Multi-source information adaptive fusion dimming method applied to two-wheeled electric vehicle

Abstract

The invention relates to a multi-source information adaptive fusion dimming method applied to a two-wheeled electric vehicle. The method comprises the following steps: acquiring environment light information, vehicle state information and a user instruction; inputting the environment light information and the vehicle state information at the current moment into a pre-trained vehicle motion model to obtain an environment light predicted value and a vehicle state predicted value at the next moment; based on the Kalman filtering state equation and the environment light information and the vehicle state information at the current moment, the environment light predicted value and the vehicle state predicted value at the next moment are corrected; according to the corrected ambient light predicted value and the vehicle state predicted value, obtaining target brightness required by the running of the two-wheeled electric vehicle at the next moment and a projection strategy; adjusting the target brightness according to a user instruction; and sending the projection strategy and the adjusted target brightness to a projection module, so that the projection module provides a light source based on the adjusted target brightness, and performs projection based on the projection strategy.

Description

Technical Field

[0001] The present application relates to the technical field of two-wheeled electric vehicles, and in particular to a multi-source information adaptive fusion dimming method applied to two-wheeled electric vehicles. Background Art

[0002] The lighting functions of traditional two-wheeled electric vehicles (such as electric bicycles and electric motorcycles) are single. They can only provide basic lighting in different riding environments such as urban roads, rural roads and mountain roads. Riders still need to rely on mobile phones and other devices to obtain information during riding, which is neither convenient nor safe.

[0003] The lighting fixtures on some mid-range and low-end models are not bright enough, failing to clearly illuminate road details at night or in low-light conditions. Furthermore, the automatic brightness adjustment function on some mid-range and high-end models is very limited, with only simple automatic brightness adjustment based on light sensors. Summary of the Invention

[0004] In order to at least to some extent overcome the problem in the related art that the lighting functions of two-wheeled electric vehicles are single and the automatic brightness adjustment function is limited, the present application provides a multi-source information adaptive fusion dimming method applied to two-wheeled electric vehicles.

[0005] The scheme of this application is as follows:

[0006] A multi-source information adaptive fusion dimming method applied to a two-wheeled electric vehicle, comprising:

[0007] Obtain ambient lighting information, vehicle status information and user instructions;

[0008] Input the current ambient light information and vehicle state information into the pre-trained vehicle motion model to obtain the ambient light prediction value and vehicle state prediction value at the next moment;

[0009] Based on the Kalman filter state equation and the current ambient light information and vehicle state information, the ambient light prediction value and vehicle state prediction value at the next moment are corrected;

[0010] According to the corrected ambient light prediction value and vehicle state prediction value, the target brightness required for the two-wheeled electric vehicle to travel at the next moment and the projection strategy are obtained;

[0011] adjusting the target brightness according to the user instruction;

[0012] The projection strategy and the adjusted target brightness are sent to the projection module, so that the projection module provides a light source based on the adjusted target brightness and performs projection based on the projection strategy.

[0013] Preferably, the method further comprises:

[0014] The target brightness is dynamically compensated according to the vehicle state prediction value at the next moment, the difference between the current vehicle state and the reference vehicle state, and the steering compensation coefficient.

[0015] Preferably, the target brightness required for the two-wheeled electric vehicle to travel at the next moment and the projection strategy are obtained based on the corrected ambient light prediction value and the vehicle state prediction value, including:

[0016] Determine whether the corrected ambient light prediction value and vehicle state prediction value conform to the preset environmental scenario;

[0017] If the preset environment scene is met, the target brightness corresponding to the preset environment scene and the projection strategy are output;

[0018] If it does not meet the preset environmental scene, the target brightness and projection strategy required for the two-wheeled electric vehicle to travel at the next moment are obtained based on the corrected ambient light prediction value and vehicle state prediction value.

[0019] Preferably, the method further comprises:

[0020] Send the projection strategy and adjusted target brightness to the projection module via the CAN bus protocol;

[0021] Receive working status feedback information sent by the projection module via the CAN bus protocol;

[0022] The working status feedback information sent by the projection module is verified by a cyclic redundancy check algorithm;

[0023] If data error occurs in the working status feedback information sent by the projection module, a data retransmission instruction is sent to the projection module.

[0024] Preferably, the method further comprises:

[0025] Monitor the quality and shape of the projected image in real time through the projection module;

[0026] Adaptively adjust the parameters of the optical lens according to the quality and shape of the projection image;

[0027] Adaptively adjust the focal length and focus of the lens based on the predicted ambient light value and projection distance at the next moment.

[0028] Preferably, the method further comprises:

[0029] Obtain vehicle operating condition information and power supply information;

[0030] Adjust the power input and power consumption of the projection module according to the current vehicle operating conditions and the current voltage and current output.

[0031] Preferably, based on the Kalman filter state equation and the ambient light information and vehicle state information at the current moment, the ambient light prediction value and the vehicle state prediction value at the next moment are corrected, including:

[0032] The predicted value of the ambient light and the predicted value of the vehicle state at the next moment are used as the predicted value, and the ambient light information and vehicle state information at the current moment are used as the observed value, and the difference between the predicted value and the observed value is calculated;

[0033] According to the size of the difference, different weights are assigned to the predicted value and the observed value for weighted averaging;

[0034] The weighted averaged predicted value and observed value are input into the Kalman filter state equation, and the corrected ambient light predicted value and vehicle state predicted value at the next moment are output.

[0035] Preferably, the method further comprises:

[0036] Establish communication with the user's mobile APP via Bluetooth or WIFI;

[0037] Receive custom projection strategies sent by users through the mobile APP;

[0038] The customized projection strategy is sent to the projection module, so that the projection module performs projection based on the customized projection strategy.

[0039] Preferably, after receiving the customized projection strategy sent by the user through the mobile APP, the method further includes:

[0040] Upload user-defined projection strategies to the cloud-based resource library and sharing community;

[0041] The method further comprises:

[0042] Receive shared projection strategies downloaded by users through cloud-based material libraries or sharing communities;

[0043] The shared projection strategy is sent to the projection module, so that the projection module performs projection based on the shared projection strategy.

[0044] Preferably, the method further comprises:

[0045] The electromagnetic shielding structure and filtering circuit are used to shield the interference of the vehicle's internal electromagnetic environment on the projection module.

[0046] The technical solution provided by this application may have the following beneficial effects:

[0047] The multi-source information adaptive fusion dimming method applied to a two-wheeled electric vehicle in the present application includes: obtaining ambient light information, vehicle status information and user instructions; inputting the ambient light information and vehicle status information at the current moment into a pre-trained vehicle motion model to obtain an ambient light prediction value and a vehicle status prediction value at the next moment; based on the Kalman filter state equation and the ambient light information and vehicle status information at the current moment, correcting the ambient light prediction value and the vehicle status prediction value at the next moment; obtaining the target brightness required for the two-wheeled electric vehicle to travel at the next moment, and a projection strategy based on the corrected ambient light prediction value and vehicle status prediction value; adjusting the target brightness according to the user instructions; sending the projection strategy and the adjusted target brightness to the projection module, so that the projection module provides a light source based on the adjusted target brightness and performs projection based on the projection strategy.

[0048] In this application, a projection module is used to replace the lighting in existing two-wheeled electric vehicles, and the ambient light information, vehicle status information and user instructions are subjected to multi-source information fusion processing. The ambient light and vehicle status at the next moment are accurately predicted through the vehicle motion model and Kalman filter state equation. Then, the target brightness required for the two-wheeled electric vehicle to travel at the next moment is obtained based on the ambient light prediction value and vehicle status prediction value at the next moment. The appropriate light source can be provided according to the current ambient light, providing users with a more convenient riding experience. The projection strategy required for the two-wheeled electric vehicle to travel at the next moment is also obtained based on the ambient light prediction value and vehicle status prediction value at the next moment. The projection module projects based on the projection strategy, and intuitively presents key information such as navigation instructions and warning signs through projection, assisting users in riding safely and providing users with a smarter travel experience.

[0049] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0051] Figure 1 This is a flow chart of a multi-source information adaptive fusion dimming method applied to a two-wheeled electric vehicle provided by one embodiment of the present application;

[0052] Figure 2 This is a comparative diagram of target brightness and projection strategies corresponding to different preset environmental scenes provided by an embodiment of the present application;

[0053] Figure 3This is a flow chart of interaction with a user in a multi-source information adaptive fusion dimming method applied to a two-wheeled electric vehicle provided by an embodiment of the present application. DETAILED DESCRIPTION

[0054] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0055] Figure 1 This is a flow chart of a multi-source information adaptive fusion dimming method for a two-wheeled electric vehicle provided by an embodiment of the present application, with reference to Figure 1 A multi-source information adaptive fusion dimming method applied to a two-wheeled electric vehicle comprises:

[0056] S11: Acquiring ambient light information, vehicle status information, and user instructions;

[0057] In practice, on-board light sensors or other optical detection equipment are used to monitor the ambient light intensity, direction, and distribution in real time. This step ensures that the system can identify the current external lighting conditions and determine whether the light source needs to be adjusted.

[0058] The vehicle's internal sensors (such as accelerometers, gyroscopes, and speed sensors) collect information about the vehicle's current dynamic state, such as speed, acceleration, steering status, and driving posture. This vehicle status information can help the system predict the vehicle's future motion trends and adjust the light source characteristics accordingly.

[0059] User commands are typically transmitted through interfaces such as buttons on the vehicle's control panel, touchscreen display, wireless remote control, handheld terminal, or voice input. Users can customize the system's brightness or projection effects, such as requesting brighter or softer lighting, to meet their individual needs.

[0060] S12: Inputting the current ambient light information and vehicle state information into a pre-trained vehicle motion model to obtain a predicted ambient light value and vehicle state value at the next moment;

[0061] Vehicle motion models, pre-trained with extensive historical data, can capture the inherent connection between ambient lighting and vehicle status. These models may utilize machine learning, neural networks, or other statistical methods to predict future states. At the current moment, the system inputs collected ambient lighting and vehicle status data into the model, which then predicts the ambient lighting conditions and vehicle dynamics for the next (short-term) moment. This step allows for foresight of future operating conditions, providing a basis for subsequent adjustments.

[0062] S13: Based on the Kalman filter state equation and the current ambient light information and vehicle state information, correct the ambient light prediction value and vehicle state prediction value at the next moment;

[0063] The Kalman filter is an optimal recursive estimation algorithm based on a linear system state-space model. It is primarily used to estimate the internal state of a dynamic system from noisy observational data. Its core idea is to iteratively integrate the system model's predictions with actual observations through a prediction-update cycle, gradually approximating the true state.

[0064] Based on the Kalman filter state equation and the current ambient light information and vehicle status information, the ambient light prediction value and vehicle status prediction value at the next moment are corrected, including:

[0065] The predicted value of the ambient light and the predicted value of the vehicle state at the next moment are used as the predicted value, and the ambient light information and vehicle state information at the current moment are used as the observed value, and the difference between the predicted value and the observed value is calculated;

[0066] According to the size of the difference, different weights are assigned to the predicted value and the observed value for weighted averaging;

[0067] The weighted averaged predicted value and observed value are input into the Kalman filter state equation, and the corrected ambient light predicted value and vehicle state predicted value at the next moment are output.

[0068] In this technical solution, the ambient light intensity sensor is used to obtain the accurate value of the current ambient light intensity in real time, and the vehicle speed sensor is used to obtain the accurate value of the current vehicle speed. The ambient light intensity and vehicle speed predicted values based on historical data and vehicle motion model are subtracted from the sensor observation values to obtain the ambient light intensity difference ΔI and vehicle speed difference Δv. For example, if the predicted light intensity value is Ipred and the observed light intensity value is Iobs, then ΔI=Iobs -Ipred; the predicted vehicle speed value is vpred, and the observed vehicle speed value is vobs, then Δv = vobs-vpred. According to the size of the difference, different weights are assigned to the observed value and the predicted value for weighted averaging to make corrections. For example, if the difference is large, it means that the predicted value has a large deviation. The observed value weight wobs can be increased (such as wobs = 0.7), and the predicted value weight wpred can be reduced to 1-wobs = 0.3. The corrected ambient light intensity estimate Iest = wobs × Iobs + wpred × Ipred, and the corrected vehicle speed estimate vest = wobs × vobs + wpred × vpred. The Kalman filter takes into account the prediction uncertainty and measurement noise through the state equation and the observation equation, thereby obtaining a more accurate state estimate for the next moment. This is critical to the accuracy and robustness of the system response.

[0069] S14: Obtaining the target brightness required for the two-wheeled electric vehicle to travel at the next moment and the projection strategy based on the corrected ambient light prediction value and the vehicle state prediction value;

[0070] The revised prediction reflects environmental changes and vehicle motion at the next moment. The system uses an algorithm to calculate a target brightness appropriate for the current driving scenario. This brightness must not only ensure safe driving and visibility, but also consider energy efficiency and comfort.

[0071] The specific algorithm is as follows:

[0072] Target brightness = α*Ienv+β*v+γ

[0073] Where Ienv represents the corrected ambient light prediction value (unit: lux); v represents the vehicle state prediction value, specifically the vehicle speed estimation value (unit: km / h);

[0074] The values of the remaining coefficients are:

[0075] α=-0.15,β=0.8,γ=5000

[0076] Output range of target brightness: 1000 lux ≤ target brightness ≤ 30000 lux.

[0077] S15: adjusting the target brightness according to user instructions;

[0078] Users may have specific preferences or practical needs, such as preferring higher brightness when traveling at night or reducing glare on rainy days. Based on the existing predicted brightness, the system fine-tunes the target brightness according to the adjustment information entered by the user.

[0079] Adjustments are not limited to numerical changes but may also involve multiple dimensions such as color temperature, contrast, and beam direction. This mechanism ensures system flexibility and personalized user experience while retaining the automated advantages of the prediction system.

[0080] S16: Sending the projection strategy and the adjusted target brightness to the projection module, so that the projection module provides a light source based on the adjusted target brightness and performs projection based on the projection strategy.

[0081] A CAN bus communication network is established between the control module and the projection module. The control module uses the CAN bus protocol to quickly and reliably transmit carefully encoded parameter setting instructions, such as light source brightness and color temperature adjustment values, to the projection module. Upon receiving the instructions, the projection module rapidly executes the corresponding operations based on its own parsing program. This includes adjusting the LED array, laser assembly, or other light source to align the actual light output with the calculated target brightness, and focusing, dispersing, or repositioning the light according to the specified strategy. Ultimately, the system achieves the goal of adaptively controlling lighting based on the external environment, vehicle motion, and user needs, providing optimal lighting and visual guidance.

[0082] In practice, advanced digital micromirror device (DMD) chips or liquid crystal display (LCD) chips are used as core projection components, and professional optical design software is used to optimize the design of projection lenses. The lens must have high resolution and be able to clearly restore the image details on the projection chip. A multi-lens combination is used, and optical problems such as aberration and chromatic aberration are corrected through precise calculation and adjustment of parameters such as lens material, curvature, thickness, and spacing. For example, low-dispersion optical glass is used to reduce chromatic aberration, and aspherical lenses are used to reduce aberration, thereby improving the clarity and uniformity of the projected image. At the same time, mechanisms are designed to adjust the focal length and focus to suit different projection distances and scene requirements.

[0083] High-efficiency heat dissipation technology is crucial. High-resolution projection modules generate significant heat during operation. Failure to dissipate heat promptly will affect chip performance and lifespan, further reducing projection quality. For example, a large copper or aluminum heat sink can be installed at the bottom of the projection module to quickly transfer heat away due to the metal's excellent thermal conductivity. Furthermore, a cooling fan or liquid cooling system can be used to further improve heat dissipation efficiency through forced convection or liquid circulation. A special heat dissipation coating can be applied to the heat sink surface to increase the heat dissipation area and enhance heat dissipation effectiveness.

[0084] Example 2

[0085] It should be noted that the method also includes:

[0086] The target brightness is dynamically compensated according to the vehicle state prediction value at the next moment, the difference between the current vehicle state and the reference vehicle state, and the steering compensation coefficient.

[0087] The specific dynamic compensation is:

[0088] L final=L base×(1+v refΔv)×δturn

[0089] Where L final represents the target brightness after dynamic compensation; L base represents the target brightness before dynamic compensation; Δv represents the difference between the current vehicle state and the reference vehicle state, specifically the difference between the current vehicle speed and the reference speed (20 km / h); δ turn represents the steering compensation coefficient (1.2 for left / right turns and 1 for straight lines).

[0090] The driver's field of vision may be limited when the vehicle turns or undergoes sudden changes in state. Dynamic compensation of target brightness helps reduce the risk of blind spots caused by inappropriate lighting, ensuring that the driver has sufficient and appropriate lighting in all driving conditions, thereby improving road safety.

[0091] Based on the predicted vehicle state at the next moment, the system can anticipate changes in the vehicle's driving process. For example, when a vehicle turns or accelerates, its dynamic characteristics will generate different lighting requirements. By introducing state differences and steering compensation coefficients, the system can dynamically adjust the target brightness, allowing the lighting output to quickly respond to real-time changes in the vehicle's motion state, thereby achieving more precise dynamic control.

[0092] It should be noted that, based on the corrected ambient light prediction value and the vehicle state prediction value, the target brightness required for the two-wheeled electric vehicle to travel at the next moment and the projection strategy are obtained, including:

[0093] Determine whether the corrected ambient light prediction value and vehicle state prediction value conform to the preset environmental scenario;

[0094] If the preset environment scene is met, the target brightness corresponding to the preset environment scene and the projection strategy are output;

[0095] If it does not meet the preset environmental scene, the target brightness and projection strategy required for the two-wheeled electric vehicle to travel at the next moment are obtained based on the corrected ambient light prediction value and vehicle state prediction value.

[0096] In this technical solution, multiple environmental scenes are pre-set, and the target brightness corresponding to each environmental scene and the projection strategy are set, such as Figure 2As shown, you can preset environmental scenarios such as urban low-speed, suburban medium-speed, high-speed at night, inclement weather, and emergency braking. When the corrected ambient light prediction values and vehicle status prediction values meet these environmental scenarios, the corresponding target brightness and projection strategy are directly output. This allows you to leverage pre-tested and optimized solutions to provide an optimal lighting configuration verified in real-world scenarios, ensuring lighting quality and safety in these environments.

[0097] If the corrected ambient light prediction value and vehicle state prediction value do not match any preset scenario, the default strategy is executed. That is, based on the corrected ambient light prediction value and vehicle state prediction value, the target brightness required for the two-wheeled electric vehicle to travel at the next moment and the projection strategy are obtained.

[0098] For example, the trigger condition for speed warning projection is: vehicle speed prediction value > preset speed threshold (default 40km / h);

[0099] The projection strategy at this time is:

[0100] 1. Digital speed display (such as "60km / h");

[0101] 2. Speeding warning icon (red exclamation mark);

[0102] 3.Safety distance warning line (calculated based on vehicle speed).

[0103] The trigger condition for the light intensity warning projection is: the ambient light prediction value < the preset light intensity threshold (default 100 lux);

[0104] The projection strategy at this time is:

[0105] 1. Night mode icon (moon symbol);

[0106] 2. Light intensity level indication (1-5 level progress bar);

[0107] 3. Road condition prompts ahead (such as "about to enter a tunnel").

[0108] Traditional two-wheeled electric vehicles have single-function lights that only provide basic illumination. Existing technologies only provide basic illumination, but this technology solution uses a projection module to project key information, such as navigation instructions and warning signs, onto the road ahead or surrounding surfaces. For example, in complex road conditions, turn arrows and hazard warning icons are projected in advance, allowing riders to intuitively access important information without having to frequently check their phones or dashboards, improving riding convenience and safety.

[0109] When the corrected ambient light prediction value and vehicle status prediction value are determined to be consistent with the preset environmental scenario, the system directly calls the optimized standard parameters, which can quickly and accurately achieve the best lighting effect in common scenarios, improving response speed and stability.

[0110] At the same time, when it is determined that the corrected ambient light prediction value and vehicle status prediction value do not conform to the preset environmental scenario, the system is not limited to a fixed strategy, but calculates the target brightness and projection strategy respectively according to the real-time prediction value, thereby achieving adaptive control in a wider range.

[0111] This dual strategy ensures that the system can not only optimize common scenarios using existing empirical data, but also flexibly respond to emergencies or non-standard environments, enhancing its overall applicability and robustness.

[0112] Example 3

[0113] It should be noted that the method also includes:

[0114] Send the projection strategy and adjusted target brightness to the projection module via the CAN bus protocol;

[0115] Receive working status feedback information sent by the projection module via the CAN bus protocol;

[0116] The working status feedback information sent by the projection module is verified by a cyclic redundancy check algorithm;

[0117] If data error occurs in the working status feedback information sent by the projection module, a data retransmission instruction is sent to the projection module.

[0118] A CAN bus communication network is established between the control module and the projection module. The control module uses the CAN bus protocol to quickly and reliably transmit carefully encoded parameter setting instructions, such as light source brightness and color temperature adjustment values, to the projection module. Upon receiving the instructions, the projection module rapidly executes the corresponding operations based on its own parsing program and transmits operating status feedback, such as the current light source brightness and whether it is functioning properly, back to the control module via the CAN bus. To ensure communication accuracy and stability, a cyclic redundancy check algorithm is used to verify the transmitted data, and any data errors are immediately retransmitted.

[0119] Example 4

[0120] It should be noted that the method also includes:

[0121] Monitor the quality and shape of the projected image in real time through the projection module;

[0122] Adaptively adjust the parameters of the optical lens according to the quality and shape of the projection image;

[0123] Adaptively adjust the focal length and focus of the lens based on the predicted ambient light value and projection distance at the next moment.

[0124] This embodiment automatically adjusts the optical lens parameters to achieve optimal projection quality by monitoring the quality and distortion of the projected image in real time. For example, if the projected image exhibits trapezoidal distortion, the system uses a motor to drive the lens elements within the lens to fine-tune the distortion, automatically correcting the distortion and restoring the projected image to its normal shape. Furthermore, the system automatically adjusts the lens' focal length and focus based on changes in ambient light and projection distance, ensuring that the projected image remains clear and sharp. This adaptive optical correction technology significantly improves the applicability and stability of the projection system, reducing the need for manual adjustments by the user.

[0125] Example 5

[0126] It should be noted that the method also includes:

[0127] Obtain vehicle operating condition information and power supply information;

[0128] Adjust the power input and power consumption of the projection module according to the current vehicle operating conditions and the current voltage and current output.

[0129] This embodiment also automatically identifies and matches the power supply voltage and current specifications of different two-wheeled electric vehicles. Through power conversion circuitry and intelligent control algorithms, the device's power input and power consumption are automatically adjusted under various operating conditions, such as vehicle startup, driving, and charging, ensuring stable operation without impacting the vehicle's existing power supply system. For example, when the vehicle is charging, the device automatically switches to low-power mode to avoid conflicts with the charging process. When the vehicle is traveling at high speed and requires stronger lighting, the power supply output is automatically increased to ensure optimal projection.

[0130] Example 6

[0131] It should be noted that, referring to Figure 3 , the method further comprises:

[0132] Establish communication with the user's mobile APP via Bluetooth or WIFI;

[0133] Receive custom projection strategies sent by users through the mobile APP;

[0134] The custom projection strategy is sent to the projection module, so that the projection module performs projection based on the custom projection strategy.

[0135] This embodiment uses advanced cross-platform development frameworks such as React Native or Flutter to ensure stable operation of the mobile app on both iOS and Android systems. A layered architecture is constructed, comprising a data layer, a business logic layer, and a presentation layer. The data layer is responsible for data interaction with the projection headlight device and local data storage, such as user-defined projection patterns and setting preferences. The business logic layer handles user editing operations, such as cropping and color adjustment of the projected content. The presentation layer provides users with a simple and intuitive user interface, displaying editing options and real-time preview effects through graphical components. A wireless communication connection is established with the user's mobile app using Bluetooth Low Energy (BLE) or Wi-Fi technology. When a user opens the app and searches for nearby devices, the app broadcasts a signal to locate the control module of the two-wheeled electric vehicle and establishes a secure connection once found. Once the connection is established, the app packages the user-edited projection content data, such as custom pattern image data and text information, according to a specific communication protocol and sends it to the control module of the two-wheeled electric vehicle. The app also receives status information returned by the control module, such as connection status and data transmission progress.

[0136] After receiving the custom projection strategy sent by the user through the mobile app, the method further includes:

[0137] Upload user-defined projection strategies to the cloud-based resource library and sharing community;

[0138] The method also includes:

[0139] Receive shared projection strategies downloaded by users through cloud-based material libraries or sharing communities;

[0140] The shared projection strategy is sent to the projection module, so that the projection module performs projection based on the shared projection strategy.

[0141] This technical solution innovatively creates a cloud-based resource library and sharing community. Users can access a rich library of projection materials and upload and share their own creations, promoting communication and creative exchange among users, forming a positive user ecosystem and further expanding product functionality and value.

[0142] Example 7

[0143] It should be noted that the method also includes:

[0144] The electromagnetic shielding structure and filtering circuit are used to shield the interference of the vehicle's internal electromagnetic environment on the projection module.

[0145] This embodiment adopts advanced electromagnetic shielding and anti-interference technology. Through a multi-layer shielding structure and filtering circuit, it effectively isolates the interference of the complex electromagnetic environment inside the vehicle on the device, ensuring the stability and clarity of the projected image.

[0146] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0147] It should be noted that, in the description of this application, the terms "first", "second", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance. In addition, in the description of this application, unless otherwise specified, the meaning of "plurality" refers to at least two.

[0148] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0149] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0150] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0151] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0152] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0153] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0154] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A multi-source information adaptive fusion dimming method applied to a two-wheeled electric vehicle, characterized in that: include: Obtain ambient lighting information, vehicle status information and user instructions; Input the current ambient light information and vehicle state information into the pre-trained vehicle motion model to obtain the ambient light prediction value and vehicle state prediction value at the next moment; Based on the Kalman filter state equation and the current ambient light information and vehicle status information, the ambient light prediction value and vehicle status prediction value at the next moment are corrected; According to the corrected ambient light prediction value and vehicle state prediction value, the target brightness required for the two-wheeled electric vehicle to travel at the next moment and the projection strategy are obtained; adjusting the target brightness according to the user instruction; The projection strategy and the adjusted target brightness are sent to the projection module, so that the projection module provides a light source based on the adjusted target brightness and performs projection based on the projection strategy.

2. The method according to claim 1, characterized in that The method further comprises: The target brightness is dynamically compensated according to the vehicle state prediction value at the next moment, the difference between the current vehicle state and the reference vehicle state, and the steering compensation coefficient.

3. The method according to claim 2, characterized in that Based on the corrected ambient light prediction value and vehicle state prediction value, the target brightness required for the two-wheeled electric vehicle to travel at the next moment and the projection strategy are obtained, including: Determine whether the corrected ambient light prediction value and vehicle state prediction value conform to the preset environmental scenario; If the preset environment scene is met, the target brightness corresponding to the preset environment scene and the projection strategy are output; If it does not meet the preset environmental scene, the target brightness and projection strategy required for the two-wheeled electric vehicle to travel at the next moment are obtained based on the corrected ambient light prediction value and vehicle state prediction value.

4. The method according to claim 2, characterized in that The method further comprises: Send the projection strategy and adjusted target brightness to the projection module via the CAN bus protocol; Receive working status feedback information sent by the projection module via the CAN bus protocol; The working status feedback information sent by the projection module is verified by a cyclic redundancy check algorithm; If data error occurs in the working status feedback information sent by the projection module, a data retransmission instruction is sent to the projection module.

5. The method according to claim 4, characterized in that The method further comprises: Monitor the quality and shape of the projected image in real time through the projection module; Adaptively adjust the parameters of the optical lens according to the quality and shape of the projection image; Adaptively adjust the focal length and focus of the lens based on the predicted ambient light value and projection distance at the next moment.

6. The method according to claim 1, characterized in that The method further comprises: Obtain vehicle operating condition information and power supply information; Adjust the power input and power consumption of the projection module according to the current vehicle operating conditions and the current voltage and current output.

7. The method according to claim 1, characterized in that Based on the Kalman filter state equation and the current ambient light information and vehicle status information, the ambient light prediction value and vehicle status prediction value at the next moment are corrected, including: The predicted value of the ambient light and the predicted value of the vehicle state at the next moment are used as the predicted value, and the ambient light information and vehicle state information at the current moment are used as the observed value, and the difference between the predicted value and the observed value is calculated; According to the size of the difference, different weights are assigned to the predicted value and the observed value for weighted averaging; The weighted averaged predicted value and observed value are input into the Kalman filter state equation, and the corrected ambient light predicted value and vehicle state predicted value at the next moment are output.

8. The method according to claim 1, characterized in that The method further comprises: Establish communication with the user's mobile APP via Bluetooth or WIFI; Receive custom projection strategies sent by users through the mobile APP; The customized projection strategy is sent to the projection module, so that the projection module performs projection based on the customized projection strategy.

9. The method according to claim 8, characterized in that After receiving the customized projection strategy sent by the user through the mobile APP, the method further includes: Upload user-defined projection strategies to the cloud-based resource library and sharing community; The method further comprises: Receive shared projection strategies downloaded by users through cloud-based material libraries or sharing communities; The shared projection strategy is sent to the projection module, so that the projection module performs projection based on the shared projection strategy.

10. The method according to claim 7, characterized in that The method further comprises: The electromagnetic shielding structure and filtering circuit are used to shield the interference of the vehicle's internal electromagnetic environment on the projection module.

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