Vehicle warning prompting methods, devices, equipment, storage media, and computer program products based on reading light language

Abstract

This application relates to the field of electric vehicle technology, and more particularly to a vehicle warning notification method, device, equipment, storage medium, and computer program product based on reading light language. When an in-vehicle alarm signal is received, the method determines the current alarm type based on a preset alarm classification strategy. The alarm types include child abandonment warnings, door hazard warnings, abnormal temperature warnings, abnormal air quality warnings, and cleaning reminders. Based on a mapping strategy between the current alarm type and alarm lighting, the method determines the target lighting mode for the reading lights. The method then controls the reading lights to turn on and execute the target lighting mode, thereby improving vehicle safety.

Description

Technical Field

[0001] This application relates to the field of electric vehicle technology, and in particular to a vehicle warning prompting method, device, equipment, storage medium, and computer program product based on reading light language. Background Technology

[0002] Current vehicle lighting signals are primarily used for external environments, such as high and low beam headlights, turn signals, and brake lights, mainly for driver communication. However, in-vehicle alerts currently rely mainly on the instrument panel, voice prompts, or the central control screen. These methods have the following problems: they cannot convey information when the owner is not in the vehicle, such as notifying the driver if children or pets are left behind after locking the car; visual cues may increase the driver's workload due to distraction while driving; and the alerts are not intuitive enough to quickly attract the attention of passengers. While roof reading lights, as traditional lighting devices, already have adjustable brightness and modes, their application remains limited to lighting needs and has not been fully developed into an interactive tool. Therefore, how to apply reading lights to in-vehicle warning alerts to improve vehicle safety has become an urgent technical problem to be solved. Summary of the Invention

[0003] The main objective of this application is to provide a vehicle warning prompting method, device, equipment, storage medium, and computer program product based on reading light language, aiming to solve the technical problem of how to improve vehicle safety.

[0004] To achieve the above objectives, this application provides a vehicle warning prompting method based on reading light language, the method comprising the following steps:

[0005] When an in-vehicle alarm signal is received, the current alarm type of the in-vehicle alarm signal is determined based on a preset alarm classification strategy. The alarm types of the in-vehicle alarm signal include child left behind warning, door risk warning, abnormal temperature warning, abnormal air quality warning, and cleaning warning.

[0006] Based on the current alarm type and alarm lighting mapping strategy, determine the target lighting mode of the reading light;

[0007] Control the reading light to turn on and execute the target lighting mode.

[0008] In one embodiment, before the step of determining the target lighting mode of the reading light according to the current alarm type and alarm lighting mapping strategy, the method further includes:

[0009] The child-leaving warning is associated with the first lighting mode, the door risk warning with the second lighting mode, the abnormal temperature warning with the third lighting mode, the abnormal air quality warning with the fourth lighting mode, and the cleaning warning with the fifth lighting mode, respectively.

[0010] Based on the association results, the alarm lighting mapping relationship strategy is generated.

[0011] In one embodiment, before the step of determining the current alarm type of the in-vehicle alarm signal based on a preset alarm classification strategy when an in-vehicle alarm signal is acquired, the method further includes:

[0012] Data such as seat pressure, door radar, interior temperature, interior air quality, and camera data are collected as data to be processed.

[0013] The data to be processed is compared with preset standard data;

[0014] Based on the comparison results, it is determined whether to issue the in-vehicle alarm signal.

[0015] In one embodiment, the step of determining the current alarm type of the in-vehicle alarm signal based on a preset alarm classification strategy when an in-vehicle alarm signal is acquired includes:

[0016] When the in-vehicle alarm signal is acquired, classification and matching are performed based on the preset alarm classification strategy, the data source of the in-vehicle alarm signal, and the data characteristics of the in-vehicle alarm signal;

[0017] If multiple results are matched by the category, the results are sorted according to a preset priority, and then deduplicated and merged to obtain the current alarm type.

[0018] In one embodiment, after the step of determining the target lighting mode of the reading light according to the current alarm type and alarm lighting mapping strategy, the method further includes:

[0019] Obtain target lighting information corresponding to the target lighting mode, the target lighting information including target brightness level, target flashing frequency, target duration, and target special effects;

[0020] Control commands are generated based on the target brightness level, the target flashing frequency, the target duration, and the target special effects.

[0021] In one embodiment, the step of controlling the reading light to turn on and execute the target lighting mode includes:

[0022] Obtain the control command;

[0023] Determine whether the reading light is in standby mode;

[0024] If so, control the reading light to turn on, and based on the control command, adjust the reading light to the target brightness level, the target flashing frequency, the target duration, and the target special effect.

[0025] Furthermore, to achieve the above objectives, this application also proposes a vehicle warning notification device based on reading light language, the vehicle warning notification device based on reading light language comprising:

[0026] The alarm type determination module is used to determine the current alarm type of the in-vehicle alarm signal based on a preset alarm classification strategy when an in-vehicle alarm signal is obtained. The alarm types of the in-vehicle alarm signal include child left behind warning, door risk warning, abnormal temperature warning, abnormal air quality warning, and cleaning warning.

[0027] The lighting mode determination module is used to determine the target lighting mode of the reading light according to the current alarm type and alarm lighting mapping relationship strategy;

[0028] The target module is used to control the reading light to turn on and execute the target lighting mode.

[0029] Furthermore, to achieve the above objectives, this application also proposes a vehicle warning prompting device based on reading light language. The device includes: a memory, a processor, and a vehicle warning prompting program based on reading light language stored in the memory and executable on the processor. The vehicle warning prompting program based on reading light language is configured to implement the steps of the vehicle warning prompting method based on reading light language as described above.

[0030] Furthermore, to achieve the above objectives, this application also proposes a storage medium storing a vehicle warning prompting program based on reading light language, wherein when the vehicle warning prompting program based on reading light language is executed by a processor, it implements the steps of the vehicle warning prompting method based on reading light language as described above.

[0031] In addition, to achieve the above objectives, this application also proposes a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the vehicle warning prompting method based on reading light language as described above.

[0032] This application, upon receiving an in-vehicle alarm signal, determines the current alarm type based on a preset alarm classification strategy. These alarm types include child abandonment warnings, door hazard warnings, abnormal temperature warnings, abnormal air quality warnings, and cleaning reminders. Based on a mapping strategy between the current alarm type and alarm lighting, it determines the target lighting mode for the reading light and controls the reading light to turn on and execute the target lighting mode. This application accurately determines the in-vehicle alarm signal type based on a preset alarm classification strategy and achieves intuitive and efficient light-based alarm prompts through the linkage between the target lighting mode and the reading light. Compared to traditional voice or screen prompts, light-based prompts can quickly attract passenger attention while reducing driver interference, effectively improving the safety of the in-vehicle environment. This method fully utilizes existing reading light hardware resources, requiring no additional equipment, expanding the functionality of the reading light, and enhancing vehicle safety. Attached Figure Description

[0033] Figure 1 This is a flowchart illustrating the first embodiment of the vehicle warning prompting method based on reading light language according to this application;

[0034] Figure 2 This is a schematic diagram of a sub-process in the second embodiment of the vehicle warning prompting method based on reading light language in this application;

[0035] Figure 3 This is a schematic diagram of a sub-process in the third embodiment of the vehicle warning prompting method based on reading light language in this application;

[0036] Figure 4 This is a schematic diagram of the module structure of the vehicle warning and alert device based on reading light language according to an embodiment of this application;

[0037] Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the vehicle warning prompting method based on reading light language in the embodiments of this application.

[0038] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0039] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of this application.

[0040] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0041] It should be noted that current vehicle lighting signals are primarily used for external environments, such as high and low beam headlights, turn signals, and brake lights, mainly for the driver to communicate with the outside world. However, in in-vehicle alerts, current methods mainly rely on the instrument panel, voice prompts, or the central control screen. These methods have the following problems: they cannot convey information when the owner is not in the vehicle, such as notifying the driver that children or pets have been left behind after locking the car; visual cues may increase the driver's workload due to distraction while driving; and the alerts are not intuitive enough to quickly attract the attention of passengers. While roof reading lights, as traditional lighting devices, already have adjustable brightness and modes, their application remains limited to lighting needs and has not been fully developed into an interactive tool. Therefore, how to apply reading lights to in-vehicle warning alerts to improve vehicle safety has become an urgent technical problem to be solved.

[0042] The main solution of this application is as follows: when an in-vehicle alarm signal is received, the current alarm type of the in-vehicle alarm signal is determined based on a preset alarm classification strategy. The alarm types of the in-vehicle alarm signal include child abandonment warning, door risk warning, abnormal temperature warning, abnormal air quality warning, and cleaning warning. The target lighting mode of the reading light is determined according to the mapping relationship strategy between the current alarm type and the alarm lighting. The reading light is then turned on and the target lighting mode is executed.

[0043] This application accurately identifies in-vehicle alarm signal types based on a preset alarm classification strategy and achieves intuitive and efficient light-based alarm prompts through the linkage of target lighting modes and reading lights. Compared to traditional voice or screen prompts, light prompts can quickly attract passenger attention while reducing driver interference, effectively improving the safety of the in-vehicle environment. This method fully utilizes existing reading light hardware resources, requiring no additional equipment, expanding the functionality of reading lights, and enhancing vehicle safety.

[0044] It should be noted that the executing entity of the method in this embodiment can be a computing service device with data processing, network communication, and program execution functions, or it can be the aforementioned vehicle warning and prompting device based on reading light language with the same or similar functions. This embodiment and the following embodiments will be described using a vehicle warning and prompting device based on reading light language as an example.

[0045] Based on this, the first embodiment of the vehicle warning prompt method based on reading light language is proposed in this application. Please refer to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the vehicle warning prompting method based on reading light language according to this application.

[0046] In this embodiment, the vehicle warning prompt method based on reading light language includes the following steps:

[0047] S1: When an in-vehicle alarm signal is received, the current alarm type of the in-vehicle alarm signal is determined based on a preset alarm classification strategy. The alarm types of the in-vehicle alarm signal include child left behind warning, door risk warning, abnormal temperature warning, abnormal air quality warning, and cleaning warning.

[0048] It should be noted that in-vehicle warning signals are abnormal status signals detected by vehicle sensors or control systems that may affect the in-vehicle environment or occupant safety. Examples include a seat sensor detecting a child left behind, or a radar sensor detecting a dangerous object near the door. The preset warning classification strategy is a set of pre-defined classification algorithms or logic based on preset vehicle rules, used to classify and identify detected warning signals and determine their specific warning type. The current warning type is the specific warning category determined according to the warning classification strategy, such as a child left behind warning or a door risk warning, which is a clear description of the warning signal. A child left behind warning is a signal generated when a child is detected still inside the vehicle after it has been locked, to prevent children from being forgotten inside. A door risk warning is a signal generated when the vehicle detects an approaching moving object (such as a vehicle or bicycle) outside the door when a passenger opens it, to avoid potential collision risks. An abnormal temperature warning is a signal generated when the in-vehicle temperature sensor detects that the in-vehicle temperature exceeds a set safe range (such as above 40°C or below 0°C), to ensure occupant safety and comfort. An air quality anomaly alert is a signal generated when the vehicle's air quality sensor detects excessive levels of carbon dioxide or other harmful gases, reminding the user to take measures such as ventilation to improve air quality. A cleaning alert is a signal generated based on the vehicle's visual sensors or user settings, used to remind the user to clean soiled areas inside the vehicle.

[0049] Specifically, the vehicle continuously monitors the in-vehicle environment and occupant behavior through built-in sensors and system modules (such as seat pressure sensors, radar, temperature sensors, and air quality sensors). Once abnormal data is detected (such as weight on the seat but no one operating the vehicle after locking it, or excessively high interior temperature), the system generates a corresponding in-vehicle alarm signal. At this point, the signal is only in a preliminary, unclassified state and requires further processing and classification.

[0050] Furthermore, based on a pre-defined alarm classification strategy, the collected alarm signals are analyzed and matched. For example: when the seat pressure sensor and infrared sensor confirm the presence of biological activity inside the vehicle and no one returns after locking the car, it is classified as a child left behind warning; when the radar detects a moving object outside the door and determines that the safe distance is insufficient, it is classified as a door risk warning; when the temperature sensor or air quality sensor exceeds the safety threshold, it is classified as a temperature abnormality warning or an air quality abnormality warning, respectively. Through strategy rules and multi-sensor data fusion, the system ultimately determines the current alarm type corresponding to the signal for use in subsequent steps.

[0051] By employing a pre-defined alarm classification strategy, the system can quickly and accurately identify in-vehicle alarm types based on various sensor inputs, effectively avoiding false alarms or missed alarms. For example, child abandonment warnings and door risk warnings can significantly reduce the risk of children being left behind or colliding with doors. Based on multi-sensor data (such as pressure, infrared, radar, air quality, etc.) and classification strategies, accurate judgment of complex environments is achieved. For instance, by combining temperature and air quality sensors, users can be simultaneously reminded to adjust the temperature and ventilate, improving the in-vehicle environment quality. The application of classification strategies reduces redundant alarm information, conveying only clear alarm types, making the prompts more intuitive and easier for users to understand, avoiding interference from multiple invalid reminders. Simultaneously, this step provides accurate alarm basis for subsequent lighting warning modes, achieving a more intelligent in-vehicle interactive experience. By classifying and processing complex signals within the vehicle, the accuracy and safety of vehicle alarms are improved, and the foundation for the execution of intelligent lighting signals is laid.

[0052] S2: Determine the target lighting mode of the reading light based on the current alarm type and alarm lighting mapping strategy;

[0053] It should be noted that the alarm lighting mapping strategy is a preset rule or logic that associates each alarm type with a specific reading light lighting mode to intuitively convey alarm information. For example: Child left behind warning → high brightness, low frequency flashing; Car door risk warning → high brightness, high frequency flashing. The target lighting mode is the final operating mode of the reading light determined according to the mapping strategy, including brightness level, flashing frequency, and special effects (such as breathing light), used to express specific alarm information.

[0054] Specifically, once the system identifies the current alarm type, it queries the corresponding target lighting mode based on the vehicle's built-in mapping strategy. For example, if the current alarm type is "Child Left Behind Warning," the system determines the target lighting mode to be "High-brightness, low-frequency flashing" by looking up the mapping table. If the current alarm type is "Door Risk Warning," the system sets the target lighting mode to "High-brightness, high-frequency flashing." The mapping table is predefined by the vehicle manufacturer or customized by the user through the vehicle's infotainment system or mobile app, ensuring flexibility and adaptability.

[0055] Furthermore, after identifying the target mode, the system further decomposes its parameters to generate executable hardware control instructions. These parameters include: brightness level: such as high brightness or low brightness; flashing frequency: such as low frequency (1 time / second) or high frequency (2 times / second); special effects mode: such as breathing light (gradually brightening and dimming) or intermittent flashing (bright for a period of time, then turning off and on again); duration: the running length of the target mode, such as 10 seconds, 30 seconds, or indefinitely until the alarm is cleared. Through the above process, the complex alarm logic is converted into specific lighting control parameters, thus completing the determination of the target lighting mode.

[0056] Each alarm type corresponds to a unique target lighting pattern, allowing users to quickly understand the alarm content through lighting effects without complex interpretation. For example, high-frequency flashing conveys emergency information, while a breathing light expresses a milder warning. Converting alarm information into lighting patterns is more intuitive than voice or screen prompts, and does not significantly distract the driver, making it particularly suitable for conveying non-emergency information (such as air quality alerts) while driving. Through flexible mapping strategies, users can customize lighting patterns according to their preferences, increasing the system's personalization and controllability while enhancing vehicle intelligence. Utilizing existing reading light hardware for alarm information prompts eliminates the need for additional equipment, reducing system complexity and cost. By combining the current alarm type with the alarm lighting mapping strategy, precise and intuitive lighting prompts are achieved, improving not only the efficiency of alarm information transmission but also enhancing vehicle safety and user experience.

[0057] S3: Control the reading light to turn on and execute the target lighting mode.

[0058] It should be noted that reading lights are lighting devices installed on the roof of a vehicle, typically used to illuminate specific areas for occupants. In this step, the reading lights are used to perform a warning and alert function. Control refers to sending specific hardware execution commands to the reading lights through the vehicle's control system, causing them to operate according to the target lighting mode.

[0059] Specifically, the target lighting mode is parsed into hardware-executable control parameters, and a start command is sent to the reading light module via the vehicle control bus (such as the CAN bus). These commands include: Activation command: activating the reading light module and starting the target mode; Parameter loading: passing specific operating parameters to the light driver unit, such as brightness level (high or low), flashing frequency (e.g., high frequency 2 times / second or low frequency 1 time / second), and special effects mode (e.g., breathing light).

[0060] Furthermore, after receiving control commands, the reading light module operates according to the target mode: Brightness control: Adjusts the light current or voltage to achieve the target brightness. Flashing mode: Controls the switching frequency of the light through PWM (Pulse Width Modulation) signals to achieve high-frequency or low-frequency flashing effects. Special effects mode: Controls the light effects according to specific algorithms; for example, a "breathing light" creates a soft prompting effect through a gradual brightening and dimming cycle. Duration: Controls the duration of the light mode's operation; it automatically turns off or switches to the default mode after reaching the set value. If the alarm status is updated (such as alarm clearing or escalation), the system can dynamically adjust the reading light's operating mode. For example, switching from low-brightness intermittent flashing to high-brightness high-frequency flashing.

[0061] By precisely controlling the brightness and mode of the reading lights, the system can quickly and intuitively convey warning information to occupants. For example, high-frequency flashing lights can quickly attract attention, responding to emergencies such as door risk warnings. Utilizing existing reading light equipment for warning prompts requires no additional hardware investment, significantly reducing system complexity and cost while leveraging the multi-functional potential of the reading lights. The light-based warning format does not rely on voice or screens, avoiding distraction of the driver's attention, while providing occupants with intuitive visual warnings, especially suitable for scenarios such as children left behind or abnormal air quality inside the vehicle. The light mode is adjusted in real time according to changes in the warning, for example, switching from a low-brightness constant light for "cleaning warnings" to a high-brightness high-frequency flashing light for "door risk warnings," ensuring the flexibility and efficiency of the warning response. Through precise control and mode execution of the reading lights, this step achieves a low-cost, efficient in-vehicle warning method, effectively improving vehicle safety and user experience.

[0062] This embodiment, upon receiving an in-vehicle alarm signal, determines the current alarm type based on a preset alarm classification strategy. These alarm types include child abandonment alerts, door hazard alerts, abnormal temperature alerts, abnormal air quality alerts, and cleaning alerts. Based on a mapping strategy between the current alarm type and alarm lighting, it determines the target lighting mode for the reading light and controls the reading light to turn on and execute the target lighting mode. This embodiment accurately determines the in-vehicle alarm signal type based on a preset alarm classification strategy and achieves intuitive and efficient light-based alarm prompts through the linkage between the target lighting mode and the reading light. Compared to traditional voice or screen prompts, light-based prompts can quickly attract passenger attention while reducing driver interference, effectively improving the safety of the in-vehicle environment. This method fully utilizes existing reading light hardware resources, requiring no additional equipment, expanding the functionality of the reading light, and enhancing vehicle safety.

[0063] Based on the first embodiment described above, a second embodiment of the vehicle warning notification method based on reading light language is proposed in this application. Please refer to... Figure 2 , Figure 2This is a schematic diagram of a sub-process in the second embodiment of the vehicle warning prompting method based on reading light language in this application.

[0064] like Figure 2 As shown, in this embodiment, before step S2, the following steps are also included:

[0065] S2a: Associate the child-leaving warning with the first lighting mode, the door risk warning with the second lighting mode, the abnormal temperature warning with the third lighting mode, the abnormal air quality warning with the fourth lighting mode, and the cleaning warning with the fifth lighting mode, respectively;

[0066] S2b: Based on the association results, generate the alarm lighting mapping relationship strategy.

[0067] It should be noted that the first to fifth lighting modes refer to the reading light operation modes defined according to the prompt type, including brightness, flashing frequency, and special effects modes (such as breathing light, constant light, intermittent flashing, etc.). Each mode corresponds to a specific prompt type. The alarm lighting mapping strategy refers to a set of rules that associate specific alarm types with their corresponding reading light lighting modes, ensuring a clear correspondence between prompt types and lighting performance for subsequent prompt execution.

[0068] Specifically, based on the vehicle's safety and interaction needs, a specific lighting mode is predefined for each alarm type. For example: a child safety warning is associated with the first lighting mode (e.g., high-brightness, low-frequency flashing), making the warning intuitive and easy to attract attention. A door hazard warning is associated with the second lighting mode (e.g., high-brightness, high-frequency flashing), emphasizing urgency and danger warnings. A temperature anomaly warning is associated with the third lighting mode (e.g., low-brightness, intermittent flashing), providing a continuous warning in non-emergency situations. An air quality anomaly warning is associated with the fourth lighting mode (e.g., low-brightness, breathing light), gently indicating an improvement in environmental quality. A cleaning warning is associated with the fifth lighting mode (e.g., low-brightness, constant illumination), expressing a routine reminder. Through these associations, a one-to-one correspondence between each alarm type and the reading light lighting mode is established, forming a preliminary lighting mapping rule.

[0069] Furthermore, after associating the aforementioned alarm types with lighting modes, a complete mapping strategy is generated based on these relationships. The mapping strategy structure includes a table mapping alarm types to target modes, recording the specific lighting mode parameters (such as brightness, frequency, and effects) associated with each alarm type. The mapping strategy can be adjusted via the vehicle's infotainment system or a mobile app. For example, users can change the "child safety warning" from "high-brightness, low-frequency flashing" to "high-brightness, constant on" to meet personalized needs. The generated mapping strategy is stored in the vehicle control system for subsequent alert functions to ensure efficient linkage between lighting alerts and alarm types.

[0070] By associating each alarm type with a specific lighting mode, alarm information is presented intuitively through lighting. Users can quickly understand the prompts without additional interpretation, improving information delivery efficiency. Different alarm types (such as emergency alarms and regular alarms) employ differentiated lighting displays, helping users quickly distinguish information priorities in various scenarios. For example, high-brightness, high-frequency flashing is suitable for door risk warnings, while low-brightness breathing lights are more suitable for air quality anomalies. Through flexible adjustments to the mapping strategy, users can customize the correspondence between alarms and lighting modes, such as setting lighting effects that better suit their preferences, further enhancing the vehicle's personalization and intelligent functions. Utilizing existing reading light hardware to implement multiple alarm functions, the mapping strategy achieves logically clear and feature-rich lighting interactions, reducing the cost of additional hardware and simplifying the design and maintenance of the alarm system. By establishing associations between alarm types and lighting modes and generating alarm-lighting mapping strategies, modular design and efficient interaction of lighting alarm functions are achieved, significantly improving vehicle safety, intelligence, and user experience.

[0071] Based on the first embodiment described above, in this embodiment, before step S1, the method further includes:

[0072] S1a: Acquire seat pressure data, door radar data, in-vehicle temperature data, in-vehicle air quality data, and camera data as data to be processed;

[0073] S1b: Compare the data to be processed with preset standard data;

[0074] S1c: Based on the comparison results, determine whether to issue the in-vehicle alarm signal.

[0075] It should be noted that seat pressure data is seat load information detected by the in-vehicle seat pressure sensor, used to determine whether an occupant (such as a child) remains in the seat. Door radar data is surrounding environmental data acquired by millimeter-wave radar or ultrasonic sensors installed near the doors, used to determine if there are any approaching moving objects (such as vehicles or pedestrians) outside the doors. Interior temperature data is the real-time temperature value of the vehicle interior monitored by the in-vehicle temperature sensor, used to detect any abnormal temperatures (such as too high or too low). Interior air quality data is the concentration data of air pollutants collected by air quality sensors (such as carbon dioxide sensors or PM2.5 detectors), used to determine whether the air quality inside the vehicle meets standards. Camera data is image or video data collected by cameras inside the vehicle or near the doors, used to assist in identifying items left behind, hazardous objects, or the cleanliness of the interior. Preset standard data refers to pre-set safety thresholds or reference values ​​for various environmental or condition parameters, used to compare with the data to be processed collected by the sensors.

[0076] Specifically, various sensors and devices are used to collect real-time environmental data inside and outside the vehicle, including: Seat pressure sensors: detect seat pressure values ​​to determine if there are any objects or living organisms on the seats. Door radar sensors: monitor moving objects around the doors, obtaining their distance and trajectory. Interior temperature sensors: detect the interior temperature of the vehicle to determine if it is within the set comfort range. Air quality sensors: measure the concentration of pollutants in the air inside the vehicle to determine if it exceeds safety standards. Cameras: capture images of the interior and exterior of the vehicle to identify special situations (such as leftover objects or soiled areas). The collected data forms a dataset to be processed, which includes various types of sensor data, providing basic information for subsequent processing.

[0077] Furthermore, the collected data to be processed is compared and analyzed with preset standard data. If the seat pressure value is higher than a certain threshold and the vehicle is locked, a child may have been left behind, and the system marks it as abnormal. If an object is detected outside the door at a distance less than a safe distance, it is determined that there is a door hazard. If the interior temperature exceeds the set range (e.g., above 40°C), it is marked as a temperature anomaly. If the carbon dioxide concentration or PM2.5 concentration exceeds the preset upper limit, the system marks it as an air anomaly. Image recognition algorithms are used to determine whether there are any leftover objects, dangerous objects, or soiled areas. Based on the comparison results, the system determines whether to trigger an alarm signal: if any comparison exceeds the threshold, a corresponding in-vehicle alarm signal is generated and associated with a specific alarm type. If all data are within the standard range, the in-vehicle condition is determined to be normal, and no alarm signal needs to be issued.

[0078] By integrating data from multiple sensors (such as pressure, radar, temperature, and air quality), the system comprehensively monitors the in-vehicle environment and occupant status, avoiding misjudgments or omissions caused by a single data source. Real-time comparison of the data to be processed with preset standard data enables rapid identification and real-time response to abnormal conditions. For example, it automatically triggers a child safety warning in a high-temperature vehicle, effectively preventing potential dangers. The preset standard data can be dynamically adjusted or additional sensors can be added (such as expanding to humidity detection) based on vehicle configuration and usage needs, enabling a wider range of abnormal detection scenarios. Automated data acquisition and alarm signal generation reduce user intervention, making the vehicle more intelligent and user-friendly, and improving the overall interactive experience. Through these steps, efficient monitoring of the in-vehicle environment and abnormal conditions is achieved, providing accurate triggering criteria for subsequent lighting warning functions, while significantly improving the vehicle's intelligence and safety.

[0079] This embodiment, upon receiving an in-vehicle alarm signal, determines the current alarm type based on a preset alarm classification strategy. These alarm types include child abandonment alerts, door hazard alerts, abnormal temperature alerts, abnormal air quality alerts, and cleaning alerts. Based on a mapping strategy between the current alarm type and alarm lighting, it determines the target lighting mode for the reading light and controls the reading light to turn on and execute the target lighting mode. This embodiment accurately determines the in-vehicle alarm signal type based on a preset alarm classification strategy and achieves intuitive and efficient light-based alarm prompts through the linkage between the target lighting mode and the reading light. Compared to traditional voice or screen prompts, light-based prompts can quickly attract passenger attention while reducing driver interference, effectively improving the safety of the in-vehicle environment. This method fully utilizes existing reading light hardware resources, requiring no additional equipment, expanding the functionality of the reading light, and enhancing vehicle safety.

[0080] Based on the second embodiment described above, a third embodiment of the vehicle warning notification method based on reading light language is proposed in this application. Please refer to... Figure 3 , Figure 3 This is a schematic diagram of a sub-process in the third embodiment of the vehicle warning prompting method based on reading light language in this application.

[0081] In this embodiment, step S1 includes:

[0082] S11: When the in-vehicle alarm signal is acquired, classification and matching are performed based on the preset alarm classification strategy, the data source of the in-vehicle alarm signal, and the data characteristics of the in-vehicle alarm signal;

[0083] S12: If multiple results are matched by the category, the multiple results are deduplicated and merged based on the preset priority to obtain the current alarm type.

[0084] It should be noted that the data source refers to the sensor or module that generates the alarm signal, such as a seat pressure sensor, door radar, or air quality sensor, used to identify the physical source of the signal. Data characteristics: Specific information contained in the alarm signal, such as pressure values, temperature values, or air pollution concentration, used for classification. Classification matching refers to associating the alarm signal with a specific alarm type according to a preset classification strategy. Preset priority sorting is a priority order defined for different alarm types based on safety or urgency, used to determine the alarm type to be processed first among multiple classification results. Deduplication and merging refers to removing redundancy through logical merging or filtering when the same alarm signal matches multiple results, ultimately outputting a unique alarm type.

[0085] Specifically, when an alarm signal is detected inside the vehicle, the system performs classification and matching based on the following logic: Based on data source: First, the source sensor of the signal is identified. For example, a seat pressure signal may match a child left behind warning, and a radar signal may match a door risk warning. Based on data characteristics: The specific data values ​​or change patterns of the signal are analyzed. For example, if the pressure sensor data is above a threshold and the vehicle is locked, it may match a "child left behind warning." If the radar sensor detects a moving object at a distance less than a safe distance, it matches a "door risk warning." Based on a preset classification strategy: The alarm signal is matched with one or more alarm types using rule-based matching algorithms (such as conditional judgment or machine learning models).

[0086] Furthermore, if the classification matching results correspond to multiple alarm types, the results are processed according to preset priorities: Priority sorting: Alarm types are arranged according to their urgency. For example, "door risk warning" takes precedence over "cleaning warning". If multiple results come from the same signal source but have overlapping features, the system will remove redundant results. For example, air quality data may trigger both "air quality anomaly warning" and "temperature anomaly warning" simultaneously; in this case, the system will merge them into a more comprehensive "environmental anomaly warning". Merging: When results involve different signal sources, the system combines multiple alarm types into a single comprehensive warning based on priority and correlation. For example, "child left behind warning" and "high temperature inside the vehicle warning" may be merged into "child left behind in high temperature warning". Finally, a unique current alarm type is output, providing clear instructions for subsequent light reminders or other actions.

[0087] Classification and matching based on data sources and characteristics effectively reduce misjudgments and ensure that alarm types accurately reflect the actual situation inside the vehicle. For example, by jointly determining "child abandonment warnings" using seat pressure and vehicle lock status, false alarms caused by a single data point are avoided. Through classification strategies and priority ordering, the system can process multiple signals simultaneously, ensuring that important alarms are delivered first while also considering secondary information. For example, when "door risk" and "air anomaly" coexist, safety-related risk information is prioritized. Deduplication and merging mechanisms reduce redundant prompts, ensuring that the alarm information received by the user is concise and easy to understand, avoiding confusion caused by repetitive or unnecessary prompts. Prioritization ensures that emergencies (such as door risks) can be handled quickly, helping to reduce accidents and improve vehicle safety. Classification strategies and priorities can be adjusted according to actual usage scenarios to adapt to different vehicle and user needs, enhancing the system's flexibility and adaptability. Through this step, the system achieves the ability to accurately extract and determine a unique alarm type from multiple source signals, providing a reliable basis for subsequent light prompts or other responses, while significantly improving vehicle intelligence and safety.

[0088] Based on the second embodiment described above, in this embodiment, after step S2, the method further includes:

[0089] S2a: Obtain target lighting information corresponding to the target lighting mode, the target lighting information including target brightness level, target flashing frequency, target duration, and target special effects;

[0090] S2b: Generate control commands based on the target brightness level, the target flashing frequency, the target duration, and the target special effects.

[0091] It should be noted that the target lighting mode is the reading light operating mode determined based on the alarm type and alarm lighting mapping relationship. It includes a set of specific lighting parameters (brightness, flashing frequency, duration, and special effects) used to intuitively convey alarm information. The target lighting information is a set of parameters describing the specific operating details of the target lighting mode, including: target brightness level: the light intensity of the reading light, which can be divided into high brightness, low brightness, etc.; target flashing frequency: the switching speed of the light, such as high-frequency flashing (2 times / second) or low-frequency flashing (1 time / second); target duration: the running length of the lighting mode, such as 10 seconds, 30 seconds, or indefinite duration; target special effects: additional dynamic performance of the light, such as a breathing light (gradual brightening and dimming cycle) or constant light (continuous illumination); control commands: a set of specific commands used to operate the reading light hardware, including light brightness, current, voltage, pulse width modulation (PWM) signals, etc., to guide the reading light to execute the target lighting mode.

[0092] Specifically, after determining the target lighting mode, specific parameters related to that mode are extracted from the internal mapping table to generate target lighting information. This includes: target brightness level: extracting the light's brightness setting, for example, "high brightness" corresponds to high current output, and "low brightness" corresponds to low current output; target blinking frequency: determining the light's blinking speed, for example, "high frequency" corresponds to blinking twice per second, and "low frequency" corresponds to blinking once per second; target duration: extracting the running time of the lighting mode, for example, "10 seconds" indicates that the mode will automatically turn off or switch after 10 seconds; target special effects: determining whether there are additional dynamic effects, for example, a "breathing light" requires the light intensity to change from low to high and then from high to low in a certain cycle. The target lighting information is stored as a parameter set for use in the next step of generating control commands.

[0093] Furthermore, based on the target lighting information, specific hardware control instructions are generated. These include: brightness instructions: converting the target brightness level into specific current or voltage parameters. For example, a high-brightness mode corresponds to a high-current output instruction; blinking instructions: using PWM (Pulse Width Modulation) technology to control the blinking frequency, achieving high-frequency or low-frequency blinking effects by setting the switching cycle; time control instructions: loading the target duration parameter and using a timer module to control the mode's runtime; special effects instructions: calling special effects algorithms (such as a breathing light) to generate a dynamically changing brightness curve and converting it into continuous current adjustment instructions. The generated control instructions are passed to the reading light driver module to guide the hardware in executing the target mode.

[0094] By combining brightness, flashing frequency, duration, and special effects, the system can accurately represent alarm information. For example, high-frequency flashing conveys emergency information, while a breathing light provides a gentle alert, improving alarm transmission efficiency and user perception. Through the separation of target lighting information and control commands, the system can flexibly adapt to different lighting hardware and user needs. For example, users can adjust brightness or flashing frequency without modifying the core logic. By parsing target lighting information and generating control commands, the system achieves diverse alert functions on existing hardware, avoiding additional hardware investment and reducing system costs. Control commands are generated based on real-time target lighting information and can quickly switch modes according to alarm changes. For example, switching from high-brightness flashing to a breathing light adapts to changes in alarm urgency. Control commands are formatted as standard hardware interface signals, ensuring compatibility with reading light driver modules and facilitating system expansion and maintenance. By acquiring target lighting information and generating control commands, the system achieves an efficient closed loop from alarm type to lighting hardware control, significantly improving vehicle intelligence and user experience while enhancing the effectiveness of alarm information transmission.

[0095] Based on the second embodiment described above, in this embodiment, step S3 includes:

[0096] S31: Obtain the control command;

[0097] S32: Determine whether the reading light is in standby mode;

[0098] S33: If so, control the reading light to turn on, and based on the control command, adjust the reading light to the target brightness level, the target flashing frequency, the target duration, and the target special effect.

[0099] It should be noted that standby mode refers to the default working state of the reading light. In this state, the reading light is not lit, but it is ready to receive control commands at any time.

[0100] Specifically, the process involves: acquiring control commands: The control commands generated in the preceding steps include parameters such as the target brightness level, flashing frequency, duration, and special effects. These commands are passed to the hardware control module as execution parameters for the reading light. Determining the reading light status: The hardware status monitoring module detects whether the reading light is in standby mode: Standby mode: The reading light is not activated and is ready to receive new commands. Non-standby mode: The reading light may be running or in a faulty state, requiring exception handling (such as interrupting the current mode or restoring the default state).

[0101] Furthermore, the system activates the reading light: If the reading light is in standby mode, the system sends an "on" signal to activate the reading light hardware module. It then adjusts to the target mode: based on the specific parameters of the control command, the reading light is gradually adjusted to the target mode: Brightness adjustment: The current or voltage is controlled to the target brightness level, for example, setting a high current output for high brightness. Flashing frequency setting: Using PWM (Pulse Width Modulation) technology, the flashing period is set according to the command to achieve high-frequency or low-frequency flashing. Duration control: The timer module is invoked to ensure that the target mode runs for the set time before automatically stopping or switching. Special effects execution: If the command includes dynamic changes (such as a breathing light), the system adjusts the brightness curve through an algorithm to achieve a gradual brightening and dimming cycle during operation. Dynamic adjustment: If a new alarm signal or command (such as an alarm escalation) is received during mode operation, the system can update the control command in real time and switch to the new target mode.

[0102] By detecting the standby status of the reading lights, the system avoids command execution failures due to device operational conflicts or malfunctions, ensuring smooth and reliable mode switching. Adjusting brightness, flashing frequency, and special effects allows the reading lights to intuitively display different alarm messages, helping users quickly understand and respond, improving the efficiency and accuracy of alerts. The system supports real-time command updates during operation, such as switching from "low brightness intermittent flashing" to "high brightness high frequency flashing," adapting to changes in alarm priority or escalation of emergencies. Separating status judgment, control commands, and hardware execution allows the system to easily adapt to different types of reading light hardware, achieving high scalability. Standby status monitoring and duration control prevent accidental triggering or prolonged operation of the lights, optimizing energy consumption and extending device lifespan. By acquiring control commands, judging the reading light status, and executing the target mode, this step achieves an efficient closed loop from logic signals to hardware control, ensuring the accuracy and real-time nature of alarm alerts, while improving vehicle safety and user experience.

[0103] This embodiment, upon receiving an in-vehicle alarm signal, determines the current alarm type based on a preset alarm classification strategy. These alarm types include child abandonment alerts, door hazard alerts, abnormal temperature alerts, abnormal air quality alerts, and cleaning alerts. Based on a mapping strategy between the current alarm type and alarm lighting, it determines the target lighting mode for the reading light and controls the reading light to turn on and execute the target lighting mode. This embodiment accurately determines the in-vehicle alarm signal type based on a preset alarm classification strategy and achieves intuitive and efficient light-based alarm prompts through the linkage between the target lighting mode and the reading light. Compared to traditional voice or screen prompts, light-based prompts can quickly attract passenger attention while reducing driver interference, effectively improving the safety of the in-vehicle environment. This method fully utilizes existing reading light hardware resources, requiring no additional equipment, expanding the functionality of the reading light, and enhancing vehicle safety.

[0104] This application also provides a vehicle warning and alert device based on reading light language, please refer to... Figure 4 , Figure 4 This is a schematic diagram of the module structure of a vehicle warning notification device based on reading light language, according to an embodiment of this application. The vehicle warning notification device based on reading light language includes:

[0105] The alarm type determination module 401 is used to determine the current alarm type of the in-vehicle alarm signal based on a preset alarm classification strategy when an in-vehicle alarm signal is obtained. The alarm types of the in-vehicle alarm signal include child left behind warning, door risk warning, abnormal temperature warning, abnormal air quality warning, and cleaning warning.

[0106] The lighting mode determination module 402 is used to determine the target lighting mode of the reading light according to the current alarm type and alarm lighting mapping relationship strategy;

[0107] The target module 403 is used to control the reading light to turn on and execute the target lighting mode.

[0108] The vehicle warning device based on reading light language provided in this application, employing the vehicle warning method based on reading light language in the above embodiments, can solve the technical problem of how to improve vehicle safety. Compared with the prior art, the beneficial effects of the vehicle warning device based on reading light language provided in this application are the same as those of the vehicle warning method based on reading light language provided in the above embodiments, and other technical features in the vehicle warning device based on reading light language are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0109] This application provides a vehicle warning prompting device based on reading light language. The vehicle warning prompting device based on reading light language includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the vehicle warning prompting method based on reading light language in the above embodiment.

[0110] The following is for reference. Figure 5 , Figure 5 This is a schematic diagram of the hardware operating environment involved in the vehicle warning prompting method based on reading light language in the embodiments of this application. It shows a structural diagram of a vehicle warning prompting device based on reading light language suitable for implementing the vehicle warning prompting method based on reading light language in the embodiments of this application. The vehicle warning prompting device based on reading light language in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (such as in-vehicle navigation terminals), etc., as well as fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The vehicle warning device based on reading light language shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0111] like Figure 5As shown, a vehicle warning device based on reading light language may include a processing unit 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 1002 or a program loaded from storage device 1003 into random access memory (RAM) 1004. RAM 1004 also stores various programs and data required for the operation of the vehicle warning device based on reading light language. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. Input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the vehicle warning device based on reading light language to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows a vehicle warning device based on reading light language with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented alternatively.

[0112] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0113] The vehicle warning device based on reading light language provided in this application, employing the vehicle warning method based on reading light language in the above embodiments, can solve the technical problem of how to improve vehicle safety. Compared with the prior art, the beneficial effects of the vehicle warning device based on reading light language provided in this application are the same as those of the vehicle warning method based on reading light language provided in the above embodiments, and other technical features in this vehicle warning device based on reading light language are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0114] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0115] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0116] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the vehicle warning prompting method based on reading light language in the above embodiments.

[0117] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0118] The aforementioned computer-readable storage medium may be included in a vehicle warning and alerting device based on reading light language; or it may exist independently and not be installed in a vehicle warning and alerting device based on reading light language.

[0119] The aforementioned computer-readable storage medium carries one or more programs that, when executed by a vehicle warning and alerting device based on a reading light language, cause the device to: upon receiving an in-vehicle alarm signal, determine the current alarm type of the in-vehicle alarm signal based on a preset alarm classification strategy, wherein the alarm types of the in-vehicle alarm signal include child abandonment warning, door risk warning, abnormal temperature warning, abnormal air quality warning, and cleaning warning; determine the target lighting mode of the reading light according to a mapping strategy between the current alarm type and alarm lighting; and control the reading light to turn on and execute the target lighting mode. Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as C or similar programming languages. The program code can be executed entirely on a user's computer, partially on a user's computer, as a standalone software package, partially on a user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer through any type of network—including a local area network (LAN) or a wide area network (WAN)—or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0120] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0121] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0122] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described vehicle warning prompting method based on reading light language, thereby solving the technical problem of how to improve vehicle safety. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the vehicle warning prompting method based on reading light language provided in the above embodiments, and will not be repeated here.

[0123] This application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the vehicle warning prompting method based on reading light language as described above.

[0124] The computer program product provided in this application can solve the technical problem of how to improve vehicle safety. Compared with the prior art, the beneficial effects of the computer program product provided in the embodiments of this application are the same as the beneficial effects of the vehicle warning prompt method based on reading light language provided in the above embodiments, and will not be repeated here.

[0125] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent scope of this application.

Claims

1. A vehicle warning prompt method based on reading light language, characterized in that, The method includes: When an in-vehicle alarm signal is received, it is classified and matched based on a preset alarm classification strategy, the data source of the in-vehicle alarm signal, and the data characteristics of the in-vehicle alarm signal. The alarm types of the in-vehicle alarm signal include child left behind warning, door risk warning, abnormal temperature warning, abnormal air quality warning, and cleaning warning. If multiple results are matched by the category, the results are sorted according to a preset priority, and then deduplicated and merged to obtain the current alarm type. Based on the current alarm type and alarm lighting mapping strategy, determine the target lighting mode of the reading light; Control the reading light to turn on and execute the target lighting mode.

2. The method as described in claim 1, characterized in that, Before the step of determining the target lighting mode of the reading light based on the current alarm type and alarm lighting mapping strategy, the method further includes: The child-leaving warning is associated with the first lighting mode, the door risk warning with the second lighting mode, the abnormal temperature warning with the third lighting mode, the abnormal air quality warning with the fourth lighting mode, and the cleaning warning with the fifth lighting mode, respectively. Based on the association results, the alarm lighting mapping relationship strategy is generated.

3. The method as described in claim 1, characterized in that, Before the step of classifying and matching the in-vehicle alarm signal based on a preset alarm classification strategy, the data source of the in-vehicle alarm signal, and the data characteristics of the in-vehicle alarm signal when the in-vehicle alarm signal is acquired, the method further includes: Data such as seat pressure, door radar, interior temperature, interior air quality, and camera data are collected as data to be processed. The data to be processed is compared with preset standard data; Based on the comparison results, it is determined whether to issue the in-vehicle alarm signal.

4. The method as described in claim 1, characterized in that, After the step of determining the target lighting mode of the reading light based on the current alarm type and alarm lighting mapping strategy, the method further includes: Obtain target lighting information corresponding to the target lighting mode, the target lighting information including target brightness level, target flashing frequency, target duration, and target special effects; Control commands are generated based on the target brightness level, the target flashing frequency, the target duration, and the target special effects.

5. The method as described in claim 4, characterized in that, The step of controlling the reading light to turn on and execute the target lighting mode includes: Obtain the control command; Determine whether the reading light is in standby mode; If so, control the reading light to turn on, and based on the control command, adjust the reading light to the target brightness level, the target flashing frequency, the target duration, and the target special effect.

6. A vehicle warning and alert device based on reading light language, characterized in that, The device comprises: The alarm type determination module is used to classify and match alarm signals when an in-vehicle alarm signal is received, based on a preset alarm classification strategy, the data source of the in-vehicle alarm signal, and the data characteristics of the in-vehicle alarm signal. The alarm types of the in-vehicle alarm signals include child abandonment warnings, door risk warnings, abnormal temperature warnings, abnormal air quality warnings, and cleaning warnings. If multiple results are matched, the multiple results are sorted according to a preset priority, deduplicated, and merged to obtain the current alarm type. The lighting mode determination module is used to determine the target lighting mode of the reading light according to the current alarm type and alarm lighting mapping relationship strategy; The target module is used to control the reading light to turn on and execute the target lighting mode.

7. A computer device, characterized in that, The device includes: a memory, a processor, and a vehicle warning prompting program based on reading light language stored in the memory and executable on the processor, the vehicle warning prompting program based on reading light language being configured to implement the steps of the vehicle warning prompting method based on reading light language as described in any one of claims 1 to 5.

8. A storage medium, characterized in that, The storage medium stores a vehicle warning prompting program based on reading light language. When the vehicle warning prompting program based on reading light language is executed by the processor, it implements the steps of the vehicle warning prompting method based on reading light language as described in any one of claims 1 to 5.

9. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the vehicle warning prompting method based on reading light language as described in any one of claims 1 to 5.

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