Accident detection and response method and device based on linkage of helmet and electric bicycle

Abstract

The invention provides an accident detection and response method and device based on linkage of a helmet and an electric bicycle, and the method comprises the steps: obtaining helmet sensor data in real time, and generating and transmitting an accident verification signal packet to the electric bicycle based on the helmet when the helmet sensor data is judged to be impacted; the accident verification signal packet is received based on the electric bicycle, verification is carried out based on the accident verification signal packet and the current bicycle state of the electric bicycle, and an accident verification result is obtained; and under the condition that the accident verification result is that the accident exists, collecting accident field data based on monitoring equipment, and uploading the accident field data to a cloud end. According to the method and the device provided by the invention, reporting can be completed within an extremely short time after an accident occurs, and accident site data can be provided, so that the technical problems of difficulty in evidence collection and slow rescue response in a shared electric bicycle accident are solved, and the riding safety guarantee of a user and the operation efficiency of a platform are greatly improved.

Description

Technical Field

[0001] This invention relates to the field of electric bicycle technology, and in particular to an accident detection and response method and device based on the linkage between a helmet and an electric bicycle. Background Technology

[0002] Safety accidents are frequent in the shared electric bicycle industry, especially falls, collisions, or sudden illnesses while riding alone. Existing vehicles generally only monitor their status via satellite positioning, unable to perceive the rider's physical condition or the impact of a collision. Furthermore, after an accident, the platform cannot immediately ascertain the extent of the rider's injury and lacks accurate accident data records, leading to delayed rescues, difficulty in determining liability, and missing accident data. Moreover, existing vehicle-mounted collision avoidance detection systems are mostly deployed in four-wheeled vehicles or high-cost smart helmets, lacking the ability to directly integrate with the shared electric bicycle system.

[0003] As a result, current electric bicycle accident monitoring relies on data from a single vehicle, making it impossible to accurately perceive the state of accidents involving people and vehicles and to respond to rescue and preserve evidence in a timely manner. Summary of the Invention

[0004] This invention provides an accident detection and response method and device based on the linkage between a helmet and an electric bicycle, which solves the defects in the prior art where accident monitoring of electric bicycles relies on data from a single vehicle, resulting in the inability to accurately perceive the state of accidents involving people and vehicles and to promptly carry out rescue responses and preserve evidence.

[0005] This invention provides an accident detection and response method based on helmet and electric bicycle linkage, comprising: Real-time acquisition of helmet sensor data; if the helmet sensor data indicates an impact, an accident verification signal packet is generated and sent to the electric motorcycle based on the helmet. Based on the electric bicycle receiving the accident verification signal packet, the accident verification result is obtained by verifying the accident verification signal packet and the current vehicle status of the electric bicycle. If the accident verification result indicates that an accident exists, the accident scene data is collected based on the monitoring equipment and uploaded to the cloud.

[0006] According to the present invention, an accident detection and response method based on helmet and electric bicycle linkage is provided, wherein the step of verifying based on the accident verification signal packet and the current vehicle state of the electric bicycle to obtain the accident verification result includes: If the accident verification signal packet indicates that the vehicle has been impacted, and the rate of change of the current vehicle state is greater than a preset change threshold, then the accident verification result indicates that an accident has occurred. The current vehicle status includes at least one of the current vehicle speed and the current vehicle location.

[0007] According to the accident detection and response method based on helmet and electric bicycle linkage provided by the present invention, the step of verifying based on the accident verification signal packet and the current vehicle state of the electric bicycle to obtain the accident verification result further includes: If the accident verification signal packet is subjected to an impact and the rate of change of the current vehicle state is not greater than a preset change threshold, the accident verification result is that no accident exists.

[0008] According to the present invention, an accident detection and response method based on helmet and electric bicycle linkage is provided, wherein the helmet sensor data includes at least one of helmet acceleration data, attitude monitoring data and height data; The helmet acceleration data is obtained based on a three-axis accelerometer; the attitude monitoring data is obtained based on a gyroscope; and the altitude data is obtained based on a barometer.

[0009] According to the present invention, an accident detection and response method based on the linkage between a helmet and an electric bicycle is provided, wherein the helmet further includes a camera and a sound pickup device; The process of collecting accident scene data based on monitoring equipment and uploading the accident scene data to the cloud includes: Based on the current multimedia data collected by the camera and audio pickup device; The current multimedia data and historical multimedia data are used as incident summary data; Based on the accident summary data, accident scene data is obtained and uploaded to the cloud.

[0010] According to the present invention, an accident detection and response method based on helmet and electric bicycle linkage is provided, wherein obtaining accident scene data based on the accident summary data includes: At least one of the helmet sensor data, the current vehicle status, and the current timestamp at which the accident verification result indicates an accident exists shall be used as accident auxiliary data; The accident summary data and the accident auxiliary data are used as the accident scene data.

[0011] The present invention also provides an accident detection and response device based on the linkage between a helmet and an electric bicycle, comprising: The initial judgment unit acquires helmet sensor data in real time. If it determines that the helmet sensor data indicates an impact, it generates and sends an accident verification signal packet to the electric motorcycle based on the helmet. The joint verification unit receives the accident verification signal packet from the electric bicycle and performs verification based on the accident verification signal packet and the current vehicle status of the electric bicycle to obtain the accident verification result. The reporting unit, in the case that the accident verification result indicates the existence of an accident, collects accident scene data based on the monitoring equipment and uploads the accident scene data to the cloud.

[0012] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the accident detection and response method based on helmet and electric bicycle linkage as described above.

[0013] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the accident detection and response method based on helmet and electric bicycle linkage as described above.

[0014] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the accident detection and response method based on helmet and electric bicycle linkage as described above.

[0015] The accident detection and response method and device based on helmet and e-bike linkage provided by this invention does not directly trigger an alarm when the helmet sensor data indicates an impact. Instead, it generates and sends an accident verification signal packet from the helmet to the e-bike. The e-bike receives the accident verification signal packet and performs secondary verification based on the current vehicle status. This dual-end data fusion and cross-verification mechanism significantly reduces the false alarm rate caused by misjudgment, ensuring the accuracy of accident detection. Simultaneously, if the accident verification result indicates an accident, it collects accident scene data from monitoring equipment and uploads it to the cloud, achieving closed-loop management of accident perception. This not only enables reporting within a very short time after an accident but also provides accident scene data, solving the technical challenges of difficult evidence collection and slow rescue response in shared e-bike accidents, greatly improving user riding safety and platform operational efficiency. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is one of the flowcharts of the accident detection and response method based on the linkage between helmet and electric bicycle provided by the present invention; Figure 2 This is the second flowchart of the accident detection and response method based on helmet and electric bicycle linkage provided by the present invention; Figure 3 This is a schematic diagram of the structure of the accident detection and response device based on the linkage between a helmet and an electric bicycle provided by the present invention; Figure 4 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0019] To address the aforementioned problems, this invention provides an accident detection and response method based on the linkage between helmets and electric bicycles, which improves the timeliness and accuracy of accident detection, enables timely and effective accident response, and thereby enhances the safety of electric bicycle riding. Figure 1 This is one of the flowcharts illustrating the accident detection and response method based on helmet and electric bicycle linkage provided by the present invention, such as... Figure 1 As shown, the method includes: Step 110: Acquire helmet sensor data in real time. If the helmet sensor data indicates an impact, generate and send an accident verification signal packet to the electric motorcycle based on the helmet.

[0020] Here, helmet sensor data refers to physical quantity data collected by sensors deployed inside the helmet, reflecting the helmet and the wearer's current posture and stress conditions. It should be noted that helmet sensor data can serve as the first-level input for accident assessment, used to detect whether the cyclist has experienced abnormal, violent movement or impact. In practical applications, helmet sensor data may include, but is not limited to, triaxial acceleration data, angular velocity data, tilt angle data, and height data.

[0021] In addition, the accident verification signal packet here refers to the communication command or data packet generated and sent to the motorcycle after the helmet initially determines that an accident may occur. It is used to trigger the secondary confirmation mechanism on the motorcycle and transform the single-sided helmet perception into a two-sided system linkage. The signal packet may contain the initially determined accident type, the timestamp of the trigger time, and the abnormal helmet sensor data.

[0022] Specifically, firstly, a communication connection needs to be established between the helmet and the electric bicycle. This connection should be a low-latency, stable, short-range communication connection. During riding, the helmet's processor continuously and in real-time reads data from the built-in sensor array. Understandably, the data characteristics of normal riding bumps and the impact of an accident differ significantly; therefore, the acquired acceleration, angular velocity, and other data can be analyzed using a pre-set algorithm.

[0023] When the helmet sensor data changes beyond a preset safety threshold, such as detecting a large instantaneous impact or a sudden tilt, the current state is determined to be an impact. In this case, to avoid false alarms based solely on helmet data (e.g., the helmet accidentally falls but the rider doesn't fall), the helmet does not directly send an alarm to the cloud. Instead, it generates an accident verification signal packet and immediately sends it to the associated motorcycle's onboard control terminal via the aforementioned communication connection, requesting joint verification. For example, the accident verification signal packet can be encapsulated with the accident type, the timestamp of the trigger time, and the abnormal helmet sensor data.

[0024] Step 120: Based on the electric bicycle receiving the accident verification signal packet, the accident verification is performed based on the accident verification signal packet and the current vehicle status of the electric bicycle to obtain the accident verification result.

[0025] The current vehicle status here can include the vehicle's real-time driving speed, the braking status of the braking system, the vehicle's tilt angle, and the vehicle's positioning status.

[0026] Specifically, upon receiving an accident verification signal packet from the helmet, the onboard control module on the e-bike is immediately triggered to enter accident verification mode. At this time, the e-bike's current vehicle status can be retrieved simultaneously. It should be noted that this verification process is essentially a multi-source data fusion judgment process, that is, determining whether the anomaly sensed by the helmet and the vehicle's operating status are logically consistent. For example, if the helmet emits an impact signal, and the vehicle status simultaneously shows rapid deceleration, rollover, or severe vibration, then the two are considered logically consistent, and the accident verification result is that an accident has occurred. Conversely, if the helmet emits an impact signal, but the vehicle is stationary and there is no vibration, it may be determined as a false alarm or the helmet simply falling off, and the accident verification result is that no accident has occurred. Understandably, through this cross-verification mechanism, interference events outside of the riding process can be effectively filtered out, and real accidents can be reported promptly to obtain accident rescue and accident scene data in a timely manner.

[0027] Step 130: If the accident verification result indicates that an accident exists, collect accident scene data based on the monitoring equipment and upload the accident scene data to the cloud.

[0028] Here, accident scene data refers to comprehensive multimedia and status data collected after an accident is confirmed, used to reconstruct the accident process, assist in liability determination, and facilitate rescue efforts. It should be noted that collecting and uploading accident scene data provides the cloud platform with objective accident evidence, including audio and video recordings before and after the accident, the geographical coordinates of the location at the time of the accident, and a summary of sensor data at the moment of the accident.

[0029] Specifically, once the accident verification results confirm the existence of an accident, the emergency response mechanism will be immediately activated to preserve evidence and report it as soon as possible. In the event of an accident verification result, monitoring equipment installed on the helmet or motorcycle can be triggered, such as cameras and microphones on the helmet or monitoring components on the motorcycle, to record or capture audio and video footage before and after the accident. Simultaneously, this video data can be packaged and encapsulated with the current accident location information, time information, and key sensor data triggered at the time of the accident to generate complete accident scene data.

[0030] It should be noted that, to ensure data security and privacy, the data can be encrypted before transmission. Subsequently, using the remote communication module mounted on the electric motorcycle, such as a 4G / 5G / NB-IoT module, the packaged accident scene data is automatically uploaded to the cloud-based emergency platform. Upon receiving the data, the cloud platform can analyze its content to determine the severity of the accident, thus providing a basis for subsequent rescue dispatch or liability determination.

[0031] The method provided in this invention, when determining that the helmet sensor data indicates an impact, does not directly trigger an alarm. Instead, it generates and sends an accident verification signal packet from the helmet to the e-bike. The e-bike receives the accident verification signal packet and performs secondary verification based on the current vehicle status. This dual-end data fusion and cross-verification mechanism significantly reduces the false alarm rate caused by misjudgment, ensuring the accuracy of accident detection. Simultaneously, if the accident verification result indicates an accident, it collects accident scene data from monitoring equipment and uploads it to the cloud, achieving closed-loop management of accident perception. This not only enables reporting within a very short time after an accident but also provides accident scene data, solving the technical challenges of difficult evidence collection and slow rescue response in shared e-bike accidents, greatly improving user riding safety and platform operational efficiency.

[0032] Based on any of the above embodiments, in step 120, verification is performed based on the accident verification signal packet and the current vehicle status of the electric bicycle to obtain the accident verification result, specifically including: If the accident verification signal packet indicates that the vehicle has been impacted, and the rate of change of the current vehicle state is greater than a preset change threshold, then the accident verification result indicates that an accident has occurred. The current vehicle status includes at least one of the current vehicle speed and the current vehicle location.

[0033] Here, the current vehicle state refers to the physical motion state parameters of the motorcycle at the time of receiving the accident verification signal packet or within a very short time window before and after that time. The current vehicle state includes at least one of the following: current vehicle speed and current vehicle location. The current vehicle speed refers to the instantaneous speed of the motorcycle, and the current vehicle location refers to the motorcycle's position information in the geographic spatial coordinate system.

[0034] Furthermore, the vehicle state change rate here refers to the magnitude of change in vehicle state parameters per unit time, reflecting the drastic change in the vehicle's motion state. Physically, it typically corresponds to the rate of change of acceleration, velocity, or displacement. The preset change threshold here refers to a pre-set critical value standard to distinguish between normal riding operations, such as normal braking and turning, and abnormal accident states, such as collision-induced sudden stop or impact displacement. This threshold can be consistent with the data type included in the current vehicle state. It should be noted that this threshold is usually set based on a large amount of experimental data or historical accident models to determine whether an accident has occurred.

[0035] Specifically, firstly, upon confirming that the received accident verification signal packet indicates that an impact event has been detected at the helmet end, the current vehicle state of the electric motorcycle can be calculated and analyzed in real time. If the current vehicle state is selected as the current vehicle speed, the derivative or difference of speed with respect to time can be calculated, i.e., the vehicle's deceleration. When it is detected that the vehicle's speed drops sharply from a high value to zero or close to zero in a very short time, and this deceleration value, i.e., the rate of change of vehicle state, exceeds a preset change threshold, such as exceeding the limit deceleration of normal emergency braking, it is determined that the vehicle has encountered an obstruction or collision.

[0036] If the current vehicle status is set to the current vehicle location, the drift speed or trajectory anomaly of the location coordinates can be monitored. If the coordinates undergo discontinuous jumps or sudden changes beyond the normal driving trajectory range within a very short period of time, and the rate of change exceeds the corresponding threshold, it is also considered an impact. Alternatively, if the current vehicle status is set to the current vehicle speed and current vehicle location, then if the detected deceleration value (i.e., the rate of change of vehicle status) exceeds a preset threshold, and the rate of change of current vehicle location also exceeds a preset threshold, it is determined that the rider has been impacted.

[0037] Therefore, only when the impact signal from the helmet and the drastic change in the state of the motorcycle exceeding the threshold are both met simultaneously will the accident verification result be locked as an accident.

[0038] The method provided in this invention effectively eliminates false alarms in scenarios where the rider and vehicle are separated by strongly correlating the impact sensing of the helmet with the dynamic characteristics of the electric bicycle. For example, if a user accidentally falls off their helmet while the bicycle is parked, although the helmet will emit an impact signal, the speed or position of the electric bicycle will not change drastically at this time, so it will not be judged as an accident. This mechanism ensures that the alarm process is only triggered in real accident scenarios where both the rider and the vehicle experience drastic physical dynamic changes simultaneously, such as a collision or a fall, thereby greatly ensuring the authenticity and reliability of the accident detection results and avoiding resource waste caused by false alarms.

[0039] Based on any of the above embodiments, step 120, which involves verifying the accident verification result based on the accident verification signal packet and the current vehicle status of the electric bicycle, further includes: If the accident verification signal packet is subjected to an impact and the rate of change of the current vehicle state is not greater than a preset change threshold, the accident verification result is that no accident exists.

[0040] Specifically, upon receiving an accident verification signal packet from the helmet, indicating that the helmet's sensors have detected a significant impact, the rate of change of the vehicle's speed and / or the rate of change of its position coordinates are calculated based on the motorcycle's current vehicle state. If the calculated rate of change of the vehicle state is within the normal range, i.e., not exceeding a preset threshold, it indicates that the motorcycle itself has not been impacted by an external force or experienced abnormal movement. For example, the helmet might be accidentally dropped after the rider has finished riding and brought the motorcycle to a complete stop; or the helmet might be slightly scratched by a tree branch while the motorcycle is moving smoothly.

[0041] Understandably, in these scenarios, although the helmet generates impact data, the vehicle's dynamic characteristics show everything is normal. Therefore, based on this dual condition, the helmet's impact signal will be determined as an isolated event or a disruptive event, leading to the conclusion that no accident occurred. Under this conclusion, subsequent alarm procedures are typically terminated or only a low-priority log entry is recorded, without triggering the upload of on-site data to the cloud.

[0042] It should be noted that by setting a verification logic that ensures the vehicle state change rate does not exceed a preset threshold, efficient filtering of false accident signals is achieved. In practical applications, helmets, as wearable devices, are highly susceptible to interference from non-accident-related human operations, such as falls or impacts. The method provided in this embodiment of the invention uses the relatively stable vehicle state of the electric bicycle as a reference system, only triggering an alarm when both the person's and the vehicle's data simultaneously exhibit accident characteristics. When only the helmet data is abnormal while the vehicle data remains stable, false alarms can be automatically identified and blocked. This not only avoids the inconvenience caused to users by frequent false alarms, such as unnecessary inquiries or alarms, but also significantly reduces the consumption of network bandwidth and cloud storage resources by uploading invalid data, greatly improving the robustness and intelligence of the entire accident detection system.

[0043] Based on any of the above embodiments, the helmet sensor data includes at least one of helmet acceleration data, attitude monitoring data, and height data; The helmet acceleration data is obtained based on a three-axis accelerometer; the attitude monitoring data is obtained based on a gyroscope; and the altitude data is obtained based on a barometer.

[0044] Here, helmet sensor data is a comprehensive dataset used to characterize the physical state of the helmet wearer at the moment of the accident. It is not a single-dimensional numerical value, but a collection of multi-source heterogeneous data, which may include at least one of helmet acceleration data, attitude monitoring data, and altitude data.

[0045] The helmet acceleration data is a physical quantity collected by a three-axis accelerometer deployed inside the helmet. Its function is to monitor the linear acceleration changes of the helmet along the X, Y, and Z spatial axes, directly reflecting the magnitude and direction of the instantaneous impact force experienced by the helmet. The attitude monitoring data is angular velocity information collected by a gyroscope, which describes the helmet's rotation, tumbling, or tilting state in space, helping to determine if the rider has rolled over or flipped. Additionally, the altitude data is a value calculated from changes in atmospheric pressure detected by a barometer. Its function is to sense changes in the helmet's vertical displacement, such as detecting whether a fall from riding height has occurred.

[0046] Specifically, during riding, the helmet's built-in microprocessor can synchronously read real-time readings from various sensor modules via bus interfaces such as I2C or SPI. For helmet acceleration data, a triaxial accelerometer can be used to continuously monitor the linear motion state. When a collision occurs, this data will present as a sharp pulse waveform, and the impact intensity can be quantified by calculating the magnitude of the triaxial composite acceleration.

[0047] For posture monitoring data, gyroscopes can be used to track changes in the spatial angle of the helmet in real time. Through integration calculations, the helmet's motion trajectory during the accident can be reconstructed, and abnormal posture features such as violent head shaking or continuous rolling can be identified.

[0048] For altitude data, barometers can be used to monitor changes in relative altitude. Although the absolute altitude of a barometer may be affected by the weather, the relative altitude difference over a short period of time can effectively identify a fall.

[0049] It should be noted that in practical applications, determining whether the helmet sensor data indicates an impact is usually based on a fusion calculation of one or more of the aforementioned data. For example, if the rate of change of acceleration data exceeds a threshold and the height data decreases significantly, it is determined to be a fall from a motorcycle; if only the acceleration data is abnormal while the height and posture do not change significantly, it may simply be a head collision with an obstacle.

[0050] The method provided in this invention, by comprehensively utilizing a triaxial accelerometer, gyroscope, and barometer, can make judgments based on comprehensive accident data including impact force, spatial attitude changes, and vertical displacement. This multi-sensor fusion mechanism significantly improves the helmet's ability to identify complex accident scenarios and can more accurately reconstruct the real physical process at the time of the accident. It provides richer and more accurate judgment basis for subsequent secondary verification on the electric bicycle, thereby effectively avoiding missed or false alarms caused by a single data dimension.

[0051] Based on any of the above embodiments, the helmet also includes a camera and a microphone; In step 130, data from the accident scene is collected based on the monitoring equipment, and the accident scene data is uploaded to the cloud, including: Based on the current multimedia data collected by the camera and audio pickup device; The current multimedia data and historical multimedia data are used as incident summary data; Based on the accident summary data, accident scene data is obtained and uploaded to the cloud.

[0052] Here, "current multimedia data" refers to the audio and video information recorded and captured in real time by the helmet's built-in data acquisition device after the accident verification results confirm the existence of an accident. Its purpose is to record the scene conditions after the accident, such as injuries, vehicle damage, and the surrounding traffic environment. "Historical multimedia data" refers to the audio and video information stored in the device's cache within a preset time period before the accident, such as 30 seconds before the accident. Its purpose is to revisit the scene in the moments before the accident to analyze the cause of the accident, such as whether there was an illegal lane change or whether there were obstacles on the road.

[0053] In addition, the accident summary data is a collection of current multimedia data and historical multimedia data, which constitutes a complete timeline record of the entire accident occurrence process.

[0054] Specifically, the helmet is not just a protective tool; it also integrates a camera and a microphone. During daily riding, the helmet's camera and microphone can continuously record, temporarily storing the collected data in a circular buffer. When the aforementioned steps confirm an accident, a data locking mechanism is immediately triggered. At this point, not only will the helmet continue to collect current multimedia data after the accident via the camera and microphone, but it will also extract historical multimedia data from the buffer immediately preceding the time of the accident.

[0055] Subsequently, these two data sets are combined or packaged to generate accident summary data containing the causes and consequences of the accident. Further, this accident summary data is encapsulated within the accident scene data and uploaded to a cloud server via a wireless network.

[0056] This invention, by introducing a camera and audio pickup device into the helmet and combining them with a historical review mechanism, achieves panoramic evidence preservation of the accident process. Unlike traditional solutions that only record the post-accident state, the method provided by this invention combines current multimedia data with historical multimedia data, providing video footage from before the accident. This is irreplaceable for reconstructing the cyclist's blind spots and determining liability. Therefore, by generating accident summary data containing a complete timeline and uploading it to the cloud, the pain points of difficulty in determining liability and obtaining evidence in traffic accidents are effectively solved, greatly protecting the legitimate rights and interests of all parties.

[0057] Based on any of the above embodiments, accident scene data is obtained based on the accident summary data, including: At least one of the helmet sensor data, the current vehicle status, and the current timestamp at which the accident verification result indicates an accident exists shall be used as accident auxiliary data; The accident summary data and the accident auxiliary data are used as the accident scene data.

[0058] Specifically, when generating the final uploaded data packet, data aggregation operations can be performed. On one hand, the helmet sensor data fragment that triggered the alarm and the current vehicle status record at the moment of the accident can be extracted and stamped with a current timestamp accurate to milliseconds. This structured numerical information is then packaged to generate accident auxiliary data. On the other hand, the generated accident summary data, which includes historical and current multimedia data, can be processed. Subsequently, these two parts of data are correlated and encapsulated to synthesize accident scene data. This data encapsulation can be a logical association, such as creating an index in the data packet header, or it can be physical compression and packaging. After encapsulation, the data packet possesses complete evidentiary value and is ready to be sent to the cloud via the network.

[0059] It should be noted that while video data alone is intuitive, it is difficult to accurately quantify impact force or vehicle speed; while sensor data alone is precise, it lacks a sense of scene reconstruction. Therefore, by integrating accident summary data with accident auxiliary data, a complete chain of evidence combining qualitative and quantitative methods is constructed. This not only greatly improves the scientific rigor and accuracy of accident liability determination but also provides a detailed data foundation for subsequent insurance claims and vehicle safety optimization.

[0060] Based on any of the above embodiments Figure 2 This is the second flowchart of the accident detection and response method based on helmet and electric bicycle linkage provided by the present invention, as shown below. Figure 2 As shown, the system mainly consists of a smart helmet, a vehicle control module, and a cloud platform. Its collaborative workflow and methodology include: First, a connection is established between the smart helmet and the vehicle's control module. To ensure real-time and secure data transmission, the two are paired and interact via a short-range encrypted communication channel.

[0061] During riding, the smart helmet continuously monitors the wearer's posture and stress levels to identify accidents. Once an abnormal impact is detected, the helmet not only records the data itself but also triggers the vehicle control module via the aforementioned communication channel. Upon receiving the signal, the vehicle control module confirms the accident by combining it with the current vehicle dynamics parameters.

[0062] It should be noted that an accident is only considered to have occurred when the accident identification result from the smart helmet and the accident confirmation result from the vehicle control module are logically consistent, meaning both ends determine an anomaly. This determination logic is the trigger point for subsequent processes. Once an accident is confirmed, the system will execute two key tasks in parallel: first, evidence preservation, i.e., locking and saving sensor data and multimedia images before and after the accident to prevent them from being overwritten or lost; second, initiating the cloud emergency process to report the accident information to a remote server. Upon receiving the alarm, the cloud platform automatically triggers an emergency response based on the accident level, such as notifying family members, insurance companies, or dispatching medical assistance. The delay from accident identification to reporting is less than 2 seconds.

[0063] The method provided in this invention firstly achieves hardware decoupling and data tight coupling between the helmet and the vehicle using short-range encrypted communication, ensuring both flexibility in wearing and communication security. Secondly, it treats accident identification and accident confirmation as two independent judgment dimensions that ultimately converge at the accident occurrence node. This dual verification mechanism fundamentally eliminates the possibility of false alarms from a single device, such as a helmet or a vehicle alone. Finally, the parallel processing of evidence preservation and cloud-based emergency response ensures that immutable on-site evidence is preserved within the golden time frame of an accident, while also achieving the fastest possible rescue response, greatly improving the system's reliability and practical value.

[0064] Based on any of the above embodiments Figure 3 This is a schematic diagram of the accident detection and response device based on the linkage between a helmet and an electric bicycle provided by the present invention, as shown below. Figure 3 As shown, the device includes: The initial judgment unit 310 acquires helmet sensor data in real time. If it determines that the helmet sensor data indicates an impact, it generates and sends an accident verification signal packet to the electric motorcycle based on the helmet. The joint verification unit 320 receives the accident verification signal packet from the electric bicycle and performs verification based on the accident verification signal packet and the current vehicle status of the electric bicycle to obtain the accident verification result. The reporting unit 330, in the case that the accident verification result indicates that an accident exists, collects accident scene data based on the monitoring equipment and uploads the accident scene data to the cloud.

[0065] The device provided in this invention, when determining that the helmet sensor data indicates an impact, does not directly trigger an alarm. Instead, it generates and sends an accident verification signal packet to the e-bike based on the helmet's signal. The e-bike receives the accident verification signal packet and performs secondary verification based on the current vehicle status. This dual-end data fusion and cross-verification mechanism significantly reduces the false alarm rate caused by misjudgment, ensuring the accuracy of accident detection. Simultaneously, if the accident verification result indicates an accident, it collects accident scene data based on monitoring equipment and uploads it to the cloud, achieving closed-loop management of accident perception. This not only enables reporting within a very short time after an accident but also provides accident scene data, solving the technical challenges of difficult evidence collection and slow rescue response in shared e-bike accidents, greatly improving user riding safety and platform operational efficiency.

[0066] Based on any of the above embodiments, the joint verification unit is specifically used for: If the accident verification signal packet indicates that the vehicle has been impacted, and the rate of change of the current vehicle state is greater than a preset change threshold, then the accident verification result indicates that an accident has occurred. The current vehicle status includes at least one of the current vehicle speed and the current vehicle location.

[0067] Based on any of the above embodiments, the joint verification unit is further specifically used for: If the accident verification signal packet is subjected to an impact and the rate of change of the current vehicle state is not greater than a preset change threshold, the accident verification result is that no accident exists.

[0068] Based on any of the above embodiments, the helmet sensor data includes at least one of helmet acceleration data, attitude monitoring data, and height data; The helmet acceleration data is obtained based on a three-axis accelerometer; the attitude monitoring data is obtained based on a gyroscope; and the altitude data is obtained based on a barometer.

[0069] Based on any of the above embodiments, the helmet further includes a camera and a sound pickup device; The reporting unit is specifically used for: Based on the current multimedia data collected by the camera and audio pickup device; The current multimedia data and historical multimedia data are used as incident summary data; Based on the accident summary data, accident scene data is obtained and uploaded to the cloud.

[0070] Based on any of the above embodiments, the reporting unit is specifically used for: At least one of the helmet sensor data, the current vehicle status, and the current timestamp at which the accident verification result indicates an accident exists shall be used as accident auxiliary data; The accident summary data and the accident auxiliary data are used as the accident scene data.

[0071] Figure 4 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 4 As shown, the electronic device may include a processor 410, a communication interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 communicate with each other through the communication bus 440. The processor 410 can call logical instructions in the memory 430 to execute an accident detection and response method based on helmet and electric bicycle linkage. The method includes: acquiring helmet sensor data in real time; if the helmet sensor data indicates an impact, generating and sending an accident verification signal packet to the electric bicycle based on the helmet; receiving the accident verification signal packet on the electric bicycle, verifying the accident based on the accident verification signal packet and the current vehicle state of the electric bicycle to obtain an accident verification result; if the accident verification result indicates an accident exists, collecting accident scene data based on monitoring equipment and uploading the accident scene data to the cloud.

[0072] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes 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.

[0073] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the accident detection and response method based on helmet and electric bicycle linkage provided by the above methods. The method includes: acquiring helmet sensor data in real time; generating and sending an accident verification signal packet to the electric bicycle based on the helmet when the helmet sensor data indicates an impact; verifying the accident verification result based on the electric bicycle receiving the accident verification signal packet and the current vehicle state of the electric bicycle; and collecting accident scene data based on monitoring equipment and uploading the accident scene data to the cloud when the accident verification result indicates an accident exists.

[0074] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the accident detection and response method based on helmet and electric bicycle linkage provided by the above methods. The method includes: acquiring helmet sensor data in real time; generating and sending an accident verification signal packet to the electric bicycle based on the helmet when the helmet sensor data indicates an impact; verifying the accident verification result based on the electric bicycle receiving the accident verification signal packet and the current vehicle state of the electric bicycle; and collecting accident scene data based on monitoring equipment and uploading the accident scene data to the cloud when the accident verification result indicates an accident exists.

[0075] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0076] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An accident detection and response method based on helmet and electric bicycle linkage, characterized in that, include: Real-time acquisition of helmet sensor data; if the helmet sensor data indicates an impact, an accident verification signal packet is generated and sent to the electric motorcycle based on the helmet. Based on the electric bicycle receiving the accident verification signal packet, the accident verification result is obtained by verifying the accident verification signal packet and the current vehicle status of the electric bicycle. If the accident verification result indicates that an accident exists, the accident scene data is collected based on the monitoring equipment and uploaded to the cloud.

2. The accident detection and response method based on helmet and electric bicycle linkage according to claim 1, characterized in that, The verification based on the accident verification signal packet and the current vehicle status of the electric bicycle, to obtain the accident verification result, includes: If the accident verification signal packet indicates that the vehicle has been impacted, and the rate of change of the current vehicle state is greater than a preset change threshold, then the accident verification result indicates that an accident has occurred. The current vehicle status includes at least one of the current vehicle speed and the current vehicle location.

3. The accident detection and response method based on helmet and electric bicycle linkage according to claim 1, characterized in that, The step of verifying the accident based on the accident verification signal packet and the current vehicle status of the electric bicycle to obtain the accident verification result also includes: If the accident verification signal packet is subjected to an impact and the rate of change of the current vehicle state is not greater than a preset change threshold, the accident verification result is that no accident exists.

4. The accident detection and response method based on helmet and electric bicycle linkage according to any one of claims 1 to 3, characterized in that, The helmet sensor data includes at least one of helmet acceleration data, attitude monitoring data, and altitude data; The helmet acceleration data is obtained based on a three-axis accelerometer; the attitude monitoring data is obtained based on a gyroscope; and the altitude data is obtained based on a barometer.

5. The accident detection and response method based on helmet and electric bicycle linkage according to claim 4, characterized in that, The helmet also includes a camera and a microphone; The process of collecting accident scene data based on monitoring equipment and uploading the accident scene data to the cloud includes: Based on the current multimedia data collected by the camera and audio pickup device; The current multimedia data and historical multimedia data are used as incident summary data; Based on the accident summary data, accident scene data is obtained and uploaded to the cloud.

6. The accident detection and response method based on helmet and electric bicycle linkage according to claim 5, characterized in that, The process of obtaining accident scene data based on the accident summary data includes: At least one of the helmet sensor data, the current vehicle status, and the current timestamp at which the accident verification result indicates an accident exists shall be used as accident auxiliary data; The accident summary data and the accident auxiliary data are used as the accident scene data.

7. An accident detection and response device based on the linkage between a helmet and an electric bicycle, characterized in that, include: The initial judgment unit acquires helmet sensor data in real time. If it determines that the helmet sensor data indicates an impact, it generates and sends an accident verification signal packet to the electric motorcycle based on the helmet. The joint verification unit receives the accident verification signal packet from the electric bicycle and performs verification based on the accident verification signal packet and the current vehicle status of the electric bicycle to obtain the accident verification result. The reporting unit, in the case that the accident verification result indicates the existence of an accident, collects accident scene data based on the monitoring equipment and uploads the accident scene data to the cloud.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the accident detection and response method based on the linkage between a helmet and a motorcycle as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the accident detection and response method based on the linkage between the helmet and the electric bicycle as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the accident detection and response method based on the linkage between the helmet and the electric bicycle as described in any one of claims 1 to 6.

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