Rule management method for intelligent seat adjustment and intelligent seat

Abstract

The invention provides a rule management method for intelligent seat adjustment and an intelligent seat, and the method comprises the steps: obtaining the number of changes corresponding to the parameters of the intelligent seat before change and the current external parameters when a user changes the parameters of the intelligent seat, and updating a first score corresponding to the parameters of the intelligent seat after change; determining a second score of the pre-change intelligent seat parameter according to the change frequency of the pre-change intelligent seat parameter; sorting each group of intelligent seat parameters corresponding to the current external parameters in the rule base according to the first score and the second score; and changing target intelligent seat parameters corresponding to the current external parameters according to a sorting result. And sorting each group of intelligent seat parameters through the first score and the second score to select the rule with the highest score, so that the changed target intelligent seat parameters can better conform to the intelligent seat use habits of the user, and optimization and iteration of the rules in the rule base are realized.

Description

Technical Field

[0001] The present application relates to the field of rule management algorithms, and in particular to a rule management method for intelligent seat adjustment and an intelligent seat. Background Art

[0002] A smart seat is a new type of smart home or car device that combines modern technology with traditional smart seat functions. It integrates sensors, artificial intelligence, the Internet of Things and other technologies, and uses intelligent seat adjustment algorithms to identify the user's physiological characteristics or environmental factors to adjust the various functional parameters of the smart seat. It can achieve real-time monitoring and automatic adjustment of human posture and health data to improve user comfort, health management capabilities and interactive experience.

[0003] However, the scoring results of the existing smart seat adjustment rules of smart seats cannot fully reflect the users' smart seat usage habits.

[0004] It should be noted that the information in the above background technology section is only used to enhance the understanding of the background technology of this application, and therefore may include technical information that is not known or easily inferred by ordinary technicians in this field. Summary of the Invention

[0005] In view of the above problems, the present application is proposed to provide a rule management method for intelligent seat adjustment and an intelligent seat that overcomes the above problems or at least partially solves the above problems, including: A rule management method for intelligent seat adjustment, the method relating to an adjustable intelligent seat, wherein the intelligent seat is preset with a rule library; wherein the rule library contains multiple sets of intelligent seat parameters; the intelligent seat is configured to call a corresponding set of target intelligent seat parameters from the rule library according to external parameters for intelligent seat adjustment; The method comprises: When the user changes the smart seat parameters, the number of changes and the current external parameters corresponding to the smart seat parameters before the change are obtained, and the first score corresponding to the smart seat parameters after the change is updated; determining a second score of the smart seat parameter before the change according to the number of changes of the smart seat parameter before the change; sorting the groups of smart seat parameters corresponding to the current external parameters in the rule base according to the first score and the second score; The target smart seat parameters corresponding to the current external parameters are changed according to the sorting result.

[0006] Furthermore, the method further comprises: When the second score of the smart seat parameter before the change is less than the preset score threshold, the smart seat parameter before the change is deleted from the rule library.

[0007] Furthermore, the smart seat is preset with at least two rule bases; the method further includes: Get current time data; A rule base for calling target smart seat parameters is determined from the at least two rule bases according to the current time data.

[0008] Furthermore, the external parameters include multidimensional pressure data; and the step of adjusting the intelligent seat by calling a corresponding set of target intelligent seat parameters from a rule library according to the external parameters includes: Determining multidimensional object model data based on the multidimensional pressure data of the external parameters; wherein, comparing multiple pressure values ​​of the multidimensional pressure data with corresponding preset pressure thresholds respectively: if the pressure value is greater than the corresponding pressure threshold, determining that the corresponding value of the multidimensional object model data is a first type of pressure value; if not, determining that the corresponding value of the multidimensional object model data is a second type of pressure value; A corresponding set of target smart seat parameters is called from a rule library according to the multidimensional object model data.

[0009] Furthermore, when the user changes the smart seat parameters, the step of obtaining the number of changes corresponding to the smart seat parameters before the change and the current external parameters, and updating the first score corresponding to the smart seat parameters after the change includes: Obtain the call count corresponding to the changed smart seat parameters; When the number of calls corresponding to the changed smart seat parameter is greater than the corresponding preset number threshold, the current score of the changed smart seat parameter is increased according to the number of calls corresponding to the changed smart seat parameter and the first score of the changed smart seat parameter is updated.

[0010] Furthermore, the method further comprises: When the user changes the smart seat parameters, obtain the call count of the smart seat parameters before the change; If the number of calls corresponding to the smart seat parameter before the change is less than the corresponding preset number threshold, deleting the smart seat parameter before the change in the rule base; If not, the second score of the smart seat parameter before the change is determined according to the number of calls and the number of changes of the smart seat parameter before the change.

[0011] Furthermore, the step of determining the second score of the smart seat parameter before the change based on the number of calls and the number of changes of the smart seat parameter before the change includes: When the number of calls corresponding to the smart seat parameter before the change is greater than the corresponding preset number threshold, increasing the current score of the smart seat parameter before the change according to the number of calls corresponding to the smart seat parameter before the change; When the number of changes corresponding to the smart seat parameter before the change is greater than the corresponding preset number threshold, reducing the current score of the smart seat parameter before the change according to the number of changes corresponding to the smart seat parameter before the change; A second score of the smart seat parameter before the change is determined according to a change result of the current score of the smart seat parameter before the change.

[0012] An adjustable smart seat, the smart seat having a preset rule base; wherein the rule base includes multiple sets of smart seat parameters; the smart seat includes a rule calling module for calling a corresponding set of target smart seat parameters from the rule base according to external parameters to perform smart seat adjustment; The smart seat also includes: a sensor trigger module, configured to obtain the number of changes corresponding to the smart seat parameters before the change and the current external parameters when the user changes the smart seat parameters, and to update the first score corresponding to the smart seat parameters after the change; a scoring module, configured to determine a second score of the smart seat parameter before the change according to the number of changes of the smart seat parameter before the change; a sorting module, configured to sort the groups of smart seat parameters corresponding to the current external parameters in the rule base according to the first score and the second score; A rule iteration module is used to change the target smart seat parameters corresponding to the current external parameters according to the sorting result.

[0013] A computer device comprises a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the method described in any embodiment of the present application.

[0014] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any embodiment of the present application is implemented.

[0015] This application has the following advantages: In an embodiment of the present application, the scoring results of the smart seat adjustment rules of the existing smart seat cannot fully reflect the user's smart seat usage habits. The present application provides a solution for scoring the smart seat parameters before the change based on the number of changes and changing the calling priority of the smart seat parameters before and after the change. Specifically, when the user changes the smart seat parameters, the number of changes corresponding to the smart seat parameters before the change and the current external parameters are obtained, and the first score corresponding to the smart seat parameters after the change is updated; the second score of the smart seat parameters before the change is determined based on the number of changes of the smart seat parameters before the change; the groups of smart seat parameters corresponding to the current external parameters in the rule library are sorted based on the first score and the second score; and the target smart seat parameters corresponding to the current external parameters are changed based on the sorting results. The groups of smart seat parameters are sorted by the first score and the second score to select the rule with the highest score, so that the target smart seat parameters after the change can better meet the user's smart seat usage habits, thereby realizing the optimization and iteration of the rules in the rule library. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for use in the description of the present application. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0017] Figure 1 This is a flowchart of a rule management method for intelligent seat adjustment provided by an embodiment of the present application; Figure 2 This is a manipulation / learning record sheet provided in one embodiment of the present application; Figure 3 This is a smart seat status table provided in one embodiment of the present application; Figure 4 This is a rule table provided in an embodiment of the present application; Figure 5 This is a sensor configuration table provided in an embodiment of the present application; Figure 6 This is a distribution diagram of smart seat pressure sensors provided by an embodiment of the present application; Figure 7 This is a sitting posture table provided in one embodiment of the present application; Figure 8 is a matrix rule table provided in an embodiment of the present application; Figure 9 is a pressure eight-dimensional characteristic vector definition table provided in an embodiment of the present application; Figure 10 This is an effect evaluation index table provided in an embodiment of the present application; Figure 11 This is a smart seat object model definition table provided in an embodiment of the present application; Figure 12 This is a smart seat object model data table provided in one embodiment of the present application; Figure 13 This is a diagram of a rule optimization implementation framework provided by an embodiment of the present application; Figure 14 This is a module diagram included in an adjustable smart chair provided in one embodiment of the present application; Figure 15 This is a structural block diagram of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0018] To make the objectives, features, and advantages of this application more readily apparent, the present application is further described below in conjunction with the accompanying drawings and specific embodiments. It is apparent that the embodiments described are only a portion of the embodiments of this application, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments in this application without inventive effort are also within the scope of protection of this application.

[0019] By analyzing the existing technology, the inventors found that the existing smart seats mainly evaluate the comfort of smart seat parameters based on the user's posture parameters. When calculating the score, multiple parameters need to be re-called and recalculated. The scoring results cannot fully reflect the user's smart seat usage habits and are prone to historical data redundancy. They are not suitable for scenarios with high computational overhead.

[0020] It should be noted that in any embodiment of the present application, the smart seat parameters in the rule base can be based on external parameters as conditions to trigger the action of smart seat adjustment, that is, the smart seat is adjusted based on the smart seat adjustment rules; a rule base may include multiple groups of smart seat adjustment rules, and the smart seat adjustment rules include triggering conditions and execution actions; wherein, the external parameters can match the triggering conditions in the smart seat adjustment rules; the smart seat parameters can match the execution actions in the smart seat adjustment rules; the smart seat adjustment rules can be IFTTT rules; the smart seat parameters in the rule base are subject to the scoring criteria of any embodiment of the present application.

[0021] Reference Figure 1 , shows a rule management method for smart seat adjustment provided by an embodiment of the present application, the method relates to an adjustable smart seat, the smart seat having a preset rule library; wherein the rule library includes multiple sets of smart seat parameters; the smart seat is configured to call a corresponding set of target smart seat parameters from the rule library according to external parameters to perform smart seat adjustment; The method comprises: S110: When the user changes the smart seat parameters, the number of changes corresponding to the smart seat parameters before the change and the current external parameters are obtained, and the first score corresponding to the smart seat parameters after the change is updated; S120: Determine a second score of the smart seat parameter before the change based on the number of changes of the smart seat parameter before the change; S130, sorting the groups of smart seat parameters corresponding to the current external parameters in the rule base according to the first score and the second score; S140. Change the target smart seat parameters corresponding to the current external parameters according to the sorting result.

[0022] In an embodiment of the present application, the scoring results of the smart seat adjustment rules of the existing smart seat cannot fully reflect the user's smart seat usage habits. The present application provides a solution for scoring the smart seat parameters before the change based on the number of changes and changing the calling priority of the smart seat parameters before and after the change. Specifically, when the user changes the smart seat parameters, the number of changes corresponding to the smart seat parameters before the change and the current external parameters are obtained, and the first score corresponding to the smart seat parameters after the change is updated; the second score of the smart seat parameters before the change is determined based on the number of changes of the smart seat parameters before the change; the groups of smart seat parameters corresponding to the current external parameters in the rule library are sorted based on the first score and the second score; and the target smart seat parameters corresponding to the current external parameters are changed based on the sorting results. The groups of smart seat parameters are sorted by the first score and the second score to select the rule with the highest score, so that the target smart seat parameters after the change can better meet the user's smart seat usage habits, thereby realizing the optimization and iteration of the rules in the rule library.

[0023] Next, a rule management method for intelligent seat adjustment in this exemplary embodiment will be further described.

[0024] It should be noted that the number of changes can be counted as the number of times a certain parameter of the smart seat is changed to another parameter of the smart seat, or as the cumulative number of changes in the internal sub-parameters of a certain parameter of the smart seat; weights can be introduced for the above counting times to perform numerical conversion to determine the number of changes; the above steps can be based on the smart seat rule management automatically triggered by the user's active adjustment of the smart seat. When the score of the rule corresponding to the smart seat parameter as a condition is higher, it means that the user is more satisfied with the smart seat rule and the more it conforms to the user's smart seat usage habits, then the rule should be given a higher score or a higher priority. This can be achieved by Figure 2 The manipulation / learning record table shown records the number of rule changes / calls, etc. It can be understood that Figure 2The creation time of the figures is written in year-month-day format for easy understanding. For specific implementation, in some embodiments, it can be converted into a timestamp format, such as converting the creation time into a UTC millisecond timestamp, such as February 1, 2025 into 1738398555000, etc.

[0025] It is understandable that the adjusted smart seat parameters may include various adjustable parameters of existing smart seats, which are not limited here. Specifically, they may include but are not limited to temperature parameters (such as cooling and / or heating adjustment), airbag parameters (simultaneous or independent inflation and deflation adjustment of single or multiple airbags in various components of the smart seat, such as single or multiple airbags in the backrest, etc.), and position parameters and / or angle parameters (simultaneous or independent electric adjustment of the position or angle of single or multiple chair components, such as raising and lowering the backrest, and rotating the backrest of the smart seat backward, etc.), etc.

[0026] The number of changes can be counted in a continuous or discontinuous manner, or a combination of continuous and discontinuous. For example, if a user sequentially changes the angle of the headrest once and the height once, the angle of the lumbar pillow twice and the height once, changes the fan level five times, and inflates an airbag in the backrest twice, thereby obtaining the changed smart seat parameters, the number of changes is now 12. For another example, if a user changes and adjusts the smart seat twice within a limited time period (e.g., one minute), within one minute, the user changes the headrest angle twice between 0 and 20 seconds, does not change it between 20 and 30 seconds, and changes the fan level three times between 50 and 60 seconds, the number of changes is now five. For another example, if the confirmation button on the smart seat is triggered or a specific user action is detected, it is confirmed that the smart seat parameters before the change have been changed to the smart seat parameters after the change, and the number of adjustments to the smart seat within that time period is then determined. For another example, the counting cycle for the number of changes is the period from the start of adjustment to the user leaving the seat. For example, from the initial adjustment by the user to the time the user leaves the seat, the angle of the headrest is changed twice and the fan gear is changed three times. At this time, the total number of changes is 5 times. For another example, the counting cycle for the number of changes is the period from the initial adjustment by the user to the specific time when the seat parameters are not changed. For example, the specific time is 15 minutes. From the initial adjustment by the user, if the next adjustment is not made within 15 minutes, this period is a counting cycle. If an adjustment is made at 14 minutes from the initial adjustment by the user, it will be postponed again for 15 minutes from this time, and this period is a counting cycle.

[0027] As an example, when determining a score based on the number of changes to smart seat parameters, different scoring weights can be set for different types of smart seat parameters to determine the score based on the number of changes to the smart seat parameters and the weight coefficient. The weight coefficient can be directly configured based on external data for different attributes, or it can be a weighted average value based on the cumulative number of adjustments, for example: If the user adjusts the temperature parameters frequently, it means that the appropriate temperature is more in line with the user's usage habits, so the changes to the smart seat parameters related to temperature will have a higher weight coefficient; If the user frequently adjusts the pressure of the smart seat backrest, it means that the appropriate pressure better matches the user's usage habits, so changes to smart seat parameters involving pressure will have a higher weight coefficient; The above description of the weight coefficient may also be applied to any other parameters involved in the scoring in this application.

[0028] As an example, corresponding to the physical structure and functions of a smart seat, the smart seat parameters may include: airbag pressure (AP), lighting parameters, heat dissipation parameters, heating parameters, and vibration parameters. Corresponding numbers, such as 0001 and 0002, can be assigned to the smart seat parameters in the rule library.

[0029] You can do this by Figure 3 The smart seat status table shown records the smart seat parameters, where in the heating / cooling mode column, 0 can represent normal mode, 1 can represent cooling mode, and 2 can represent heating mode; in the vibration mode column, 0 can represent no vibration and 1 can represent vibration; in the lighting mode column, 0 can represent off and 1 can represent enabled.

[0030] You can do this by Figure 4 The rule table shown records the correspondence between external parameters and smart seat parameters.

[0031] As an example, the external parameter may be obtained by at least one sensor provided on the smart seat; the sensor may include: Pressure sensor, used to collect pressure data and set a safety range; Temperature sensor, used to collect temperature data and set a safety range; Heart rate sensor, used to obtain the user's heart rate (beats / minute); A blood pressure sensor is used to obtain the user's blood pressure value; Blood pressure sensor, used to obtain the user's blood oxygen saturation value; Accelerometer to capture user's micro-movements; Wherein, the temperature sensor may include: An ambient temperature sensor is provided at a position of the smart seat that is not in contact with the user and is used to obtain the ambient temperature of the smart seat; A smart seat surface temperature sensor is provided at a position where the smart seat contacts the user and is used to obtain the user's contact surface temperature; The temperature control device temperature sensor is installed at the heating or cooling device of the smart seat and is used to obtain the temperature at the heating or cooling device.

[0032] In some embodiments, the external parameters can also be obtained through device input (such as an electronic screen, a remote control, an APP, etc.), such as data that the above-mentioned sensors cannot or are difficult to obtain, such as data that the above-mentioned sensors need to input related data, such as user operation input of the data of the above-mentioned sensors, such as user information (such as height, weight) or other time and environmental information.

[0033] In a specific implementation, referring to Figure 5 ,The sensor can be configured as follows: the pressure sensor collects the pressure distribution of the seat cushion and the backrest at a frequency of 10 Hz (with a resolution of 0.1 kPa, such as hip pressure 0.25 MPa, waist pressure 0.3 MPa), where the pressure sensor may include 26 pieces of pressure sensors, which can be ,e.g. Figure 6 The configuration is performed as shown in S1-S26. The heart rate sensor measures the pulse waveform at 1-second intervals (for example, a heart rate of 80 beats / minute), the blood pressure sensor updates the blood pressure value every 10 minutes (for example, 60-130 mmHg), the blood oxygen sensor updates the blood oxygen saturation value every 10 minutes (for example, 90-100), the temperature sensor updates the ambient and body temperature values ​​every 5 minutes (for example, 28°C / 36.5°C), and the acceleration sensor detects micro-movements at a frequency of 50 Hz (for example, a leg shaking acceleration of 0.5g).

[0034] It should be noted that, corresponding to the type of sensor, the external parameters may include: multi-dimensional pressure data, temperature data, user status information data, environmental data, etc.

[0035] As described in step S110, when the user changes the smart seat parameters, the number of changes corresponding to the smart seat parameters before the change and the current external parameters are obtained, and the first score corresponding to the smart seat parameters after the change is updated.

[0036] It should be noted that when a user proactively changes the smart seat parameters, it means that the user is not satisfied with the pre-change smart seat parameters corresponding to the current external parameters, but may be more satisfied with the post-change smart seat parameters. Therefore, it is necessary to obtain the number of changes corresponding to the pre-change smart seat parameters and the current external parameters to reduce the score of the pre-change smart seat parameters. At the same time, it is necessary to update the first score corresponding to the post-change smart seat parameters. The first score can be a preset initial score directly assigned or a score recalculated based on the scoring conditions. The post-change smart seat parameters can be new temporary smart seat parameters generated in the rule base, or they can be smart seat parameters preset in the rule base, or they can be historical smart seat parameters generated during use.

[0037] As described in step S120, the second score of the smart seat parameter before the change is determined according to the number of changes of the smart seat parameter before the change.

[0038] It should be noted that if the corresponding number of changes is too many, it means that the user is not satisfied with the changed smart seat parameters under the current external parameter conditions, so the score of the smart seat parameters before the change should be reduced.

[0039] As an example, if the initial score of the smart seat parameter before the change is 0 points, the score will be reduced by 0.5 points each time the change is made to determine the second score of the smart seat parameter before the change; multiple change number thresholds can also be set, and when a specific threshold is reached, the corresponding score will be reduced to determine the second score of the smart seat parameter before the change.

[0040] As described in step S130, the groups of smart seat parameters corresponding to the current external parameters in the rule base are sorted according to the first score and the second score.

[0041] It should be noted that when the score of the smart seat parameter before the change decreases, the smart seat parameter with the highest score under the current external parameter conditions may change. Therefore, it is necessary to re-sort the smart seat parameters of each group according to the score to select the optimal smart seat parameter.

[0042] As described in step S140, the target smart seat parameters corresponding to the current external parameters are changed according to the sorting result.

[0043] It should be noted that by changing the target smart seat parameters corresponding to the current external parameters, the smart seat can automatically select the smart seat parameters with the highest score in the rule library to better suit the user's smart seat usage habits, and will also be dynamically refreshed as the user's usage habits change; If the target smart seat parameter after the change is the smart seat parameter before the change, it means that the smart seat parameter before the change is still the smart seat parameter with the highest score in the rule base, and the rule has not been iterated; If the target smart seat parameters after the change are not the smart seat parameters before the change, it means that the smart seat parameters before the change are no longer the smart seat parameters with the highest scores in the rule base, and the rules have been iterated. At this time, the target smart seat parameters corresponding to the current external parameters can be the smart seat parameters after the change, or other smart seat parameters in the rule base.

[0044] In one embodiment of the present application, the method further includes the following steps: When the second score of the smart seat parameter before the change is less than the preset score threshold, the smart seat parameter before the change is deleted from the rule library.

[0045] It should be noted that the smart seat can be preset with a periodic optimization thread for deleting smart seat parameters with lower scores; when the score of any smart seat parameter in the rule base is less than the preset score threshold, the smart seat parameter with a score less than the preset score threshold will be deleted from the rule base.

[0046] In a specific embodiment of the present application, the following steps may be included: When the number of smart seat parameter groups in the rule base is greater than a corresponding preset value, at least one group of smart seat parameters is deleted from the rule base according to the sorting result.

[0047] It should be noted that by setting a rule quantity threshold to eliminate rules with the lowest scores, when the number of smart seat parameter groups in the rule base exceeds the rule quantity threshold, the smart seat parameters with lower scores are deleted from the rule base based on the ranking results and the number of smart seat parameter groups exceeding the rule quantity threshold. For example, the rule quantity threshold can be set to 200, and then the smart seat parameters ranked after 200th in the score ranking will be automatically deleted.

[0048] Therefore, by deleting rules with scores below the threshold and / or low rankings, not only will the smart seat parameters retained in the rule library become more and more in line with the user's usage habits after the smart seat has been used for a certain period of time, but the program's retrieval rate can also be improved and memory can be saved under limited computing overhead.

[0049] In one embodiment of the present application, the smart chair may have at least two rule bases in addition to having only one preset rule base; the method further includes the following steps: Get current time data; A rule base for calling target smart seat parameters is determined from the at least two rule bases according to the current time data.

[0050] It should be noted that the current time data may include current single-day time period data; the current single-day time period data is used to indicate the time period of the day, such as morning (6:00-12:00), noon (12:00-14:00), afternoon (14:00-18:00), evening (18:00-24:00) and late night (0:00-6:00); The current time data may also include date data, and the date data is used to indicate a working day or a rest (holiday) day, etc.; Considering that time has a significant impact on the user's state of chair usage, for example, users tend to work more in the morning and take a lunch break at noon; they tend to work more on weekdays and relax more on weekends, etc. Adding the time indicator condition can better fit the actual usage situation and further improve the accuracy of smart chair learning; The current time data may also include current season data; if the current season data is winter, the rule base corresponding to winter is called; if the current season data is summer, the rule base corresponding to summer is called; if the current season data is other seasons, the general rule base is called; Since seasonal rules are only used frequently in the current season and are rarely used outside the current season, for example, users usually only use smart seat parameters to turn on the heating mode through smart seats in winter. Therefore, introducing current time data as a rule calling condition can improve the hit rate of the rules and be more in line with current usage scenarios.

[0051] In a specific embodiment of the present application, the smart chair may have at least two preset rule bases in addition to only one preset rule base; the method further includes the following steps: Get current spatial data; A rule base for calling target smart seat parameters is determined from the at least two rule bases according to the current spatial data.

[0052] It should be noted that current spatial data can be obtained through GPS; regional rules can distinguish the usage habits of people in different regions; and seasonal rules and regional rules can enable smart seats to automatically adapt to seasonal changes in different regions when calling rules.

[0053] In one embodiment of the present application, the external parameters include: multi-dimensional pressure data; the specific process of "calling a corresponding set of target smart seat parameters from the rule library based on the external parameters to adjust the smart seat" can be further explained in combination with the following description.

[0054] As described in the following steps, multidimensional object model data is determined based on the multidimensional pressure data of the external parameters; wherein, multiple pressure values ​​of the multidimensional pressure data are respectively compared with corresponding preset pressure thresholds: if the pressure value is greater than the corresponding pressure threshold, the corresponding value of the multidimensional object model data is determined to be a first type of pressure value; if not, the corresponding value of the multidimensional object model data is determined to be a second type of pressure value; A corresponding set of target smart seat parameters is called from a rule library according to the multidimensional object model data.

[0055] It should be noted that the multiple pressure values ​​of the multi-dimensional pressure data can be simplified into two types of pressure values ​​to simplify the external parameters. Since the multiple pressure values ​​of the multi-dimensional pressure data will produce a large number of values ​​under slight changes, simplifying the external parameters avoids the generation of a large number of rules when the smart seat introduces pressure parameters as the conditions of the rules, that is, reducing the number of rules in the rule base, and can also more accurately identify the user's sitting posture; wherein, the first type of pressure value can be 1, and the second type of pressure value can be 0. When the user's sitting posture changes, or when a certain part lacks support, it is easier to trigger the corresponding rules. This can be done through Figure 7 The posture table shown represents the pressure sensor PressureSensor state matrix, where 0 represents no pressure and 1 represents pressure.

[0056] Similarly, you can also set a conversion threshold for temperature to reduce the number of rules when the temperature parameter is used as a rule condition; for example, convert 25 degrees and 26 degrees into the same type of object model data. Figure 8 The rule matrix table shown reflects the smart seat parameter patterns corresponding to different temperature / time conditions.

[0057] In a specific implementation, an input range can be set for the external parameters. Only when the obtained external parameters are within the input range, the external parameters are further processed or the smart seat parameters in the rule library are called to adjust the smart seat. For example, in some scenarios, if a pet stays on the smart seat for a certain period of time, the external parameters corresponding to the pet will not be within the input range in most cases. That is, the physical model verification of the external parameters corresponding to the pet is illegal, so the physical model data will not be generated, or the external parameters cannot be matched when converted to multi-dimensional pressure calibration, or the generated physical model data will be judged invalid and the data will be discarded, so no new rules will be generated or scoring will be performed.

[0058] In a specific embodiment of the present application, the external parameters include: eight-dimensional sitting posture data; The specific process of "calling a corresponding set of target smart seat parameters from the rule library based on external parameters" can be further explained in combination with the following description.

[0059] As described in the following steps, multi-dimensional object model data is determined based on the eight-dimensional sitting posture data; A corresponding set of target smart seat parameters is called from a rule library according to the multidimensional object model data.

[0060] It should be noted that the eight-dimensional sitting posture data may include: average pressure intensity, pressure center offset, pressure change frequency, maximum pressure gradient, left-right pressure symmetry, pressure difference before and after 30 seconds, periodic pattern intensity, and abnormal pulse signal ratio; Among them, the average pressure intensity is used to determine whether there is excessive pressure; the pressure center offset is used to detect whether the sitting posture is crooked; the pressure change frequency is used to identify user micro-movements (such as leg shaking); the maximum pressure gradient is used to prevent local blood circulation obstruction; the left and right pressure symmetry is used for spinal health assessment; the pressure difference before and after 30 seconds is used to determine whether a state of relaxation has been entered; the periodic pattern intensity is used to detect regular body shaking; the abnormal pulse signal ratio is used to detect sudden discomfort or equipment failure. The eight-dimensional sitting posture data can be used to represent the user's use status of the smart chair, and similar sitting postures can be integrated to avoid repeated or similar sitting postures that generate unnecessary rules and lead to rule redundancy. By optimizing the number of rules in the rule base, the number of rules in the rule base is reduced, thereby improving the hit rate of rules under various conditions. Figure 9 The pressure eight-dimensional feature vector definition table shown reflects the contents of the eight-dimensional sitting posture data.

[0061] In one embodiment of the present application, the specific process of step S110, "when the user changes the smart seat parameters, obtaining the number of changes corresponding to the smart seat parameters before the change and the current external parameters, and updating the first score corresponding to the smart seat parameters after the change" can be further explained in combination with the following description.

[0062] As described in the following steps, the call count corresponding to the changed smart seat parameters is obtained; When the number of calls corresponding to the changed smart seat parameter is greater than the corresponding preset number threshold, the current score of the changed smart seat parameter is increased according to the number of calls corresponding to the changed smart seat parameter and the first score of the changed smart seat parameter is updated.

[0063] It should be noted that if the corresponding number of calls is large, it means that the user has a high demand for the changed smart seat parameters under the current external parameter conditions, resulting in repeated use of rules, so the score of the changed smart seat parameters should be increased.

[0064] In a specific embodiment of the present application, the following steps may be included: Obtaining the usage time corresponding to the changed smart seat parameters; When the usage time corresponding to the changed smart seat parameter is greater than the corresponding preset time threshold, the current score of the changed smart seat parameter is increased according to the usage time corresponding to the changed smart seat parameter to update the first score of the changed smart seat parameter.

[0065] It should be noted that the preset time threshold can be a fixed value or the average usage time of the smart seat parameters in the rule base. For example, for every 30 minutes the usage time corresponding to the smart seat parameters exceeds the average usage time, 0.01 points will be added.

[0066] In one embodiment of the present application, the method further includes the following steps: When the user changes the smart seat parameters, obtain the call count of the smart seat parameters before the change; If the number of calls corresponding to the smart seat parameter before the change is less than the corresponding preset number threshold, deleting the smart seat parameter before the change in the rule base; If not, the second score of the smart seat parameter before the change is determined according to the number of calls and the number of changes of the smart seat parameter before the change.

[0067] It should be noted that the number of calls may be the total number of times the smart seat calls the corresponding smart seat parameters. If the corresponding number of calls is too small, it means that the smart seat parameters are useless temporary smart seat parameters generated during use, so they can be deleted.

[0068] In a specific embodiment of the present application, the following steps may be included: When the user changes the smart seat parameters, obtain the usage time corresponding to the smart seat parameters before the change; If the usage time corresponding to the smart seat parameter before the change is less than the corresponding preset time threshold, deleting the smart seat parameter before the change in the rule base; If not, the second score of the smart seat parameter before the change is determined according to the usage time and the number of changes of the smart seat parameter before the change.

[0069] It should be noted that the usage time can be the total time the user continuously uses the corresponding smart seat parameters. If the corresponding usage time is too short, it means that the user is not satisfied with the changed smart seat parameters under the current external parameter conditions, or useless temporary smart seat parameters are generated during use, so they can be deleted.

[0070] As an example, multiple conditions can be set for deleting smart seat parameters. For example, if the smart seat parameter is used for less than 5 minutes and the score of the smart seat parameter is less than 30 points, it will be deleted from the rule base.

[0071] Reference Figure 10 As an example, the acceptance rate and average comfort time can also be automatically adjusted as scoring conditions.

[0072] For example, the automatic adjustment acceptance rate indicates the proportion of automatic adjustments without manual modification to the total number of adjustments. When the automatic adjustment acceptance rate is greater than 75%, it means that the user is relatively satisfied with the current smart seat parameters and can be awarded extra points; otherwise, points will be deducted.

[0073] For example, the average comfort time indicates the continuous use time of the pressure sensor. When the average comfort time is greater than or equal to 30 minutes, it means that the user is relatively satisfied with the current smart seat parameters and can add points; otherwise, points will be deducted.

[0074] In one embodiment of the present application, the specific process of "determining the second score of the smart seat parameter before the change based on the number of calls and changes of the smart seat parameter before the change" can be further explained in combination with the following description.

[0075] As described in the following steps, when the number of calls corresponding to the smart seat parameter before the change is greater than the corresponding preset number threshold, the current score of the smart seat parameter before the change is increased according to the number of calls corresponding to the smart seat parameter before the change; When the number of changes corresponding to the smart seat parameter before the change is greater than the corresponding preset number threshold, reducing the current score of the smart seat parameter before the change according to the number of changes corresponding to the smart seat parameter before the change; A second score of the smart seat parameter before the change is determined according to a change result of the current score of the smart seat parameter before the change.

[0076] It should be noted that the scoring in any embodiment of the present application can also be a combined scoring of multiple conditions, and the score can be increased and / or decreased by multiple conditions. The process of changing the score corresponding to any smart seat parameter can be combined with the process of changing the score corresponding to the smart seat parameter in other embodiments of the present application.

[0077] In a specific embodiment of the present application, rule management can be performed specifically through the following steps: The collected external parameters and seat parameters are digitally constructed through the physical model. For example, the external parameters of the smart seat, including but not limited to pressure sensors, temperature sensors, etc., and the seat parameters of the smart seat, including but not limited to adjusting the airbag pressure, heating / ventilation, vibration motor and other features are abstracted into their corresponding Figure 11 The physical model description of the feature is defined, such as the feature identifier, value type, value range, unit, etc.

[0078] When the user uses the chair, the collected user posture data and seat adjustment parameters, that is, the collected external parameters and seat parameters of the smart chair, are matched with the physical model and the physical model legitimacy is verified, and the legal data that meets the physical model definition is updated in real time and integrated into the preliminary physical model data, such as Figure 12 As shown; Based on the preliminary physical model data, the physical model data is configured into an executable set of rules consisting of conditions and actions using the IFTTT rule engine and rule learning algorithm. The rules use external parameters as conditions and configurable smart seat parameters as actions (such as adjusting airbag pressure, heating / ventilation, and lighting mode). When a user uses the seat, changes in seat parameters or changes in the external conditions of the smart seat trigger the rule engine to determine the executable conditional rules. By extracting multiple sets of different rules (multiple sets of object model data rules) and optimizing these rules through intelligent learning, the engine outputs action execution instructions corresponding to the optimal or highest-scoring rule that meets the current external parameters, thereby adjusting the smart seat. Intelligent learning optimization refers to iterating the rules using a rule management method for smart seat adjustment according to an embodiment of the present application, optimizing the rules through intelligent learning, or iteratively optimizing using other intelligent learning methods (such as DeepSeek).

[0079] It can be understood that in some embodiments, the external parameters and seat parameters of the smart chair can be checked to see if they meet the numerical type and range of the defined physical model. If they meet the numerical type and range definitions preset by the physical model, they are defined as correct data, and the next step of data processing is continued; conversely, if the data collected by the sensor does not meet the definition of data by the physical model, it is defined as abnormal data, and the next step of data processing can be avoided, thereby reducing the amount of calculation; at the same time, when the smart chair recognizes abnormal data, it can remind the user to check whether the status of the sensor providing the abnormal data is normal, thereby avoiding erroneous external parameters interfering with the normal operation of the chair; when some sensors are abnormal, the manufacturer can also be notified at the same time, so that the sensors can be replaced in advance, further improving the after-sales experience.

[0080] It is understandable that in some embodiments, when the external parameters collected by the sensor meet the numerical type and range of the defined physical model, the external parameters are assigned to the identifier corresponding to the physical model, thereby forming physical model data.

[0081] It is understood that in some embodiments, some variable rules can be pre-set. These variable rules can be based on the average scores of a large number of users before the smart seat leaves the factory, and then uploaded to the server via the cloud. They can also be set based on experience, thereby reducing the possibility of automatic seat adjustment failures due to the lack of rules with the same external parameters when the seat is expected to automatically adjust, further shortening the training time for smart seats. In addition, in some embodiments, fixed conditional rules can also be set. For example, if a sensor such as blood oxygen or heart rate is excessively abnormal, an online alarm or hospital notification can be sent, further improving the user's health protection.

[0082] It is understandable that if the external parameters or smart seat parameters collected by the smart seat are not simplified, a large number of rules may be generated. In order to reduce the number of rules and avoid excessive redundancy of rules, or because there are too many external parameters and seat parameters, most of the external parameters and seat parameters have no corresponding rules, which is not conducive to score optimization or automatic adjustment, in some embodiments, the rules need to be processed as follows to reduce the number of rules: For pressure rules, multiple sensors on the seat cushion, backrest, or headrest are used. By setting a certain threshold, any pressure exceeding this threshold is set to 1, and any pressure below this threshold is set to 0, thus generating a sitting pressure map. This threshold can be a factory preset or the average pressure value of the area over a recent period, such as the seat cushion area.

[0083] Replacing the physical model's pressure data with only two states, 0 and 1, allows for more accurate identification of the user's sitting posture and simplifies the rules. When the user's sitting posture changes or a certain part lacks support, the corresponding rules are more likely to be triggered.

[0084] The actions performed, that is, the seat parameters, can be simplified. For example, the heating has three levels, including low heat, medium heat and high heat.

[0085] In some embodiments, in addition to reducing the accuracy of external parameters or seat parameters to reduce rules, rules can also be reduced by allowing differences in certain parameters. For example, only a certain proportion of external parameters need to be the same (such as the external conditions of rule one, seat cushion temperature 10°C, seat cushion pressure 1000PA, backrest pressure 900PA and the external conditions of rule two, seat cushion temperature 10°C, seat cushion pressure 1000PA, backrest pressure 1000PA, etc.), or the external parameters with high specific weights are the same or weighted the same, or there is a certain proportion of fluctuation in the external parameters of two identical fields (such as the seat cushion temperature of 10°C in rule one and the seat cushion temperature of 12°C in rule two). They can all be considered to be the same external conditions, thereby conveniently satisfying the reasonable number of rules required by this patent for comparison under the same external conditions.

[0086] In a specific implementation, in order to further increase the accuracy of recognition, a positioning device such as GPS or Beidou and a time acquisition device are placed in the chair, and the time and location can be obtained as a condition indicator.

[0087] In one specific implementation, the smart chair will be pre-configured with several execution modes before leaving the factory, such as: leisure mode (chair partially reclines, lumbar airbag inflates, etc.), work mode (chair does not recline, lumbar and back airbags inflate, etc.), and rest mode (chair reclines, head and neck airbags inflate, etc.). These execution modes can be obtained through uploaded rules with high satisfaction or through other methods such as research. When the user then executes an action command on the chair in the corresponding mode, it is equivalent to customizing the current mode. If the user uses it multiple times, the system will identify this customized mode as the user's favorite during subsequent intelligent learning optimization and will retain it as a habit. At the same time, information such as time and location will be recorded in the conditional rules to facilitate subsequent learning optimization. By pre-setting execution modes, unnecessary user actions are reduced, and the number of temporary rules generated is reduced, allowing future intelligent learning to more quickly learn rules that align with user habits.

[0088] It should be noted that the conditions and execution actions of some rules are not necessarily related. In addition to setting up one rule base, you can also set up two or more rule bases, that is, to simultaneously associate the conditions and actions of the smart seat through two or more physical model rules, so that the conditions and execution actions are more consistent, and the accuracy of the rules will be improved through future intelligent learning optimization. For example, the physical model rules stored in rule base A are mainly about the external parameters of the pressure sensor and the seat parameters for executing airbag inflation and deflation actions. Rule base B is mainly about the external parameters of the temperature sensor and the seat parameters for executing heating and cooling actions. Other rule bases include those about lunch breaks / working time and chair reclining / upright actions.

[0089] It should be noted that using a smaller rule base can integrate factors such as temperature and pressure and take corresponding actions, so that multiple factors are no longer isolated. Learning through multiple factors is more accurate and suitable for cloud-based intelligent learning optimization or use in situations with large amounts of data. Using a larger rule base can reduce the interference of irrelevant factors and is suitable for use in local intelligent learning optimization or when the amount of data is small.

[0090] In some embodiments, if the conditions of the IFTTT rule are met, the smart chair will execute according to the IFTTT rule. If there is a preset IFTTT rule, it will be executed according to the preset IFTTT rule. If there is no preset IFTTT rule, or if an action instruction is given to the preset IFTTT rule and the execution action is changed, it will be recorded as a temporary rule and assigned a corresponding number. When the conditions are the same, the conditions will be scored according to the next intelligent learning algorithm or the method of this patent, and the rule with the highest score will be selected for execution.

[0091] In some embodiments, rules can also be optimized through the cloud: The user end is connected to the cloud model and the smart seat end respectively; The user end may include an APP (such as a mobile phone APP, a computer APP, or a related APP set on the smart chair end, etc.), or may include a smart chair controller on the smart chair end, etc.; It is understandable that the smart chair end can be connected to the user end such as APP, smart chair controller, etc. through various connection methods (including but not limited to Bluetooth such as BLE or other wireless connection methods, wired connection, etc.).

[0092] The user terminal is periodically synchronized with the cloud model, and the user terminal interacts with the smart chair terminal in real time.

[0093] The smart seat can connect to the user end, such as an app or smart seat controller. Each time it is used, it reports current rule information, usage duration, smart seat status, and GPS information (city) to the cloud for data analysis. Due to computing power limitations, the smart seat (edge ​​side) can use a simpler rule learning mechanism, such as iterating rules using a rule management method for smart seat adjustment described in an embodiment of this application, or performing intelligent learning that requires less sample data. Furthermore, the cloud can optimize more intelligent learning methods, such as using deep learning (such as DeepSeek) to analyze user habits based on big data models, or using this patented method as a foundation, or configuring learning models (such as DeepSeek) according to this patented method for intelligent learning optimization. The cloud can handle greater computational resources, allowing the smart seat to utilize more external parameters, intelligent parameters, or rules for optimization, resulting in more detailed intelligent adjustment. Furthermore, more learning data allows for more precise learning optimization. The smart seat can also learn independently of the cloud. The cloud's analysis of the smart seat data and the rule-generated recommendations are transferred to the smart seat through the user end such as the APP, smart seat controller, etc. for optimization.

[0094] The framework for rule optimization implementation is as follows Figure 13As shown, since the computing power of the smart seat itself is relatively weak, it can automatically generate certain rules in offline mode through methods such as the patented method or simple intelligent learning methods (such as the lower or low computing power version of deepseek, etc.). However, the online cloud platform has strong computing and reasoning capabilities. Therefore, it can use large models or other intelligent learning methods (such as the higher or high computing power version of deepseek, etc.) to analyze user habits, generate rules, and then execute them by the rule engine on the edge side (i.e., the local smart seat), ensuring real-time performance and taking into account both flexibility and efficiency.

[0095] In some embodiments, it is understood that in addition to the method of this patent, the smart seat can also use other intelligent learning methods for learning optimization, such as through deep learning models (such as DeepSeek models, etc.), or through some other existing intelligent learning methods, to learn and optimize the control, adjustment or rules of the smart seat. Specifically, it can be to use big data or large models to analyze user habits, or to predict the expected reward of each action through contextual features (such as user portraits, environmental information, etc.). In addition, the method of this patent can also be combined with other intelligent learning methods for learning optimization, such as using the method of this patent as the basic method of intelligent learning, or setting the intelligent learning model according to the method of this patent, or integrating the method of this patent and the intelligent learning model for intelligent learning optimization. In addition, various intelligent learning models can be used in various stages of the scoring method of this patent, and there is no limitation here. For example, you can rely on the intelligent learning model to first simplify the various parameters of the object model (for example: simplify the external parameters through the eight-dimensional sitting posture data or multi-dimensional pressure data described in the above embodiment), and then use the method of this patent or other intelligent learning methods to optimize, reduce redundancy, and avoid too many rules. You can also use intelligent learning algorithms (such as deepseek, etc.) to learn relevant features in analyzing the user's habits of using a chair or controlling and adjusting the chair, and combine them with the use of this patent method for further optimization.

[0096] It can be understood that in addition to the method of this patent, by using intelligent learning algorithms (such as deepseek models, etc.) on the seat side or in the cloud of the smart seat, the system can continuously learn features from data (such as any one or combination of the following data: the user's habits of using the chair or the user's control and adjustment of the chair, or the external parameters and seat parameters of the smart seat described in this patent, etc.), optimize rules and strategies, and then use the optimized rules and strategies to control and adjust various parameters or actions of the smart seat, etc., through a continuous "learning-optimization-execution" reciprocating cycle, learning, optimization, execution, and then looping back to learning, optimization, and execution, it can perceive and analyze information in more dimensions and make more complex responses, making the smart seat more adaptable to the user's usage habits, and making the control and adjustment of the smart seat more intelligent, thereby significantly improving the comfort, safety and user satisfaction of the seat. However, using intelligent learning algorithms (such as deepseek, etc.) directly on the seat side or in the cloud of the smart chair to control or adjust the chair, or to learn and optimize the control and adjustment methods of the chair, may require a large amount of data and computing power support. Therefore, other intelligent learning algorithms are combined or mixed with this patented method. When the amount of data is small or the computing power is weak, this patented method is used. When the amount of data is large or the computing power is strong, an intelligent learning algorithm (such as various big data models, deepseek, UCB algorithm, etc.) is used. This can further balance the conflicts between the power consumption, computing power, storage space, accuracy, and intelligence of the smart chair.

[0097] As for other types of embodiments of the present application, since these embodiments are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0098] Reference Figure 14 A diagram showing the modules included in an adjustable smart chair provided in an embodiment of the present application is provided. An embodiment of the present application provides an adjustable smart chair, wherein the smart chair is pre-set with at least one rule base; wherein the rule base contains multiple sets of smart chair parameters; the smart chair includes a rule calling module for calling a corresponding set of target smart chair parameters from the rule base based on external parameters to perform smart seat adjustment; The smart seat also includes: The sensor trigger module 1410 is configured to obtain the number of changes to the smart seat parameters before the change and the current external parameters when the user changes the smart seat parameters, and to update the first score corresponding to the smart seat parameters after the change; A scoring module 1420 is configured to determine a second score of the smart seat parameter before the change based on the number of changes of the smart seat parameter before the change; a sorting module 1430, configured to sort the groups of smart seat parameters corresponding to the current external parameters in the rule base according to the first score and the second score; The rule iteration module 1440 is configured to change the target smart seat parameters corresponding to the current external parameters according to the sorting result.

[0099] In one embodiment of the present application, the smart chair further includes: The rule deletion module is used to delete the smart seat parameter before the change from the rule library when the second score of the smart seat parameter before the change is less than a preset score threshold.

[0100] In one embodiment of the present application, the smart chair is preset with at least two rule bases; the smart chair further includes: Time acquisition module, used to obtain current time data; A rule base determination module is used to determine a rule base for calling target smart seat parameters from the at least two rule bases based on the current time data.

[0101] In one embodiment of the present application, the external parameters include: multi-dimensional pressure data; The rule calling module includes: a physical model determination submodule, configured to determine multidimensional physical model data based on the multidimensional pressure data of the external parameters; wherein, the plurality of pressure values ​​of the multidimensional pressure data are respectively compared with corresponding preset pressure thresholds; if the pressure value is greater than the corresponding pressure threshold, the corresponding value of the multidimensional physical model data is determined to be a first-category pressure value; if not, the corresponding value of the multidimensional physical model data is determined to be a second-category pressure value; The rule calling submodule is used to call a corresponding set of target smart seat parameters from a rule library according to the multidimensional object model data.

[0102] In one embodiment of the present application, the sensor trigger module 1410 includes: A call count acquisition submodule, used to obtain the call count corresponding to the changed smart seat parameters; The first score change submodule is used to increase the current score of the changed smart seat parameter according to the number of calls corresponding to the changed smart seat parameter and update the first score of the changed smart seat parameter when the number of calls corresponding to the changed smart seat parameter is greater than the corresponding preset number threshold.

[0103] In one embodiment of the present application, the smart chair further includes: The call count acquisition module is used to obtain the call count corresponding to the smart seat parameters before the change when the user changes the smart seat parameters; a determination module, configured to delete the smart seat parameter before the change from the rule base if the number of calls corresponding to the smart seat parameter before the change is less than a corresponding preset number threshold; If not, the second score of the smart seat parameter before the change is determined according to the number of calls and the number of changes of the smart seat parameter before the change.

[0104] In one embodiment of the present application, the determination module includes: a call count comparison submodule, configured to increase a current score of the smart seat parameter before the change according to the call count corresponding to the smart seat parameter before the change, when the call count corresponding to the smart seat parameter before the change is greater than a corresponding preset threshold; a change count comparison submodule, configured to reduce a current score of the smart seat parameter before the change according to the number of changes corresponding to the smart seat parameter before the change, when the number of changes corresponding to the smart seat parameter before the change is greater than a corresponding preset number threshold; The second score changing submodule is used to determine a second score of the smart seat parameter before the change according to the change result of the current score of the smart seat parameter before the change.

[0105] Reference Figure 15 , shows a block diagram of a computer device provided in an embodiment of the present application. The computer device 12 is suitable for implementing the embodiments of the present invention and may specifically include the following: Computer device 12 is implemented as a general-purpose computing device. Components of computer device 12 may include, but are not limited to, one or more processing units 16, system memory 28, and a bus 18 connecting various system components (including system memory 28 and processing units 16). Computer device 12 may be a device connected to the bus.

[0106] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. Examples of these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MCA) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0107] The computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0108] System memory 28 may include computer system readable media in the form of volatile memory, such as RAM 30 (random access memory) and / or cache 32. Computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be configured to read and write to non-removable, non-volatile magnetic media (commonly referred to as a "hard drive"). Although Figure 15 Although not shown, a magnetic disk drive for reading and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), as well as an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of various embodiments of the present invention.

[0109] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may include an implementation of a network environment. Program modules 42 generally perform the functions and / or methodologies of the embodiments described herein.

[0110] The computer device 12 may also communicate with one or more external devices 14 (e.g., a keyboard, a pointing device, a display 24, etc.), one or more devices that enable a user to interact with the computer device 12, and / or any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., a network card, a modem, etc.). Such communication may be performed through an I / O interface 22 (input / output interface). Furthermore, the computer device 12 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network (e.g., the Internet)) through a network adapter 20. Figure 15 As shown, the network adapter 20 communicates with the other modules of the computer device 12 via the bus 18. Figure 15 Not shown, other hardware and / or software modules may be used in conjunction with computer device 12, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0111] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28 , such as implementing a rule management method for intelligent seat adjustment provided by any embodiment of the present invention.

[0112] That is, when the program is executed by the processor, it is implemented as follows: when the user changes the smart seat parameters, the number of changes corresponding to the smart seat parameters before the change and the current external parameters are obtained, and the first score corresponding to the smart seat parameters after the change is updated; determining a second score of the smart seat parameter before the change according to the number of changes of the smart seat parameter before the change; sorting the groups of smart seat parameters corresponding to the current external parameters in the rule base according to the first score and the second score; The target smart seat parameters corresponding to the current external parameters are changed according to the sorting result.

[0113] The computer device 12 is merely an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0114] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a rule management method for intelligent seat adjustment as provided in any embodiment of the present application.

[0115] That is, when the program is executed by the processor, it is implemented as follows: when the user changes the smart seat parameters, the number of changes corresponding to the smart seat parameters before the change and the current external parameters are obtained, and the first score corresponding to the smart seat parameters after the change is updated; determining a second score of the smart seat parameter before the change according to the number of changes of the smart seat parameter before the change; sorting the groups of smart seat parameters corresponding to the current external parameters in the rule base according to the first score and the second score; The target smart seat parameters corresponding to the current external parameters are changed according to the sorting result.

[0116] Computer storage media can take the form of any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, RAM, read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, a portable CD-ROM, an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0117] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0118] Program code embodied on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, radio frequency (RF), etc., or any suitable combination of the foregoing.

[0119] The computer program code for performing the operations of the present invention may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a LAN or WAN, or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0120] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0121] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or terminal device that includes the element.

[0122] The above is a detailed introduction to the rule management method and smart seat for intelligent seat adjustment provided by this application. Specific examples are used in this article to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method of this application and its core idea; at the same time, for technical personnel in this field, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on this application.

Claims

1. A rule management method for intelligent seat adjustment, characterized in that: The method relates to an adjustable smart seat, wherein the smart seat is preset with a rule library; wherein the rule library includes multiple sets of smart seat parameters; the smart seat is used to call a corresponding set of target smart seat parameters from the rule library according to external parameters to perform smart seat adjustment; The method comprises: When the user changes the smart seat parameters, the number of changes and the current external parameters corresponding to the smart seat parameters before the change are obtained, and the first score corresponding to the smart seat parameters after the change is updated; determining a second score of the smart seat parameter before the change according to the number of changes of the smart seat parameter before the change; sorting the groups of smart seat parameters corresponding to the current external parameters in the rule base according to the first score and the second score; The target smart seat parameters corresponding to the current external parameters are changed according to the sorting result.

2. The method according to claim 1, characterized in that The method further comprises: When the second score of the smart seat parameter before the change is less than the preset score threshold, the smart seat parameter before the change is deleted from the rule library.

3. The method according to claim 1, characterized in that The smart seat is preset with at least two rule bases; the method further includes: Get current time data; A rule base for calling target smart seat parameters is determined from the at least two rule bases according to the current time data.

4. The method according to claim 1, wherein The external parameters include: multi-dimensional pressure data; the step of calling a corresponding set of target smart seat parameters from a rule library according to the external parameters to adjust the smart seat includes: Determining multidimensional object model data based on the multidimensional pressure data of the external parameters; wherein, comparing multiple pressure values ​​of the multidimensional pressure data with corresponding preset pressure thresholds respectively: if the pressure value is greater than the corresponding pressure threshold, determining that the corresponding value of the multidimensional object model data is a first type of pressure value; if not, determining that the corresponding value of the multidimensional object model data is a second type of pressure value; A corresponding set of target smart seat parameters is called from a rule library according to the multidimensional object model data.

5. The method according to claim 1, wherein The step of obtaining the number of changes corresponding to the smart seat parameters before the change and the current external parameters when the user changes the smart seat parameters, and updating the first score corresponding to the smart seat parameters after the change, includes: Obtain the call count corresponding to the changed smart seat parameters; When the number of calls corresponding to the changed smart seat parameter is greater than the corresponding preset number threshold, the current score of the changed smart seat parameter is increased according to the number of calls corresponding to the changed smart seat parameter and the first score of the changed smart seat parameter is updated.

6. The method according to claim 1, characterized in that The method further comprises: When the user changes the smart seat parameters, obtain the call count of the smart seat parameters before the change; If the number of calls corresponding to the smart seat parameter before the change is less than the corresponding preset number threshold, deleting the smart seat parameter before the change in the rule base; If not, the second score of the smart seat parameter before the change is determined according to the number of calls and the number of changes of the smart seat parameter before the change.

7. The method according to claim 6, characterized in that The step of determining the second score of the smart seat parameter before the change based on the number of calls and the number of changes of the smart seat parameter before the change includes: When the number of calls corresponding to the smart seat parameter before the change is greater than the corresponding preset number threshold, increasing the current score of the smart seat parameter before the change according to the number of calls corresponding to the smart seat parameter before the change; When the number of changes corresponding to the smart seat parameter before the change is greater than the corresponding preset number threshold, reducing the current score of the smart seat parameter before the change according to the number of changes corresponding to the smart seat parameter before the change; A second score of the smart seat parameter before the change is determined according to a change result of the current score of the smart seat parameter before the change.

8. An adjustable smart chair, characterized in that: The smart seat is preset with a rule library; wherein the rule library contains multiple sets of smart seat parameters; the smart seat includes a rule calling module for calling a corresponding set of target smart seat parameters from the rule library according to external parameters to perform smart seat adjustment; The smart seat also includes: a sensor trigger module, configured to obtain the number of changes corresponding to the smart seat parameters before the change and the current external parameters when the user changes the smart seat parameters, and to update the first score corresponding to the smart seat parameters after the change; a scoring module, configured to determine a second score of the smart seat parameter before the change according to the number of changes of the smart seat parameter before the change; a sorting module, configured to sort the groups of smart seat parameters corresponding to the current external parameters in the rule base according to the first score and the second score; A rule iteration module is used to change the target smart seat parameters corresponding to the current external parameters according to the sorting result.

9. A computer device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program implements the method according to any one of claims 1 to 7 when executed by the processor.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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