Electric vehicle intelligent switch control method and two-wheeled electric vehicle

Abstract

The invention discloses an electric vehicle intelligent switch control method and a two-wheeled electric vehicle, and relates to the technical field of electric vehicle automatic control. The method comprises the steps that the Bluetooth signal intensity between user side intelligent equipment and a vehicle side is collected in real time, and filtering processing is carried out; meanwhile, human body gait features are obtained in real time; when the intensity of the filtered Bluetooth signal is gradually increased and reaches a set threshold value, and the human body gait feature is recognized as a vehicle owner, the vehicle is automatically started; and when the intensity of the filtered Bluetooth signal is gradually reduced to be smaller than a set threshold value and the human body gait feature is identified as a vehicle owner, the vehicle is automatically stopped. According to the method, automatic startup and shutdown of the vehicle can be realized without any operation of a user, a vehicle owner can be identified, and the vehicle use safety is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle automatic control, in particular to an electric vehicle intelligent switch control method and a two-wheeled electric vehicle. Background Art

[0002] Traditional two-wheeled electric vehicles still generally use mechanical keys or remote control buttons to unlock the vehicle. When the user forgets to bring the key or remote control or loses the key, the electric vehicle cannot be used normally, which reduces the user's car experience. Although some smarter electric vehicles can also use mobile phone APP to control the vehicle's power on and off, when the user's hands are occupied, the above control method is not convenient for manual operation. The above all reflect the problem that traditional two-wheeled electric vehicles are not intelligent enough.

[0003] As the two-wheeled electric vehicle industry moves towards intelligent development, there is an urgent need for an intelligent vehicle control technology that can provide users with a convenient vehicle experience while ensuring safety and realize automatic switching of the vehicle. Summary of the invention

[0004] In view of the above problems and technical requirements, the inventors have proposed an intelligent switch control method for an electric vehicle and a two-wheeled electric vehicle. The technical solution of the present invention is as follows:

[0005] In a first aspect, the present application provides an electric vehicle intelligent switch control method, comprising the following steps:

[0006] Collect the Bluetooth signal strength between the user's smart device and the vehicle in real time and perform filtering;

[0007] At the same time, human gait characteristics are obtained in real time;

[0008] When the strength of the filtered Bluetooth signal gradually increases and reaches the set threshold, and the human gait characteristics are recognized as the owner, the vehicle is automatically started;

[0009] When the strength of the filtered Bluetooth signal gradually decreases to less than the set threshold and the human gait characteristics are recognized as the owner of the vehicle, the vehicle will be automatically shut down.

[0010] A further technical solution is to filter the Bluetooth signal strength, including:

[0011] The real-time collected Bluetooth signal strength is stored in a data queue of a certain length in sequence;

[0012] Deburring the data in the queue;

[0013] The filtered data is averaged according to different weight ratios;

[0014] Repeat the above operation for the continuously updated data queue to obtain a continuous and stable Bluetooth signal strength.

[0015] A further technical solution is to perform burr removal on the data in the queue, including:

[0016] Each data in the queue is traversed. If the value mutation of the current data relative to the previous and next data exceeds the first threshold, the current data is considered as glitch data and is removed from the queue; otherwise, the current data is retained.

[0017] A further technical solution is to perform burr removal on the data in the queue, and further includes:

[0018] When it is detected that the acceleration of the user-side smart device exceeds the speed threshold, the first threshold is correspondingly increased by a certain proportion for each 1g increase in the exceeded acceleration.

[0019] A further technical solution is to average the filtered data according to different weight ratios, including:

[0020] For the filtered data queue, a first weight coefficient is assigned to the data far from the center of the queue, a second weight coefficient is assigned to the data near the center of the queue, and a third weight coefficient is assigned to the data between far and near the center of the queue;

[0021] The data assigned to the same weight coefficient are weighted and summed, and then divided by the total number of data with this type of weight coefficient. Finally, the three types of data are added together to get the average value of the current data queue;

[0022] Among them, the first weight coefficient is the smallest, the second weight coefficient is the largest, and the sum of the first, second and third weight coefficients is 100%.

[0023] Its further technical solution is to average the filtered data according to different weight ratios, and also includes:

[0024] When it is detected that the air humidity is greater than the second threshold, the first weight coefficient is adjusted to increase, and the second weight coefficient is adjusted to decrease, and the sum of the first, second and third weight coefficients is still 100%.

[0025] A further technical solution is that the process of identifying human gait features includes:

[0026] The six-axis sensor in the user-side smart device collects data in six dimensions of the human body during walking in real time;

[0027] The OpenShoe algorithm is used to obtain the current user's gait characteristics, including step length, step speed, and step frequency;

[0028] The human gait features of the current user are compared with the pre-stored gait features of the car owner. When the similarity is higher than a third threshold, the current user is considered to be the car owner.

[0029] A further technical solution is that the process of identifying human gait features also includes:

[0030] When the vibration sensor in the user-side smart device detects that the high pulse collected within one second is lower than the fourth threshold, it is considered that the current user is in a non-walking state;

[0031] The data collected in non-walking state are removed from the data, and then the OpenShoe algorithm is used to obtain the human gait characteristics of the current user.

[0032] Its further technical solution is that the method further comprises:

[0033] The human gait feature data is recorded every time the vehicle is started. The data volume of each type of feature data is accumulated to form a normally distributed data column, so as to update the owner's gait features.

[0034] In a second aspect, the present application provides a two-wheeled electric vehicle, including a central controller and a Bluetooth module placed in the vehicle. The central controller and the user-end smart device transmit data through the Bluetooth module. The central controller contains a computer program. When the computer program is executed by the central controller, the steps of the method provided in the first aspect are implemented.

[0035] The beneficial technical effects of the present invention are:

[0036] This method obtains a continuous and stable Bluetooth signal strength value through filtering, judges the approach and departure of a human body according to the trend change of the Bluetooth signal strength value, and sets a threshold judgment as the condition for turning the vehicle on and off. In addition, by comparing the gait characteristics of the human body, it is judged whether the vehicle is used by the owner himself, which is the primary condition for turning the vehicle on and off. For a two-wheeled electric vehicle equipped with this method, the user can automatically turn the vehicle on and off without any operation, and the owner can also be identified to ensure the safety of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a logic diagram of the electric vehicle intelligent switch control method provided in this application.

[0038] Figure 2 It is a schematic diagram of the system architecture of a two-wheeled electric vehicle provided in this application. DETAILED DESCRIPTION

[0039] The specific implementation of the present invention will be further described below in conjunction with the accompanying drawings.

[0040] Please refer to Figure 1 As shown, an embodiment of the present application provides an electric vehicle intelligent switch control method, which specifically includes the following steps:

[0041] Step 1: Collect the Bluetooth signal strength between the user-side smart device and the vehicle-side in real time and perform filtering. The user-side smart device is usually a mobile phone or smart watch with Bluetooth function, which can be paired with the Bluetooth module on the two-wheeled electric vehicle, and the Bluetooth signal strength between the user-side and the vehicle-side can be measured by RSSI detection technology.

[0042] Step 2: At the same time, the gait characteristics of the human body are obtained in real time. Since each person has specific posture and behavior characteristics when walking, in this embodiment, the six-axis sensor in the user-side smart device can be used to collect six-dimensional data of the human body during walking (i.e., walking data) in real time, and then the gait characteristics of the human body can be further obtained through a specific algorithm.

[0043] Step 3: When the strength of the filtered Bluetooth signal gradually increases and reaches the set threshold, and the human gait characteristics are recognized as the owner of the vehicle, the vehicle is automatically started.

[0044] Step 4: When the strength of the filtered Bluetooth signal gradually decreases to less than the set threshold, and the human gait characteristics are recognized as the owner of the vehicle, the vehicle is automatically shut down.

[0045] In this embodiment, two conditions are set for the automatic on / off of the vehicle. One is to judge the approach and departure of the human body through the trend change of the Bluetooth signal strength value, and set a threshold judgment; the threshold supports free calibration by the user to adapt to the difference in the strength of Bluetooth signals of different mobile phones and improve the reliability of the method; the calibration method can be that the user holds the mobile phone and stands at the position where the vehicle is believed to be able to be turned on and off within the Bluetooth communication distance range at both ends, and the Bluetooth signal strength value detected at the position is used as the set threshold. The other is to judge whether the owner is using the vehicle by comparing the gait characteristics of the human body. The vehicle can only be automatically turned on and off when both conditions are met at the same time.

[0046] Since the Bluetooth signal strength value is extremely unstable and easily affected by environmental factors (temperature, humidity, electromagnetic waves, etc.) and motion interference, the present application filters the real-time collected Bluetooth signal strength, specifically including: storing the real-time collected Bluetooth signal strength in a data queue of a certain length in order, such as collecting one data every 10ms, and collecting 1s continuously as a data column. Deburring the data in the queue, that is, traversing each data in the queue, if the numerical mutation of the current data relative to the previous and next data exceeds the first threshold (such as 30%), the current data is considered to be burr data and is removed from the queue, otherwise the current data is retained. The filtered data is averaged according to different weight ratios, and the above operation is repeated for the continuously updated data queue to obtain a continuous and stable Bluetooth signal strength.

[0047] In addition, in order to reduce interference during the movement process, when it is detected that the acceleration of the user-side smart device exceeds the speed threshold (such as 1.5g), it is judged as the unstable period of the Bluetooth signal. At this time, the Bluetooth signal strength will change more dramatically, so the judgment condition for deburring will be adjusted accordingly, that is, compared with the speed threshold, for every 1g increase in acceleration exceeding the speed threshold, the first threshold will be increased by a certain proportion (such as 5%).

[0048] Among them, the filtered data is averaged according to different weight ratios, specifically including: for the filtered data queue, the first weight coefficient is assigned to the data far from the center of the queue, the second weight coefficient is assigned to the data close to the center of the queue, and the third weight coefficient is assigned to the data between the far and near the center of the queue. Then the data assigned to the same weight coefficient are weighted and summed, and then divided by the total number of data of this type of weight coefficient, and finally the three types of data are added to obtain the average value of the current data queue, that is, the Bluetooth signal strength value of this 1s is obtained. When allocating the weight ratio, the first weight coefficient is set to be the smallest, the second weight coefficient is set to be the largest, and the sum of the first, second and third weight coefficients is 100%. For example, in this embodiment, the first weight coefficient is given as 10%, the second weight coefficient is 70%, and the third weight coefficient is 20%. Continue to collect the Bluetooth signal strength of 0.1 to 1.1s in the above manner to obtain the Bluetooth signal strength value of 1.1s, so as to obtain a continuous and stable Bluetooth signal strength every 0.1s.

[0049] In addition, in order to reduce the interference of environmental factors, when the air humidity is detected to be greater than the second threshold value (such as 80%), the first weight coefficient is adjusted to increase and the second weight coefficient is reduced. The sum of the first, second and third weight coefficients is still 100%. For example, in this embodiment, the first weight coefficient is adjusted to 30%, the second weight coefficient is 60%, and the third weight coefficient is 10%. In this way, the Bluetooth signal strength value of this 1s can be increased to avoid excessive air humidity covering the Bluetooth signal, resulting in a weak signal that cannot be detected.

[0050] In step 2, the collected walking data is used to obtain the human gait characteristics of the current user, including step length, pace, and step frequency, using the OpenShoe algorithm, and the gait characteristics of the owner are obtained by the same method. In step 3, the human gait characteristics of the current user are compared with the pre-stored gait characteristics of the owner. When the similarity is higher than the third threshold, the current user is considered to be the owner. In addition, in order to ensure the accuracy of the OpenShoe algorithm, when the vibration sensor in the user-side smart device detects that the high pulse collected within one second is lower than the fourth threshold (such as 30 times), the current user is considered to be in a non-walking state. The accuracy of the OpenShoe algorithm is low when in a non-walking state, so it is necessary to exclude the data collected in the non-walking state from the walking data, and then use the OpenShoe algorithm to obtain the human gait characteristics of the current user.

[0051] Since a person's gait will change according to the individual's current physical condition, such as walking slower when feeling unwell, or walking faster when in a hurry, this method will also record the human gait feature data each time the vehicle is started. The accumulated data volume of each type of feature data forms a normally distributed data column, which updates the owner's gait features, making the next human gait feature recognition more accurate and adaptable.

[0052] Based on the same inventive concept, another embodiment of the present application further provides a two-wheeled electric vehicle, such as Figure 2 As shown, it includes a central controller and a Bluetooth module placed in the vehicle. The central controller and the user-end smart device transmit data (including Bluetooth data and walking data) through the Bluetooth module. The central controller contains a computer program. When the computer program is executed by the central controller, the steps of the electric vehicle intelligent switch control method introduced above are implemented.

[0053] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0054] The above is only a preferred embodiment of the present application, and the present invention is not limited to the above embodiments. It is understood that other improvements and changes directly derived or associated by those skilled in the art without departing from the spirit and concept of the present invention should be considered to be included in the protection scope of the present invention.

Claims

1. An electric vehicle intelligent switch control method, characterized in that: The method comprises: Collect the Bluetooth signal strength between the user's smart device and the vehicle in real time and perform filtering; At the same time, human gait characteristics are obtained in real time; When the strength of the filtered Bluetooth signal gradually increases and reaches a set threshold, and the human gait characteristics are identified as the owner of the vehicle, the vehicle is automatically started; When the strength of the filtered Bluetooth signal gradually decreases to less than a set threshold and the human gait characteristics are identified as the vehicle owner, the vehicle is automatically shut down.

2. The electric vehicle intelligent switch control method according to claim 1, characterized in that: The Bluetooth signal strength is filtered, including: The real-time collected Bluetooth signal strength is stored in a data queue of a certain length in sequence; Deburring the data in the queue; The filtered data is averaged according to different weight ratios; Repeat the above operation for the continuously updated data queue to obtain a continuous and stable Bluetooth signal strength.

3. The electric vehicle intelligent switch control method according to claim 2, characterized in that: The deburring of the data in the queue includes: Each data in the queue is traversed. If the value mutation of the current data relative to the previous and next data exceeds the first threshold, the current data is considered as glitch data and is removed from the queue; otherwise, the current data is retained.

4. The electric vehicle intelligent switch control method according to claim 3, characterized in that: The deburring of the data in the queue further includes: When it is detected that the acceleration of the user-side smart device exceeds the speed threshold, the first threshold is correspondingly increased by a certain proportion for every 1g increase in the exceeded acceleration.

5. The electric vehicle intelligent switch control method according to claim 2, characterized in that: The filtered data is averaged according to different weight ratios, including: For the filtered data queue, a first weight coefficient is assigned to the data far from the center of the queue, a second weight coefficient is assigned to the data near the center of the queue, and a third weight coefficient is assigned to the data between far and near the center of the queue; The data assigned to the same weight coefficient are weighted and summed, and then divided by the total number of data with this type of weight coefficient. Finally, the three types of data are added together to get the average value of the current data queue; Among them, the first weight coefficient is the smallest, the second weight coefficient is the largest, and the sum of the first, second and third weight coefficients is 100%.

6. The electric vehicle intelligent switch control method according to claim 5, characterized in that: The averaging of the filtered data according to different weight ratios also includes: When it is detected that the air humidity is greater than the second threshold, the first weight coefficient is adjusted to increase, and the second weight coefficient is adjusted to decrease, and the sum of the first, second and third weight coefficients is still 100%.

7. The electric vehicle intelligent switch control method according to claim 1, characterized in that: The recognition process of the human gait feature includes: The six-axis sensor in the user-side smart device collects data of six dimensions of the human body during walking in real time; Using the OpenShoe algorithm to obtain the human gait characteristics of the current user, including step length, step speed, and step frequency; The human gait features of the current user are compared with the pre-stored gait features of the vehicle owner. When the similarity is higher than a third threshold, it is considered that the current user is the vehicle owner.

8. The electric vehicle intelligent switch control method according to claim 7, characterized in that: The recognition process of the human gait feature also includes: When the vibration sensor in the user-side smart device detects that the high pulse collected within one second is lower than the fourth threshold, it is considered that the current user is in a non-walking state; The data collected in a non-walking state is removed from the data, and the human gait characteristics of the current user are obtained using the OpenShoe algorithm.

9. The electric vehicle intelligent switch control method according to claim 7, characterized in that: The method further comprises: The human gait characteristic data is recorded each time the vehicle is started, and the data volume of each type of characteristic data is accumulated to form a normally distributed data column, so as to update the gait characteristics of the vehicle owner.

10. A two-wheeled electric vehicle, characterized in that: It includes a central controller and a Bluetooth module placed in a car, the central controller and the user-end smart device transmit data through the Bluetooth module, the central controller contains a computer program, and when the computer program is executed by the central controller, the steps of the method described in any one of claims 1 to 9 are implemented.

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