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CN-122018400-A - AI control interaction system of massage armchair

CN122018400ACN 122018400 ACN122018400 ACN 122018400ACN-122018400-A

Abstract

The invention provides an AI control interaction system of a massage chair, which relates to the technical field of intelligent control and man-machine interaction, and comprises a strategy generation and scheduling execution module, wherein the strategy generation and scheduling execution module is used for generating a candidate control strategy based on an updated user portrait and the current state of the massage chair, determining a target control strategy from the candidate control strategy according to a preset scheduling rule, outputting a control instruction for an execution mechanism of the massage chair to drive the massage chair to work.

Inventors

  • WEI WENZHI
  • DENG MINGJIANG

Assignees

  • 迪斯健康科技(北京)有限公司

Dates

Publication Date
20260512
Application Date
20260205

Claims (10)

  1. 1. An AI control interaction system for a massage armchair, comprising: The signal acquisition and interaction module is used for acquiring pressure contact signals, pose displacement signals and running state signals of the massage armchair and receiving user interaction instructions; the user identification and portrait learning module is used for identifying different users and establishing corresponding user portraits, wherein the user portraits comprise a preference parameter set and a safety threshold set of the users, and the user portraits are updated according to a preset portrait updating rule, and the portrait updating rule comprises the steps of carrying out incremental updating on the preference parameter set and synchronously updating the safety threshold set when detecting that the manual adjustment action of the user on massage parameters or the execution feedback of the pressure contact signal and the gesture displacement signal representation meets an updating triggering condition; The strategy generation and scheduling execution module is used for generating candidate control strategies based on the updated user portrait and the current state of the massage armchair, determining target control strategies from the candidate control strategies according to preset scheduling rules, and outputting control instructions for the massage armchair executing mechanism to drive the massage armchair to work; And the safety constraint and closed loop correction module is used for carrying out constraint verification on the control instruction based on the safety threshold set, the pressure contact signal, the pose displacement signal and the running state signal before and during the execution of the target control strategy, and carrying out online correction on the control instruction when detecting that the control parameter in the control instruction violates the constraint condition of the safety threshold set, wherein the online correction comprises limiting the control parameter to a safety range, switching to a conservation strategy or triggering safety shutdown.
  2. 2. The AI control interaction system of a massage chair as set forth in claim 1 wherein said pressure contact signal is collected by a pressure sensor array disposed on a seat cushion and/or a backrest, said position displacement signal is collected by a back angle sensor and/or a leg rest displacement sensor, and said operational status signal comprises a current signal of a massage machine motor, a temperature signal of a heating unit, and an air pressure signal of an air bag circuit.
  3. 3. The AI control interaction system of a massage armchair of claim 1 wherein said signal acquisition and interaction module comprises a panel hand control and a mobile terminal or a voice interface, and adds source identification to instructions of different interaction interfaces for said scheduling rules to make source priority determination.
  4. 4. The AI control interaction system of a massage armchair of claim 1, wherein the user recognition and representation learning module is configured to perform user recognition based on mobile terminal account login information or voiceprint features, and bind a result of the user recognition as a unique user identifier to automatically invoke a corresponding user representation upon each activation.
  5. 5. The AI control interaction system of a massage chair as set forth in claim 1 wherein said user representation is stored using structured data, said set of preference parameters comprises a target massage location, intensity level, cadence parameter and single duration, said set of safety thresholds comprises an upper cartridge compression force or motor current limit, an upper air bag pressure limit, an upper heating temperature limit, and a tabu location indicator.
  6. 6. The AI control interaction system of a massage armchair of claim 1 wherein said portrait update rules include weighting and fusing historical preferences and current session preferences when incremental update is performed on said preference parameter set, weighting the current session preferences in the fusion as a preset learning rate, and employing a non-increasing rule when update is performed on said safety threshold set, said non-increasing rule being that the updated safety threshold is not higher than the pre-update safety threshold.
  7. 7. The AI control interaction system of a massage chair of claim 1 wherein said updated trigger conditions include a user manually adjusting a same massage parameter a number of times in a single session reaching a preset number of times threshold, a pressure distribution deviation characterized by a pressure contact signal continuing to reach a preset deviation threshold and continuing to reach a preset duration threshold, a chair back angle or leg rest displacement variation characterized by a pose displacement signal reaching a preset variation threshold and continuing to reach a preset duration threshold.
  8. 8. The AI control interaction system of a massage chair as set forth in claim 1 wherein said candidate control strategy comprises coordinated control parameters for a massage movement driving mechanism and an air bag inflation/deflation mechanism, said strategy generation and dispatch execution module calculates a comfort level index based on a pressure contact signal and/or a manual user adjustment action, and said comfort level index is used as a basis for determining a target control strategy, said comfort level index comprising a pressure distribution uniformity, a contact loss rate, and a manual adjustment frequency per unit time.
  9. 9. The AI control interaction system of a massage chair as set forth in claim 1 wherein the scheduling rules include an emergency stop command having a highest priority, a panel hand controller command, and a mobile terminal or voice command, wherein when commands of different sources are concurrent and control parameters conflict with each other, a high priority command is used to override a low priority command, and a deferred execution process is performed on the overridden command.
  10. 10. The AI control interaction system of a massage armchair of claim 1, wherein said constraint check comprises at least an upper constraint check of an air bag target pressure, a movement pressing force or a motor current, a heating target temperature and a back or leg rest travel range, when detecting that a control parameter in a control command will trigger any upper constraint, executing a clipping process and/or a strategy replacement process through said on-line correction, said conservative strategy comprising reducing movement strength and speed, reducing air bag target pressure and executing pressure relief, closing or derating heating and adjusting back and leg rest to a preset safety posture, triggering said safety shutdown and generating an event record containing trigger cause and trigger signal value when a state violating a safety threshold set continues to reach a preset duration threshold.

Description

AI control interaction system of massage armchair Technical Field The invention relates to the technical field of intelligent control and man-machine interaction, in particular to an AI control interaction system of a massage armchair. Background The existing massage chairs generally comprise a main control board, a driving module, a massage machine core, an air bag, a heating mechanism, a leg stretching mechanism and other actuating mechanisms, and mode selection and parameter setting are realized through a hand controller, a panel or a movable end. Part of products are introduced into voice interaction, APP linkage or simple body type and gesture detection, and are used for adjusting parameters such as strength, speed, position and the like on the basis of a preset program, and the whole control mostly adopts a program control scheme of fixed flow or limited parameter configuration. However, the existing scheme has the common problems that the comfort and the safety are difficult to be compatible, the multi-user preference learning and the portrait curing are insufficient, the repeated use still needs frequent manual adjustment, and the comfort and the safety are difficult to be compatible. Disclosure of Invention Aiming at the defects of the prior art, the invention provides an AI control interaction system of a massage armchair, which aims to solve the technical problems of difficult compatibility of comfort and safety, insufficient preference curing, frequent manual adjustment and the like by methods of signal acquisition, user image learning, strategy generation and scheduling execution, safety constraint correction and the like. In order to achieve the purpose, the invention is realized by the following technical scheme that the AI control interaction system of the massage armchair comprises: The signal acquisition and interaction module is used for acquiring pressure contact signals, pose displacement signals and running state signals of the massage armchair and receiving user interaction instructions; the user identification and portrait learning module is used for identifying different users and establishing corresponding user portraits, wherein the user portraits comprise a preference parameter set and a safety threshold set of the users, and the user portraits are updated according to a preset portrait updating rule, and the portrait updating rule comprises the steps of carrying out incremental updating on the preference parameter set and synchronously updating the safety threshold set when detecting that the manual adjustment action of the user on massage parameters or the execution feedback of the pressure contact signal and the gesture displacement signal representation meets an updating triggering condition; The strategy generation and scheduling execution module is used for generating candidate control strategies based on the updated user portrait and the current state of the massage armchair, determining target control strategies from the candidate control strategies according to preset scheduling rules, and outputting control instructions for the massage armchair executing mechanism to drive the massage armchair to work; And the safety constraint and closed loop correction module is used for carrying out constraint verification on the control instruction based on the safety threshold set, the pressure contact signal, the pose displacement signal and the running state signal before and during the execution of the target control strategy, and carrying out online correction on the control instruction when detecting that the control parameter in the control instruction violates the constraint condition of the safety threshold set, wherein the online correction comprises limiting the control parameter to a safety range, switching to a conservation strategy or triggering safety shutdown. Preferably, the pressure contact signal is collected by a pressure sensing array arranged on the seat cushion and/or the backrest, the pose displacement signal is collected by a seat back angle sensor and/or a leg rest displacement sensor, and the running state signal comprises a current signal of a massage movement motor, a temperature signal of a heating unit and an air pressure signal of an air bag loop. Preferably, the signal acquisition and interaction module comprises a panel hand controller and a mobile terminal or a voice interface, and adds source identifiers to instructions of different interaction interfaces so as to judge source priority of the scheduling rules. Preferably, the user identification and portrait learning module performs user identification based on mobile terminal account login information or voiceprint features, and binds the result of user identification as a unique user identification so as to automatically call the corresponding user portrait when each time is started. Preferably, the user portrait adopts structured data storage, the preference parameter set comprises a target mas