Self-adaptive safety control system of intelligent child electric vehicle

Abstract

The invention relates to the technical field of safety control, and provides a self-adaptive safety control system for an intelligent child electric vehicle, which comprises an identification and analysis module, a control module and a self-adaptive safety control module, and is characterized in that the identification and analysis module adopts a multi-modal sensor to identify and analyze the age of a driver; the behavior monitoring module obtains driving operation information; the battery management module monitors the state of a battery of the child electric vehicle; the safety control module performs risk assessment through a risk assessment model according to the driving operation information and the driver age, and generates a safety control instruction of the child electric vehicle motor based on a risk assessment result according to the driving operation information and the driver age in combination with the current battery information; and the early warning intervention module carries out early warning prompt according to the risk assessment result and carries out control intervention according to feedback information of the early warning prompt. Intelligent safety upgrading from passive protection to active intervention is achieved, the child electric vehicle can conduct safety control in a self-adaptive mode according to the age of a driver, the intelligent level of the child electric vehicle is improved, and the safety of the driver is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of safety control technology, and in particular to an intelligent adaptive safety control system for children's electric vehicles. Background Technology

[0002] With the development of technology and the improvement of people's living standards, children's electric vehicles, as a new type of children's toy and means of transportation, have become increasingly popular among parents and children. Currently, the functions of children's electric vehicles on the market are becoming increasingly complex, and their speeds have also increased. However, their safety control systems remain relatively simple and passive, unable to adapt to the needs of children of different ages. For younger or beginner children, the fixed maximum speed may still be too fast, posing a risk of loss of control. For older, more experienced children, the fixed low speed cannot meet their entertainment needs, leading to a decreased driving experience. Furthermore, existing children's electric vehicles lack monitoring and guidance of children's driving behavior, failing to recognize dangerous driving behaviors such as frequent rapid acceleration and sharp turns at high speeds, and unable to provide warnings or interventions. This leads to children developing bad driving habits and creating safety hazards. Therefore, existing technological solutions have significant safety risks and management deficiencies. Thus, there is an urgent need for an intelligent adaptive safety control system for children's electric vehicles to at least solve some of the above problems. Summary of the Invention

[0003] The purpose of this invention is to provide an intelligent adaptive safety control system for children's electric vehicles, which realizes an intelligent safety upgrade from passive protection to active intervention, enabling children's electric vehicles to adaptively control safety based on the driver's age, improving the intelligence level of children's electric vehicles and ensuring the safety of the driver.

[0004] To achieve the above objectives, the present invention provides the following technical solution: an intelligent child electric vehicle adaptive safety control system, comprising: The identification and analysis module is used to identify and analyze the driver's age using multimodal sensors to determine the driver's age; The behavior monitoring module is used to acquire driving operation information; The battery management module is used to monitor the status of the battery in children's electric vehicles and determine the current battery information. The safety control module is used to conduct risk assessment based on driving operation information and driver age through a risk assessment model, obtain risk assessment results, and based on the risk assessment results, analyze the motor of the child electric vehicle according to the driving operation information, driver age, and current battery information to determine safety control commands. Then, the module controls the motor's operating state according to the safety control commands to achieve driving control of the child electric vehicle. The early warning and intervention module is used to issue early warnings based on the risk assessment results and to implement control interventions based on the feedback information from the early warnings.

[0005] Preferably, the behavior monitoring module includes: a first monitoring unit and a second monitoring unit; The first monitoring unit is used to monitor the driver's driving operation behavior, determine the driver's operation behavior, and obtain driving operation information; The second monitoring unit is used to receive and monitor the driving control information sent by the mobile control terminal, determine the driving control information, and obtain driving operation information.

[0006] Preferably, the early warning and intervention module includes: an early warning unit and an intervention unit; The warning unit is used to issue a warning signal based on the risk assessment result. When the risk assessment result indicates that the current driving operation poses a safety hazard to the driver, it will issue a dangerous driving warning in conjunction with the driving operation information. The intervention unit is used to obtain driving operation information after a dangerous driving warning is issued through the behavior monitoring module at a preset time, obtain warning feedback information, and perform control intervention based on the warning feedback information.

[0007] Preferably, the safety control module performs a risk assessment based on driving operation information and the driver's age using a risk assessment model, including: The target baseline risk assessment model is obtained by loading the corresponding baseline risk assessment model based on the driver's age; Perform current state feature analysis on children's electric vehicles to obtain their state feature vectors. Risk assessment results are determined by combining the target baseline risk assessment model with the characteristic vector of child electric vehicles.

[0008] Preferably, the safety control command includes: a first safety control command and a second safety control command. When generating a safety control command based on the risk assessment results, driving operation information, driver age, and current battery information, a safe driving control command is generated based on the driving operation information, driver age, and current battery information to obtain the first safety control command; a power safety supply control command is generated based on the first safety control command to obtain the second safety control command.

[0009] Preferably, safe driving control commands are generated based on driving operation information combined with the driver's age and current battery information, including: The battery status of children's electric vehicles is determined based on current battery information and battery safety threshold analysis. Calculate the battery adaptive coefficient based on the battery status; Based on the analysis of the battery's actual power supply using the battery adaptive coefficient, the current safe power boundary is determined. Using the current safe power boundary as a constraint, safe driving control commands are determined within the current safe power boundary based on driving operation information and driver age.

[0010] Preferably, generating a power safety supply control command based on the first safety control command includes: Based on the analysis of the first safety control command, the battery demand of the motor operation control of the child electric vehicle is determined. Verification was conducted based on battery demand information and current safe power limits; After successful verification, the battery demand information is combined with the battery adaptive coefficient analysis to determine the battery output control information, thus obtaining the second safety control command.

[0011] Preferably, when the battery management module monitors the status of the battery of the children's electric vehicle, it acquires the current battery information in real time and provides charging prompts and charging management based on the current battery information.

[0012] Preferably, the intelligent child electric vehicle adaptive safety control system further includes: a remote monitoring module; The remote monitoring module is used to monitor and record the use of children's electric vehicles, obtain information on their usage, acquire risk assessment results during their use, statistically record the risk assessment results, and then report on safe driving based on the usage of the children's electric vehicles and the statistical data.

[0013] Preferably, the remote monitoring module includes: an electronic fence configuration unit, which is used to obtain the usage range information set by the parents for the child electric vehicle, and generate an electronic fence based on the usage range information; then the early warning and intervention module provides early warning prompts and gradual control intervention based on the real-time location of the child electric vehicle and the electronic fence.

[0014] The present invention has achieved the following beneficial effects: This invention achieves an intelligent safety upgrade from passive protection to active intervention, enabling children's electric vehicles to adaptively control safety based on the driver's age. It fully considers the differences in perception between drivers of different ages, which not only ensures the driver's safety but also enhances the driver's user experience, effectively improving the intelligence level of children's electric vehicles.

[0015] This invention utilizes an identification and analysis module to obtain the driver's age, enabling the determination of safety control commands based on the driver's age when controlling the children's electric vehicle. It fully considers the different sensitivities of different ages to vehicle status, providing greater safety and a better user experience. Furthermore, it employs multi-modal sensors for multi-factor information collection and driver age identification analysis, reducing age analysis errors and improving the accuracy of driver age determination. A behavior monitoring module acquires driving operation information in real time, allowing for timely responses after the driver or accompanying person performs driving operations, avoiding prolonged waiting times that negatively impact the user experience. A battery management module monitors the battery, ensuring sufficient energy storage to prevent insufficient power from affecting the child's use, and ensuring stable power output to prevent over-discharge from affecting battery life. This ensures stable power output under different operating conditions, preventing performance fluctuations caused by insufficient or excessive power supply that could affect the control stability of the electric vehicle's motor operation, thus improving the control stability of the intelligent children's electric vehicle's adaptive safety control system. The safety control module utilizes a risk assessment model to conduct precise risk assessments, anticipating potential hazards and proactively intervening to enhance the safety management of child-friendly electric vehicles, reducing the probability of accidents. Furthermore, safety control commands are directly applied to the motor control of the electric vehicle, enabling real-time control of its driving status and improving control efficiency. Risk assessments are also combined with the driver's age to avoid different safety hazards for drivers of different ages under the same driving conditions, meeting the needs of drivers of all ages and ensuring safe driving of child-friendly electric vehicles. This improves the control flexibility of the electric vehicles, maximizes driver safety during operation, and enhances the driving experience. The early warning intervention module provides timely alerts when the current driving operation poses a safety hazard, increasing driver vigilance and preventing accidents caused by improper operation. This helps cultivate standardized driving habits and provides a guarantee for driving safety.

[0016] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the application.

[0017] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0018] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the intelligent child electric vehicle adaptive safety control system described in this invention; Figure 2 A schematic diagram illustrating the steps of risk assessment in the safety control module of the intelligent child electric vehicle adaptive safety control system described in this invention; Figure 3 A schematic diagram illustrating the steps of generating a first safety control command in the adaptive safety control system for an intelligent children's electric vehicle according to the present invention; Figure 4 A schematic diagram illustrating the steps of generating a second safety control command in the adaptive safety control system for an intelligent children's electric vehicle according to the present invention; Figure 5 This is another schematic diagram of the intelligent child electric vehicle adaptive safety control system described in this invention. Detailed Implementation

[0019] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0020] This invention provides an intelligent adaptive safety control system for children's electric vehicles, comprising: an identification and analysis module, a behavior monitoring module, a safety control module, and an early warning and intervention module. Figure 1 As shown, the identification and analysis module and the behavior monitoring module are connected to the security control module, and the early warning and intervention module is connected to both the behavior monitoring module and the early warning and intervention module.

[0021] The identification and analysis module is used to identify and analyze the driver's age using multimodal sensors to determine the driver's age.

[0022] The identification and analysis module uses multimodal sensors to identify and analyze the driver's age. This involves acquiring various types of information through the multimodal sensors, including: collecting multiple driver information such as weight, driving height, and facial image information; performing age analysis on each type of information separately: analyzing weight to obtain first identification data, analyzing driving height to obtain second identification data, analyzing facial image information to obtain third identification data, and so on; statistically analyzing the first, second, and third identification data, and then determining the driver's age through comprehensive analysis. The multimodal sensors include weight pressure sensors, ultrasonic or infrared sensors, and CMOS image sensors. The data acquired by the multimodal sensors is used only for driver age analysis; the collected information is discarded immediately after the identification and analysis are completed and is not retained.

[0023] The behavior monitoring module is used to acquire driving operation information.

[0024] The driving operation information includes: driving operation information obtained based on the driver's operating behavior and driving operation information obtained based on driving control information issued by the mobile control terminal.

[0025] The battery management module is used to monitor the status of the battery in children's electric vehicles and determine the current battery information.

[0026] Among them, the battery management module adaptively supplies power to the motor of the children's electric vehicle, ensuring that the motor operates normally based on the power supply from the battery, and guaranteeing the control of the motor's operation.

[0027] The safety control module is used to conduct a risk assessment based on driving operation information and driver age using a risk assessment model, obtain the risk assessment result, and generate safety control commands based on the risk assessment result, driving operation information, driver age, and current battery information. In turn, the module controls the motor's operating status according to the safety control commands to achieve driving control of the child electric vehicle.

[0028] The risk assessment model is an artificial intelligence analysis model that uses driving operation information and driver age as input. Based on this information, it assesses potential safety hazards to the driver during the use of the child electric vehicle and determines the risk assessment result, which is then output as the model's output. When controlling the motor's operation according to safety control commands to achieve control of the child electric vehicle, the model simultaneously controls both the battery and motor to ensure proper driving control.

[0029] The early warning and intervention module is used to issue early warnings based on the risk assessment results and to implement control interventions based on the feedback information from the early warnings.

[0030] Specifically, the early warning intervention module only issues warnings when the risk assessment indicates that the current driving operation poses a safety hazard to the driver. Otherwise, the module does not need to respond to the risk assessment results. The feedback information for the warning refers to any new driving actions taken by the driver or supervisor based on the warning prompt.

[0031] The aforementioned technical solution achieves an intelligent safety upgrade from passive protection to active intervention, enabling child-friendly electric vehicles to adaptively control safety based on the driver's age. It fully considers the perceptual differences of drivers of different ages, ensuring driver safety and enhancing the user experience, effectively improving the intelligence level of child-friendly electric vehicles. The recognition and analysis module can obtain the driver's age, allowing for the determination of safety control commands based on the driver's age when controlling the child-friendly electric vehicle. It fully considers the different sensitivities to vehicle status at different ages, providing greater safety and a better user experience. Furthermore, the use of multi-modal sensors for multi-factor information collection and driver age recognition analysis reduces age analysis errors and improves the accuracy of driver age identification. The behavior monitoring module can acquire driving operation information in real time, enabling timely responses after the driver or accompanying person performs driving operations, avoiding long waiting times that negatively impact the user experience of the child-friendly electric vehicle. The battery management module monitors the battery, ensuring sufficient energy storage to prevent insufficient power from affecting children's use of the electric vehicle, and ensuring stable energy output to prevent over-discharge from affecting battery life. It guarantees stable energy output under different operating conditions, avoiding performance fluctuations caused by insufficient or excessive power supply that could affect the control stability of the electric vehicle's motor operation, thus improving the control stability of the intelligent child electric vehicle's adaptive safety control system. The safety control module utilizes a risk assessment model for precise risk assessment, predicting potential dangers in advance and proactively intervening to enhance safety management and reduce the probability of accidents. Furthermore, safety control commands are directly applied to the electric vehicle's motor control, enabling real-time control of the electric vehicle's driving status and improving control efficiency. The safety control module also incorporates driver age for risk assessment, preventing different safety hazards for drivers of different ages under the same driving conditions, meeting the needs of drivers of all ages, ensuring safe driving of the electric vehicle, improving control flexibility, maximizing driver safety during operation, and enhancing the driver's driving experience. The early warning and intervention module provides timely warnings when the current driving operation poses a safety hazard to the driver, thereby increasing the driver's vigilance, preventing accidents caused by improper operation, helping to form standardized driving habits, and providing a guarantee for driving safety.

[0032] In one embodiment of the present invention, the behavior monitoring module includes: a first monitoring unit and a second monitoring unit; the first monitoring unit and the second monitoring unit do not affect each other, and while the first monitoring unit monitors the driver's driving operation behavior, the second monitoring unit also receives and monitors the driving control information sent by the mobile control terminal.

[0033] The first monitoring unit is used to monitor the driver's driving operation behavior, determine the driver's operation behavior, and obtain driving operation information.

[0034] The first monitoring unit monitors the driver's driving behavior in real time, and obtains the driver's driving behavior and driving operation information when the driver performs a driving operation.

[0035] The second monitoring unit is used to receive and monitor the driving control information sent by the mobile control terminal, determine the driving control information, and obtain driving operation information.

[0036] The mobile control terminal is a wireless controller used for driving control of the child electric vehicle. The second monitoring unit receives and monitors the driving control information sent by the mobile control terminal, and also monitors the device in the child electric vehicle that receives the driving control information in real time. When the child electric vehicle receives the driving control information, it determines the driving control information and obtains the driving operation information.

[0037] The above technical solution achieves dual-dimensional monitoring and protection through the first and second monitoring units, enabling the behavior monitoring module to not only acquire the driver's driving operations in real time, but also the control operations of accompanying persons on the child electric vehicle in real time. This allows for intelligent adaptive safety control of the child electric vehicle in any form of use, providing more comprehensive protection for the safety of using the child electric vehicle.

[0038] In one embodiment of the present invention, the early warning intervention module includes: an early warning unit and an intervention unit; The warning unit is used to issue a warning signal based on the risk assessment result. When the risk assessment result indicates that the current driving operation poses a safety hazard to the driver, it provides a dangerous driving warning in conjunction with the driving operation information.

[0039] Specifically, when the risk assessment indicates that the current driving operation poses a safety hazard to the driver, a first warning is immediately triggered when a dangerous driving warning is issued based on the driving operation information. Simultaneously with the first warning, the correct driving operation is determined through analysis of the driving behavior, and then a second warning is issued based on the correct driving operation. Here, the first warning is typically a purely musical prompt or a voice prompt such as "Please be careful." The second warning is a guiding voice prompt for the correct driving operation.

[0040] The intervention unit is used to obtain driving operation information after a dangerous driving warning is issued through the behavior monitoring module at a preset time, obtain warning feedback information, and perform control intervention based on the warning feedback information.

[0041] When implementing control intervention based on feedback information, the behavior monitoring module acquires the feedback information within a preset time frame and determines whether new driving operation information appears within that time. If new driving operation information appears within the preset time, no control intervention is required; the safety control module can analyze the adjusted driving operation information. If no new driving operation information appears within the preset time, a control intervention is necessary. In this case, the intervention type is determined based on the current driving status of the child electric vehicle, and the intervention plan is determined based on the driver's age. For example, the intervention measures include: how long to bring the child electric vehicle to a complete stop, how long to decelerate to the minimum speed, etc. Intervention types include: power limiting, steering assist, forced deceleration, and system shutdown. The preset time is generally 0.8 seconds by default, but can be adjusted according to actual needs.

[0042] The aforementioned technical solution, through its early warning intervention module, not only enhances the vigilance of drivers or mobile control operators by providing early warning units, enabling them to recognize inappropriate driving operations and promptly perform safe driving maneuvers, thus standardizing their driving behavior, but also raises their awareness of dangerous driving through the early warning unit. This allows for timely countermeasures, reducing safety hazards and ensuring driver safety. The intervention unit can intervene promptly in driving operations when necessary, preventing collisions or rollovers of the child-friendly electric vehicle. Furthermore, the intervention unit incorporates preset time feedback analysis, giving drivers or mobile control operators brief reaction time and preventing confusion caused by multiple parties attempting to operate the vehicle, ensuring the effectiveness and accuracy of the motor's response control commands. In addition, when determining intervention measures based on the feedback analysis results, the current driving status of the child-friendly electric vehicle is considered, avoiding sudden intervention that could cause collisions or discomfort due to inertia, further ensuring driver safety and a positive experience.

[0043] In one embodiment provided by the present invention, such as Figure 2 As shown, the safety control module performs a risk assessment based on driving operation information and the driver's age using a risk assessment model, including: S1. Load the corresponding baseline risk assessment model according to the driver's age to obtain the target baseline risk assessment model.

[0044] In this step, the baseline risk assessment model is stored in the model database. It is a baseline model pre-trained based on the general ability data of children in that age group. Driving operation information and vehicle status information are used as input data. Through driving operation predictive analysis, the model comprehensively assesses the risk response and individual response of children in that age group, and outputs the comprehensive risk value as the model's result. Here, either one baseline risk assessment model corresponds to one age (e.g., a baseline risk assessment model for 2-year-olds, a baseline risk assessment model for 3-year-olds), or one baseline risk assessment model corresponds to one age gradient (e.g., the same baseline risk assessment model for 2-3 year olds, and the same baseline risk assessment model for 4-5 year olds).

[0045] Furthermore, the model database regularly utilizes accumulated training samples to update model parameters through online learning algorithms, and introduces a memory decay mechanism to validate the baseline risk assessment model. This ensures that the baseline risk assessment model can adapt to children's growth, guarantees the accuracy of the baseline risk assessment model, and keeps the accuracy of risk assessment at a high level, providing continuous and effective safety protection for children of different ages.

[0046] S2. Perform current state feature analysis on the children's electric vehicle to obtain the state feature vector of the children's electric vehicle.

[0047] In this step, the current state features include: current driving operation information, current vehicle state information, and current driving state information. Here, current driving operation information refers to the driving operation information being analyzed, such as acceleration, turning, and emergency braking. Current vehicle state information refers to the current driving status information of the children's electric vehicle, including: current speed, acceleration, and battery output power. Current driving state information refers to the bumpiness experienced by the children's electric vehicle during driving, such as the bump index. When analyzing the current state features of the children's electric vehicle, structured analysis and processing are performed on the current state features such as current driving operation information, current vehicle state information, and current driving state information to obtain the children's electric vehicle state feature vector.

[0048] S3. Conduct risk analysis by combining the target baseline risk assessment model with the characteristic vector of child electric vehicles to determine the risk assessment results.

[0049] In this step, risk analysis is conducted using a target baseline risk assessment model combined with the characteristic vector of the child electric vehicle. This includes: predicting and analyzing the vehicle speed after the driving operation based on the current driving operation information and vehicle status information to obtain the predicted driving speed; analyzing the driver's sensitivity to the predicted driving speed, and assessing the driver's risk response in terms of cognitive ability and motor coordination under the predicted driving speed to determine the first risk assessment data. For example, the first risk assessment data for a 3-year-old driver at a predicted driving speed of 3 km / h is 80, and for a 6-year-old driver it is 50. The stability change data resulting from the control changes of the child electric vehicle is analyzed and predicted based on the current driving operation information and vehicle status information, and the prediction is revised based on the current driving status information to obtain the stability change characteristic information of the child electric vehicle. Then, the driver's response under the stability change characteristic information of the child electric vehicle is assessed to determine the second risk assessment data. Weights are determined for the first and second risk assessment data, and the weights are combined with the first and second risk assessment data for comprehensive analysis to obtain a comprehensive risk value. The risk assessment result is determined based on the comprehensive risk value and fuzzy mapping rules. Here, the risk assessment results include: the current driving operation poses a safety hazard to the driver, and the current driving operation does not pose a safety hazard to the driver. The fuzzy mapping rule is the mapping relationship between the comprehensive risk value and the risk assessment results. For example, when the comprehensive risk value is in the range [0, 60], the risk assessment result is that the current driving operation poses no safety hazard to the driver; when the comprehensive risk value is in the range (60, 100], the risk assessment result is that the current driving operation poses a safety hazard to the driver.

[0050] The aforementioned technical solution employs a multi-dimensional risk assessment model to more accurately evaluate whether driving operations pose safety hazards to the driver. This allows for proactive prevention, avoiding physical and psychological harm to the driver or property damage to the child electric vehicle after an accident, effectively reducing the probability of accidents. During risk assessment, the baseline risk assessment model allows for direct loading based on the driver's age, reducing time consumption and improving efficiency. It also considers the perceptual differences among drivers of different ages, enhancing accuracy. Furthermore, by analyzing the current state characteristics of the child electric vehicle, it achieves structured and standardized information on current driving operations, vehicle status, and driving conditions. This provides data support for the risk analysis of the target baseline risk assessment model, enabling efficient and accurate risk analysis by combining the target baseline risk assessment model with the child electric vehicle's feature vectors. This ensures the feasibility and orderliness of risk analysis using the target baseline risk assessment model combined with the child electric vehicle's feature vectors, more accurately identifying the risks associated with driving operations.

[0051] In one embodiment of the present invention, the safety control command includes: a first safety control command and a second safety control command. When generating the safety control command based on the risk assessment result, driving operation information, driver age, and current battery information, a safe driving control command is generated based on the driving operation information, driver age, and current battery information to obtain the first safety control command; a power safety supply control command is generated based on the first safety control command to obtain the second safety control command.

[0052] Specifically, when generating safety control commands based on the risk assessment results, driving operation information, driver age, and current battery information, the commands are generated only if the risk assessment results indicate that the current driving operation poses no safety hazard to the driver.

[0053] Furthermore, such as Figure 3 As shown, safe driving control commands are generated based on driving operation information, driver age, and current battery information, including: A1. Analyze and determine the battery status of the children's electric vehicle based on the current battery information and battery safety thresholds.

[0054] In this step, battery safety thresholds include: low charge threshold, critical charge threshold, high temperature threshold, and maximum permissible power. When analyzing and judging the battery status of a child electric vehicle using battery safety thresholds, the real-time parameter data of the battery is determined based on the current battery information. This real-time parameter data is then compared with the battery safety thresholds to determine the battery status. Here, the real-time parameter data includes: current charge level, current temperature, and current instantaneous power. Specifically, this includes: comparing the current charge level with the low charge threshold to determine if the battery is in a low charge state; and if so, further comparing the current charge level with the critical charge threshold to determine if the battery is in a critical charge state. The current temperature is compared with the high temperature threshold to determine if the battery is in a high temperature state. Instantaneous power is calculated to determine the current instantaneous power, and then compared with the maximum permissible power to determine if the battery is in a high-load state.

[0055] A2. Calculate the battery adaptive coefficient based on the battery status.

[0056] This step involves calculating the battery adaptive coefficient based on the battery state, including: calculating the low-charge power limiting coefficient; calculating the high-temperature derating coefficient; and determining the battery adaptive coefficient based on the low-charge power limiting coefficient and the high-temperature derating coefficient. Specifically, the low-charge power limiting coefficient is determined based on the battery state. If the battery is not in a low-charge state (i.e., the battery charge is not less than the low-charge threshold), the low-charge power limiting coefficient is 1. If the battery is in a low-charge state but not a critical state, a linear or piecewise function is used to limit power to avoid sudden voltage drops. Examples of linear or piecewise functions include: ,in, For low power consumption, This represents the current battery charge. For low battery threshold, The critical charge threshold is used. If the battery is in a critical charge state, an extremely conservative strategy must be implemented to protect it, and the low-charge power limitation factor is 0.1. The high-temperature derating factor is determined based on the battery temperature state. If the battery is not in a high-temperature state (i.e., its current temperature is not greater than the high-temperature threshold), the high-temperature derating factor is 1. If the battery is in a high-temperature state, a derating strategy is used to determine the high-temperature derating factor. Here, the derating strategy is a linear mapping relationship, for example: ,in, This is the high-temperature derating factor. To adjust the parameters, This is the current temperature of the battery. The high-temperature threshold is used. When determining the battery adaptive coefficient based on the low-power limitation coefficient and the high-temperature derating coefficient, the magnitudes of the low-power limitation coefficient and the high-temperature derating coefficient are compared, and the minimum value between the low-power limitation coefficient and the high-temperature derating coefficient is taken as the final battery adaptive coefficient.

[0057] A3. Analyze the actual power supply of the battery based on the battery adaptive coefficient to determine the current safe power boundary.

[0058] In this step, when analyzing the actual power supply of the battery based on the battery adaptive coefficient, the battery output is actually revised according to the battery adaptive coefficient to determine the boundaries of the maximum instantaneous power and continuous power that the battery can safely provide to the motor under the current conditions, thereby obtaining the current safe power boundary.

[0059] A4. Using the current safe power boundary as a constraint, determine the safe driving control command within the current safe power boundary based on driving operation information and driver age.

[0060] In this step, driving control commands are stored in a control command database. Driving control command templates, pre-determined based on driving behavior analysis of child electric vehicles, are stored to form the control command database. When determining safe driving control commands within the current safe power boundary based on driving operation information and driver age, driving analysis is performed based on the driving operation information to determine driving behavior and information. The driving information is revised based on the driver's age to obtain target driving information. Target commands are then retrieved from the control command database based on the driving behavior to determine the target driving control command. This target driving control command is then supplemented and improved based on the target driving information to initially determine the safe driving control command. Finally, constraints are applied using the current safe power boundary to limit the initially determined safe driving control command, resulting in the final safe driving control command and the first safe control command.

[0061] Furthermore, such as Figure 4 As shown, a power safety supply control command is generated based on the first safety control command, including: B1. Analyze the battery requirements of the motor operation control of the child electric vehicle based on the first safety control command, and determine the battery requirement information.

[0062] In this step, when analyzing the battery requirements of the motor operation control of the child electric vehicle based on the first safety control command, the motor parameters of the child electric vehicle are determined. Then, based on the first safety control command and the motor parameters of the child electric vehicle, an operation simulation analysis is performed to determine the battery requirements of the motor operation control of the child electric vehicle and obtain battery requirement information.

[0063] B2. Verify based on battery demand information and current safe power limits; In this step, when verifying the battery demand information in conjunction with the current safe power boundary, it is verified whether the battery demand information is within the current safe power boundary range. If it is, the verification passes; otherwise, the verification fails.

[0064] B3. After successful verification, the battery demand information is combined with the battery adaptive coefficient analysis to determine the battery output control information, and the second safety control command is obtained.

[0065] In this step, when determining the battery output control information by combining the battery demand information with the battery adaptive coefficient analysis, the battery adaptive coefficient is used to revise the battery demand information to determine the target power supply data. The battery signal conversion analysis is performed according to the target power supply data to determine the battery signal control information. Based on the battery signal control information, the battery output control command is generated to obtain the second safety control command.

[0066] The aforementioned technical solution generates not only safety driving control commands but also battery output control commands when generating safety control commands. This ensures the motor of the children's electric vehicle operates safely when responding to these commands, guaranteeing both safe driving control and safe battery output. It prevents over-discharge from affecting battery life and ensures stable battery output under various operating conditions, avoiding performance fluctuations caused by insufficient or excessive power supply. This allows for stable driving of the children's electric vehicle, preventing instability and potential safety hazards to the driver. Furthermore, the analysis and calculation of the battery adaptive coefficient ensures that the actual battery condition is fully considered when determining the first and second safety control commands. This guarantees the battery maintains optimal performance under different operating conditions, preventing battery overload and potential fire hazards, and also preventing battery damage from affecting the normal use of the children's electric vehicle. This ensures the first safety control command effectively controls the motor's operation, achieving safe driving control of the children's electric vehicle.

[0067] In one embodiment of the present invention, when the battery management module monitors the status of the battery of a children's electric vehicle, it obtains the current battery information in real time and provides charging prompts and charging management based on the current battery information.

[0068] When providing charging reminders and managing charging based on current battery information, the system determines the current battery level and analyzes whether charging is necessary. If so, a charging reminder is given. Furthermore, during the charging process, the system analyzes whether the battery has reached its rated capacity. If so, charging is stopped automatically by cutting off the power.

[0069] The above technical solution, by providing charging reminders and management based on current battery information, not only enables the battery management module to promptly remind users to charge when needed, preventing parents from forgetting to charge the batteries of children's electric vehicles and affecting their use, but also ensures timely power disconnection once the battery reaches its rated capacity, preventing damage to the battery from continued charging after it has reached its rated capacity and protecting its lifespan.

[0070] In one embodiment provided by the present invention, such as Figure 5 As shown, the intelligent child electric vehicle adaptive safety control system also includes a remote monitoring module. This remote monitoring module is connected to both the safety control module and the early warning intervention module. The remote monitoring module is typically a mobile app, installed on the parent's mobile device according to their needs, and differs from the mobile control terminal.

[0071] The remote monitoring module is used to monitor and record the use of children's electric vehicles, obtain information on their usage, acquire risk assessment results during their use, statistically record the risk assessment results, and then report on safe driving based on the usage of the children's electric vehicles and the statistical data.

[0072] When obtaining risk assessment results, the results are directly acquired and statistically recorded from the safety control module. When reporting on safe driving based on the usage of the child electric vehicle and the statistically recorded data, reports are made separately according to the driver's usage method and the child electric vehicle's usage method. Here, the usage method of the child electric vehicle includes: driver operation and mobile control terminal operation.

[0073] Furthermore, the remote monitoring module includes an electronic fence configuration unit, which is used to obtain the usage range information set by the parents for the child electric vehicle and generate an electronic fence based on the usage range information; then the early warning and intervention module provides early warning prompts and gradual control intervention based on the real-time location of the child electric vehicle and the electronic fence.

[0074] The electronic fence is set up in the remote monitoring module. Parents can pre-define the location of the child electric vehicle based on its usage range in the electronic fence configuration unit. After receiving the usage range information set by the parents, the electronic fence configuration unit generates an electronic fence for that range and sets the effective time. Generally, the effective time of the electronic fence is long-term. Parents can also temporarily deactivate the fence or set up a new one as needed. For example, a temporary safe zone can be set up in a park, with the effective time set to 16:00-17:00 on the same day. A permanent safe zone can be set up in one's own yard. Here, the default effective time for a permanent safe zone is all day, but this default effective time can also be adjusted. The early warning and intervention module uses the real-time location of the child electric vehicle combined with an electronic fence to provide early warnings and gradual control intervention. It acquires the child electric vehicle's location in real time, determines its current position, analyzes the distance between the current location and the electronic fence boundary within the effective time frame, and determines a minimum distance value. When the minimum distance value is less than a warning threshold, an electronic fence warning is issued. If, after the warning is issued, the minimum distance between the current location and the electronic fence boundary gradually increases but does not fall below the warning threshold, the warning is lifted. If the minimum distance between the current location and the electronic fence boundary gradually decreases, and the minimum distance value is less than half the warning threshold, a control intervention request is sent to the safety control module. The safety control module then retrieves a locking command based on the control intervention request, causing the motor of the child electric vehicle to gradually stop running, thus stopping the vehicle from responding to driving operation information. When the child electric vehicle moves away from the electronic fence boundary within a safe area, the locking command is released, and normal use resumes, allowing the vehicle to continue responding to driving operation information. Here, electronic fence warnings are usually voice prompts, such as: "You are about to leave the safe zone, please be careful." The alert level can be preset by parents or determined based on the maximum speed of the child-sized electric vehicle.

[0075] For example, parents pre-mark their yard as the usage area in the electronic fence configuration unit of the remote monitoring module, generating an electronic fence and determining its location and validity period. When a child uses a child-sized electric vehicle within the safe area, if the vehicle approaches the electronic fence, an electronic fence warning will be issued with a voice prompt: "You are about to leave the safe area, please be aware of your activity range." If the driver adjusts their driving after hearing the warning, moving the electric vehicle away from the electronic fence boundary, the warning is deactivated and the voice prompt stops. If the driver continues to drive towards the electronic fence boundary after hearing the warning, a control intervention request will be sent to the safety control module when the minimum distance between the electric vehicle's current position and the boundary is less than half the warning threshold. The safety control module will then retrieve a locking command based on the intervention request, causing the electric vehicle's motor to gradually stop operating and the vehicle to cease responding to driving inputs. Once the electric vehicle returns to the designated usage area, the locking command will be deactivated, and normal use will resume, allowing the vehicle to respond to driving inputs again. If you need to take your child to a park or other public place to use the electric car, you need to disable or temporarily disable the electronic fence of your yard in the remote monitoring module. If you disable it, the electronic fence will be permanently disabled. If you temporarily disable it, the electronic fence will be enabled again when the electric car returns to your yard (the area where the electronic fence is used).

[0076] The aforementioned technical solution utilizes a remote monitoring module to supervise children's electric vehicles. This allows parents to monitor the driver's actions even when they cannot supervise their children, enabling timely education and correction of improper driving practices, thus standardizing children's driving behavior and enhancing their safety awareness. Furthermore, the electronic fence setting limits the driving range of the electric vehicles, allowing children to use them more effectively even without parental supervision. This prevents children from driving the vehicles to dangerous areas without parental knowledge, reducing the supervisory burden on parents and giving them greater peace of mind when allowing their children to use the vehicles. Children can also enjoy the fun of using electric vehicles in a safe environment, improving both safety and ease of use.

[0077] Those skilled in the art should understand that the terms "first" and "second" in this invention merely refer to different application stages.

[0078] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0079] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. An intelligent adaptive safety control system for children's electric vehicles, characterized in that, include: The identification and analysis module is used to identify and analyze the driver's age using multimodal sensors to determine the driver's age; The behavior monitoring module is used to acquire driving operation information; The battery management module is used to monitor the status of the battery in children's electric vehicles and determine the current battery information. The safety control module is used to conduct risk assessment based on driving operation information and driver age through a risk assessment model, obtain risk assessment results, and based on the risk assessment results, analyze the motor of the child electric vehicle according to the driving operation information, driver age, and current battery information to determine safety control commands. Then, the module controls the motor's operating state according to the safety control commands to achieve driving control of the child electric vehicle. The early warning and intervention module is used to issue early warnings based on the risk assessment results and to implement control interventions based on the feedback information from the early warnings.

2. The intelligent child electric vehicle adaptive safety control system according to claim 1, characterized in that, The behavior monitoring module includes: a first monitoring unit and a second monitoring unit; The first monitoring unit is used to monitor the driver's driving operation behavior, determine the driver's operation behavior, and obtain driving operation information; The second monitoring unit is used to receive and monitor the driving control information sent by the mobile control terminal, determine the driving control information, and obtain driving operation information.

3. The intelligent child electric vehicle adaptive safety control system according to claim 1, characterized in that, The early warning and intervention module includes: an early warning unit and an intervention unit; The warning unit is used to issue a warning signal based on the risk assessment result. When the risk assessment result indicates that the current driving operation poses a safety hazard to the driver, it provides a dangerous driving warning in conjunction with the driving operation information. The intervention unit is used to obtain driving operation information after a dangerous driving warning is issued through the behavior monitoring module at a preset time, obtain warning feedback information, and perform control intervention based on the warning feedback information.

4. The intelligent child electric vehicle adaptive safety control system according to claim 1, characterized in that, The safety control module performs a risk assessment based on driving operation information and the driver's age using a risk assessment model, including: The target baseline risk assessment model is obtained by loading the corresponding baseline risk assessment model based on the driver's age; Perform current state feature analysis on children's electric vehicles to obtain their state feature vectors. Risk assessment results are determined by combining the target baseline risk assessment model with the characteristic vector of child electric vehicles.

5. The intelligent child electric vehicle adaptive safety control system according to claim 1, characterized in that, The safety control instructions include: a first safety control instruction and a second safety control instruction. When generating safety control instructions based on risk assessment results, driving operation information, driver age, and current battery information, a safe driving control instruction is generated based on the driving operation information, driver age, and current battery information to obtain the first safety control instruction; a power safety supply control instruction is generated based on the first safety control instruction to obtain the second safety control instruction.

6. The intelligent child electric vehicle adaptive safety control system according to claim 5, characterized in that, Based on driving operation information combined with the driver's age and current battery information, safe driving control commands are generated, including: The battery status of children's electric vehicles is determined based on current battery information and battery safety threshold analysis. Calculate the battery adaptive coefficient based on the battery status; Based on the analysis of the battery's actual power supply using the battery adaptive coefficient, the current safe power boundary is determined. Using the current safe power boundary as a constraint, safe driving control commands are determined within the current safe power boundary based on driving operation information and driver age.

7. The intelligent child electric vehicle adaptive safety control system according to claim 6, characterized in that, Based on the first security control command, a power security supply control command is generated, including: Based on the analysis of the first safety control command, the battery demand of the motor operation control of the child electric vehicle is determined. Verification was conducted based on battery demand information and current safe power limits; After successful verification, the battery demand information is combined with the battery adaptive coefficient analysis to determine the battery output control information, thus obtaining the second safety control command.

8. The intelligent child electric vehicle adaptive safety control system according to claim 1, characterized in that, When monitoring the status of the battery in a children's electric vehicle, the battery management module obtains the current battery information in real time and provides charging prompts and management based on the current battery information.

9. The intelligent child electric vehicle adaptive safety control system according to claim 1, characterized in that, The intelligent children's electric vehicle adaptive safety control system also includes: a remote monitoring module; The remote monitoring module is used to monitor and record the use of children's electric vehicles, obtain information on their usage, acquire risk assessment results during their use, statistically record these results, and then report on safe driving based on the usage data and the statistical records.

10. The intelligent child electric vehicle adaptive safety control system according to claim 9, characterized in that, The remote monitoring module includes an electronic fence configuration unit, which is used to obtain the usage range information set by the parents for the child electric vehicle and generate an electronic fence based on the usage range information; then the early warning and intervention module provides early warning prompts and gradual control intervention based on the real-time location of the child electric vehicle and the electronic fence.

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