Shared electric bicycle intelligent helmet linkage lock control method and system based on Internet of Things

Abstract

The invention particularly relates to a shared electric bicycle intelligent helmet linkage lock control method and system based on the Internet of Things. The system comprises a lock control module; the helmet module is used for collecting state information of the helmet of the electric bicycle, analyzing the state information to obtain a wearing evaluation coefficient, presetting a threshold value of the wearing evaluation coefficient, comparing the obtained wearing evaluation coefficient with the wearing evaluation coefficient threshold value, and unlocking the maximum speed of the electric bicycle if the wearing evaluation coefficient is greater than the wearing evaluation coefficient threshold value; a server module; and an application module. According to the invention, an intelligent linkage mechanism guarantees the riding safety from the source; during riding, the helmet module continuously monitors the state of the helmet, and a wearing evaluation coefficient is obtained through complex analysis of level, position and pressure information; if the coefficient does not reach the standard, the speed of the electric bicycle is limited, and the user is forced to correctly wear the helmet; the whole-course management and control greatly improves the wearing rate of the helmet, reduces safety accidents caused by the fact that the helmet is not worn or improperly worn, and constructs a solid defense line for the life safety of a rider.

Description

Technical Field

[0001] The present invention relates to the technical field of shared electric motorcycles, and in particular to a method and system for controlling the linkage locking of an intelligent helmet of a shared electric motorcycle based on the Internet of Things. Background Art

[0002] With the increasing severity of urban traffic congestion and people's growing demand for green travel, shared electric motorcycles are a convenient and environmentally friendly way of travel; However, shared electric motorcycles have also exposed a series of safety issues during use, among which riders not wearing helmets is a prominent problem that needs to be solved urgently.

[0003] At present, although some shared electric motorcycle companies have equipped their vehicles with helmets, the lack of effective management and technical means has led to a low helmet wearing rate; Some riders take chances and think that not wearing a helmet will not cause any danger, thus neglecting their own safety. At the same time, the traditional shared electric bike lock control system and the helmet are independent of each other and cannot achieve effective linkage.

[0004] For example, users can unlock the motorcycle without taking off the helmet, or ride normally without wearing the helmet correctly, which greatly increases the safety risk during riding.

[0005] For example, publication number: CN117953614A discloses a method and device for detecting helmet wearing. Although the method of judging whether a user is wearing a helmet by the relationship between the RFID signal strength and the threshold in different states is simple, the basis for the judgment is relatively single and it is impossible to accurately judge whether the user is wearing the helmet correctly while riding.

[0006] In order to solve these problems, a shared electric motorcycle intelligent helmet linkage lock control method and system based on the Internet of Things is needed. Through the intelligent linkage between the electric motorcycle and the helmet, it is ensured that the rider can wear the helmet correctly when using the shared electric motorcycle, thereby improving the riding safety. Summary of the invention

[0007] The purpose of the present invention is to solve the above-mentioned problems and to propose a method and system for the linkage locking control of a shared electric motorcycle smart helmet based on the Internet of Things.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions: The shared electric motorcycle intelligent helmet linkage lock control system based on the Internet of Things includes: Lock control module: receives the lock switch command contained in the communication and performs the corresponding lock switch operation; the lock switch command is divided into the motorcycle lock switch command and the helmet lock switch command; Helmet module: Collect the status information of the e-bike helmet, analyze it to obtain a wearing evaluation coefficient, and unlock the maximum speed of the e-bike; Server module: Communicate with the lock control module and the user's mobile application, parse and process the received information, and send instructions and response messages to the corresponding devices; Application program module: Communicate with the server module, send the user's operation request to the server, and receive the response information returned by the server.

[0009] Preferably, the lock control module specifically includes: When receiving the signal that the user has completed the car rental process through the mobile application and the information of the helmet has also been confirmed, the main control system of the e-bike will send an unlocking instruction to the control circuit of the lock control module; after receiving the instruction, the control circuit immediately drives the motor of the electronic lock to open the lock catch, and the user can take away the helmet; After the main control system of the e-bike receives the signal that the helmet has been taken away, it unlocks the electronic lock of the e-bike and limits the maximum speed of the e-bike within a preset range.

[0010] Preferably, in the helmet module, collecting the status information of the e-bike helmet specifically includes: the horizontal status information of the helmet, the position information of the helmet, and the pressure information inside the helmet.

[0011] Preferably, the process of obtaining the wearing evaluation coefficient includes: After analyzing the horizontal status information of the helmet and the position information of the helmet, obtain a position coefficient; After analyzing the pressure information inside the helmet, obtain a pressure deviation coefficient; And comprehensively process the position coefficient and the pressure deviation coefficient to obtain a wearing evaluation coefficient.

[0012] Preferably, the process of obtaining the position coefficient includes: Analyze the horizontal status information of the helmet, including: After the e-bike is unlocked and it is sensed that the helmet is separated from the vehicle, obtain the acceleration of the helmet in the horizontal and vertical directions through the gravity sensors inside the helmet; The gravity sensors inside the helmet are distributed at the position of the center line of the helmet and on both sides of the helmet with the center line of the helmet as the symmetry line; Obtain the acceleration data of the gravity sensors distributed at the position of the center line of the helmet in the z-axis direction in three-dimensional space; Calculate the deviation between the vertical direction and the gravitational acceleration; by calculating the z-axis acceleration component And the difference from the gravitational acceleration g, and record it as the gravity deviation value; Obtain the allowable value range of the preset gravity deviation value, compare the obtained gravity deviation value with the allowable value range of the gravity deviation value. If the allowable value range of the gravity deviation value is within the allowable value range of the gravity deviation value, record this gravity deviation value as the normal gravity deviation value; Obtain all the normal gravity deviation values obtained at the preset time interval, and divide the number of all normal deviation values by the number of all gravity deviation values to obtain the normal ratio; Starting from the gravity sensors on the helmet respectively, draw straight lines along the gravity direction. Denote the straight line at the center of the helmet as the central gravity line, and the straight lines on both sides of the helmet as side line one and side line two respectively; Draw straight lines perpendicular to the central gravity line and side line one, and the central gravity line and side line two respectively, and denote them as short line one and short line two respectively; Short line one and short line two are respectively the shortest distances between the central gravity line and side line one and side line two; Obtain short line one and short line two when the helmet is in a horizontal state, and use them as the standard short line one and standard short line two; Perform difference calculations on short line one and short line two with standard short line one and standard short line two respectively, and take the absolute value to obtain line one difference and line two difference; Take the larger value of line one difference and line two difference as the judgment line value; Preset the allowable range of the judgment line value, and record the judgment line value within the allowable range of the judgment line value as the normal judgment value; Analyze the position information of the helmet, including arranging detection points on the helmet and the electric bicycle, and obtain the normal line ratio after analyzing the positions between the detection points; Perform weighted calculation on the normal ratio, normal judgment value and normal line ratio to obtain the position coefficient.

[0013] Preferably, the obtaining process of the pressure deviation coefficient includes: Obtain the pressure values monitored by each pressure monitoring point inside the helmet; And preset the allowable fluctuation range corresponding to each pressure value respectively, match each pressure value with its corresponding allowable fluctuation range, and record the pressure value not within the allowable fluctuation range as the abnormal pressure value; Count the number of abnormal pressure values, and divide the number of abnormal pressure values by the number of pressure values to obtain the abnormal ratio; Arrange the abnormal pressure values in descending order according to the numerical value, extract the maximum abnormal pressure value and the minimum abnormal pressure value among them, and perform difference calculation on them and then take the absolute value and denote it as the abnormal extreme value; Mark the detection points corresponding to each abnormal pressure value, and project them onto the same plane in sequence, connect the marked points with straight lines to form a closed area, and denote it as the projected closed area; Project the helmet onto the same plane and calculate the projected area of the helmet, denoted as the helmet projected area; Divide the projected enclosed area by the helmet projected area to obtain the abnormal area ratio; After comprehensively analyzing the abnormal ratio, abnormal extreme value, and abnormal area ratio, obtain the pressure deviation coefficient.

[0014] Preferably, the logic for unlocking the maximum speed of the e-bike includes: Preset a threshold for the wearing evaluation coefficient, compare the obtained wearing evaluation coefficient with the wearing evaluation coefficient threshold. If the wearing evaluation coefficient is greater than the wearing evaluation coefficient threshold, unlock the maximum speed of the e-bike; if the wearing evaluation coefficient is less than the wearing evaluation coefficient threshold, limit the speed of the e-bike within a preset range.

[0015] Preferably, the server module specifically includes: Adopt a database management system to store and manage data; When the user initiates an unlocking request on the application, verify the user's identity information; Query the status databases of the e-bike and the helmet, check whether the target e-bike and helmet are in an available state, whether there are any faults or occupied by other users; after confirming that all conditions are met, generate an unlocking instruction according to preset rules and algorithms, and send the instruction to the communication modules of the e-bike and the helmet through the communication network; During the user's ride, continuously receive data uploaded by the sensors of the e-bike and the helmet, and analyze the data using preset data analysis algorithms.

[0016] Preferably, the application module specifically includes: Before use, the user needs to download the corresponding application on the mobile phone, then complete the registration process, fill in personal basic information, and conduct real-name authentication; After completing the registration, the user opens the application and uses the positioning function of the mobile phone to find nearby available e-bikes and helmets, as well as the current remaining battery power of the e-bike; The application interface intuitively displays the location distribution of the vehicles and helmets, and the user can make a reservation according to their own needs; Within the reserved time, the user arrives at the vehicle parking point, sends an unlocking instruction through the application, and after verification, the e-bike and the helmet are unlocked; After the ride is over, the user parks the e-bike in the designated area, completes the return operation through the application, calculates the fee based on the riding duration and mileage, and the user completes the payment on the application; Manage and authenticate the user's usage permissions.

[0017] Internet of Things-based intelligent helmet linkage lock control method for shared electric bicycles, including: Linkage lock control: Receive the electric bicycle lock opening / closing instruction and helmet lock opening / closing instruction contained in the communication, and perform corresponding lock opening / closing operations; Helmet status analysis: Collect the status information of the electric bicycle helmet, analyze it to obtain a wearing evaluation coefficient, and unlock the maximum speed of the electric bicycle based on the obtained wearing evaluation coefficient; Backend server: Communicate with the lock control module and the user mobile application, parse and process the received information, and send instructions and response messages to the corresponding devices; Mobile application: Communicate with the server module, send the user's operation request to the server, and receive the response information returned by the server.

[0018] In summary, due to the adoption of the above technical solutions, the beneficial effects of the present invention are: 1. Through the intelligent linkage mechanism, the present invention ensures riding safety from the source; during riding, the helmet module continuously monitors the helmet status, and obtains a wearing evaluation coefficient through complex analysis of horizontal, position, and pressure information; if the coefficient does not meet the standard, the speed of the electric bicycle is restricted, forcing the user to wear the helmet correctly; this full-process control greatly improves the helmet wearing rate, reduces safety accidents caused by non-wearing or improper wearing, and builds a solid defense line for the life safety of riders.

[0019] 2. By adopting a professional database management system, the present invention comprehensively stores and analyzes various types of data; the operator can accurately optimize the vehicle placement layout according to the user's usage habits, such as common riding routes and peak usage periods, making the vehicle distribution more in line with the needs and reducing the problem of difficult vehicle finding; at the same time, through the vehicle and helmet status data, maintenance, repair, and charging can be arranged in a timely manner, improving resource utilization rate and service life. Description of the Drawings

[0020] In the following description of the exemplary embodiments in conjunction with the drawings, more details, features, and advantages of the present application are disclosed. In the drawings: Figure 1 is the flowchart of the present invention; Detailed Embodiments

[0021] The following will describe several embodiments of the present application in more detail with reference to the drawings so that those skilled in the art can implement the present application. The present application can be embodied in many different forms and purposes and should not be limited to the embodiments described herein. These embodiments are provided to make the present application comprehensive and complete, and to fully convey the scope of the present application to those skilled in the art. The embodiments do not limit the present application.

[0022] Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the relevant art and / or the context of this specification, and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

[0023] Please refer to Figure 1 As shown, the present invention provides a technical solution: An intelligent helmet linkage lock control system for shared electric bicycles based on the Internet of Things, comprising: A lock control module: receiving the lock / unlock instructions contained in the communication and performing corresponding lock / unlock operations; the lock / unlock instructions are divided into electric bicycle lock / unlock instructions and helmet lock / unlock instructions; The lock control module specifically includes: When receiving the signal that the user has completed the car rental process through the mobile application and the information of the helmet has also been confirmed, the main control system of the electric bicycle will send an unlock instruction to the control circuit of the lock control module; after receiving the instruction, the control circuit immediately drives the motor of the electronic lock to open the lock catch, and the user can take away the helmet; After the main control system of the electric bicycle receives the signal that the helmet has been taken away, it unlocks the electronic lock of the electric bicycle and limits the maximum speed of the electric bicycle within a preset range; The electric bicycle and the helmet are equipped with Bluetooth and low-power Bluetooth technologies for short-range communication with the helmet; when the helmet approaches the electric bicycle, the Bluetooth modules of both automatically search and pair to establish a data transmission channel to realize information interaction between the helmet and the electric bicycle, such as the helmet sending the wearing status information to the electric bicycle, and the electric bicycle feeding back the vehicle start status to the helmet, etc.; At the same time, the electric bicycle is also equipped with a 4G / 5G communication module. Through this module, the electric bicycle can communicate with the background server at a long distance and high speed; the electric bicycle uploads its own status data, such as battery power, speed, location, and various event information during the riding process, such as emergency braking, collision warning, etc., to the server in real time; The server can also send various instructions to the electric bicycle through the 4G / 5G network, such as remotely locking the vehicle or controlling the vehicle in case of an abnormality; it can also remotely push system software updates to improve the performance and functions of the vehicle; A helmet module: collecting the status information of the electric bicycle helmet, analyzing it to obtain a wearing evaluation coefficient, and unlocking the maximum speed of the electric bicycle; In the helmet module, collecting the status information of the electric bicycle helmet specifically includes: the horizontal status information of the helmet, the position information of the helmet, and the pressure information inside the helmet; The process of obtaining the wearing evaluation coefficient includes: After analyzing the horizontal state information and the position information of the helmet, a position coefficient is obtained; After analyzing the pressure information inside the helmet, a pressure deviation coefficient is obtained; And after comprehensively processing the position coefficient and the pressure deviation coefficient, a wearing evaluation coefficient is obtained; After normalizing the position coefficient and the pressure deviation coefficient, the position coefficient and the pressure deviation coefficient are respectively used as the two right-angled sides of a right triangle, and the remaining side is connected to form a complete right triangle. With the center of the right-angled side where the pressure deviation coefficient is located as the center and half of the pressure deviation coefficient as the radius, a circle is drawn, and the circle cuts the right triangle, and the remaining area of the right triangle is calculated, which is denoted as the wearing evaluation coefficient; The process of obtaining the position coefficient includes: Analyzing the horizontal state information of the helmet, including: After the electric bicycle is unlocked and it is sensed that the helmet is separated from the vehicle, the accelerations of the helmet in the horizontal and vertical directions are obtained through the gravity sensor inside the helmet; There are the following ways to judge the separation of the helmet from the vehicle: Hall magnetic induction technology: Hall magnetic induction components and magnets are respectively installed on the helmet and the electric bicycle; when the helmet is placed at a specific position of the vehicle, the Hall magnetic induction component is close to the magnet and is in an induction state; when the helmet is removed and separated from the vehicle, the Hall magnetic induction component detects a magnetic field change and generates a corresponding signal change, thereby judging that the helmet is separated from the vehicle; For example, the helmets of some shared electric bicycles are installed in the card slots of the basket. There are magnets at the card slots and Hall sensors inside the helmets. When the helmet is taken out of the card slot, the sensor can sense the disappearance of the magnetic field and thus send out a separation signal; NFC or RFID technology: An NFC (Near Field Communication) tag or an RFID (Radio Frequency Identification) tag is installed on the helmet, and a corresponding reader is set on the electric bicycle; when the helmet is close to the vehicle, the reader of the vehicle can read the tag information to confirm that the helmet and the vehicle are in a connected state; when the helmet is taken away and the reader cannot read the tag information, it can be judged that the helmet is separated from the vehicle; in addition to judging the separation state, this technology can also be used to verify whether the helmet and the vehicle match; Bluetooth connection detection: The helmet and the electric bicycle are connected via Bluetooth; a Bluetooth connection status monitoring program is set in the vehicle system. When the helmet and the vehicle are normally connected, the Bluetooth signal is stable; once the helmet is taken away from the vehicle and exceeds the effective range of the Bluetooth signal, the Bluetooth connection will be interrupted or the signal strength will be greatly weakened, and the electric bicycle judges that the helmet is separated from the vehicle accordingly; The gravity sensors inside the helmet are distributed at the position of the helmet center line (such as the top center of the helmet), and on both sides of the helmet with the helmet center line as the symmetry line; Obtain the acceleration data of the gravity sensors distributed at the position of the helmet center line in the z-axis direction in three-dimensional space; Calculate the deviation between the vertical direction and the gravitational acceleration; by calculating the difference between the z-axis acceleration component And the gravitational acceleration g, and record it as the gravity deviation value; Adopt the formula ; where Reflects the deviation degree of the acceleration in the vertical direction from the theoretical gravitational acceleration; Preset the allowable value range of the gravity deviation value, compare the obtained gravity deviation value with the allowable value range of the gravity deviation value. If the allowable value range of the gravity deviation value is within the allowable value range of the gravity deviation value, then record this gravity deviation value as the normal gravity deviation value; Obtain all the normal gravity deviation values obtained at the preset time interval, and divide the number of all normal deviation values by the number of all gravity deviation values to obtain the normal ratio; Starting from the gravity sensors on the helmet respectively, draw straight lines along the gravity direction. Record the straight line at the center of the helmet as the center gravity line, and the straight lines on both sides of the helmet as the side line one and side line two respectively; Draw straight lines perpendicular to the center gravity line and side line one, and the center gravity line and side line two respectively, and record them as short line one and short line two respectively; short line one and short line two are respectively the shortest distances between the center gravity line and side line one and side line two; Obtain short line one and short line two when the helmet is in a horizontal state, and use them as the standard short line one and standard short line two; Perform difference calculations on short line one and short line two with the standard short line one and standard short line two respectively, and take the absolute value to obtain the line one difference and line two difference; take the larger value of the line one difference and line two difference as the judgment line value; Preset the allowable range of the judgment line value, and record the judgment line value within the allowable range of the judgment line value as the normal judgment value; Analyze the position information of the helmet, including arranging detection points on the helmet and the electric bicycle, and obtain the normal line ratio after analyzing the positions between the detection points; Specifically include: After obtaining that the normal gravity deviation value is within the allowable range and reaches the preset duration, starting from the detection points on the helmet, sequentially obtain the distances between the helmet detection points and each detection point on the electric bicycle, and record them as the position lines; Preset the allowable fluctuation ranges of each position line respectively, and match each position line with the allowable fluctuation range of the corresponding position line in sequence. Mark the position lines within the allowable fluctuation range of the position line as normal position lines; Obtain and count the number of normal position lines, and divide it by the number of all position lines to obtain the normal line ratio; Perform weighted calculation on the normal ratio, normal judgment value, and normal line ratio to obtain the position coefficient; Preset the weight factors of the normal ratio, normal judgment value, and normal line ratio. Multiply the normal ratio, normal judgment value, and normal line ratio with their corresponding weight factors respectively and then sum them up to obtain the position coefficient; The process of obtaining the pressure deviation coefficient includes: Obtain the pressure values monitored by each pressure monitoring point inside the helmet; And preset the allowable fluctuation ranges corresponding to each pressure value respectively. Match each pressure value with its corresponding allowable fluctuation range, and mark the pressure values not within the allowable fluctuation range as abnormal pressure values; Count the number of abnormal pressure values, and divide the number of abnormal pressure values by the number of pressure values to obtain the abnormal ratio; Arrange each abnormal pressure value in descending order according to the numerical value, extract the maximum abnormal pressure value and the minimum abnormal pressure value among them, and calculate the difference between them and then take the absolute value and mark it as the abnormal extreme value; Mark the detection points corresponding to each abnormal pressure value, and project them onto the same plane in sequence. Connect each marked point with a straight line to form a closed area, and the formed closed area is the largest closed area that can be formed by each marked point; and mark it as the projected closed area; Project the helmet onto the same plane as well, and calculate the projected area of the helmet, marked as the helmet projected area; Divide the projected closed area by the helmet projected area to obtain the abnormal area ratio; Perform comprehensive analysis on the abnormal ratio, abnormal extreme value, and abnormal area ratio to obtain the pressure deviation coefficient; Preset the weight factors of the abnormal ratio, abnormal extreme value, and abnormal area ratio. Multiply the abnormal ratio, abnormal extreme value, and abnormal area ratio with their corresponding weight factors respectively to obtain the pressure deviation coefficient; The logic for unlocking the maximum speed of the electric bicycle includes: Preset the threshold of the wearing evaluation coefficient. Compare the obtained wearing evaluation coefficient with the wearing evaluation coefficient threshold. If the wearing evaluation coefficient is greater than the wearing evaluation coefficient threshold, unlock the maximum speed of the electric bicycle; if the wearing evaluation coefficient is less than the wearing evaluation coefficient threshold, limit the speed of the electric bicycle within the preset range; The method for obtaining and the size of the preset wearing assessment coefficient threshold can be based on the following content: Acquisition method: Collect position coefficients, pressure deviation coefficients and corresponding comprehensive wearing assessment coefficients of many helmets worn correctly and incorrectly, and construct a data set; use data analysis algorithms, such as cluster analysis and decision trees, to find threshold boundaries that can effectively distinguish between correct and incorrect wearing states; in addition, it is also necessary to consider various factors in actual riding scenarios, such as changes in helmet status under different road conditions and riding speeds, classify and screen the data, and thus determine a reasonable threshold; Threshold size: determined according to actual application scenarios and safety requirements. In scenarios that pursue higher safety standards, the threshold will be set higher to ensure that the maximum speed is unlocked only when the helmet is worn very properly. In scenarios that have certain requirements for riding convenience and relatively low safety risks, the threshold may be appropriately lowered. For example, in urban streets with heavy traffic and complex road conditions, to ensure the safety of cyclists, the threshold can be set to a value that can strictly screen out the correct wearing of helmets; in relatively safe closed parks or scenic spots, the threshold can be appropriately adjusted to improve the user's riding experience while ensuring basic safety; Server module: communicates with the lock control module and the user's mobile phone application, parses and processes the received information, and sends instructions and response messages to the corresponding devices; The server module includes: Use database management systems, such as MySQL, Oracle, etc., to safely and reliably store and efficiently manage the massive amounts of data generated in the system; These data include user information, including user registration information, real-name authentication information, credit rating, etc.; motorcycle information, such as vehicle model, frame number, purchase time, maintenance record, current location, power level, etc.; helmet information, including helmet number, manufacturer, usage status, last charging time, etc.; riding records, which record in detail the start time, end time, riding route, riding speed, power consumption, etc. of each ride; safety event records, such as the time, location, and severity of collision events; By storing and organizing these data, operators can gain a deeper understanding of users’ usage habits; For example, users’ frequently used cycling routes and peak usage periods can be used to optimize vehicle deployment layout and scheduling strategies; At the same time, by analyzing the usage frequency and status data of vehicles and helmets, the repair, maintenance and charging of vehicles and helmets can be arranged in a timely manner to improve the utilization rate and service life of resources; When a user initiates an unlocking request on the application, the user's identity information will be verified. By comparing with the user information database, it is confirmed whether the user is registered, has completed real-name authentication, and whether the credit rating meets the usage requirements; Query the status database of the electric bicycle and helmet, and check whether the target electric bicycle and helmet are in an available state, whether there are any faults or occupied by other users; After confirming that all conditions are met, an unlocking instruction is generated according to the preset rules and algorithms, and this instruction is sent to the communication modules of the electric bicycle and helmet through the communication network; During the user's ride, continuously receive the data uploaded by the sensors of the electric bicycle and helmet, and use the preset data analysis algorithm to analyze these data in real time; For example, when it is detected that the speed of the electric bicycle exceeds the preset safe speed threshold and the helmet module simultaneously detects abnormal data, it is comprehensively judged that a dangerous situation may have occurred, and corresponding emergency measures are immediately initiated, such as sending an alarm message to the user's application to remind the user to pay attention to safety, and at the same time sending a warning message to the background operation personnel so that they can take rescue actions in time or remotely control the vehicle; The operation personnel can view the geographical location distribution of all shared electric bicycles and helmets in real time in the background, and clearly see the real-time positions of each vehicle and each helmet on the electronic map, which is convenient for vehicle scheduling and resource management; At the same time, the detailed status information of the electric bicycle and helmet can be obtained in real time, such as the battery level, speed, driving direction of the electric bicycle, and the wearing status and battery level of the helmet; Once it is found that a vehicle or helmet has a fault, such as the electric bicycle has too low battery level, insufficient tire pressure, or abnormal helmet communication, etc., the operation personnel immediately send an instruction to the corresponding vehicle or helmet through the server module for remote processing; For example, for an electric bicycle with too low battery level, the operation personnel can remotely send an instruction to mark it as in a state to be charged and guide the nearby maintenance personnel to go for processing; For a helmet with abnormal communication, the communication module can be remotely restarted to try to repair it; In addition, the operation personnel can also remotely upgrade and maintain the system through the server module to ensure that the system always maintains the latest functions and the best stability, providing users with a better and safer shared travel service; Application program module: Communicate with the server module, send the user's operation request to the server, and receive the response information returned by the server; Specifically include: Before using, the user needs to download the corresponding application on the mobile phone, then complete the registration process, fill in personal basic information, and conduct real-name authentication to ensure the legality and security of use; After completing registration, the user opens the application and uses the positioning function of the mobile phone to find available electric bicycles and helmets nearby, as well as the current remaining battery power of the electric bicycle; The application interface intuitively displays the location distribution of vehicles and helmets, and the user can make reservations according to their own needs; Within the reserved time, the user arrives at the vehicle parking point and sends an unlocking instruction through the application. After successful verification, the electric bicycle and helmet are unlocked; during the riding process, the application uses the sensors of the mobile phone itself and the communication functions with the electric bicycle and helmet to display real-time riding data, such as the current speed and riding mileage; After the ride is over, the user parks the electric bicycle in the designated area, completes the return operation through the application, calculates the fee based on the riding duration and mileage, the user completes the payment on the application, and views the detailed riding history records, including information such as the time, route, and fee of each ride; Carry out refined management and strict authentication of the user's usage rights; Based on the information provided by the user during registration and various factors such as the credit rating accumulated during subsequent use, different permissions are assigned to the user. Newly registered users usually need to pay a certain amount of deposit after completing real-name authentication to obtain the right to use electric bicycles and helmets; As the number of user uses increases and a good credit record accumulates, their credit rating will gradually increase; old users with a higher credit rating may enjoy preferential treatments, such as riding without a deposit, which greatly improves the user experience; they may also have the right to give priority to using vehicles. When vehicles are in short supply, the system will give priority to allocating available vehicles to them; At the same time, the permission management module will supervise each operation of the user in real time. If it is found that the user has violated regulations such as maliciously damaging the electric bicycle or helmet, the system will reduce the user's credit rating according to the severity of the circumstances and even restrict their usage rights to maintain a good usage environment for shared resources.

[0024] An intelligent helmet linkage lock control method for shared electric bicycles based on the Internet of Things, including: Linkage lock control: Receive the electric bicycle lock opening and closing instructions and helmet lock opening and closing instructions contained in the communication, and perform corresponding lock opening and closing operations; Helmet status analysis: Collect the status information of the electric bicycle helmet, analyze it to obtain a wearing evaluation coefficient, and unlock the maximum speed of the electric bicycle based on the obtained wearing evaluation coefficient; After analyzing the horizontal status information of the helmet and the position information of the helmet, a position coefficient is obtained; After analyzing the pressure information inside the helmet, a pressure deviation coefficient is obtained; And the position coefficient and the pressure deviation coefficient are comprehensively processed to obtain a wearing evaluation coefficient; Backend server: Communicates with the lock control module and the user's mobile application, parses and processes the received information, and sends instructions and response messages to the corresponding devices; Mobile application: Communicates with the server module, sends the user's operation requests to the server, and receives the response information returned by the server.

[0025] The above formulas are all obtained by collecting a large amount of data for software simulation and selecting a formula close to the true value. The influence weight factors and specific coefficient values in the formula are set by those skilled in the art according to the actual situation and can be adjusted and modified later.

[0026] The above description of the embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. The shared electric motorcycle intelligent helmet linkage lock control system based on the Internet of Things is characterized by: include: Lock control module: receives the lock switch command contained in the communication and performs the corresponding lock switch operation; The switch lock command is divided into the motorcycle switch lock command and the helmet switch lock command; Helmet module: collects the status information of motorcycle helmets, obtains the wearing assessment coefficient after analysis, presets the threshold of the wearing assessment coefficient, compares the obtained wearing assessment coefficient with the wearing assessment coefficient threshold, and unlocks the maximum speed of the motorcycle if the wearing assessment coefficient is greater than the wearing assessment coefficient threshold; Server module: communicates with the lock control module and the user's mobile phone application, parses and processes the received information, and sends instructions and response messages to the corresponding devices; Application module: communicates with the server module, sends the user's operation request to the server, and receives the response information returned by the server.

2. The shared electric motorcycle intelligent helmet linkage lock control system based on the Internet of Things according to claim 1 is characterized in that: The lock control module specifically includes: When receiving a signal that the user has completed the rental process through the mobile phone application and the information of the helmet has been confirmed, the main control system of the motorcycle will send an unlocking command to the control circuit of the lock control module; after receiving the command, the control circuit will immediately drive the motor of the electronic lock to open the lock, and the user can take away the helmet; After receiving the signal that the helmet has been taken away, the main control system of the motorcycle unlocks the electronic lock of the motorcycle and limits the maximum speed of the motorcycle to a preset range.

3. The shared electric motorcycle intelligent helmet linkage lock control system based on the Internet of Things according to claim 1 is characterized in that: In the helmet module, the status information of the motorcycle helmet is collected, including: the horizontal status information of the helmet, the position information of the helmet, and the pressure information inside the helmet.

4. The shared electric motorcycle intelligent helmet linkage lock control system based on the Internet of Things according to claim 3 is characterized in that: The process of obtaining the wearing assessment coefficient includes: After analyzing the horizontal state information and the position information of the helmet, the position coefficient is obtained; After analyzing the pressure information inside the helmet, the pressure deviation coefficient is obtained; The position coefficient and pressure deviation coefficient are processed comprehensively to obtain the wearing evaluation coefficient.

5. The shared electric motorcycle intelligent helmet linkage lock control system based on the Internet of Things according to claim 4 is characterized in that: The process of obtaining the position coefficient includes: Analyze the horizontal status information of the helmet, including: After the motorcycle is unlocked, when the helmet is sensed to be separated from the vehicle, the helmet's acceleration in the horizontal and vertical directions is obtained through the gravity sensor inside the helmet; The gravity sensors in the helmet are distributed at the center line of the helmet and at both sides of the helmet with the center line of the helmet as the symmetry line; Acquire acceleration data of the gravity sensor distributed on the center line of the helmet in the z-axis direction in three-dimensional space; Calculate the deviation between the vertical direction and the gravitational acceleration; calculate the z-axis acceleration component The difference from the gravitational acceleration g is recorded as the gravity deviation value; Preset an allowable value range of the gravity deviation value, compare the obtained gravity deviation value with the allowable value range of the gravity deviation value, and if the allowable value range of the gravity deviation value is within the allowable value range of the gravity deviation value, record the gravity deviation value as the normal gravity deviation value; Obtaining all normal gravity deviation values ​​obtained at a preset time interval, and dividing the number of all normal deviation values ​​by the number of all gravity deviation values ​​to obtain a normal ratio; Take the gravity sensor on the helmet as the starting point and draw straight lines along the direction of gravity. The straight line at the center of the helmet is recorded as the center gravity line, and the straight lines on both sides of the helmet are recorded as side line 1 and side line 2 respectively. Draw straight lines perpendicular to the center mass line and side line 1, and to the center mass line and side line 2, and record them as short line 1 and short line 2 respectively; short line 1 and short line 2 are the shortest distances between the center mass line and side line 1 and side line 2 respectively; Obtain the short line 1 and the short line 2 when the helmet is in a horizontal state, and use them as the standard short line 1 and the standard short line 2; Calculate the difference between short line 1 and short line 2 and standard short line 1 and standard short line 2 respectively, and take the absolute value to obtain line 1 difference and line 2 difference; take the larger value of line 1 difference and line 2 difference as the judgment line value; Preset the allowable range of the judgment line value, and record the judgment line value within the allowable range of the judgment line value as a normal judgment value; Analyze the position information of the helmet, including setting up detection points on the helmet and the motorcycle, and analyzing the positions between the detection points to obtain a normal line ratio; The position coefficient is obtained by weighted calculation of the normal proportion, normal judgment value and normal line ratio.

6. The shared electric motorcycle intelligent helmet linkage lock control system based on the Internet of Things according to claim 5 is characterized in that: The process of obtaining the pressure deviation coefficient includes: Obtain the pressure values ​​monitored at each pressure monitoring point in the helmet; The allowable fluctuation ranges corresponding to the respective pressure values ​​are preset, and the respective pressure values ​​are matched with the respective corresponding allowable fluctuation ranges, and the pressure values ​​not within the allowable fluctuation ranges are recorded as abnormal pressure values; Counting the number of abnormal pressure values, and dividing the number of abnormal pressure values ​​by the number of pressure values ​​to obtain an abnormal ratio; Arrange the abnormal pressure values ​​in descending order according to their numerical values, extract the maximum abnormal pressure value and the minimum abnormal pressure value, calculate their difference, and take the absolute value as the abnormal extreme value; Mark the detection points corresponding to each abnormal pressure value, and project them on the same plane in sequence, connect each marked point with a straight line to form a closed area, which is recorded as the projected closed area; Project the helmet onto the plane and calculate the projection area of ​​the helmet, which is recorded as the helmet projection area; Divide the projected enclosed area by the helmet projected area to get the abnormal area ratio; The pressure deviation coefficient is obtained by comprehensive analysis of the abnormal ratio, abnormal extreme value and abnormal area ratio.

7. The shared electric motorcycle intelligent helmet linkage lock control system based on the Internet of Things according to claim 6 is characterized in that: The logic for unlocking the maximum speed of a motorcycle includes: A threshold of the wearing assessment coefficient is preset, and the acquired wearing assessment coefficient is compared with the wearing assessment coefficient threshold. If the wearing assessment coefficient is greater than the wearing assessment coefficient threshold, the maximum speed of the motorcycle is unlocked; if the wearing assessment coefficient is less than the wearing assessment coefficient threshold, the speed of the motorcycle is limited to a preset range.

8. The shared electric motorcycle intelligent helmet linkage lock control system based on the Internet of Things according to claim 1 is characterized in that: The server module includes: Use database management system to store and manage data; When a user initiates an unlocking request on the app, the user's identity information will be verified; Query the motorcycle and helmet status database to check whether the target motorcycle and helmet are in a usable state, whether there is a fault or occupied by other users; after confirming that all conditions are met, generate an unlocking instruction according to the preset rules and algorithms, and send the instruction to the communication module of the motorcycle and helmet through the communication network; While the user is riding, the system continuously receives data uploaded from the motorcycle and helmet sensors, and uses the preset data analysis algorithm to analyze the data.

9. The shared electric motorcycle intelligent helmet linkage lock control system based on the Internet of Things according to claim 1 is characterized in that: Application modules, including: Before using, users need to download the corresponding application on their mobile phones, complete the registration process, fill in their basic personal information, and conduct real-name authentication; After completing the registration, the user opens the app and uses the phone’s location function to find nearby available motorcycles and helmets, as well as the motorcycle’s current remaining battery power; The application interface intuitively displays the location distribution of vehicles and helmets, and users can make reservations based on their needs; During the scheduled time, the user arrives at the parking spot and sends an unlocking command through the app. After verification, the motorcycle and helmet are unlocked. After the ride, the user parks the motorcycle in a designated area and returns the motorcycle through the app. The fee is calculated based on the ride time and mileage, and the user completes the payment on the app. Manage and authenticate user access rights.

10. A method for controlling the linkage lock of a smart helmet for a shared electric motorcycle based on the Internet of Things, according to any one of claims 1 to 9, characterized in that: include: Linkage lock control: Receive the motorcycle lock switch command and helmet lock switch command contained in the communication, and perform the corresponding lock switch operation; Helmet status analysis: collect the status information of motorcycle helmets, analyze them and get the wearing assessment coefficient, then unlock the maximum speed of the motorcycle based on the obtained wearing assessment coefficient; Backend server: communicates with the lock control module and the user's mobile phone application, parses and processes the received information, and sends instructions and response messages to the corresponding devices; Mobile application: communicates with the server module, sends the user's operation request to the server, and receives the response information returned by the server.

Images