Travel scene presetting and service method based on safety seat and child seat

Abstract

The invention discloses a travel scene presetting and service method based on a safety seat and a child seat, and the method comprises the steps: recording the travel behavior data of a user, and generating a user activity list based on learning and / or customization; storing the user activity list, wherein the user activity list comprises conditions and feedback; monitoring parameters of a current scene according to conditions in the user activity list; matching corresponding feedback from the user activity list according to the monitored scene condition; according to the matched feedback, the safety seat is controlled to execute corresponding actions, and travel scenarized service is completed. The safety seat can provide accurate and intimate scene services by learning user behaviors or allowing user customization, and the system generates a personalized activity list according to the use habits of the user, so that the requirements of different families are met, and the comfort is enhanced; various condition signals are processed through an activity list and a learning algorithm, the hardware complexity and the number of sensors are reduced, and the cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile safety products, and in particular to a travel scene presetting and service method based on a safety seat and a child seat. Background Art

[0002] With the advancement of technology, more and more child safety seats are endowed with electric functions, such as ventilation and heat dissipation, seat heating, electric rotation, electric adjustment of seat back angle and electric adjustment of headrest height. The introduction of these functions can theoretically provide more targeted travel scenario services according to user needs. For example, in the welcome mode, when the user opens the door, the child safety seat can automatically rotate to a position facing the user, making it easier for the child to sit in and then turn back to normal use. For another example, the seat can automatically start the ventilation or heating function under different temperature conditions to provide children with a more comfortable riding experience.

[0003] However, in actual applications, due to the complexity and changeability of life scenarios, it is difficult to accurately capture the real needs of users by relying solely on sensors. For example, the existing welcome mode usually relies on distance sensors to detect the opening of the door, thereby triggering the rotation of the seat, but the opening of the door does not necessarily mean that the user needs to use a child safety seat, resulting in a mismatch between function triggering and user needs. In addition, travel scenario services based on sensor perception have the problem of high costs: the process of capturing signals through sensors and realizing travel scenario services is complicated, which increases hardware and R&D costs. Summary of the invention

[0004] To solve at least one aspect of the above problems, the present invention first provides a travel scenario preset and service method based on a safety seat, comprising the steps of: recording user travel behavior data, and generating a user activity list based on learning and / or customization; storing the user activity list, wherein the user activity list includes conditions and feedback; monitoring the parameters of the current scene according to the conditions in the user activity list; matching corresponding feedback from the user activity list according to the monitored scene conditions; and controlling the safety seat to perform corresponding actions according to the matched feedback to complete the travel scenario service.

[0005] Optionally, the step of recording user travel behavior data includes: recording the user's operation history data on the safety seat through the safety seat's sensor, an environmental detection module or a terminal connected to the safety seat, the operation history data including time, environmental parameters, sensor signals and user operation instructions.

[0006] Optionally, the conditions in the user activity list include time, environmental parameters, sensor signals and preconditions, and the feedback in the user activity list includes electric rotation of the safety seat, fan activation and heating function activation.

[0007] Optionally, the step of monitoring parameters of the current scene includes monitoring time parameters, temperature and humidity, geographic location, sensor signals and precondition signals to obtain complete scene information.

[0008] Optionally, the step of controlling the safety seat to perform corresponding actions includes the following actions: controlling the safety seat to rotate to a specified direction according to matching feedback; activating the seat ventilation or heating function according to temperature or time conditions; and adjusting the seat position after confirming that the child is seated according to a sensor signal.

[0009] Optionally, generation and use of the user activity list supports hierarchical logic management, allowing conditions and feedback to gradually perform complex scenario-based operations according to a hierarchical relationship.

[0010] Optionally, the method further includes the following steps: the user turns on or off the travel scenario service through the safety seat or an interactive interface of a terminal connected to the safety seat, and can switch between manual adjustment and automatic control modes.

[0011] Optionally, the step of generating a user activity list based on learning or customization includes: when based on learning, analyzing the user travel behavior data through a learning algorithm, extracting the correspondence between conditions and feedback, and generating a user activity list; when based on customization, the user sets or adjusts the conditions and feedback in the user activity list through an interactive interface to generate a user activity list.

[0012] Optionally, the method further includes the step of allowing the user to perform secondary editing on the user activity list through the interactive interface to optimize the logical relationship between conditions and feedback.

[0013] Optionally, the learning algorithm in the step of generating the user activity list based on learning adopts a decision tree, a Bayesian algorithm, a K nearest neighbor algorithm and / or a neural network learning algorithm.

[0014] Compared with the prior art, the travel scenario preset and service method based on the safety seat in the present invention can provide accurate and considerate scenario services by learning user behavior or allowing user customization, and the system generates a personalized activity list based on the user's usage habits to meet the needs of different families and enhance comfort; it processes multiple conditional signals through activity lists and learning algorithms to reduce hardware complexity and the number of sensors, thereby reducing costs; it can also avoid false actions caused by a single sensor triggering, and the feedback accuracy is high.

[0015] In addition, the present invention provides a child seat for implementing the travel scenario preset and service method based on the safety seat as described above.

[0016] Compared with the prior art, the child seat described in the present invention and the above-mentioned travel scenario preset and service method based on the safety seat have the same advantages over the prior art, which will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of a travel scenario preset and service method based on a safety seat according to an embodiment of the present invention;

[0018] Figure 2 A flowchart of generating a user activity list based on learning according to an embodiment of the present invention;

[0019] Figure 3 A flowchart of generating a user activity list based on customization according to an embodiment of the present invention;

[0020] Figure 4 A schematic diagram of supporting hierarchical logic management for the generation and use of a user activity list according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0022] The embodiment of the present invention provides a travel scene preset and service method based on a safety seat, combined with Figure 1 As shown, the steps include:

[0023] Record user travel behavior data and generate user activity lists based on learning and / or customization; the system records the user's interaction with the child safety seat through the child safety seat itself or connected devices (such as smartphone applications or cloud services);

[0024] Storing the user activity list, the user activity list including conditions and feedback; the user activity list is stored in the form of structured data, including the corresponding relationship between conditions (such as time, environment, sensor signals, etc.) and feedback (such as seat rotation, heating, fan start, etc.); the generated user activity list is stored in the firmware of the safety seat, or stored in a terminal connected to the safety seat or in the cloud through a networked device, so as to facilitate cross-device access and management;

[0025] According to the conditions in the user activity list, the parameters of the current scene are monitored; the system monitors the current scene conditions in real time through sensor modules and external devices (such as GPS, environmental monitoring devices), including time, temperature and humidity, vehicle location, sensor signals, etc.;

[0026] According to the monitored scene conditions, the corresponding feedback is matched from the user activity list; the data is analyzed in real time and compared with the conditions in the user activity list. If the parameters of the current scene match the list conditions, the next feedback matching stage is entered;

[0027] Based on the matching feedback, the safety seat is controlled to perform corresponding actions to complete the travel scenario service; the corresponding feedback is extracted from the user activity list according to the matching conditions, and the safety seat is controlled to perform corresponding actions; the actions performed are such as rotating the seat to a specified direction, turning on or off the ventilation and cooling function, or adjusting the seat back angle, headrest height, etc.; example scenarios include: at 7:00 am on weekdays, the seat automatically rotates 90° to face the door, which is convenient for children to sit; on hot summer afternoons, the seat ventilation system automatically starts to maintain seat comfort.

[0028] Among them, the system uses learning algorithms (such as decision trees, Bayesian algorithms, neural networks, etc.) to analyze historical data, extract the correlation between conditions and feedback, and form a user activity list. For example, during the high temperature period in summer (i.e., condition), the user manually turns on the seat ventilation function many times (i.e., feedback). After learning, the system generates corresponding conditional feedback rules; users manually add or edit conditions and feedback through the interactive interface connected to the safety seat. For example, users set "7:00 am on weekdays" (i.e., condition) and "seat rotates 90° to the direction of the door" (i.e., feedback) in the application.

[0029] The travel scenario preset and service method based on the safety seat in this embodiment can provide accurate and considerate scenario services by learning user behavior or allowing user customization, and the system generates a personalized activity list based on the user's usage habits to meet the needs of different families and enhance comfort; it processes multiple conditional signals through activity lists and learning algorithms to reduce hardware complexity and the number of sensors, thereby reducing costs; it can also avoid false actions caused by a single sensor triggering, and the feedback accuracy is high.

[0030] Optionally, the step of recording the user's travel behavior data includes: recording the user's operation history data of the safety seat through the safety seat sensor, environmental detection module or terminal connected to the safety seat, and the operation history data includes time, environmental parameters, sensor signals and user operation instructions. Time data such as use time, date, holiday mark, etc., environmental data such as temperature and humidity, vehicle location, driving route, etc., user behavior data such as seat rotation direction, fan on or off, heating use frequency and other operation records; real-time environment and user operation data are collected through the sensor module, and the data is stored in a timestamp format to facilitate the learning algorithm to analyze historical behavior habits. Time data, for example: record the specific time when a seat rotation operation occurs, such as 7:00:00 am on XX / XX / XXXX. Environmental data, for example: when the vehicle is in a high temperature environment (above 30°C), record whether the user starts the ventilation function. Sensor signal, for example: record the automatic rotation operation triggered after the seat detects that the child is sitting. User operation instructions, for example: the user manually sets the seat to "welcome mode" through a smartphone application.

[0031] Among them, the sensors of the safety seat include a position sensor (for detecting the seat rotation angle), a weight sensor (for detecting the child's seating status), a temperature sensor (for monitoring the seat surface temperature), and a pressure sensor (for sensing user touch operations); the environmental detection module includes a temperature and humidity sensor, a light sensor, and a vehicle positioning module, which is used to capture relevant information of the current environment, such as outside temperature, temperature inside the vehicle, and vehicle geographic location; the terminal device connected to the safety seat is through a mobile application or an in-vehicle information system (such as an in-vehicle touch screen), which records the operating instructions sent by the user through the human-computer interaction interface, such as adjusting the seat angle, turning on the heating function, etc.

[0032] In this embodiment, comprehensive user behavior information is captured through multi-dimensional records such as time, environmental parameters, sensor signals, and user operation instructions. Accurate timestamps and multi-dimensional data help the learning algorithm to establish a deep association between conditions and feedback, thereby improving the accuracy and reliability of user activity list generation. Multi-dimensional data is used to verify scenario conditions (such as simultaneously satisfying time, temperature, and sensor signals) to avoid false triggering caused by a single condition and improve the accuracy of system response.

[0033] Optionally, combined Figure 2 , Figure 3 As shown, the step of generating a user activity list based on learning or customization includes:

[0034] When based on learning, the user travel behavior data is analyzed by a learning algorithm, the corresponding relationship between conditions and feedback is extracted, and a user activity list is generated; the system analyzes the user's travel behavior data by a learning algorithm, extracts the corresponding relationship between conditions and feedback, and automatically generates a user activity list; data collection is to obtain user behavior data such as time, temperature and humidity, sensor signals, etc. through sensors, environmental detection modules, etc.; algorithm analysis is to use a learning algorithm to analyze user behavior data and identify the corresponding relationship between conditions (such as time, environmental parameters, etc.) and feedback (such as seat rotation, ventilation or heating); list generation is to generate a structured user activity list based on the analysis results, including the definition of conditions and feedback;

[0035] When based on customization, users set or adjust the conditions and feedback in the user activity list through the interactive interface to generate a user activity list; users set or adjust the conditions and feedback in the activity list through the interactive interface to generate or modify the user activity list; the interactive interface design can provide a user-friendly interface, allowing users to select or define conditions and feedback. Users can add, modify or delete conditions and feedback based on the existing list according to actual needs, ensuring that the list content meets personalized needs.

[0036] In this embodiment, learning-based means automatically generating a user activity list through an algorithm, which reduces the user's operating burden and enhances the intelligence level of the system; customization-based means that users can customize the content of the activity list according to their own habits and actual needs, ensuring the accuracy and personalization of scenario-based services; the system supports both automatic learning and manual customization to meet the preferences and needs of different users and enhance the convenience and diversity of user experience.

[0037] Optionally, the method further includes the steps of: allowing the user to perform secondary editing on the user activity list through the interactive interface to optimize the logical relationship between conditions and feedback; and allowing the user to further optimize and adjust the generated user activity list through the interactive interface to improve the logical relationship between conditions and feedback to make it more in line with actual needs.

[0038] The specific implementation method is to provide users with full editing rights to the activity list, including adding, deleting, modifying conditions and feedback; through a simple and intuitive interface design, users can easily understand and operate the logical relationship of the list, such as adjusting the priority or hierarchical structure of the conditions through drag and drop operations; provide real-time feedback function, allowing users to preview the optimization effect after editing is completed or verify the accuracy of the logic through simulation scenarios; the user's modified list can be stored instantly and applied to subsequent travel scenario services without the need to regenerate the activity list.

[0039] In this embodiment, allowing users to perform secondary editing of the activity list helps to better adapt to personalized needs or cope with special scenarios, and enhance the flexibility and controllability of the system; users can adjust the matching relationship between conditions and feedback according to different time periods, environmental conditions or usage habits to ensure more accurate system operation; the secondary editing function enables the system to achieve intelligent response through learning algorithms while retaining the user's ability to manually optimize, forming an effective combination of intelligence and manual intervention.

[0040] Optionally, the learning algorithm in the step of generating the user activity list based on learning adopts a decision tree, a Bayesian algorithm, a K nearest neighbor algorithm and / or a neural network learning algorithm.

[0041] Among them, the decision tree algorithm divides the relationship between conditions and feedback through hierarchical logic to generate a clear decision path. For example, the time conditions on weekdays give priority to matching steering feedback, while on weekends, the ventilation or heating function in rest mode is given priority. The Bayesian algorithm dynamically adjusts the condition weights and feedback priorities in the user activity list based on the probabilistic relationship between conditions and feedback. For example, when the trigger probability of a certain scene is high (such as starting the ventilation function under high temperatures in summer), the system automatically increases its priority; the K nearest neighbor algorithm analyzes the combination of conditions and feedback of similar scenes based on user historical behavior data, and performs real-time matching and optimization. For example, under specific ambient temperature and geographical location conditions, the system automatically matches the feedback logic that is most similar to adjacent scenes. The neural network algorithm mines the deep association between conditions and feedback through complex multi-layer nonlinear mapping to generate a highly optimized user activity list, such as learning the user's behavior patterns under the interaction of multiple conditions to generate multi-dimensional scenario service logic.

[0042] For example, by combining environmental conditions and learning algorithms (taking the Bayesian algorithm as an example), for the conditions of summer, weekdays, 16:30-17:30, temperature above 25°C, and the geographical location (path) of the school, it is considered that the child is being picked up from school, and ventilation and heat dissipation are started in advance; by combining time conditions, environmental conditions and learning algorithms (taking the decision tree as an example), when it is a weekday and the geographical location (departure) is home, it is considered that the child is being sent to school, and the child seat is turned towards the door at 6:30 in advance. When it is detected that the child is sitting (after a delay), the child seat is turned back to the child seat. If the child is not seated within the time limit, the child seat is automatically turned back to the child seat.

[0043] In this embodiment, the introduction and application of multiple algorithms enable the system to flexibly adapt to the learning needs of different scenarios, significantly improve the generation quality of user activity lists, and enhance the accuracy and reliability of the learning process; the coordinated use of different algorithms improves the system's adaptability to diverse scenarios and can accurately respond to the needs of simple and complex scenarios.

[0044] Optionally, the conditions in the user activity list include time, environmental parameters, sensor signals and preconditions, and the feedback in the user activity list includes electric rotation of the safety seat, fan activation and heating function activation.

[0045] Among them, time includes absolute time (such as specific hours and minutes) and relative time (such as the time interval after an event). For example, the condition can be "7:00 am on weekdays" or "2 seconds after the child is seated." Environmental parameters include environmental data such as temperature, humidity, light intensity, and vehicle geographic location. For example, the condition can be "the temperature inside the car is higher than 30°C" or "the vehicle's geographic location is close to the school." The sensor signal comes from the real-time signal detected by the sensor of the safety seat, including the child sitting, the door opening, the seat angle change, etc. For example, the conditions can be "the door opening is detected" and "the child is detected sitting." The precondition refers to the subsequent condition after a specific condition or event is triggered. For example, the condition can be "after detecting that the child is sitting, the seat has been rotated to face the door."

[0046] Electric rotation means that the safety seat can be automatically rotated to a specified angle, such as feedback can be "the seat rotates 90° counterclockwise to face the door". Fan start means that the built-in fan is started according to the set conditions to provide ventilation for the seat, such as feedback can be "seat ventilation is started when the temperature inside the car is higher than 30°C". Heating function activation means that the built-in heating module is started according to the set conditions to provide warmth for the seat, such as feedback can be "seat heating is started when the temperature inside the car is lower than 10°C". Each condition is connected to the feedback through timestamps and logical associations to form a complete activity chain. The system monitors the current scene in real time, dynamically matches it with the conditions in the user's activity list, and triggers the corresponding feedback. For example, when it is detected that "it is 7:00 am on weekdays, the temperature outside the car is lower than 10°C and the child is seated", the feedback of "the seat rotates 90° counterclockwise and the heating is started" is triggered.

[0047] In this embodiment, the conditions cover time, environmental parameters, sensor signals and preconditions, which support the precise definition and triggering of complex scenarios and improve the user experience. Through the preconditions, a coherent multi-step operation logic is constructed to meet the user's complex scenario needs. For example, the seat is automatically rotated and reset after the child sits down, and the fan or heating function is started according to the temperature, and the operation is smooth and natural. The feedback operation is automatically executed, reducing the need for manual adjustment by the user and enhancing convenience. The feedback function can be triggered in combination to realize multi-functional linkage operation and provide a better riding experience. The matching of conditions and feedback is based on actual user needs, reducing the triggering of irrelevant services and improving operation accuracy.

[0048] Optionally, the step of monitoring parameters of the current scene includes monitoring time parameters, temperature and humidity, geographic location, sensor signals and precondition signals to obtain complete scene information.

[0049] Among them, the time parameters include absolute time (specific year, month, day, hour, minute) and relative time (time interval after the event is triggered), such as "7:00 am" or "3 seconds after the door is opened". Temperature and humidity are sensed in real time by the environmental detection module inside and outside the car; the geographical location is obtained by the vehicle positioning module. The geographical location information or driving path of the current vehicle; the sensor signal includes the signal of the built-in sensor of the safety seat, such as the child's seat signal, the seat angle change signal, the pressure sensor signal, etc.; the precondition signal is the state or condition signal before the trigger, which is used to determine whether the logical chain of the operation is met, such as the precondition signal includes "the door is open" or "the seat has been rotated 90°". Parameters such as time, temperature and humidity, and geographic location are collected through independent sensor modules. Sensor signals are acquired in real time through built-in sensors in the seats, and precondition signals are generated through scene logic deduction. All parameters are analyzed in real time through software modules to extract key scene information. For example, if it is monitored that "7:00 a.m. on Monday, the temperature outside the car is 10°C, and the vehicle is at home", the system combines the data to determine that the possible scene is "sending children to school". The parameters are associated to form a complete scene description to avoid service errors due to insufficient single data. The time "7:00" is combined with the geographic location "home" and the child's seat signal to accurately determine the scene requirements of "sending children to school". This multi-dimensional monitoring enhances the accuracy of scene recognition and avoids false triggering or erroneous service based on a single parameter. The precondition logic also improves continuity and prevents unnecessary operations.

[0050] Optionally, the step of controlling the safety seat to perform corresponding actions includes the following actions:

[0051] Control the safety seat to rotate to the specified direction based on the matching feedback; The safety seat is adjusted to the predetermined direction through the electric rotation function according to the feedback instructions in the user's activity list. For example, in the welcome mode, the seat turns to the door position to facilitate children getting on and off the vehicle; The seat's built-in motor component controls the rotation angle according to the feedback instructions. For example, when the vehicle is in the pick-up mode, the seat automatically rotates 90° counterclockwise to face the door;

[0052] Activate seat ventilation or heating functions based on temperature or time conditions; The safety seat responds to temperature or time conditions through built-in ventilation and heating modules to provide a comfortable seat environment. The temperature monitoring module collects the seat surface or interior ambient temperature in real time and matches it with the conditions in the user's activity list to trigger ventilation or heating functions. For example, when the interior temperature is higher than 30°C, the seat ventilation function is automatically turned on, and when it is lower than 5°C in the morning in winter, the heating function is automatically turned on.

[0053] The seat position is adjusted after confirming that the child is seated based on the sensor signal; the safety seat detects whether the child is seated through the built-in pressure sensor or contact sensor, and adjusts the seat position based on the feedback instruction to ensure safety and comfort. The pressure sensor senses the force state of the seat. When the signal that the child is seated is detected, the seat angle, depth or height is adjusted through the electric adjustment module. For example, after the child is seated, the seat automatically adjusts to a comfortable reclining angle. If it is detected that the child is not seated, the default position is maintained or returned to the initial state.

[0054] In this embodiment, through multi-dimensional feedback control, such as seat rotation, temperature regulation and position adjustment, the product is significantly improved in terms of intelligence, safety and comfort, meeting the actual needs of users for the multi-scenario use of child safety seats.

[0055] Optionally, the generation and use of the user activity list supports hierarchical logic management, allowing conditions and feedback to gradually perform complex scenario operations according to hierarchical relationships; the user activity list manages conditions and feedback in the form of hierarchical logic, and gradually resolves complex scenario operations through hierarchical relationships, such as high-level conditions triggering low-level conditions, and low-level conditions further determine the feedback content. During the generation process of the user activity list, a hierarchical logical structure is constructed according to the correlation between conditions and feedback:

[0056] First-level conditions: global trigger conditions, such as time and date (such as "7:00 a.m. on weekdays").

[0057] Second-level conditions: detailed scenario conditions, such as user operations and environmental parameters (such as "temperature is higher than 30°C" or "child seated").

[0058] Feedback: Perform seat actions (such as rotation, heating or position adjustment) based on matching layer conditions.

[0059] Combination Figure 4 As shown, an embodiment is as follows:

[0060] First-level condition: 7:00 a.m. on weekdays, trigger the welcome mode (90° counterclockwise rotation);

[0061] The second condition: detect when the child is seated, trigger the seat to rotate back to its original position (90° clockwise) and adjust to the appropriate angle.

[0062] In addition, it supports progressive execution through hierarchical conditions to achieve complex scene operations with multiple steps and multiple actions, and establishes an execution sequence through logical association between conditions and feedback. For example:

[0063] First level condition (high level): 6:30 am on weekdays, the vehicle departure point is home;

[0064] The second condition (low level): combines time and sensor signals to confirm whether the child is seated;

[0065] Feedback: Trigger the seat to rotate to the door layer by layer, reset after a delay, and adjust the seat heating status.

[0066] In this embodiment, through the conditional parsing capability of hierarchical logic, the system can efficiently handle complex travel scenarios; users can freely define and adjust the hierarchical relationship between conditions and feedback according to actual needs to meet diverse scenario requirements; the hierarchical logic between conditions and feedback reduces the interference of irrelevant conditions on feedback execution; in addition, hierarchical conditions can be dynamically matched during real-time monitoring to improve the system's adaptability to actual scenarios.

[0067] Optionally, the method further includes the following steps: the user turns on or off the travel scenario service through the safety seat or an interactive interface of a terminal connected to the safety seat, and can switch between manual adjustment and automatic control modes.

[0068] Among them, users can independently turn on or off the travel scenario service function through the interactive interface of the terminal that comes with the safety seat or is connected to it (such as mobile phone APP, vehicle-mounted central control screen, etc.). The interactive interface contains a switch button for switching the activation status of the travel scenario service, and the status indicator shows the activation or deactivation status of the current scenario service; the interactive interface operation sends instructions to the safety seat control module through the communication module (such as Bluetooth, Wi-Fi), and the safety seat adjusts the scenario service mode according to the instructions. Support users to switch between manual adjustment and automatic control modes, providing flexible operation methods; automatic control mode: the system automatically executes travel scenario services according to the user activity list and scenario parameters; manual adjustment mode: users can directly control the seat functions (such as rotation, heating, ventilation, etc.) through the interactive interface, and temporarily disable the automatic control logic; when the user switches to manual adjustment mode, the system suspends the automatic feedback execution of the user activity list, and when switching back to automatic mode, it resumes the response to the user activity list.

[0069] In this embodiment, users can choose to turn scenario-based services on or off according to actual needs, avoiding wasting system resources or causing unnecessary operations when services are not needed, thereby enhancing the flexibility and autonomy of user operations; intuitively controlling service status and mode switching through an interactive interface, lowering the operating threshold, improving system usability, and enhancing the customization and convenience of user experience; the automatic control mode provides intelligent services and reduces user intervention, and the manual adjustment mode meets the user's personalized operation needs for seat functions, taking into account both automation and personalization needs.

[0070] Another embodiment of the present invention provides a child seat for implementing the travel scenario preset and service method based on the safety seat as described above.

[0071] The child seat in this embodiment can provide accurate and considerate scene services by learning user behavior or allowing user customization, and the system generates a personalized activity list based on the user's usage habits to meet the needs of different families and enhance comfort. It processes multiple conditional signals through activity lists and learning algorithms to reduce hardware complexity and the number of sensors, thereby reducing costs. It can also avoid false actions caused by a single sensor, and has high feedback accuracy.

[0072] Although the disclosure is disclosed as above, the protection scope of the disclosure is not limited thereto. Those skilled in the art may make various changes and modifications without departing from the spirit and scope of the disclosure, and these changes and modifications will fall within the protection scope of the present invention.

Claims

1. A travel scenario presetting and service method based on a safety seat, characterized in that: Includes steps: Record user travel behavior data and generate user activity lists based on learning and / or customization; storing the user activity list, the user activity list comprising conditions and feedback; Monitoring parameters of the current scene according to the conditions in the user activity list; Matching corresponding feedback from the user activity list according to the monitored scene conditions; Based on the matching feedback, the safety seat is controlled to perform corresponding actions to complete travel scenario services.

2. The travel scenario presetting and service method based on the safety seat according to claim 1 is characterized in that: The step of recording the user's travel behavior data includes: recording the user's operation history data on the safety seat through the safety seat's sensor, environmental detection module or a terminal connected to the safety seat, wherein the operation history data includes time, environmental parameters, sensor signals and user operation instructions.

3. The travel scenario presetting and service method based on the safety seat according to claim 1 is characterized in that: The conditions in the user activity list include time, environmental parameters, sensor signals and preconditions, and the feedback in the user activity list includes electric rotation of the safety seat, fan activation and heating function activation.

4. The travel scenario presetting and service method based on the safety seat according to claim 1, characterized in that: The step of monitoring the parameters of the current scene includes monitoring time parameters, temperature and humidity, geographic location, sensor signals and precondition signals to obtain complete scene information.

5. The travel scenario presetting and service method based on the safety seat according to claim 1, characterized in that: The step of controlling the safety seat to perform corresponding actions includes the following actions: Control the safety seat to rotate to a specified direction according to the matching feedback; Activate seat ventilation or heating based on temperature or time conditions; The seat position is adjusted based on the sensor signal after confirming that the child is seated.

6. The travel scenario presetting and service method based on the safety seat according to claim 1 is characterized in that: The generation and use of the user activity list supports hierarchical logic management, allowing conditions and feedback to gradually perform complex scenario-based operations according to hierarchical relationships.

7. The travel scenario presetting and service method based on a safety seat according to claim 1, characterized in that: The method also includes the following steps: the user turns on or off the travel scenario service through the safety seat or an interactive interface of a terminal connected to the safety seat, and can switch between manual adjustment and automatic control modes.

8. The travel scenario presetting and service method based on a safety seat according to claim 1, characterized in that: The step of generating a user activity list based on learning or customization includes: When based on learning, the user travel behavior data is analyzed by a learning algorithm, the corresponding relationship between conditions and feedback is extracted, and a user activity list is generated; When based on customization, the user sets or adjusts the conditions and feedback in the user activity list through the interactive interface to generate the user activity list.

9. The travel scenario presetting and service method based on the safety seat according to claim 8, characterized in that: The method also includes the step of allowing the user to perform secondary editing on the user activity list through the interactive interface to optimize the logical relationship between conditions and feedback.

10. The travel scenario presetting and service method based on a safety seat according to claim 8, characterized in that: The learning algorithm in the step of generating the user activity list based on learning adopts a decision tree, a Bayesian algorithm, a K nearest neighbor algorithm and / or a neural network learning algorithm.

11. A child seat, characterized in that: Used to implement the travel scenario preset and service method based on the safety seat as described in any one of claims 1-10.

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