A safety protection method, device and equipment for an electric motorcycle and a storage medium

Abstract

This application discloses a safety protection method, device, equipment, and storage medium for electric motorcycles. The method includes determining the time-series acceleration change rate data of a target electric motorcycle based on time-series acceleration data acquired periodically from an inertial sensor, and determining the acceleration change rate curve of the target electric motorcycle based on the time-series acceleration change rate data; if the acceleration change rate curve indicates an abnormal situation with the target electric motorcycle, then acquiring the current longitudinal axis angular velocity of the target electric motorcycle from the inertial sensor; determining the target dangerous state of the target electric motorcycle based on the time-series acceleration data, the time-series acceleration change rate data, and the current longitudinal axis angular velocity; and determining a target safety protection strategy from candidate safety protection strategies based on the target dangerous state, and implementing the target safety protection strategy for safety protection. This solution improves the safety of electric motorcycles.

Description

Technical Field

[0001] This application relates to the field of vehicle engineering technology, and more particularly to the field of vehicle safety protection technology, specifically to a safety protection method, device, equipment, and storage medium for an electric motorcycle. Background Technology

[0002] Electric motorcycles are lightweight and maneuverable, but they also have the disadvantage of weak protection. In the event of a collision or rollover, the driver is very likely to be thrown off the motorcycle and suffer a secondary impact, resulting in serious personal injury.

[0003] Current safety measures for electric motorcycles are mostly passive, such as wearing helmets and installing crash bars. These measures can only passively absorb impact forces after an accident, and cannot actively predict or intervene in accident risks, thus their protective effect is limited. Some models have begun to introduce sensors for safety monitoring, but existing sensors mostly monitor single parameters (such as speed sensors and distance sensors), making it difficult to comprehensively and in real time capture the dynamic attitude changes of electric motorcycles, especially at the moment of collision and rollover, and unable to quickly and accurately identify dangerous situations and trigger effective safety protection actions. Summary of the Invention

[0004] This application provides a safety protection method, device, equipment, and storage medium for electric motorcycles to improve the accuracy and reliability of hazard identification for electric motorcycles.

[0005] According to one aspect of this application, a safety protection method for an electric motorcycle is provided. This method is applied to an onboard controller of the target electric motorcycle; the target electric motorcycle further includes an inertial sensor; the inertial sensor is located at the center of gravity of the target electric motorcycle and is serially connected to the onboard controller; the method includes: Based on the time-series acceleration data acquired from the inertial sensor at regular intervals, the time-series acceleration change rate data of the target electric motorcycle is determined, and based on the time-series acceleration change rate data, the acceleration change rate curve of the target electric motorcycle is determined. If it is determined that the target electric motorcycle is in an abnormal condition based on the acceleration rate of change curve, then the current longitudinal axis angular velocity of the target electric motorcycle is obtained from the inertial sensor. The target dangerous state of the target electric motorcycle is determined based on the time-series acceleration data, the time-series acceleration change rate data, and the current longitudinal axis angular velocity. Based on the target dangerous state, a target safety protection strategy is determined from the candidate safety protection strategies, and the target safety protection strategy is adopted to protect the target electric motorcycle.

[0006] According to another aspect of this application, a safety protection device for an electric motorcycle is provided, the device being disposed on the on-board controller of the target electric motorcycle; the target electric motorcycle further includes an inertial sensor; the inertial sensor is located at the center of gravity of the target electric motorcycle and is serially connected to the on-board controller; the device includes: The data processing module is used to determine the time-series acceleration change rate data of the target electric motorcycle based on the time-series acceleration data acquired from the inertial sensor at regular intervals, and to determine the acceleration change rate curve of the target electric motorcycle based on the time-series acceleration change rate data. An anomaly detection module is used to obtain the current longitudinal axis angular velocity of the target electric motorcycle from the inertial sensor if it is determined from the acceleration change rate curve that the target electric motorcycle has an abnormal situation. The state determination module is used to determine the target dangerous state of the target electric motorcycle based on the time-series acceleration data, the time-series acceleration change rate data, and the current longitudinal axis angular velocity. The safety protection module is used to determine a target safety protection strategy from candidate safety protection strategies based on the target dangerous state, and to adopt the target safety protection strategy to provide safety protection for the target electric motorcycle.

[0007] According to another aspect of this application, an electronic device is provided, the electronic device comprising: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement any of the safety protection methods for electric motorcycles provided in the embodiments of this application.

[0008] According to another aspect of this application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements any of the safety protection methods for electric motorcycles provided in the embodiments of this application.

[0009] According to another aspect of this application, a computer program product is provided, including a computer program that, when executed by a processor, implements any of the safety protection methods for electric motorcycles provided in the embodiments of this application.

[0010] This application determines the time-series acceleration change rate data of a target electric motorcycle based on time-series acceleration data acquired periodically from an inertial sensor, and determines the acceleration change rate curve of the target electric motorcycle based on the time-series acceleration change rate data. If the acceleration change rate curve indicates an abnormal situation with the target electric motorcycle, the current longitudinal axis angular velocity of the target electric motorcycle is acquired from the inertial sensor. Based on the time-series acceleration data, the time-series acceleration change rate data, and the current longitudinal axis angular velocity, the target dangerous state of the target electric motorcycle is determined. Based on the target dangerous state, a target safety protection strategy is determined from candidate safety protection strategies and implemented to protect the target electric motorcycle. This technical solution, by detecting abnormal situations with the electric motorcycle based on the acceleration change rate curve, and by comprehensively considering data such as the electric motorcycle's acceleration, acceleration change rate, and angular velocity, identifies the dangerous state of the electric motorcycle and takes targeted protective measures accordingly. This further improves the accuracy and reliability of hazard identification on the basis of achieving automated real-time hazard identification of electric motorcycles. Attached Figure Description

[0011] Figure 1 This is a flowchart of a safety protection method for an electric motorcycle according to Embodiment 1 of this application; Figure 2 This is a flowchart of a safety protection method for an electric motorcycle according to Embodiment 2 of this application; Figure 3 This is a structural schematic diagram of a safety protection device for an electric motorcycle according to Embodiment 3 of this application; Figure 4 This is a schematic diagram of the structure of an electronic device that implements the safety protection method for electric motorcycles according to Embodiment 4 of this application. Detailed Implementation

[0012] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0013] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0014] Furthermore, it should be noted that the collection, storage, use, processing, transmission, provision, and disclosure of time-series acceleration data and time-series acceleration rate of change data, etc., involved in the technical solution of this application, all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0015] Example 1 Figure 1 This is a flowchart of a safety protection method for an electric motorcycle according to Embodiment 1 of this application. This embodiment is applicable to situations involving real-time hazard identification and targeted safety protection of a target electric vehicle. This protection can be implemented by a safety protection device on the electric motorcycle, which can be implemented in hardware and / or software. The safety protection device can be configured in a computer device, such as the onboard controller of the target electric motorcycle. The target electric motorcycle also includes an inertial sensor located at the center of gravity of the motorcycle and serially connected to the onboard controller. Figure 1 As shown, the method includes: S110. Based on the time-series acceleration data acquired from the inertial sensor at regular intervals, determine the time-series acceleration change rate data of the target electric motorcycle, and based on the time-series acceleration change rate data, determine the acceleration change rate curve of the target electric motorcycle.

[0016] Inertial sensors, including accelerometers and gyroscopes, are used to measure the motion state of an object and sense dynamic parameters such as acceleration and angular velocity. This inertial sensor collects motion data of the target electric motorcycle in real time based on a preset sampling period. The target electric motorcycle refers to the electric motorcycle currently undergoing real-time hazard detection and safety protection. Time-series acceleration data refers to the acceleration data of the target electric motorcycle at different time points within a first time window. Time-series acceleration rate of change data refers to the acceleration rate of change data of the target electric motorcycle at different time points within the first time window. The acceleration rate of change curve is a curve used to characterize the rate of change of acceleration at different time points for each coordinate axis; this curve reflects the trend and magnitude of the acceleration rate of change.

[0017] Optionally, based on a first time window, the time-series acceleration data of the target electric motorcycle at the current time point and before the current time point is acquired from the inertial sensor; wherein, the time-series acceleration data is the time-series acceleration data of each coordinate axis of the target electric motorcycle relative to the center of gravity position of the vehicle body in the current coordinate system; based on the time-series acceleration data, the time-series acceleration change rate data of the target electric motorcycle is determined, and based on the time-series acceleration change rate data, the acceleration change rate curve of the target electric motorcycle relative to each coordinate axis of the current coordinate system is determined.

[0018] Here, the first time window refers to the pre-configured time period for data acquisition from the inertial sensor. The current time node refers to the time node when the vehicle controller is currently acquiring data from the inertial sensor. The vehicle's center of gravity position refers to the position of the electric motorcycle's center of mass. The current coordinate system refers to the three-dimensional spatial coordinate system centered on the vehicle's center position.

[0019] For example, based on a preset data acquisition cycle, the system periodically acquires temporal acceleration data of the target electric motorcycle from the inertial sensor, including horizontal axis temporal acceleration data, vertical axis temporal acceleration data, depth axis acceleration data, and vertical axis angular velocity data, at the current time point and the first time window before the current time point. A Kalman filter algorithm is used to filter the temporal acceleration data to remove outliers. The filtered temporal acceleration data is then used to calculate the temporal acceleration rate of change data of the target electric motorcycle. Based on the time points relative to each coordinate axis of the current coordinate system and their corresponding acceleration rates of change in the temporal acceleration rate of change data, an acceleration rate of change curve for the target electric motorcycle is generated.

[0020] Understandably, replacing single-time-node data with time-series data can effectively avoid misjudgments of danger caused by analyzing single-time-node data in complex road conditions such as bumpy roads, and improve the accuracy of hazard identification; using acceleration rate curves can reflect the dynamic performance of vehicles under different conditions; by analyzing the shape and change characteristics of the curves, the system can more accurately identify abnormal driving behaviors (such as sudden acceleration or deceleration), which helps to determine whether there is a danger.

[0021] S120. If it is determined from the acceleration rate of change curve that there is an abnormality in the target electric motorcycle, then the current longitudinal axis angular velocity of the target electric motorcycle is obtained from the inertial sensor.

[0022] Abnormal situations refer to situations where the target electric motorcycle is not being driven normally, such as sudden acceleration or deceleration. The current longitudinal axis angular velocity refers to the roll rate of the target electric motorcycle about the longitudinal axis of a three-dimensional coordinate system centered on the center of the motorcycle.

[0023] S130. Based on the time-series acceleration data, the time-series acceleration rate of change data, and the current longitudinal axis angular velocity, determine the target dangerous state of the target electric motorcycle.

[0024] The target hazard state refers to the current driving state of the target electric motorcycle that is finally determined after hazard identification, which may include at least one of the following: collision hazard state, rollover hazard state, and no hazard state.

[0025] In one alternative implementation, after determining the target dangerous state of the target electric motorcycle, if the target dangerous state is a non-dangerous state, the time-series acceleration data, time-series acceleration rate of change data, acceleration rate of change curve and current longitudinal axis angular velocity are integrated to obtain an abnormal misidentification log, and the abnormal misidentification log is fed back to the developers.

[0026] The "no-hazard state" refers to a state where the target electric motorcycle is not at risk of collision or rollover. The "abnormal misidentification log" records abnormal events or misjudgments that occur during the monitoring of the electric motorcycle; when the system incorrectly identifies a "no-hazard state" as a "hazard state," such a log is generated. This log contains detailed information related to acceleration data, rate of change data, rate of change curves, and longitudinal angular velocity, for subsequent analysis and improvement.

[0027] Understandably, providing abnormal misidentification logs to developers allows them to analyze and understand the reasons for system misjudgments, enabling them to make necessary adjustments and improvements, optimize the hazard identification logic of the vehicle controller, and improve the accuracy and reliability of hazard identification.

[0028] S140. Based on the target's dangerous state, determine the target safety protection strategy from the candidate safety protection strategies, and adopt the target safety protection strategy to protect the target electric motorcycle.

[0029] Among them, candidate safety protection strategies refer to a series of preset safety protection measures in the vehicle control system. Target safety protection strategies refer to the safety protection measures to be taken in response to a dangerous target condition.

[0030] For example, when the target hazard control state is a collision hazard state, the target safety protection strategy may be as follows: send a command to the electric motorcycle's motor controller via the vehicle controller to immediately cut off the motor power output to avoid secondary injuries caused by the motor's continued drive during the accident; trigger the front airbag; send a command to the electric motorcycle's electronic braking system to implement emergency braking, reduce the vehicle's sliding speed, and simultaneously activate the hazard warning lights to warn surrounding vehicles to avoid the accident; obtain the current location information through the vehicle positioning module, combine it with the vehicle identification information, and automatically send a distress signal, including the accident location and vehicle information, to preset emergency contacts, traffic police, and emergency medical services.

[0031] For example, when the target hazard control state is a rollover hazard state, the target safety protection strategy may be as follows: send a command to the electric motorcycle's motor controller via the vehicle controller to immediately cut off the motor power output to avoid secondary injury caused by the motor's continuous drive during the accident; trigger the side airbags, with the airbag triggering time controlled within the triggering delay threshold (e.g., ≤50ms); send a command to the electric motorcycle's electronic braking system to implement emergency braking, reduce the vehicle's sliding speed, and simultaneously activate the hazard warning lights to alert surrounding vehicles to avoid the accident; obtain the current location information through the vehicle positioning module, combine it with the vehicle identification information, and automatically send a distress signal, including the accident location and vehicle information, to preset emergency contacts, traffic police, and emergency medical departments.

[0032] This application embodiment determines the time-series acceleration change rate data of a target electric motorcycle based on time-series acceleration data acquired periodically from an inertial sensor, and determines the acceleration change rate curve of the target electric motorcycle based on the time-series acceleration change rate data. If the acceleration change rate curve indicates an abnormal situation with the target electric motorcycle, the current longitudinal axis angular velocity of the target electric motorcycle is acquired from the inertial sensor. Based on the time-series acceleration data, the time-series acceleration change rate data, and the current longitudinal axis angular velocity, the target dangerous state of the target electric motorcycle is determined. Based on the target dangerous state, a target safety protection strategy is determined from candidate safety protection strategies and implemented to protect the target electric motorcycle. This technical solution, by detecting abnormal situations with the electric motorcycle based on the acceleration change rate curve, and by comprehensively considering data such as the electric motorcycle's acceleration, acceleration change rate, and angular velocity, identifies the dangerous state of the electric motorcycle and takes targeted protective measures accordingly. This further improves the accuracy and reliability of hazard identification on the basis of achieving automated real-time hazard identification of electric motorcycles.

[0033] Example 2 Figure 2 This is a flowchart of a safety protection method for an electric motorcycle according to Embodiment 2 of this application. Based on the technical solutions of the above embodiments, this embodiment refines the process of "determining the target dangerous state of the target electric motorcycle based on time-series acceleration data, time-series acceleration rate of change data, and current longitudinal axis angular velocity" to "determining a target threshold set from a candidate threshold set based on the current acceleration in the time-series acceleration data; wherein the target threshold set includes a target acceleration threshold, a target angular velocity threshold, and a target acceleration rate of change threshold; performing collision hazard identification on the target electric motorcycle based on the target acceleration threshold, the target acceleration rate of change threshold, the time-series acceleration data, and the time-series acceleration rate of change data, obtaining a collision identification result for the target electric motorcycle; performing rollover hazard identification on the target electric motorcycle based on the target acceleration threshold, the target angular velocity threshold, the current longitudinal axis angular velocity, and the time-series acceleration data, obtaining a rollover identification result for the target electric motorcycle; and determining the target dangerous state of the target electric motorcycle based on the collision identification result and the rollover identification result." It should be noted that for parts not detailed in this embodiment, please refer to the relevant descriptions in other embodiments. Figure 2 As shown, the method includes: S210. Based on the time-series acceleration data acquired from the inertial sensor, determine the time-series acceleration change rate data of the target electric motorcycle, and based on the time-series acceleration change rate data, determine the acceleration change rate curve of the target electric motorcycle.

[0034] S220. If it is determined from the acceleration rate of change curve that there is an abnormality in the target electric motorcycle, then the current longitudinal axis angular velocity of the target electric motorcycle is obtained from the inertial sensor.

[0035] S230. Based on the current acceleration in the time-series acceleration data, determine the target threshold set from the candidate threshold set; wherein, the target threshold set includes the target acceleration threshold, the target angular velocity threshold, and the target acceleration rate of change threshold.

[0036] The candidate threshold set refers to a predefined set of selectable thresholds used for safety monitoring to determine whether the electric motorcycle's motion is normal; these thresholds may be set based on factors such as historical data, vehicle performance, or safety standards. The target threshold set refers to the specific set of thresholds determined from the candidate threshold set that matches the current time-series acceleration data. The target acceleration threshold is a specific value in the target threshold set used to determine whether the electric motorcycle's acceleration exceeds a safe range. The target angular velocity threshold is another specific value in the target threshold set, designed to monitor the electric motorcycle's angular velocity (i.e., rotational speed around the longitudinal axis). The target rate of change of acceleration threshold is a threshold used to determine whether the electric motorcycle's rate of acceleration change is within a safe range.

[0037] S240. Based on the target acceleration threshold, the target acceleration rate of change threshold, the time-series acceleration data, and the time-series acceleration rate of change data, a collision hazard identification is performed on the target electric motorcycle to obtain the collision identification result of the target electric motorcycle.

[0038] The collision identification result refers to the identification result used to characterize whether the target electric motorcycle is at risk of collision, which can be either no collision risk or a collision risk.

[0039] Optionally, the target acceleration threshold includes a target collision acceleration threshold; correspondingly, if the absolute value of the horizontal axis temporal acceleration in the temporal acceleration data is greater than the target collision acceleration threshold within a preset continuous time period, and the current horizontal axis acceleration change rate in the temporal acceleration change rate data is greater than the target acceleration change rate threshold, then the target electric motorcycle is determined to be in collision danger; and / or, if the absolute value of the vertical axis temporal acceleration in the temporal acceleration data is greater than the target collision acceleration threshold within a preset continuous time period, and the current vertical axis acceleration change rate in the temporal acceleration change rate data is greater than the target acceleration change rate threshold, then the target electric motorcycle is determined to be in collision danger.

[0040] The target collision acceleration threshold refers to the acceleration level used to detect potential collisions. If the electric motorcycle's acceleration exceeds this threshold, it may indicate a collision risk. The horizontal axis temporal acceleration refers to the horizontal axis acceleration data of the target electric motorcycle at different time points within a first time window. The preset continuous time period refers to a predefined time range during acceleration monitoring; within this time period, if the absolute value of the acceleration continuously exceeds the target collision acceleration threshold, it may indicate that the vehicle is in a collision-hazardous state. The current horizontal axis acceleration change rate refers to the rate at which the electric motorcycle's horizontal axis acceleration changes at the current moment. The vertical axis temporal acceleration refers to the vertical axis acceleration data of the target electric motorcycle at different time points within the first time window. The current vertical axis acceleration change rate refers to the rate at which the electric motorcycle's vertical axis acceleration changes at the current moment.

[0041] For example, when the absolute value of the acceleration along the X-axis or Y-axis is greater than the set collision determination threshold for three consecutive time points, and the rate of change of acceleration is greater than 5 m / s², 3 At that time, it was determined that the electric motorcycle was at risk of collision.

[0042] S250. Based on the target acceleration threshold, target angular velocity threshold, current longitudinal axis angular velocity, and time-series acceleration data, perform rollover hazard identification on the target electric motorcycle and obtain the rollover identification result of the target electric motorcycle.

[0043] Among them, the rollover recognition result refers to the recognition result used to characterize whether the target electric motorcycle has a rollover hazard, which can be either no rollover hazard or a rollover hazard has occurred.

[0044] Optionally, the target acceleration threshold may also include a target rollover acceleration threshold; correspondingly, if the current longitudinal axis angular velocity is greater than the target angular velocity threshold, and the absolute value of the current depth axis acceleration in the time-series acceleration data is less than the target angular velocity threshold, then the target electric motorcycle is determined to be in rollover danger.

[0045] The target rollover acceleration threshold refers to the depth axis acceleration level used to detect potential rollovers; if the electric motorcycle's depth axis acceleration exceeds this threshold, it may indicate a collision risk. The current depth axis acceleration refers to the rate of change of the electric motorcycle's depth axis acceleration at the current moment.

[0046] Furthermore, the longitudinal angular velocity of the target electric motorcycle is obtained from the inertial sensor over a certain period of time. If the current longitudinal angular velocity is greater than the target angular velocity threshold, and the longitudinal angular velocity over a certain period of time is greater than the target angular velocity threshold, and the absolute value of the current depth axis acceleration in the temporal acceleration data is less than the target angular velocity threshold, then the target electric motorcycle is determined to be in danger of rolling over.

[0047] Among them, the temporal longitudinal angular velocity refers to the longitudinal angular velocity data of the target electric motorcycle at different time points within a certain period of time.

[0048] For example, when the absolute value of the roll rate around the Y-axis is greater than the set roll determination threshold and the duration exceeds 0.2s, while the absolute value of the Z-axis acceleration is less than 1m / s², 2 At that time, it was determined that the electric motorcycle was in danger of rolling over.

[0049] S260. Based on the collision recognition results and rollover recognition results, determine the target dangerous state of the target electric motorcycle.

[0050] S270. Based on the target's dangerous state, determine the target safety protection strategy from the candidate safety protection strategies, and adopt the target safety protection strategy to protect the target electric motorcycle.

[0051] In one optional implementation, after providing safety protection for the target electric motorcycle, the time-series acceleration data and time-series longitudinal angular velocity data of the target electric motorcycle in the future time period after the current time point can be obtained from the inertial sensor; based on the time-series acceleration data and time-series longitudinal angular velocity data in the future time period, collision risk identification and rollover risk identification of the target electric motorcycle can be performed, and if the target electric motorcycle is found to be safe, the target danger state can be lifted.

[0052] The future time period refers to the time period from the current moment when data needs to be acquired from the inertial sensor.

[0053] For example, if the temporal acceleration data and temporal longitudinal angular velocity data of the target electric motorcycle obtained from the inertial sensor for a future time period after the current time point are both within the safe threshold range, then the dangerous state is determined to be lifted.

[0054] This application embodiment determines the time-series acceleration change rate data of a target electric motorcycle based on time-series acceleration data acquired periodically from an inertial sensor, and determines the acceleration change rate curve of the target electric motorcycle based on the time-series acceleration change rate data. If the acceleration change rate curve indicates an abnormality in the target electric motorcycle, the current longitudinal axis angular velocity of the target electric motorcycle is acquired from the inertial sensor. Based on the current acceleration in the time-series acceleration data, a target threshold set is determined from a candidate threshold set. The target threshold set includes a target acceleration threshold, a target angular velocity threshold, and a target acceleration change rate threshold. Based on the target acceleration threshold, the target... The system uses a combination of acceleration rate of change threshold, time-series acceleration data, and time-series acceleration rate of change data to identify collision hazards in a target electric motorcycle, resulting in a collision identification result. Based on the target acceleration threshold, target angular velocity threshold, current longitudinal angular velocity, and time-series acceleration data, it also identifies rollover hazards in the target electric motorcycle, resulting in a rollover identification result. Based on the collision and rollover identification results, the system determines the target hazard state of the electric motorcycle. Based on this hazard state, it selects a target safety protection strategy from candidate safety protection strategies and implements it to protect the target electric motorcycle. This technical solution, by detecting abnormal situations in electric motorcycles based on acceleration rate of change curves, and by comprehensively analyzing data such as acceleration, acceleration rate of change, and angular velocity, identifies the hazard state of the electric motorcycle and implements targeted protective measures. This not only achieves automated real-time hazard identification of electric motorcycles but also further improves the accuracy and reliability of hazard identification.

[0055] Example 3 Figure 3 This is a structural schematic diagram of a safety protection device for an electric motorcycle according to Embodiment 3 of this application. It is applicable to situations requiring real-time hazard identification and targeted safety protection of a target electric vehicle. The safety protection device can be implemented in hardware and / or software and can be configured in a computer device, such as the onboard controller of the target electric motorcycle. The target electric motorcycle also includes an inertial sensor located at the center of gravity of the motorcycle and serially connected to the onboard controller. Figure 3 As shown, the device includes: The data processing module 310 is used to determine the time-series acceleration change rate data of the target electric motorcycle based on the time-series acceleration data acquired from the inertial sensor at regular intervals, and to determine the acceleration change rate curve of the target electric motorcycle based on the time-series acceleration change rate data. The anomaly identification module 320 is used to obtain the current longitudinal axis angular velocity of the target electric motorcycle from the inertial sensor if it is determined from the acceleration change rate curve that there is an anomaly in the target electric motorcycle. The state determination module 330 is used to determine the target dangerous state of the target electric motorcycle based on the time-series acceleration data, the time-series acceleration change rate data and the current longitudinal axis angular velocity. The safety protection module 340 is used to determine the target safety protection strategy from the candidate safety protection strategies based on the target's dangerous state, and to adopt the target safety protection strategy to protect the target electric motorcycle.

[0056] This application embodiment determines the time-series acceleration change rate data of a target electric motorcycle based on time-series acceleration data acquired periodically from an inertial sensor, and determines the acceleration change rate curve of the target electric motorcycle based on the time-series acceleration change rate data. If the acceleration change rate curve indicates an abnormal situation with the target electric motorcycle, the current longitudinal axis angular velocity of the target electric motorcycle is acquired from the inertial sensor. Based on the time-series acceleration data, the time-series acceleration change rate data, and the current longitudinal axis angular velocity, the target dangerous state of the target electric motorcycle is determined. Based on the target dangerous state, a target safety protection strategy is determined from candidate safety protection strategies and implemented to protect the target electric motorcycle. This technical solution, by detecting abnormal situations with the electric motorcycle based on the acceleration change rate curve, and by comprehensively considering data such as the electric motorcycle's acceleration, acceleration change rate, and angular velocity, identifies the dangerous state of the electric motorcycle and takes targeted protective measures accordingly. This further improves the accuracy and reliability of hazard identification on the basis of achieving automated real-time hazard identification of electric motorcycles.

[0057] Optionally, the status determination module 330 includes: The threshold filtering unit is used to determine the target threshold set from the candidate threshold set based on the current acceleration in the time-series acceleration data; wherein, the target threshold set includes the target acceleration threshold, the target angular velocity threshold, and the target acceleration rate of change threshold; The collision identification unit is used to identify the collision risk of the target electric motorcycle based on the target acceleration threshold, the target acceleration change rate threshold, the time-series acceleration data, and the time-series acceleration change rate data, and to obtain the collision identification result of the target electric motorcycle. The rollover recognition unit is used to identify the rollover hazard of the target electric motorcycle based on the target acceleration threshold, the target angular velocity threshold, the current longitudinal axis angular velocity, and the time-series acceleration data, and to obtain the rollover recognition result of the target electric motorcycle. The state determination unit is used to determine the target dangerous state of the target electric motorcycle based on the collision recognition results and rollover recognition results.

[0058] Optionally, the target acceleration threshold includes a target collision acceleration threshold; correspondingly, the collision recognition unit is specifically used for: If the absolute value of the horizontal axis temporal acceleration in the temporal acceleration data is greater than the target collision acceleration threshold within a preset continuous time period, and the current horizontal axis acceleration change rate in the temporal acceleration change rate data is greater than the target acceleration change rate threshold, then the target electric motorcycle is determined to be in collision danger. And / or, if the absolute value of the longitudinal acceleration in the temporal acceleration data is greater than the target collision acceleration threshold within a preset continuous time period, and the current longitudinal acceleration change rate in the temporal acceleration change rate data is greater than the target acceleration change rate threshold, then the target electric motorcycle is determined to be in collision danger.

[0059] Optionally, the target acceleration threshold may also include a target roll acceleration threshold; correspondingly, the roll recognition unit is specifically used for: If the current longitudinal axis angular velocity is greater than the target angular velocity threshold, and the absolute value of the current depth axis acceleration in the time-series acceleration data is less than the target angular velocity threshold, then the target electric motorcycle is determined to be in danger of rolling over.

[0060] Optionally, the security module 340 is also used for: After determining the target dangerous state of the target electric motorcycle, if the target dangerous state is not dangerous, the time-series acceleration data, time-series acceleration rate of change data, acceleration rate of change curve and current longitudinal axis angular velocity are integrated to obtain the abnormal misidentification log, and the abnormal misidentification log is fed back to the developers.

[0061] Optionally, the security module 340 is also used for: After providing safety protection for the target electric motorcycle, the system obtains the time-series acceleration data and time-series longitudinal angular velocity data of the target electric motorcycle in the future time period after the current time point from the inertial sensor; based on the time-series acceleration data and time-series longitudinal angular velocity data in the future time period, the system identifies the target electric motorcycle for collision risk and rollover risk, and removes the target danger state if it is found that the target electric motorcycle is not in danger.

[0062] The safety protection device for electric motorcycles provided in this application can execute the safety protection method for electric motorcycles provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects for executing the safety protection method for each electric motorcycle.

[0063] According to embodiments of this application, this application also provides an electronic device, a readable storage medium, and a computer program product.

[0064] Example 4 Figure 4 This is a schematic diagram of the structure of an electronic device 410 implementing the safety protection method for an electric motorcycle according to an embodiment of this application. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0065] like Figure 4 As shown, the electronic device 410 includes at least one processor 411 and a memory, such as a read-only memory (ROM) 412 or a random access memory (RAM) 413, communicatively connected to the at least one processor 411. The memory stores computer programs executable by the at least one processor. The processor 411 can perform various appropriate actions and processes based on the computer program stored in the ROM 412 or loaded from storage unit 418 into the RAM 413. The RAM 413 may also store various programs and data required for the operation of the electronic device 410. The processor 411, ROM 412, and RAM 413 are interconnected via a bus 414. An input / output (I / O) interface 415 is also connected to the bus 414.

[0066] Multiple components in electronic device 410 are connected to I / O interface 415, including: input unit 416, such as keyboard, mouse, etc.; output unit 417, such as various types of displays, speakers, etc.; storage unit 418, such as disk, optical disk, etc.; and communication unit 419, such as network card, modem, wireless transceiver, etc. Communication unit 419 allows electronic device 410 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0067] Processor 411 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 411 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 411 performs the various methods and processes described above, such as safety protection methods for electric motorcycles.

[0068] In some embodiments, the safety protection method for an electric motorcycle can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 418. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 410 via ROM 412 and / or communication unit 419. When the computer program is loaded into RAM 413 and executed by processor 411, one or more steps of the safety protection method for an electric motorcycle described above can be performed. Alternatively, in other embodiments, processor 411 can be configured as the safety protection method for an electric motorcycle by any other suitable means (e.g., by means of firmware).

[0069] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0070] Computer programs used to implement the methods of this application may be written in any combination of one or more programming languages. These computer programs may be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable electric motorcycle safety device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs may be executed entirely on the machine, partially on the machine, or as a standalone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0071] In the context of this application, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0072] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0073] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0074] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0075] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this application can be achieved, and this is not limited herein.

[0076] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A safety protection method for an electric motorcycle, characterized in that, An onboard controller for a target electric motorcycle; the target electric motorcycle further includes an inertial sensor; the inertial sensor is located at the center of gravity of the target electric motorcycle and is serially connected to the onboard controller; the method includes: Based on the time-series acceleration data acquired from the inertial sensor at regular intervals, the time-series acceleration change rate data of the target electric motorcycle is determined, and based on the time-series acceleration change rate data, the acceleration change rate curve of the target electric motorcycle is determined. If it is determined that the target electric motorcycle is in an abnormal condition based on the acceleration rate of change curve, then the current longitudinal axis angular velocity of the target electric motorcycle is obtained from the inertial sensor. The target dangerous state of the target electric motorcycle is determined based on the time-series acceleration data, the time-series acceleration change rate data, and the current longitudinal axis angular velocity. Based on the target dangerous state, a target safety protection strategy is determined from the candidate safety protection strategies, and the target safety protection strategy is adopted to protect the target electric motorcycle.

2. The method according to claim 1, characterized in that, The step of determining the target dangerous state of the target electric motorcycle based on the time-series acceleration data, the time-series acceleration rate of change data, and the current longitudinal axis angular velocity includes: Based on the current acceleration in the time-series acceleration data, a target threshold set is determined from the candidate threshold set; wherein, the target threshold set includes a target acceleration threshold, a target angular velocity threshold, and a target acceleration rate of change threshold; Based on the target acceleration threshold, the target acceleration rate of change threshold, the time-series acceleration data, and the time-series acceleration rate of change data, a collision risk identification is performed on the target electric motorcycle to obtain the collision identification result of the target electric motorcycle. Based on the target acceleration threshold, the target angular velocity threshold, the current longitudinal axis angular velocity, and the time-series acceleration data, the target electric motorcycle is identified for rollover hazard, and the rollover identification result of the target electric motorcycle is obtained. Based on the collision recognition results and the rollover recognition results, the target dangerous state of the target electric motorcycle is determined.

3. The method according to claim 2, characterized in that, The target acceleration threshold includes a target collision acceleration threshold; correspondingly, the step of identifying the collision hazard of the target electric motorcycle based on the target acceleration threshold, the target acceleration rate of change threshold, the time-series acceleration data, and the time-series acceleration rate of change data, to obtain the collision identification result of the target electric motorcycle, includes: If the absolute value of the horizontal axis temporal acceleration in the temporal acceleration data is greater than the target collision acceleration threshold within a preset continuous time period, and the current horizontal axis acceleration change rate in the temporal acceleration change rate data is greater than the target acceleration change rate threshold, then it is determined that the target electric motorcycle is in danger of collision. And / or, if the absolute value of the longitudinal acceleration in the temporal acceleration data is greater than the target collision acceleration threshold within a preset continuous time period, and the current longitudinal acceleration change rate in the temporal acceleration change rate data is greater than the target acceleration change rate threshold, then the target electric motorcycle is determined to be in collision danger.

4. The method according to claim 2, characterized in that, The target acceleration threshold also includes a target rollover acceleration threshold; correspondingly, the step of identifying the rollover hazard of the target electric motorcycle based on the target acceleration threshold, the target angular velocity threshold, the current longitudinal axis angular velocity, and the time-series acceleration data, to obtain the rollover identification result of the target electric motorcycle, includes: If the current longitudinal axis angular velocity is greater than the target angular velocity threshold, and the absolute value of the current depth axis acceleration in the time-series acceleration data is less than the target angular velocity threshold, then the target electric motorcycle is determined to be in danger of rolling over.

5. The method according to claim 1, characterized in that, After determining the target hazardous state of the target electric motorcycle, the method further includes: If the target danger state is a non-danger state, the time-series acceleration data, the time-series acceleration rate of change data, the acceleration rate of change curve, and the current longitudinal axis angular velocity are integrated to obtain an anomaly misidentification log, and the anomaly misidentification log is fed back to the developers.

6. The method according to claim 1, characterized in that, After providing safety protection for the target electric motorcycle, the method further includes: The time-series acceleration data and time-series longitudinal angular velocity data of the target electric motorcycle in the future time period after the current time point are obtained from the inertial sensor. Based on the time-series acceleration data and time-series longitudinal angular velocity data within the future time period, the target electric motorcycle is identified for collision risk and rollover risk. If the target electric motorcycle is found to be safe, the target danger status is lifted.

7. A safety protection device for an electric motorcycle, characterized in that, An onboard controller configured on a target electric motorcycle; the target electric motorcycle also includes an inertial sensor; the inertial sensor is located at the center of gravity of the target electric motorcycle and is serially connected to the onboard controller; the device includes: The data processing module is used to determine the time-series acceleration change rate data of the target electric motorcycle based on the time-series acceleration data acquired from the inertial sensor at regular intervals, and to determine the acceleration change rate curve of the target electric motorcycle based on the time-series acceleration change rate data. An anomaly detection module is used to obtain the current longitudinal axis angular velocity of the target electric motorcycle from the inertial sensor if it is determined from the acceleration change rate curve that the target electric motorcycle has an abnormal situation. The state determination module is used to determine the target dangerous state of the target electric motorcycle based on the time-series acceleration data, the time-series acceleration change rate data, and the current longitudinal axis angular velocity. The safety protection module is used to determine a target safety protection strategy from candidate safety protection strategies based on the target dangerous state, and to adopt the target safety protection strategy to provide safety protection for the target electric motorcycle.

8. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the safety protection method for electric motorcycles as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the safety protection method for electric motorcycles as described in any one of claims 1-6.

10. A computer program product comprising a computer program that, when executed by a processor, implements the safety protection method for an electric motorcycle according to any one of claims 1-6.

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