CN-121970971-A - Self-adaptive adjusting method and device for intelligent seat and intelligent seat

Abstract

The application discloses a self-adaptive adjusting method and device for an intelligent seat and the intelligent seat, and belongs to the technical field of seat adjustment. The self-adaptive adjusting method comprises the steps of obtaining multi-mode sensing data acquired by a sensor assembly, carrying out fusion processing on the multi-mode sensing data to obtain a feature vector, determining a correction control parameter corresponding to a proportional-integral-derivative control algorithm based on the feature vector, determining a target adjusting quantity based on the correction control parameter by adopting the proportional-integral-derivative control algorithm, adjusting an executing mechanism according to the target adjusting quantity, determining a hierarchical adjusting strategy according to the feature vector, and carrying out hierarchical adjustment on the executing mechanism. Therefore, the dynamic self-adaptive adjustment of the intelligent seat is realized, the intelligent seat can be adapted to different users, the adjustment response difference of the intelligent seat to different users is also considered, the problems of overshoot or adjustment delay and the like are avoided, and the intelligent seat can be accurately adjusted for different users and under different states of the users.

Inventors

• FENG XIAOWEI
• Request for anonymity

Assignees

• 杭州黑白调科技有限公司

Dates

Publication Date

20260505

Application Date

20251223

Claims (10)

1. 1. A method of adaptive adjustment of a smart seat, the smart seat comprising a sensor assembly and an actuator, the method comprising: acquiring multi-mode sensing data acquired by the sensor assembly, and carrying out fusion processing on the multi-mode sensing data to obtain a feature vector; determining a correction control parameter corresponding to a proportional-integral-derivative control algorithm based on the feature vector; Determining a target adjustment amount by using the proportional-integral-derivative control algorithm based on the corrected control parameter; Adjusting the actuating mechanism according to the target adjustment amount; And determining a hierarchical adjustment strategy according to the feature vector, and performing hierarchical adjustment on the executing mechanism.
2. 2. The adaptive modulation method of claim 1, wherein the multi-modal sensing data comprises any one or more of pressure sensing data, inertial sensing data, millimeter wave radar data, and/or The feature vector includes any one or more of a body type vector, a posture vector, and a fatigue index.
3. 3. The adaptive tuning method of claim 1, wherein the feature vector comprises a body type vector and a fatigue index, and the determining the corrected control parameter corresponding to the proportional-integral-derivative control algorithm based on the feature vector comprises: inquiring in a pre-stored body type-parameter mapping table according to the body type vector, and determining initial control parameters corresponding to the proportional-integral-derivative control algorithm; and fine tuning the initial control parameters based on the body type vector and the fatigue index to obtain corrected control parameters.
4. 4. The adaptive tuning method of claim 3, wherein fine tuning the initial control parameters based on the body type vector and the fatigue index to obtain revised control parameters comprises: determining an error vector between a target value and an actual value of the actuator based on the body type vector and the fatigue index; and fine-tuning the initial control parameters according to the error vector by a recursive least square algorithm according to a preset interval time length to obtain the corrected control parameters.
5. 5. The adaptive adjustment method according to claim 1, wherein the actuator includes a shoulder rest adjustment module, a lumbar rest adjustment module, and a seat depth adjustment module, the target adjustment amounts include a shoulder rest adjustment amount, a lumbar rest adjustment amount, and a seat depth adjustment amount, and the adjusting the actuator according to the target adjustment amounts includes: Adjusting the shoulder rest adjustment module according to the shoulder rest adjustment amount, and/or Adjusting the waist support adjusting module according to the waist support adjusting amount, and/or And adjusting the seat depth adjusting module according to the seat depth adjusting amount.
6. 6. The adaptive tuning method of claim 1, wherein the feature vector comprises a fatigue index, wherein the determining a hierarchical tuning strategy based on the feature vector and performing hierarchical tuning of the actuator comprises: Performing a first level adjustment on the actuator if the fatigue index is determined to be less than a first threshold; performing a secondary adjustment on the actuator if the fatigue index is determined to be greater than the first threshold and less than a second threshold; performing a three-level adjustment of the actuator if the fatigue index is determined to be greater than the second threshold and less than a third threshold; In the event that the fatigue index is determined to be greater than the third threshold, a four-stage adjustment is made to the actuator.
7. 7. The adaptive tuning method of claim 5, wherein, The first-stage adjustment of the actuator comprises: controlling the actuator to maintain the current state, and/or The performing the secondary adjustment on the actuator comprises: Controlling part of the adjusting modules in the actuating mechanism to adjust for a first duration, and/or The three-stage adjustment of the actuating mechanism comprises the following steps: controlling part of the adjusting modules in the actuating mechanism to adjust for a second time period, and/or The four-stage adjustment of the actuating mechanism comprises: controlling all the adjusting modules in the executing mechanism to adjust and keeping the third time length; the first time period is smaller than the second time period, and the second time period is smaller than the third time period.
8. 8. The adaptive adjustment method according to claim 1, wherein the sensor assembly includes a pressure sensor, an inertial measurement unit, and a millimeter wave radar, and further comprising, after the determining a hierarchical adjustment strategy from the feature vector and the hierarchical adjustment of the actuator: the pressure of the intelligent seat is monitored in real time through the pressure sensor; And under the condition that the pressure is less than a pressure threshold value and lasts for a fourth time period, controlling the actuating mechanism to reset, controlling the inertial measurement unit and the millimeter wave radar to be closed, and controlling the pressure sensor to keep running.
9. 9. An adaptive adjustment device for an intelligent seat, the intelligent seat comprising a sensor assembly and an actuator, the adaptive adjustment device comprising: The data processing module is used for acquiring the multi-mode sensing data acquired by the sensor assembly, and carrying out fusion processing on the multi-mode sensing data to acquire a feature vector; The first determining module is used for determining a correction control parameter corresponding to the proportional-integral-derivative control algorithm based on the feature vector; The second determining module is used for determining a target adjustment quantity by adopting the proportional-integral-derivative control algorithm based on the corrected control parameter; The first adjusting module is used for adjusting the executing mechanism according to the target adjusting quantity; And the second adjusting module is used for determining a hierarchical adjusting strategy according to the characteristic vector and performing hierarchical adjustment on the executing mechanism.
10. 10. A smart seat comprising one or more processors and a memory, the memory storing a computer program that, when executed by the processor, implements the adaptive adjustment method of any one of claims 1-8.

Description

Self-adaptive adjusting method and device for intelligent seat and intelligent seat Technical Field The application relates to the technical field of seat adjustment, in particular to an adaptive adjustment method and device for an intelligent seat, the intelligent seat and a computer readable storage medium. Background At present, most people stay in an office for a long time and keep sitting postures for a long time, and the seats lack dynamic adjustment capability and are difficult to adapt to different users, so that the problems of local overhigh pressure (such as buttocks and waist), uneven spinal stress and the like easily occur after the users sit for a long time, and chronic diseases such as lumbar muscle strain, lumbar disc herniation and the like can be caused after long-term use. Disclosure of Invention The embodiment of the application provides an adaptive adjustment method of an intelligent seat, an adaptive adjustment device, the intelligent seat and a computer readable storage medium, which are used for solving at least one technical problem. The self-adaptive adjustment method of the intelligent seat comprises a sensor assembly and an actuating mechanism, and comprises the following steps: acquiring multi-mode sensing data acquired by the sensor assembly, and carrying out fusion processing on the multi-mode sensing data to obtain a feature vector; determining a correction control parameter corresponding to a proportional-integral-derivative control algorithm based on the feature vector; Determining a target adjustment amount by using the proportional-integral-derivative control algorithm based on the corrected control parameter; Adjusting the actuating mechanism according to the target adjustment amount; And determining a hierarchical adjustment strategy according to the feature vector, and performing hierarchical adjustment on the executing mechanism. In certain embodiments, the multimodal sensory data includes any one or more of pressure sensory data, inertial sensory data, millimeter wave radar data, and/or The feature vector includes any one or more of a body type vector, a posture vector, and a fatigue index. In some embodiments, the feature vector includes a body type vector and a fatigue index, and the determining the correction control parameter corresponding to the proportional-integral-derivative control algorithm based on the feature vector includes: inquiring in a pre-stored body type-parameter mapping table according to the body type vector, and determining initial control parameters corresponding to the proportional-integral-derivative control algorithm; and fine tuning the initial control parameters based on the body type vector and the fatigue index to obtain corrected control parameters. In some embodiments, the fine tuning the initial control parameter based on the body type vector and the fatigue index to obtain a corrected control parameter includes: determining an error vector between a target adjustment amount and an actual adjustment amount of the actuator based on the body type vector and the fatigue index; and fine-tuning the initial control parameters according to the error vector by a recursive least square algorithm according to a preset interval time length to obtain the corrected control parameters. In some embodiments, the feature vector includes a fatigue index, the determining a hierarchical adjustment strategy based on the feature vector and performing hierarchical adjustment of the actuator includes: Performing a first level adjustment on the actuator if the fatigue index is determined to be less than a first threshold; performing a secondary adjustment on the actuator if the fatigue index is determined to be greater than the first threshold and less than a second threshold; performing a three-level adjustment of the actuator if the fatigue index is determined to be greater than the second threshold and less than a third threshold; In the event that the fatigue index is determined to be greater than the third threshold, a four-stage adjustment is made to the actuator. In certain embodiments, the first-stage adjustment of the actuator comprises: controlling the actuator to maintain the current state, and/or The performing the secondary adjustment on the actuator comprises: Controlling part of the adjusting modules in the actuating mechanism to adjust for a first duration, and/or The three-stage adjustment of the actuating mechanism comprises the following steps: controlling part of the adjusting modules in the actuating mechanism to adjust for a second time period, and/or The four-stage adjustment of the actuating mechanism comprises: controlling all the adjusting modules in the executing mechanism to adjust and keeping the third time length; the first time period is smaller than the second time period, and the second time period is smaller than the third time period. In some embodiments, the sensor assembly includes a pressure sensor, an inertial measureme