Safety warning system and signal control method for electric bicycle

Abstract

The application discloses a safety warning system and signal control method for an electric bicycle, which comprises a central processing module, a hand-off monitoring module, a collision avoidance module, a vehicle speed adjusting module and an alarm module, wherein the central processing module is used for processing input signals and outputting control signals; the hand-off monitoring module comprises a pressure detection unit arranged at a handle and a speed detection unit arranged at an electric motor, and the pressure detection unit and the speed detection unit are electrically connected with the central processing module; the collision avoidance module is electrically connected with the central processing module and is used for detecting the distance information between the electric bicycle and surrounding vehicles in real time; and the alarm module comprises a vibration early warning unit, a sound early warning unit and a display unit. The application can effectively prevent a driver from driving the electric bicycle by using a single hand, can detect the distance between the electric bicycle and a front vehicle, and can remind the driver to keep a safe driving distance, so that rear-end accidents can be avoided.

Description

Technical Field

[0001] This invention relates to the field of electric bicycle safety measures technology, specifically to a safety warning system and signal control method for electric bicycles. Background Technology

[0002] Traffic congestion is widespread in cities of all sizes across my country, and electric bicycles, as a convenient, fast, and energy-efficient mode of transportation, have become a popular choice for many commuters. The number of electric bicycles on the road is increasing year by year, and coupled with the complex road traffic conditions in my country, electric bicycle safety has become a significant factor in traffic accidents. Many electric bicycle riders operate with one hand, and in emergencies, sudden braking with one hand can lead to instability and rollovers, or even failure to detect hazard signals, resulting in accidents. This poses a significant safety risk. Furthermore, when the following distance is too small, a sudden slowdown or lane change by the vehicle in front can easily cause a rear-end collision. Summary of the Invention

[0003] The purpose is to provide a safety warning system and signal control method for electric bicycles, which solves the technical problems that electric bicycle riders may cause safety accidents by driving with one hand or not driving within a safe driving distance.

[0004] This invention is achieved through the following technical solution:

[0005] This invention provides a safety warning system for electric bicycles, comprising:

[0006] Central processing module, which processes input signals and outputs control signals;

[0007] The release-of-hand monitoring module includes a pressure detection unit located at the handle and a speed detection unit located at the motor. Both the pressure detection unit and the speed detection unit are electrically connected to the central processing module.

[0008] The collision avoidance module is electrically connected to the central processing module and is used to acquire image information of vehicles ahead in real time.

[0009] The vehicle speed adjustment module is electrically connected to the central processing unit and is used to adjust the speed of the electric bicycle.

[0010] An alarm module is provided, comprising a vibration warning unit, an audio warning unit, and a display unit, all of which are electrically connected to a central processing module.

[0011] Furthermore, the collision avoidance module includes a vehicle image acquisition unit, which is electrically connected to the central processing module. The central processing module extracts feature points of the target vehicle from the image information acquired by the vehicle image acquisition unit and calculates the real-time vehicle distance based on the monocular geometric ranging model.

[0012] Furthermore, it also includes a tire detection module, which includes a tire pressure sensor and a tire image acquisition unit. The tire pressure sensor and the tire image acquisition unit are electrically connected to the central processing module. The central processing module obtains real-time tire image information from the tire image acquisition unit and compares the real-time tire image with abnormal tire images in an abnormal tire image database established using Python web crawlers and Matlab image optimization, thereby controlling the alarm module to remind the driver in real time.

[0013] Furthermore, it also includes an anti-theft module, which has a human image acquisition unit that is electrically connected to the central processing module.

[0014] Furthermore, the vibration warning unit is a vibrator distributed on the electric bicycle seat, the sound warning unit is a buzzer installed on the handlebars of the electric bicycle, and the display unit is a touch screen installed on the handlebars of the electric bicycle.

[0015] A signal control method for a safety warning system for electric bicycles is also provided, applied to the safety warning system for electric bicycles described in the first aspect above, comprising:

[0016] The system acquires real-time pressure signals collected by the pressure detection unit in the hands-free monitoring module, real-time speed signals collected by the speed detection unit, and vehicle image signals collected by the anti-collision module.

[0017] The pressure and speed signals are processed, and the alarm module and vehicle speed regulation module are controlled to operate.

[0018] The system processes the vehicle image signal and controls the alarm module to operate.

[0019] Furthermore, processing the pressure signal and speed signal, and controlling the operation of the alarm module and the vehicle speed adjustment module includes:

[0020] The real-time pressure value is compared with the preset pressure value and the real-time speed value is compared with the preset speed value. If the real-time pressure value is less than the preset pressure value and the real-time speed value is greater than the preset speed value, a control command is sent to the alarm module. The vibration warning unit and the sound warning unit in the alarm module respond to the control command and issue an alarm signal.

[0021] Obtain the response time of the alarm signal, and then compare the response time with the preset time;

[0022] If the response time of the alarm signal is longer than the preset time, a deceleration command is sent to the vehicle speed adjustment module, and the electric bicycle automatically decelerates. When the real-time speed value decelerates to below the preset speed value, the alarm module and the vehicle speed adjustment module stop working at the same time.

[0023] If the warning signal response time is less than the preset time and the real-time pressure value is greater than the preset pressure value, the warning module will stop working.

[0024] Furthermore, processing the vehicle image signal and controlling the operation of the alarm module includes:

[0025] A1. Collect images of vehicles ahead using an infrared thermal imager;

[0026] A2. Then, the ISODATA clustering algorithm is used to identify the vehicles in the image and extract the feature points of the target vehicles;

[0027] A3. Then calculate the real-time vehicle distance based on the feature points and the monocular geometric ranging model;

[0028] A3. If the real-time vehicle distance is not within the preset safe vehicle distance range, an alarm will be sent to the driver via the alarm module.

[0029] Furthermore, it also includes:

[0030] The system acquires real-time tire image information and matches the real-time tire image with a preset abnormal tire image. If the match is successful, the display unit will display "Tire Abnormality" and alert the driver through the sound warning unit.

[0031] The system obtains real-time tire pressure information. If the real-time tire pressure is not within the preset tire pressure range, the display unit will show "abnormal tire pressure" and alert the driver through the sound warning unit.

[0032] Furthermore, it also includes:

[0033] The system acquires real-time motion image information of a person and matches it with preset abnormal image information. If the match is successful, the sound warning unit will issue an alarm signal to alert the thief and record the person's facial image information and send it to the mobile phone.

[0034] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0035] 1. During operation, if the driver's hands are removed from the handlebars, the hands-off detection module will detect the pressure at the handlebars and the speed at the wheels, and transmit this information to the central processing module. After processing, the central processing module will control the alarm module to remind the driver to grip the handlebars again and control the speed adjustment module to slow down the electric bicycle, thereby effectively preventing accidents caused by the driver driving with one hand.

[0036] 2. The anti-collision module enables the electric bicycle to capture images of vehicles ahead. After capturing the images, the module sends them to the central processing module. The central processing module calculates the real-time distance to the vehicle ahead based on the vehicle images and other information, and controls the alarm module to remind the driver to maintain a safe driving distance. This effectively prevents rear-end collisions with other vehicles. Attached Figure Description

[0037] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0038] Figure 1 This is a flowchart illustrating the workflow of the safety warning system of the present invention.

[0039] Figure 2 This is a block diagram illustrating the principle of a geometric ranging method based on ranging feature points and monocular vision.

[0040] Figure 3 This is a flowchart of the tire abnormality detection process of the present invention. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0042] This invention provides a safety warning system for electric bicycles, such as... Figure 1 As shown, it includes:

[0043] The central processing module is used to process input signals and output control signals. The central processing module has a central processor that can be pre-programmed with control, execution and other programs.

[0044] The hands-free detection module includes a pressure detection unit located at the handlebars and a speed detection unit located at the motor. Both the pressure detection unit and the speed detection unit are electrically connected to the central processing module. The pressure detection unit is a capacitive sensor distributed on the handlebars of the electric bicycle, and the speed detection unit is a speed sensor located on the motor.

[0045] The collision avoidance module is electrically connected to the central processing module and is used to acquire image information of vehicles ahead in real time.

[0046] The vehicle speed adjustment module is electrically connected to the central processing unit and is used to adjust the speed of the electric bicycle.

[0047] The alarm module includes a vibration warning unit, an audio warning unit, and a display unit, all of which are electrically connected to the central processing module. The audio warning unit is a buzzer installed on the handlebars of the electric bicycle. The vibration warning unit is a vibrator located on the seat of the electric bicycle. The display unit is a touch screen installed on the handlebars and can display information such as the distance to the vehicle.

[0048] Furthermore, the collision avoidance module includes a vehicle image acquisition unit, which is electrically connected to the central processing module. The central processing module extracts feature points of the target vehicle from the image information acquired by the vehicle image acquisition unit and calculates the real-time vehicle distance based on the monocular geometric ranging model.

[0049] like Figure 2 As shown, in order to achieve accurate vehicle distance monitoring, the vehicle image acquisition unit uses an infrared thermal imager. The specific workflow for real-time vehicle distance detection is as follows:

[0050] 1. Images of vehicles ahead are captured using an infrared thermal imager.

[0051] 2. Then, the ISODATA clustering algorithm is used to identify vehicles in the image and extract feature points of the target vehicles.

[0052] The specific process is as follows:

[0053] (1) Set appropriate initial parameter attributes, take the infrared feature vectors of the images that are farthest apart as the initial cluster centers, use Euclidean distance as the metric, calculate the centroid coordinates of the connected regions of the candidate targets, and use this two-dimensional sample vector set as the initial feature vector set [n1,n2,n2...] of the clustering algorithm;

[0054] (2) Read in the data and determine one or more final cluster centers according to the ISODATA clustering algorithm.

[0055] (3) Using the cluster center as the center, find all candidate target connected regions belonging to these cluster center points.

[0056] (4) Find the minimum bounding rectangle of all target connected regions belonging to the cluster center point. The minimum bounding rectangle region corresponds to the position where the target vehicle in front can be detected.

[0057] (5) When two or more minimum bounding rectangles are connected, find the minimum bounding rectangle region of the multiple connected rectangles. This region corresponds to the position of the target vehicle in front that may be detected.

[0058] (6) Determine the coordinates of the feature point, that is, find the infrared image coordinates (u1, v1) of the midpoint of the lower side of the smallest bounding rectangle. This point corresponds to the ranging feature point of the target vehicle in front of the infrared image; find the coordinates (u2, v2) of the lower mapping point of the vehicle's position on the infrared image plane.

[0059] 3. Then, calculate the real-time vehicle distance based on the feature points and the monocular geometric ranging model.

[0060] The calculated distance between this vehicle and the target vehicle ahead is h. c h c =h1+h2, where h1 is the distance from the front of the vehicle to the nearest field of view of the vehicle-mounted infrared thermal imager, and h2 is the distance from the nearest field of view obtained from the image to the target vehicle in front.

[0061] The imaging process of monocular vision is usually based on the pinhole imaging principle and is described through three transformations of four coordinate systems. Let P be any point on the road plane, and its position on the road plane be (X...). P ,Y P Point P's mapping point on the image plane is P, and its position on the image plane is (x...). P ,y P Using geometric relationships, the mapping relationship between image coordinates and road plane coordinates in a monocular vision ranging system can be easily derived:

[0062]

[0063] in,

[0064] Where H and W are the height and width of the two-dimensional space of the image plane, respectively, h is the installation height of the infrared thermal imager, 2β0 is the horizontal field of view of the infrared thermal imager, 2α0 is the vertical field of view of the infrared thermal imager lens, and γ0 is the downward angle of the infrared thermal imager.

[0065] The road plane coordinates X1, X2, Y1, Y2 are derived from the image plane coordinates u1, u2, v1, v2 based on the geometric mapping relationship mentioned above.

[0066] According to the formula and h c =h1+h2 gives the distance between the current vehicle and the target vehicle ahead.

[0067] When the real-time detected distance to another vehicle is outside the preset safe range, the alarm module will alert the driver through display, sound, and vibration alarms, thereby reducing the risk of rear-end collisions during driving.

[0068] Furthermore, in hot weather, excessively high tire pressure can lead to tire blowouts. Additionally, tire cracks and severe wear pose safety hazards during driving. The system also includes a tire detection module, comprising a tire pressure sensor and a tire image acquisition unit. These are electrically connected to a central processing module. The central processing module acquires real-time tire image information from the tire image acquisition unit and compares it with abnormal tire images in an abnormal tire image database built using Python web scraping and Matlab image optimization. This comparison controls an alarm module to alert the driver in real time. The tire pressure sensor monitors tire pressure in real time to prevent accidents caused by tire blowouts. The central processing module also has communication capabilities, enabling information exchange between the central processing module and the Internet of Things (IoT).

[0069] like Figure 3 As shown, the specific process for detecting tire wear, cracks, and other conditions is as follows:

[0070] First, an abnormal tire image database was established using Python web scraping and Matlab image optimization techniques on the Internet of Things.

[0071] Specifically, the main body of the Python crawler is constructed by defining functions, and it is set to retrieve 150 images at a time. By changing the parameters in the functions, abnormal tire image information can be retrieved separately, and an abnormal tire image database can be established.

[0072] To improve the accuracy of the acquired abnormal tire images, Matlab is a well-established method for image texture feature extraction. This application uses Matlab to perform feature matching on the abnormal tire images obtained from Python web crawlers, calculates the similarity between images, filters out images of the same abnormal tire, and removes other interfering factors to improve the accuracy of abnormal image acquisition. While there are many texture feature extraction methods, this application selects local binary pattern for feature extraction.

[0073] Specifically, the image is reprocessed using Local Binary Patterns (LCPs). LCPs can extract local features as a similarity criterion and have advantages such as rotation invariance and grayscale invariance. The result records the differences between a pixel and its surrounding pixels in the form of a pixel map. The formula for LCPs is as follows:

[0074]

[0075] Among them, (X) c ,y c ) is the center pixel, and its brightness is i. c ; and then i p It is the brightness of adjacent pixels.

[0076] To further improve image similarity, this study chose to use Euclidean distance to quantitatively assess the similarity between images; a smaller Euclidean distance indicates higher similarity. The formula for Euclidean distance is as follows:

[0077]

[0078] Second, by comparing real-time captured tire images with abnormal tire images in the database, it is possible to determine whether the tire is abnormal.

[0079] Specifically, the SURF image algorithm is used to compare the real-time acquired tire image information with the abnormal tire database information.

[0080] Furthermore, given the high incidence of electric bicycle theft, an anti-theft module is included to prevent substantial economic losses caused by theft. This anti-theft module features a human image acquisition unit, which is electrically connected to the central processing module.

[0081] Furthermore, the human image acquisition unit is mounted on the bracket of the rearview mirror. This human image acquisition unit is a rotatable miniature camera. The miniature camera can monitor vehicle information on the electric bicycle from multiple angles and send the real-time acquired human image and motion information to the central processing module.

[0082] The central processing module connects wirelessly to the Internet of Things. Drivers can use their mobile phones or PCs to update preset abnormal tire images and abnormal human action images in real time. Abnormal tire images include images of severely worn tires and cracks, while abnormal human action images include images of non-drivers using tools to damage the vehicle.

[0083] Another aspect of the present invention provides a signal control method for a safety warning system for electric bicycles, applied to the safety warning system for electric bicycles provided in the first aspect above, comprising:

[0084] The system acquires real-time pressure signals collected by the pressure detection unit in the hands-free monitoring module, real-time speed signals collected by the speed detection unit, and vehicle image signals collected by the anti-collision module.

[0085] The pressure and speed signals are processed, and the alarm module and the vehicle speed regulation module are controlled to operate.

[0086] The system processes the vehicle image signal and controls the alarm module to operate.

[0087] In practical use, preset values ​​can be set according to the driver's needs. In this embodiment, the preset time is set to 5 seconds and the preset speed value is set to 2.78 m / s. The pressure sensor on the handlebars of the electric bicycle detects the pressure information on the handlebars in real time and sends the pressure value to the central processing module in real time. The speed sensor on the wheel of the electric bicycle sends the speed value of the wheel to the central processing module in real time. Then, the central processing module compares the pressure value with the preset pressure value and the preset speed value with the real-time speed of the wheel. If the real-time pressure value is less than the preset pressure value and the real-time speed value is greater than 2.78 m / s, the central processing module sends a command to the alarm module. The sound warning unit in the alarm module reminds the driver to grip the handlebars again by sound, and the vibration warning unit on the seat reminds the driver to grip the handlebars again by vibration. If the sound or vibration time exceeds 5 seconds, the central processing module sends a deceleration command to the speed adjustment module, and then the electric bicycle decelerates at 1 m / s. 2 When the acceleration decelerates to below 2.78 m / s, the alarm module also stops working, meaning the buzzer and vibrator stop operating. If the sound or vibration lasts for 5 seconds and the driver grips the handlebars again, the alarm module stops working, and the electric bicycle continues to operate normally.

[0088] Furthermore, processing the pressure signal and speed signal, and controlling the operation of the alarm module and the vehicle speed adjustment module includes:

[0089] The real-time pressure value is compared with the preset pressure value and the real-time speed value is compared with the preset speed value. If the real-time pressure value is less than the preset pressure value and the real-time speed value is greater than the preset speed value, a control command is sent to the alarm module. The vibration warning unit and the sound warning unit in the alarm module respond to the control command and issue an alarm signal.

[0090] Obtain the response time of the alarm signal, and then compare the response time with the preset time;

[0091] If the response time of the alarm signal is longer than the preset time, a deceleration command is sent to the vehicle speed adjustment module, and the electric bicycle automatically decelerates. When the real-time speed value decelerates to below the preset speed value, the alarm module and the vehicle speed adjustment module stop working at the same time.

[0092] If the warning signal response time is less than the preset time and the real-time pressure value is greater than the preset pressure value, the warning module will stop working.

[0093] Furthermore, processing the vehicle image signal and controlling the operation of the alarm module includes:

[0094] A1. Collect images of vehicles ahead using an infrared thermal imager;

[0095] A2. Then, the ISODATA clustering algorithm is used to identify the vehicles in the image and extract the feature points of the target vehicles;

[0096] A3. Then calculate the real-time vehicle distance based on the feature points and the monocular geometric ranging model;

[0097] A3. If the real-time vehicle distance is not within the preset safe vehicle distance range, an alarm will be sent to the driver via the alarm module.

[0098] Furthermore, it also includes:

[0099] The system acquires real-time tire image information and matches the real-time tire image with a preset abnormal tire image. If the match is successful, the display unit will display "Tire Abnormality" and alert the driver through the sound warning unit.

[0100] The system obtains real-time tire pressure information. If the real-time tire pressure is not within the preset tire pressure range, the display unit will show "abnormal tire pressure" and alert the driver through the sound warning unit.

[0101] Furthermore, it also includes:

[0102] The system acquires real-time motion image information of a person and matches it with preset abnormal image information. If the match is successful, the sound warning unit will issue an alarm signal to alert the thief and record the person's facial image information and send it to the mobile phone.

[0103] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A safety warning system for electric bicycles, characterized in that, include: Central processing module, which processes input signals and outputs control signals; The release-of-hand monitoring module includes a pressure detection unit located at the handle and a speed detection unit located at the motor. Both the pressure detection unit and the speed detection unit are electrically connected to the central processing module. The collision avoidance module is electrically connected to the central processing module. The collision avoidance module is an infrared thermal imager used to collect image information of vehicles ahead in real time. The central processing module can identify vehicles in the images collected by the infrared thermal imager and extract the feature points of the target vehicles through the ISODATA clustering algorithm, and calculate the real-time vehicle distance based on the feature points and the monocular geometric ranging model. The vehicle speed adjustment module is electrically connected to the central processing unit and is used to adjust the speed of the electric bicycle. An alarm module includes a vibration warning unit, an audio warning unit, and a display unit. The vibration warning unit is a vibrator distributed on the electric bicycle seat, the audio warning unit is a buzzer installed on the electric bicycle handlebars, and the display unit is a touch screen installed on the electric bicycle handlebars. The vibration warning unit, audio warning unit, and display unit are all electrically connected to the central processing module. The pressure detection unit is used to collect real-time pressure signals; The speed detection unit is used to collect real-time speed signals; This collision avoidance module is used to acquire vehicle image signals; The central processing module is also used to compare the real-time pressure value with the preset pressure value and the real-time speed value with the preset speed value. If the real-time pressure value is less than the preset pressure value and the real-time speed value is greater than the preset speed value, a control command is sent to the alarm module. The vibration warning unit and the sound warning unit in the alarm module are used to simultaneously respond to the control command and issue an alarm signal; The central processing module is also used to acquire the response time of the alarm signal and then compare the response time with the preset time; if the response time of the alarm signal is greater than the preset time, a deceleration command is sent to the vehicle speed adjustment module. The speed regulation module is used to receive a deceleration command sent by the central processing module when the response time of the alarm signal is greater than the preset time, so that the electric bicycle can automatically decelerate at an acceleration of 1m / s² until the real-time speed of the electric bicycle drops below the preset speed value. The central processing module is also used to control the alarm module and the vehicle speed adjustment module to stop working simultaneously when the real-time speed value of the electric bicycle drops below the preset speed value, and to control the warning module to stop working if the response time of the warning signal is less than the preset time and the real-time pressure value is greater than the preset pressure value; the central processing module is also used to process the vehicle image signal and control the alarm module to work.

2. A safety warning system for electric bicycles according to claim 1, characterized in that, The collision avoidance module includes a vehicle image acquisition unit, which is electrically connected to a central processing module. The central processing module extracts feature points of the target vehicle from the image information acquired by the vehicle image acquisition unit and calculates the real-time vehicle distance based on a monocular geometric ranging model.

3. A safety warning system for electric bicycles according to claim 1, characterized in that, It also includes a tire detection module, which includes a tire pressure sensor and a tire image acquisition unit. The tire pressure sensor and the tire image acquisition unit are electrically connected to the central processing module. The central processing module obtains real-time tire image information from the tire image acquisition unit and compares the real-time tire image with abnormal tire images in an abnormal tire image database established using Python web crawlers and Matlab image optimization, thereby controlling the alarm module to remind the driver in real time.

4. A safety warning system for electric bicycles according to claim 1, characterized in that, It also includes an anti-theft module, which has a human image acquisition unit that is electrically connected to the central processing module.

5. A safety warning system for electric bicycles according to claim 1, characterized in that, The vibration warning unit is a vibrator, which is located on the electric bicycle seat. The sound warning unit is a buzzer, which is located on the handlebars of the electric bicycle. The display unit is a touch screen, which is located on the handlebars of the electric bicycle.

6. A signal control method for a safety warning system for electric bicycles, applied to the safety warning system for electric bicycles as described in any one of claims 1-5, characterized in that, include: The system acquires real-time pressure signals collected by the pressure detection unit in the hands-free monitoring module, real-time speed signals collected by the speed detection unit, and vehicle image signals collected by the anti-collision module. The pressure and speed signals are processed, and the alarm module and vehicle speed regulation module are controlled to operate. Process the vehicle image signal and control the alarm module to operate; The process of processing the pressure and speed signals and controlling the operation of the alarm module and the vehicle speed adjustment module includes: The real-time pressure value is compared with the preset pressure value and the real-time speed value is compared with the preset speed value. If the real-time pressure value is less than the preset pressure value and the real-time speed value is greater than the preset speed value, a control command is sent to the alarm module. The vibration warning unit and the sound warning unit in the alarm module respond to the control command and issue an alarm signal. Obtain the response time of the alarm signal, and then compare the response time with the preset time; If the response time of the alarm signal is longer than the preset time, a deceleration command is sent to the vehicle speed adjustment module, and the electric bicycle automatically decelerates. When the real-time speed value decelerates to below the preset speed value, the alarm module and the vehicle speed adjustment module stop working at the same time. If the warning signal response time is less than the preset time and the real-time pressure value is greater than the preset pressure value, the warning module will stop working.

7. A signal control method for a safety warning system for electric bicycles according to claim 6, characterized in that, in, Processing the vehicle image signal and controlling the alarm module includes: A1. Collect images of vehicles ahead using an infrared thermal imager; A2. Then, the ISODATA clustering algorithm is used to identify the vehicles in the image and extract the feature points of the target vehicles; A3. Then calculate the real-time vehicle distance based on the feature points and the monocular geometric ranging model; A3. If the real-time vehicle distance is not within the preset safe vehicle distance range, an alarm will be sent to the driver via the alarm module.

8. A signal control method for a safety warning system for electric bicycles according to claim 6, characterized in that, Also includes: The system acquires real-time tire image information and matches the real-time tire image with a preset abnormal tire image. If the match is successful, the display unit will display "Tire Abnormality" and alert the driver through the sound warning unit. The system obtains real-time tire pressure information. If the real-time tire pressure is not within the preset tire pressure range, the display unit will show "abnormal tire pressure" and alert the driver through the sound warning unit.

9. A signal control method for a safety warning system for an electric bicycle according to claim 6, characterized in that, Also includes: The system acquires real-time motion image information of a person and matches it with preset abnormal image information. If the match is successful, the sound warning unit will issue an alarm signal to alert the thief and record the person's facial image information and send it to the mobile phone.

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