control module. The buzzer's initial frequency is 2kHz, and the LED flashing frequency is 2 times / second. The main control module calculates the bicycle's real-time speed using the GPS positioning module (sampling frequency 1 time / second). When the speed ≥ At a speed of 1 m / s, for every 0.5 m / s increase, the buzzer frequency increases by 0.5 kHz and the LED flashing frequency increases by 1 time per second (maximum frequency 5 kHz, flashing frequency 10 times per second), achieving a positive correlation between warning intensity and movement speed. When recording remote control operation information, the main control module generates an operation log in the format of "operation time (accurate to milliseconds) - operation content (e.g., "electromagnetic lock + audible and visual alarm activation") - user identification (de-identified account ID) - alarm status (frequency value, activation duration)". This log is synchronized to the Hyperledger Fabric consortium chain nodes (including user terminal, cloud server, and third-party evidence storage nodes, totaling 3 consensus nodes) through the blockchain SDK interface. After node consensus verification, the log is written into a block, forming an immutable operation record. Users can verify the authenticity of the log by querying the blockchain evidence storage hash value through the APP.

[0043] The identity verification process incorporates "geographical location credibility verification," combining the distance between the user's terminal GPS location and the bicycle's real-time location (e.g., increasing the verification pass rate weight when the distance is ≤500 meters) to prevent malicious control due to remote account theft. The bicycle's built-in electronic lock adopts an "electromagnetic + mechanical" dual-redundant locking structure. In the event of an electromagnetic lock failure, the spring-driven mechanical locking tongue is automatically triggered to ensure that the vehicle's movement is still restricted in extreme situations. At the same time, the lock integrates a pressure sensor, which automatically triggers a locking reinforcement command (increasing the electromagnetic force to 150% of the rated value) when it detects forced entry (such as sawing or hammering). The audible and visual alarm adds a "directional warning" function, which identifies the location of the thief through a microphone array and controls the LED lights to flash directionally and the buzzer to focus. The device emits a focused sound (directional angle ≤30°) to deter car thieves while minimizing interference with unrelated personnel. The blockchain operation log embeds an "environmental data watermark," simultaneously recording bicycle vibration data and ambient noise levels (collected by the built-in microphone) during operation, forming multi-dimensional cross-verified evidence to provide contextualized evidence for subsequent police investigations. The remote control commands employ a "dynamic key + timeliness" design. Each time a command is generated, the cloud server randomly generates a one-time key (valid for 30 seconds), which is encrypted and bound to the command content to prevent interception and reuse. It also supports user-preset "emergency control whitelists," allowing designated contacts to skip some verification steps and quickly trigger control commands in emergencies, balancing security and emergency response efficiency.

[0044] Specifically, in some possible embodiments, the following steps are included: Receive automatic alarm trigger signals and bicycle geographical location information from multiple bicycle terminals within the same management area, and count the number of alarms per unit time. Determine whether the number of alarms counted exceeds the preset cluster warning threshold. If it does, mark the area as a high-risk area. Send cluster warning information to the administrator terminal of the high-risk area, and push risk warnings to the bicycle user terminals within 500 meters of the high-risk area that have not triggered the automatic alarm mechanism; Real-time alarm data and bicycle distribution density data of high-risk areas are synchronized to the urban public safety management platform to provide data support for the deployment of anti-theft police forces in the area. At the same time, the intelligent monitoring equipment around the high-risk area is linked to automatically focus on the area. The alarm threshold for bicycles that have not triggered the automatic alarm mechanism in the vicinity of high-risk areas is automatically tightened, thereby improving the sensitivity of anomaly detection and continuously monitoring the status of bicycles in the area.

[0045] When receiving signals from multiple bicycle terminals within the same management area, the management area is divided into urban geographic grids (using a 1km×1km square grid, identified by GeoHash encoding with a precision of 6 bits). Each bicycle terminal (with a built-in 4G communication module) sends an alarm signal (including alarm type, trigger time) and latitude and longitude information (error ≤ 5 meters) to the area management server via the MQTT protocol after triggering the automatic alarm mechanism. The server uses a sliding window statistical method (window duration set to 5 minutes) to accumulate the number of alarms within the grid in real time (the trigger count increments by 1 for each received alarm signal). When determining the cluster warning threshold, the threshold is based on the number of bicycles within the grid. The number of bicycles is dynamically set (based on historical registration data, with a threshold of 3 alarms per 5 minutes for every 100 bicycles, and less than 100 bicycles counted as 100). If the number of alarms exceeds the threshold (e.g., if a grid has 200 bicycles, the threshold is 6 alarms per 5 minutes, and 7 alarms are triggered), the server automatically marks the grid as a high-risk area and generates the area boundary coordinates (latitude and longitude of the four corners of the grid). During the information push phase to administrators and users, the high-risk area administrator terminal (such as the monitoring host in the community security room or the law enforcement terminal of the local police officer) receives the cluster warning information (including the area boundary, number of alarms, and time of the first alarm) through a dedicated communication link. The information format is JS. The ON structure, along with the server's geofencing algorithm (based on the Haversine formula to calculate distance), filters out non-alarm bicycle users within a 500-meter radius of high-risk areas and pushes risk alerts via the app (the message being "Your bicycle is near a high-risk area; we recommend checking your vehicle's condition or temporarily securing it"). The push success rate is ≥99%. During data synchronization and monitoring linkage, the regional management server synchronizes real-time alarm data (alarm time, location distribution) and bicycle distribution density data (number of bicycles per 100m² within the grid) to the city's public safety management platform (connected to the public security "Skynet" system) via a dedicated public security VPN, while simultaneously calling ONVIF. The protocol interface controls surrounding intelligent monitoring devices (cameras), enabling them to automatically adjust the pan-tilt angle to focus on the center coordinates of the high-risk area (error ≤ 10 meters), and the monitoring images are transmitted back to the platform in real time. When automatically tightening the alarm threshold, the server sends a threshold adjustment command to bicycle terminals within 500 meters of the high-risk area that have not alarmed, reducing the vibration amplitude threshold by 20% (e.g., from 5m / s² to 4m / s²), the lock pressure threshold by 15% (e.g., from 100N to 85N), and shortening the data acquisition cycle to 50% of the original cycle (e.g., from 1 second / time to 0.5 seconds / time), improving the sensitivity of anomaly detection, and the continuous monitoring period is 1 hour after the high-risk area is cleared.

[0046] The cluster alert threshold incorporates adaptive adjustment based on the time dimension. For example, the threshold is reduced by 30% during weekday morning peak hours (7:00-9:00) and nighttime hours (22:00-6:00) (due to the high density of people during morning peak hours leading to false alarms, and the higher risk of theft and robbery at night). Combined with the historical alarm time distribution of the grid over the past 30 days, a personalized threshold curve is generated to reduce misjudgments during non-risk periods. High-risk areas are further subdivided into "core areas" (areas with concentrated alarms, radius 200 meters) and "buffer zones" (surrounding areas within 300 meters). Core area administrators (such as property security guards) receive dispatch instructions with electronic map positioning, while buffer zone users receive self-check guidance including the distribution of surrounding monitoring points. During data synchronization, "theft and robbery behavior characteristic tags" are added, which are then used to further analyze the data. The alarm data is analyzed to extract typical theft and robbery patterns in the area (such as tool type and time of the crime) to assist the police in deploying targeted forces (such as carrying tools to detect breaching tools). When linked with intelligent monitoring equipment, cameras with AI recognition capabilities are prioritized to automatically identify individuals carrying lock-picking tools or abnormally moving bicycles, mark them as suspicious targets, and push the information to the management terminal. The alarm threshold tightening adopts a "gradient adjustment" strategy. When high risk is marked for the first time, the threshold is reduced by 20%. If the number of alarms continues to increase within 10 minutes, it is further reduced by 10% to avoid a surge in false alarms caused by a single large adjustment. At the same time, abnormal detection data before and after the threshold adjustment are recorded to form a "risk-response" effect evaluation report, providing a basis for subsequent threshold optimization.

[0047] Specifically, in some possible embodiments, the following steps are included: After the automatic alarm mechanism is triggered, the city's traffic network database and historical theft trajectory database are accessed to obtain relevant reference data; By using big data analysis algorithms, the real-time geographical location information, speed of movement, and reference data from urban traffic network databases and historical theft trajectory databases of stolen bicycles are comprehensively calculated to predict possible escape routes. Select IoT devices such as shared charging stations and smart stations along the escape route as temporary tracking nodes, update the timestamps of the stolen bicycles passing through IoT device nodes in real time, and dynamically correct the tracking path. Filter the monitoring point information along the escape route, combine real-time traffic data and dynamically corrected tracking path, and mark the optimal tracking path; The predicted escape route, information on monitoring points along the way, temporary tracking node data, and the optimal tracking path are synchronized to the user's terminal to provide tracking guidance for the user.

[0048] Upon triggering the automatic alarm mechanism, the main control module uses HTTPS to access the city's traffic network database (connected to the Gaode Map open platform API, containing structured data such as road type, speed limit, traffic light distribution, and lane number, updated every 15 minutes) and the cloud-based historical stolen trajectory database (stores trajectory data of bicycle theft cases nationwide over the past 3 years, with a sample size of ≥100,000, each trajectory containing latitude and longitude sequences, movement speed curves, escape destination types, etc.) to obtain road network data within a 5-kilometer radius of the stolen bicycle's real-time location and historical stolen trajectory reference data for the same area over the past 6 months; and then uses big data analysis algorithms (employing LSTM long short-term memory network combined with Dijkstra's algorithm) to obtain road network data within a 5-kilometer radius of the stolen bicycle's real-time location and historical stolen trajectory reference data for the same area over the past 6 months. The short path algorithm integrates the real-time geographical location information of the stolen bicycle (latitude and longitude accuracy ±3 meters), real-time movement speed (calculated from the distance and time difference between two consecutive GPS positioning, in km / h, sampling frequency 1 time / second), road network data, and historical theft trajectory data. Input features include current location, movement direction, road congestion index, and historical escape hotspot routes. The algorithm outputs the top 3 most probable escape routes (confidence ≥80%), prioritizing the avoidance of congested road sections and monitoring blind spots. When selecting temporary tracking nodes, the algorithm queries the IoT device catalog interface for shared charging piles (State Grid smart AC charging piles, supporting Bluetooth 5.0 and LoR) within 100 meters of the escape route. The system utilizes communication devices and smart stations (shared bicycle maintenance stations with built-in WiFi gateways) as temporary tracking nodes. When a stolen bicycle (with a built-in Bluetooth broadcast module, broadcasting 10 times / second) enters the node's communication range (≤50 meters), the node automatically collects the bicycle's Bluetooth signal strength and reception timestamp, uploading it to the cloud in real time via the MQTT protocol. The cloud then corrects the escape route deviation based on the timestamp sequence from multiple nodes (correction accuracy ≤10 meters). When filtering monitoring point information, it calls the unified interface of the public security Skynet system and social public monitoring (supporting the ONVIF protocol) to extract monitoring device information within a 50-meter range along the escape route (including monitoring ID, installation location, lens orientation, online status, etc.), and combines this information with Gaode Maps. Real-time traffic data (congestion index 0-10, level 0 is smooth traffic) and dynamically corrected tracking paths are used. The optimal tracking path is marked by AI algorithm, prioritizing road sections with dense monitoring (at least one effective monitor every 500 meters), smooth roads (congestion index ≤ 3), and high pedestrian traffic. Finally, through the APP's map visualization module, the predicted escape route (marked with dashed lines), monitoring points along the way (marked with red icons, click to view the monitoring unit), temporary tracking nodes (marked with blue icons, showing signal reception time), and the optimal tracking path (marked with solid lines, with estimated tracking time) are synchronized to the user's terminal. At the same time, voice navigation guidance is provided (such as "Drive 500 meters east along XX Road, there is a Skynet monitor 100 meters ahead").

[0049] In the escape route prediction stage, a theft behavior feature recognition model is introduced. By analyzing historical stolen trajectory data, the behavioral preferences of the thieves are extracted (such as avoiding highway exits and choosing secluded roads or non-motorized vehicle lanes for escape). Combined with real-time captured movement characteristics of the stolen bicycles (such as whether they frequently change lanes or speed), the route prediction weights are dynamically adjusted to improve the prediction confidence to over 90%. The temporary tracking nodes have added a "signal reverse positioning" function, which uses triangulation based on the Bluetooth signal strength received by multiple nodes to further narrow down the location range of the stolen bicycle (positioning accuracy ≤ 5 meters). At the same time, the nodes can send a low-power wake-up command to the bicycle to trigger the bicycle's built-in auxiliary positioning module (such as a UWB positioning tag), improving the tracking reliability in complex environments. The monitoring point selection adds "AI priority sorting", which uses image recognition algorithms to predict the coverage of the monitoring equipment on the escape route. The system prioritizes marking unobstructed, high-definition (resolution ≥ 1080P) surveillance footage that supports moving target tracking, and automatically sends "focus" requests to the monitoring management platform to ensure that the monitoring equipment focuses on capturing images of stolen bicycles and related personnel. The information synchronized to user terminals includes an "emergency linkage entry point," allowing users to send assistance tracking requests to nearby police stations and property security departments with a single click. These requests include the optimal tracking route, real-time location, and contact information. Simultaneously, the platform automatically calculates the distance difference between the user and the stolen bicycle and recommends suitable solutions such as "self-driving tracking," "cycling tracking," or "alarm waiting." The historical theft trajectory database incorporates a "spatiotemporal dynamic update" mechanism, adding the latest theft case trajectory data monthly and adjusting trajectory analysis weights according to season and time period (e.g., 10:00 PM to 4:00 AM is a high-incidence period), enabling the algorithm to adapt to changes in theft patterns in different spatiotemporal scenarios.

[0050] Specifically, in some possible embodiments, the following steps are included: Real-time monitoring of satellite signal strength of GPS positioning module to determine whether signal strength meets preset accuracy requirements; If the satellite signal strength of the GPS positioning module is lower than the preset value, the Beidou positioning module will be automatically activated to receive the positioning data collected by the Beidou positioning module and perform dual-mode fusion with the positioning data collected by the GPS positioning module. If dual-mode positioning still cannot meet the accuracy requirements, a signal relay request is sent to nearby connected IoT devices to receive position calibration data fed back by the IoT devices. By fusing and calculating the dual-mode positioning data from the GPS positioning module and the Beidou positioning module, as well as the location calibration data fed back by the IoT device, the positioning accuracy is improved to within 5 meters, and the precise geographical location information of the bicycle is updated. The updated precise geographical location information of the bicycle is synchronized to the shared bicycle control system around the stolen bicycle, triggering the shared bicycle control system to perform a temporary locking function on the shared bicycles it manages, forming a local enclosure area without affecting public passage and slowing down the movement speed of the stolen bicycle.

[0051] When monitoring the satellite signal strength of the GPS positioning module in real time, the UBlox NEO-M8U GPS module (supporting the L1 band, tracking sensitivity -167dBm) is used. The carrier-to-noise ratio (CN0) value is read through the module's built-in signal quality register. The preset accuracy requirement is "CN0 ≥ 35dB-Hz and three consecutive positioning errors ≤ 10 meters". The main control module reads the signal strength data every second and makes a judgment. If the GPS signal strength is lower than the preset value (e.g., CN0 < 35dB-Hz or positioning error > 10 meters), the main control module automatically wakes up the Beidou positioning module (model: Hexin Xingtong UM220-IVN, supporting the BD2B1 band) through the SPI interface. After the module starts, it completes satellite acquisition and positioning within 10 seconds and outputs data such as latitude, longitude, altitude, and timestamp. The main control module adopts Kalman CMOS. The filtering algorithm performs dual-mode fusion of GPS and BeiDou positioning data (fusion period 500ms), and optimizes the positioning results through weighted averaging (weights are dynamically allocated according to signal strength, with modules with strong signals accounting for 60%-80% of the weight), reducing the positioning error to within 8 meters. If the accuracy requirements are still not met after dual-mode fusion (error > 8 meters, such as in scenarios with tall buildings obstructing the view or in tunnels), the main control module sends a signal relay request through the NB-IoT communication module (model: BC95-B5) to connected IoT devices (smart streetlights, shared charging station cabinets, traffic signal control boxes, etc., the device list is synchronized in real time by the cloud IoT platform). The request packet includes the bicycle's unique ID and the current approximate location (…). Based on the dual-mode fusion results and positioning accuracy requirements, after receiving the request, the IoT device uses its built-in high-precision positioning module (such as the Qianxun Location RTK module, with an accuracy of ±1 meter) to feed back its own coordinates and the RSSI value of the received bicycle signal (used for triangulation) as position calibration data. When fusing the dual-mode positioning data and the position calibration data, the Extended Kalman Filter (EKF) algorithm is used to correct the state equation of the dual-mode positioning using the IoT device coordinates as observations. With the assistance of triangulation from three or more IoT devices, the positioning accuracy is finally improved to within 5 meters (95% confidence level), and the precise geographical location information of the bicycle (including latitude and longitude, positioning time, and accuracy level) is updated. The updated... Location information is synchronized via an encrypted TCP link to the shared bicycle control system (connected to Hello and Meituan shared bicycle APIs) within a 300-meter radius of the stolen bicycle. This triggers the system to temporarily lock the shared bicycles it manages. The locking strategy is as follows: within a 50-meter radius of the stolen bicycle's current location, select 10-20 shared bicycles (avoiding main roads and fire lanes) according to the principle of "dense at the road edges and sparse in the middle"), and lock the electronic locks of these bicycles within 3 seconds (only restricting riding, not affecting pushing), forming a semi-enclosed area to slow down the stolen bicycle's movement (tests have shown that this can reduce the movement speed by 40%-60%). The locking duration is 30 minutes (which can be remotely released by the user or the police).

[0052] In the positioning module switching logic, "environmental adaptive learning" is introduced. Machine learning algorithms record the GPS / BeiDou signal attenuation characteristics in different scenarios (such as high-rise buildings in commercial areas, tunnels, and tree-lined roads), generating a scenario-signal strength mapping model. When a known weak signal scenario is detected, the BeiDou module is pre-activated 5 seconds in advance, reducing the dual-mode switching delay (from 10 seconds to 3 seconds). In the IoT device relay link, "dynamic node filtering" is added, prioritizing IoT devices equipped with UWB (Ultra-Wideband) positioning modules as calibration nodes. Utilizing UWB's centimeter-level ranging capability (range accuracy ±30cm), combined with the TOA (Time of Arrival) algorithm, the distance between the bicycle and the node is calculated, improving the location calibration data accuracy to within 2 meters, especially suitable for GPS / BeiDou failure scenarios such as indoor or underground parking garages. The shared bicycle enclosure strategy adopts "dynamic density adjustment," using Gaode Map road width data to adjust the density of narrow roads (width...) The number of bicycles enclosed within a 5-meter radius is reduced to 5-8 (to avoid congestion), while the number on wider roads (≥5 meters) is increased to 15-20 (to enhance the enclosure effect). Simultaneously, a warning sound ("A bicycle has been stolen nearby, please assist in interception") is sent to the built-in speakers of the bicycles within the enclosure area to increase on-site deterrence. The positioning data fusion algorithm introduces "dynamic confidence weighting," adjusting the weights in real time based on the historical error rates of each positioning source (e.g., GPS error rate <5% in sunny weather, >15% in rainy weather). Data sources with high error rates have their weight reduced to below 20%, ensuring the fusion result always leans towards more reliable positioning information. A new "emergency positioning backup" mechanism is added. When all positioning methods fail (e.g., the device is blocked), the bicycle's built-in low-power Bluetooth beacon (broadcast period 1 second) is triggered. Authorized user terminals in the vicinity (e.g., other bicycle users' apps) can receive the beacon signal and upload it to the cloud, forming a "crowdsourced positioning network" to fill positioning blind spots.

[0053] Specifically, in some possible embodiments, the following steps are included: Receive user-defined permission management rules, including temporary authorization objects, authorization duration, and operation permission scope; When a temporarily authorized object initiates a device access request, the identity information and authorization validity period of the temporarily authorized object are verified, and the corresponding operation permissions are granted after confirming that it meets the authorization rules. Real-time monitoring of the operation behavior of temporarily authorized objects; if an operation exceeding the authorized scope is detected, the access rights of the temporarily authorized object are immediately terminated and an abnormal operation alert is sent to the original user. When generating permission usage reports, embed correlation data analysis of abnormal operations of temporary authorized objects, including the geographical location of the operation, device terminal information, and cross-validation with the abnormal status records of bicycles, to help users trace the source of security risks; When the authorization period expires, the authorization permissions of the temporary authorized object will be automatically revoked, and the permission usage report containing the cross-validation results will be synchronized to the original user terminal.

[0054] When receiving user-defined permission management rules, users configure them through the permission management interface of the terminal APP. The interface provides visual operation options: temporary authorized objects can be added via mobile phone number, APP account, or Bluetooth near-field pairing (manual input or selection from contacts is supported), the authorization duration can be set from 1 hour to 7 days (accurate to the minute), and the scope of operation permissions is divided into three levels ("Basic View": view only real-time location; "Limited Control": view location + remote unlock; "Full Control": all function permissions). After setting, the rules are stored in encrypted JSON format in the cloud permission database (using AES-256 encryption, the key is bound to the user account). When a temporarily authorized user initiates a device access request (such as scanning a bicycle's QR code via an app or entering a device ID), the cloud server first verifies its identity information (by comparing it with a pre-set mobile phone number / account or verifying Bluetooth pairing records), then checks the authorization validity period (the system time must be within the authorization start-end time range). After successful dual verification, a temporary access token (valid for 15 minutes, single-use) is issued to the temporarily authorized user's terminal, and corresponding permissions are granted (e.g., "Limited Control" permissions only allow calls to the unlock and location query interfaces). While monitoring the temporarily authorized user's actions in real time, the cloud server records the date of each interface call. The system records (including operation type, time, terminal IP, and geographic location) and verifies the legitimacy of operations using a permission whitelist (e.g., calling the unlock interface with "basic view" permission is considered unauthorized). If an operation exceeding the scope is detected, the temporary access token is immediately revoked (using a token blacklist mechanism), and an abnormal operation alert (including the type of unauthorized operation, the time of occurrence, and the temporary authorized object identifier) ​​is sent to the original user's terminal via an app pop-up and SMS. When generating a permission usage report, the report includes all operation records during the authorization period (normal operations are marked in green, and abnormal operations are marked in red), and embeds related data analysis of abnormal operations: geographic location (temporary authorized object operation). The system compares the terminal's GPS location with the bicycle's real-time location, and includes device terminal information (terminal model, operating system, IP address). It also associates the information with abnormal bicycle status records (such as whether vibration alarms are triggered during unauthorized operations). Cross-validation is performed using a timeline (e.g., if the time difference between an abnormal operation and the occurrence of bicycle vibration is less than 30 seconds, it is marked as a high-risk association). After the authorization period expires, the cloud server automatically executes the permission revocation command (deletes temporary authorization records and clears the terminal's cached access tokens) and pushes a permission usage report (PDF format with electronic signature) containing the cross-validation results to the original user's terminal via the APP. The user can view the details or export the report with one click.

[0055] The access control rules introduce a "behavioral trust level" mechanism, dynamically adjusting the scope of permissions based on the historical operation records of temporarily authorized users (such as whether there have been any unauthorized behaviors and the compliance rate of operations). For example, the initial authorization only grants "basic viewing," and after three consecutive compliant operations, it can be upgraded to "limited control," improving the flexibility and security of permission allocation. The identity verification process adds "multi-factor dynamic verification." In addition to basic identity information, based on the authorization risk level (such as "full control" being high risk), the temporarily authorized user is required to provide biometric features (fingerprint / face, collected by the terminal APP and verified locally) or real-time scene photos (captured of the bicycle's surrounding environment and compared with vehicle features stored in the cloud), reducing the risk of account theft. Real-time monitoring uses an "AI abnormal behavior recognition" algorithm, which analyzes the operating habits of temporarily authorized users (such as frequently used operations). A behavioral baseline is established based on the time period and average operation interval. When an operation deviating from the baseline is detected (such as frequent calls to the unlock interface late at night), an alert is triggered even if the user has not exceeded their authority, and a risk warning is sent to the original user, thus achieving "pre-intervention before exceeding authority". The cross-validation of the permission usage report introduces "blockchain notarization", which synchronizes the key data of abnormal operations (time stamp, geographic location hash, terminal information digest) to the consortium blockchain node to form an immutable traceability certificate. If a dispute occurs later, the authenticity of the report can be verified through the hash value. A new "emergency permission revocation" function has been added. The original user can "freeze" all temporary authorizations with one click through the APP. The freezing command uses the highest priority communication link (such as SMS + 4G push) to ensure that it takes effect within 3 seconds, and at the same time triggers the bicycle's built-in sound and light alarm (lasting for 10 seconds), which is suitable for emergency scenarios where the identity of the temporary authorization object is questionable.

[0056] Another embodiment of this application provides a bicycle GPS anti-theft and automatic alarm system, wherein, see reference Figure 2 A bicycle GPS anti-theft and automatic alarm system, comprising: Command receiving and parameter configuration module 100: used to receive system deployment commands sent by users through terminal devices, and configure the working parameters of the GPS positioning module and status detection sensor on the bicycle, including positioning frequency, data acquisition cycle and transmission protocol; Data acquisition and transmission module 200: used to receive bicycle geographical location information collected by GPS positioning module and physical status data collected by status detection sensor in real time through wireless communication interface; Data storage and labeling module 300: Used to store the received bicycle geographic location information and physical status data to the cloud database, and to complete data labeling according to preset data classification rules; Anomaly Detection Module 400: Used to call the preset alarm detection algorithm, combine the preset alarm threshold and allowable movement range, analyze the labeled data, and determine whether the bicycle has engaged in illegal movement or vandalism. Automatic alarm and information push module 500: If an anomaly is detected, it immediately triggers an automatic alarm mechanism and sends alarm information containing the anomaly type and real-time geographical location information to the user terminal via SMS, telephone or application push. Remote control response module 600: It is used to receive control requests sent by users through terminal devices, respond to the requests and grant control permissions to users, allowing users to view the real-time geographical location information, historical trajectory and abnormal details of bicycles, and execute user-initiated instructions to remotely start or stop the automatic alarm mechanism.

[0057] The bicycle GPS anti-theft and automatic alarm system provided in this embodiment can achieve the steps of the aforementioned embodiments due to the functions of each module and the logical connection between them. Therefore, it can achieve the same technical effect as the aforementioned embodiments. For the principle analysis, please refer to the relevant description of the steps of the aforementioned bicycle GPS anti-theft and automatic alarm method, which will not be repeated here.

[0058] This application also provides a bicycle GPS anti-theft and automatic alarm device, including a memory and a processor. The memory stores a computer program that can be loaded by the processor and executed as described above for a bicycle GPS anti-theft and automatic alarm method.

[0059] This application embodiment also provides a storage medium storing a computer program that can be loaded by a processor and executed as described above for a bicycle GPS anti-theft and automatic alarm method.

[0060] The storage medium provided in this embodiment can achieve the same technical effect as the aforementioned embodiments because the computer program therein, after being loaded and run on the processor, will implement the various steps of the aforementioned embodiments. For the principle analysis, please refer to the relevant description of the aforementioned method steps, which will not be repeated here.

[0061] The storage medium includes, for example, various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0062] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0063] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0064] Furthermore, features defined by the terms "first" and "second" may explicitly or implicitly include at least one of those features. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., and unless otherwise explicitly specified, is used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features.

[0065] Therefore, any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0066] The embodiments described in this specific implementation are preferred embodiments of this application and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A method for GPS anti-theft and automatic alarm for bicycles, characterized in that, include: Receive system deployment instructions sent by users through terminal devices, and configure the operating parameters of the GPS positioning module and status detection sensor on the bicycle, including positioning frequency, data acquisition cycle and transmission protocol; The system receives real-time bicycle location information from the GPS positioning module and physical status data from the status detection sensor via a wireless communication interface. The received bicycle location information and physical status data are stored in a cloud database, and the data is labeled according to the preset data classification rules; The system calls a preset alarm judgment algorithm, combines preset alarm thresholds and allowed movement ranges, analyzes the labeled data, and determines whether the bicycle has engaged in illegal movement or vandalism. If an anomaly is detected, an automatic alarm mechanism is immediately triggered, sending an alarm message containing the anomaly type and real-time geographic location information to the user terminal via SMS, telephone, or application push. It receives control requests sent by users through terminal devices, responds to the requests and grants control permissions to users, allowing them to view the bicycle's real-time geographical location information, historical trajectory and abnormal details, and executes user-initiated instructions to remotely start or stop the automatic alarm mechanism.

2. The bicycle GPS anti-theft and automatic alarm method according to claim 1, characterized in that, Includes the following steps: Configure the operating parameters of the state detection sensor group, which includes a vibration sensor, a pressure sensor, and a tilt sensor, and set the vibration amplitude threshold, the lock pressure threshold, and the vehicle body tilt angle threshold, respectively; It receives vibration amplitude data collected by vibration sensors, lock pressure data collected by pressure sensors, and vehicle tilt angle data collected by tilt sensors in real time. The received multi-dimensional sensor data is fused with the bicycle's geographical location information collected by the GPS positioning module to generate a comprehensive analysis dataset; The integrated analysis dataset is compared with the historical anomaly data sample library stored in the cloud database. The preset thresholds of each sensor in the state detection sensor group are dynamically optimized through machine learning algorithms to achieve adaptive adjustment of the thresholds. Each data point in the comprehensive analysis dataset is compared with its corresponding preset threshold. When any data point exceeds the preset threshold, it is determined to be in an abnormal state.

3. The bicycle GPS anti-theft and automatic alarm method according to claim 1, characterized in that, Includes the following steps: Receive remote control commands sent by the user through a terminal device, the commands including the linkage control requirements of the bicycle's built-in electronic lock; Verify the identity and permissions of remote control commands to confirm their legitimacy; An electromagnetic locking control signal is sent to the bicycle's built-in electronic lock. Upon receiving the signal, the bicycle's built-in electronic lock activates the electromagnetic locking mechanism, restricting wheel rotation and unlocking the lock. While sending the electromagnetic lock control signal, the built-in sound and light alarm of the bicycle is triggered to emit a high-frequency warning sound and flashing light. The alarm frequency of the built-in sound and light alarm is positively correlated with the real-time moving speed of the stolen bicycle. The faster the speed, the higher the warning intensity. Record detailed information about remote control operations, including operation time, operation content, user identification, and the activation status of the bicycle's built-in audible and visual alarm. Synchronize the operation records to the blockchain node to form an immutable operation log.

4. The bicycle GPS anti-theft and automatic alarm method according to claim 1, characterized in that, Includes the following steps: Receive automatic alarm trigger signals and bicycle geographical location information from multiple bicycle terminals within the same management area, and count the number of alarms per unit time. Determine whether the number of alarms counted exceeds the preset cluster warning threshold. If it does, mark the area as a high-risk area. Send cluster warning information to the administrator terminal of the high-risk area, and push risk warnings to the bicycle user terminals within 500 meters of the high-risk area that have not triggered the automatic alarm mechanism; Real-time alarm data and bicycle distribution density data of high-risk areas are synchronized to the urban public safety management platform to provide data support for the deployment of anti-theft police forces in the area. At the same time, the intelligent monitoring equipment around the high-risk area is linked to automatically focus on the area. The alarm threshold for bicycles that have not triggered the automatic alarm mechanism in the vicinity of high-risk areas is automatically tightened, thereby improving the sensitivity of anomaly detection and continuously monitoring the status of bicycles in the area.

5. The bicycle GPS anti-theft and automatic alarm method according to claim 1, characterized in that, Includes the following steps: After the automatic alarm mechanism is triggered, the city's traffic network database and historical theft trajectory database are accessed to obtain relevant reference data; By using big data analysis algorithms, the real-time geographical location information, speed of movement, and reference data from urban traffic network databases and historical theft trajectory databases of stolen bicycles are comprehensively calculated to predict possible escape routes. Select IoT devices such as shared charging stations and smart stations along the escape route as temporary tracking nodes, update the timestamps of the stolen bicycles passing through IoT device nodes in real time, and dynamically correct the tracking path. Filter the monitoring point information along the escape route, combine real-time traffic data and dynamically corrected tracking path, and mark the optimal tracking path; The predicted escape route, information on monitoring points along the way, temporary tracking node data, and the optimal tracking path are synchronized to the user's terminal to provide tracking guidance for the user.

6. The bicycle GPS anti-theft and automatic alarm method according to claim 1, characterized in that, Includes the following steps: Real-time monitoring of satellite signal strength of GPS positioning module to determine whether signal strength meets preset accuracy requirements; If the satellite signal strength of the GPS positioning module is lower than the preset value, the Beidou positioning module will be automatically activated to receive the positioning data collected by the Beidou positioning module and perform dual-mode fusion with the positioning data collected by the GPS positioning module. If dual-mode positioning still cannot meet the accuracy requirements, a signal relay request is sent to nearby connected IoT devices to receive position calibration data fed back by the IoT devices. By fusing and calculating the dual-mode positioning data from the GPS positioning module and the Beidou positioning module, as well as the location calibration data fed back by the IoT device, the positioning accuracy is improved to within 5 meters, and the precise geographical location information of the bicycle is updated. The updated precise geographical location information of the bicycle is synchronized to the shared bicycle control system around the stolen bicycle, triggering the shared bicycle control system to perform a temporary locking function on the shared bicycles it manages, forming a local enclosure area without affecting public passage and slowing down the movement speed of the stolen bicycle.

7. The bicycle GPS anti-theft and automatic alarm method according to claim 1, characterized in that, Includes the following steps: Receive user-defined permission management rules, including temporary authorization objects, authorization duration, and operation permission scope; When a temporarily authorized object initiates a device access request, the identity information and authorization validity period of the temporarily authorized object are verified, and the corresponding operation permissions are granted after confirming that it meets the authorization rules. Real-time monitoring of the operation behavior of temporarily authorized objects; if an operation exceeding the authorized scope is detected, the access rights of the temporarily authorized object are immediately terminated and an abnormal operation alert is sent to the original user. When generating permission usage reports, embed correlation data analysis of abnormal operations of temporary authorized objects, including the geographical location of the operation, device terminal information, and cross-validation with the abnormal status records of bicycles, to help users trace the source of security risks; When the authorization period expires, the authorization permissions of the temporary authorized object will be automatically revoked, and the permission usage report containing the cross-validation results will be synchronized to the original user terminal.

8. A bicycle GPS anti-theft and automatic alarm system, characterized in that, include: Command receiving and parameter configuration module: used to receive system deployment commands sent by users through terminal devices, and configure the working parameters of the GPS positioning module and status detection sensor on the bicycle. The working parameters include positioning frequency, data acquisition cycle and transmission protocol. Data acquisition and transmission module: used to receive bicycle geographical location information collected by GPS positioning module and physical status data collected by status detection sensor in real time through wireless communication interface; Data storage and labeling module: This module stores the received bicycle geographic location information and physical status data in a cloud database and labels the data according to preset data classification rules. Anomaly Detection Module: Used to call the preset alarm detection algorithm, combine the preset alarm threshold and allowable movement range, analyze the labeled data, and determine whether the bicycle has engaged in illegal movement or vandalism. Automatic alarm and information push module: If an anomaly is detected, the automatic alarm mechanism will be triggered immediately, and alarm information containing the anomaly type and real-time geographical location information will be sent to the user terminal via SMS, telephone or application push. Remote control response module: This module receives control requests sent by users through terminal devices, responds to the requests, and grants control permissions to users, allowing them to view the bicycle's real-time geographical location information, historical trajectory, and anomaly details. It also executes user-initiated commands to remotely start or stop the automatic alarm mechanism.

9. A bicycle GPS anti-theft and automatic alarm device, characterized in that, include: The memory and processor, wherein the memory stores a computer program that can be loaded by the processor and executed as described in any one of claims 1-7 for bicycle GPS anti-theft and automatic alarm.

10. A storage medium, characterized in that, The computer program is stored and can be loaded by a processor and executed as described in any one of claims 1-7 for bicycle GPS anti-theft and automatic alarm.

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