WO2018171019A1

WO2018171019A1 - Multivariable optimization driving and control system and method capable of intelligently adapting surface

Info

Publication number: WO2018171019A1
Application number: PCT/CN2017/084095
Authority: WO
Inventors: 辛志宇; 闵苏; 叶鹏
Original assignee: 魔玛智能科技（上海）有限公司
Priority date: 2017-03-20
Filing date: 2017-05-12
Publication date: 2018-09-27
Also published as: CN107168111B; CN107168111A

Abstract

A multivariable optimization driving and control system and method capable of intelligently adapting a surface. The system comprises: a data calling module, a data access module, a user behavior and state mode recognition module, a user environmental adaption decision module, a multivariable optimization calculation module, a distributed type driving subsystem control module, and distributed type multiple driving subsystem modules. The system and method are applicable for the optimization control of multi-target tasks of a plurality of driving systems and capable of cooperatively changing actions on complex surfaces under the control of a global optimization strategy, have good user experience, and can satisfy a personalized and customized preference strategy and calculation method of a user; driving according to a simple target value is changed to the multivariable optimization driving in considerations of optimization strategies for personalized customization of a user, multi-environmental adaptation and driving recourses, so that user experience is improved by means of data driving and artificial intelligence, and changing actions on complex surfaces can be cooperatively executed by means of a control system of the global optimization strategy.

Description

Multivariable optimized drive control system and method for intelligent adaptive surface

Technical field

The present invention relates to the field of computer and intelligent system control technologies, and in particular, to a multi-variable optimized drive control system and method for intelligently accommodating surfaces.

Background technique

With the advent of the artificial intelligence era, multidimensional data collection and user pattern recognition provide more possibilities for smart products to better adapt to the user experience. In a human-computer interaction system, complex drive changes involving the surface of a contact object tend to have multiple goals and multiple tasks at the same time. In order to optimize the user experience, this complex drive change requires special optimization strategy calculations for a variety of variable factors. At the same time, the drive system itself also needs to integrate software and hardware systems, and specifically optimize the design for the implementation of multi-variable optimization strategies.

In a self-adjusting surface support system, it is often necessary to drive the system to simultaneously make the same or different changes and adjustments to multiple points and multiple areas of the surface. In order to optimize the user experience, these multi-variable driver implementations often need to be coordinated or completed simultaneously or simultaneously. At the same time, the driver system also needs a relatively flexible optimization strategy to implement the resource allocation scheme to meet the implementation requirements of different tasks for the user's individual needs and the real-time changing environment state. In the past systems, due to the centralized drive system and the single variable task module, the above task requirements could not be achieved. The invention realizes the above multi-variable cooperation and achieves the requirement of optimal driving execution effect through a distributed modular optimization driving system and method combining software and hardware.

Summary of the invention

In view of the deficiencies in the prior art, it is an object of the present invention to provide a multi-variable optimized drive control system and method for intelligently accommodating surfaces.

The multi-variable optimized driving control system for intelligent adaptive surface provided by the invention comprises: a data calling module, a data access module, a user behavior and state pattern recognition module, a user environment adaptation decision module, a multivariable optimization solving module, and a distributed Drive subsystem control module and distributed multiple drive subsystem modules, wherein:

The data invoking module is configured to transmit the contact object duration and real-time behavior and status tag data retrieved from the data access module to the user behavior and state pattern recognition module and the user environment adaptation decision module;

a data access module for accessing the diachronic and real-time behavior and status tag data of the contact object;

The user behavior and state pattern recognition module is configured to compare the acquired real-time and chronological contact object behavior and state tag data with the pattern category features in the database, and perform pattern recognition and classification marking on the current user mode category, and the current user mode The category value is written into the data access module;

The user environment adaptation decision module is configured to obtain a current user mode category from the data access module, and retrieve a user environment adaptation target value group corresponding to the current user mode category from the data access module, and output the target value group to at most The variable optimization solving module obtains the returned driving target value group and outputs to the distributed driving subsystem control module;

The multi-variable optimization solving module is configured to generate a driving strategy for the current user mode category, parse, optimize and correct the user environment adaptive target value group according to the driving strategy, and output the driving target value group to the user environment adaptive decision module;

The distributed driving subsystem control module is configured to receive a driving target value group that is adapted by the user environment to the decision module output, generate a corresponding driving value group and/or a task command, and output to the corresponding driving subsystem module for cooperative driving execution to complete the target. Task, and returning an operation value, the operation value is saved as a device driver record in the data access module;

A plurality of distributed drive subsystem modules for performing drive tasks for supporting surface adjustment.

Preferably, the data access module comprises: a data temporary storage module and a database, wherein: the data temporary storage module stores the current user mode category value, and the database stores the duration, real-time behavior and status tag data of the contact object.

Preferably, the contact object comprises: a partial or all body area where the user lies, sits, and is in contact with the support surface.

Preferably, the multivariate optimization solution module comprises: a user customized optimization strategy module, a user environment adaptation optimization strategy module, a driving resource optimization strategy module, and a target task solving decision module, specifically:

The user customization optimization policy module is configured to set a user environment adaptation optimization strategy and a driving resource optimization strategy scheme corresponding to the current user mode category under different user personalized requirement conditions;

Specifically, different user personalization strategies include:

1), sleep quality priority strategy:

Adopt a comfort priority strategy and/or an immediate adaptation priority strategy during the sleep phase;

The light sleep stage uses a comfort priority strategy and/or a non-perceive adaptation priority strategy;

The attitude-priority strategy and/or the non-perceive adaptation priority strategy are adopted in the deep sleep phase;

When the sleep duration requirement is met, an uncomfortable strategy and/or an immediate adaptation priority strategy is employed during the sleep awakening period;

2), special posture to maintain priority strategy:

A gesture priority strategy is adopted for all sleep stages;

At the same time, when the user is in the target posture, the global pressure equalization strategy is adopted;

When the user is in a non-target posture, an uncomfortable strategy is adopted;

4), physiological indicators monitoring priority strategy:

Monitor the contact priority strategy during all sleep stages;

The user environment adaptation optimization policy module is configured to set different adjustment strategies, and a priority relationship of target value adjustment between driving variables under the policy.

Specifically, in the application scenario of the smart bed, there is an adjustment height variable based on the user's posture, and an adjustment pressure variable based on user comfort. Therefore, under different user environment adaptation strategies, the priority and weight relationship of these adjustment variables are also Different. E.g:

1. The attitude priority strategy, the strategy of adjusting the attitude (height deformation) variable to the target value priority;

2. Comfort priority strategy, the strategy of adjusting the pressure variable to the target value priority;

3. The global pressure balance strategy, a strategy of balancing the pressure variables of various parts of the body to the target value;

4. Focus on the partial pressure balance strategy, and adjust the pressure variable of the key parts to the target value priority strategy;

5. Monitor the contact priority strategy and keep the area where the monitoring sensor module is located in a reasonable contact state;

6, uncomfortable strategy.

The driving resource optimization policy module is configured to set a calling scheme of different driving resources under different driving resource optimization strategies; specifically, for example:

1. Instant adaptation of priority strategies;

2. Non-perceived adaptation priority strategy.

The target task solving decision module is configured to accept a user environment adaptation target value group output by the user environment adaptation decision module, and generate the condition constraint constraint of the user customization optimization strategy module, the user environment adaptation optimization strategy module, and the driving resource optimization strategy module. The driving target value group adapted to the driving operation is output to the driving subsystem control module.

Preferably, the distributed drive subsystem control module comprises: an instruction accepting and communication module, a driver execution module,

The instruction accepting and communicating module is configured to accept a driving value group and a driving task command output by the driving execution module, and output a driving value group and/or a task command to the corresponding driving subsystem module;

The driver execution module is configured to accept the driving target value group output by the multivariate optimization solving module, generate a driving value group and The task command is driven to the instruction accept and communication module, and the return operation value is saved in the data access module.

The multi-variable optimization driving control method for intelligent adaptive surface provided by the invention comprises the following steps:

Data invocation step: transferring the contact object duration and real-time behavior and status tag data retrieved from the data access module to the user behavior and state pattern recognition module and the user environment adaptation decision module;

Data access step: accessing the duration and real-time behavior and status tag data of the contact object;

User behavior and status pattern recognition step: compare the acquired real-time and duration contact object behavior and status tag data with the pattern category features in the database, and perform pattern recognition and classification marking on the current user mode category, and the current user mode category value Write to the data access module;

The user environment adapts to the decision step: obtaining the current user mode category from the data access module, and calling the user environment adaptation target value group corresponding to the current user mode category from the data access module, and outputting the target value group to multivariate optimization The solving module obtains the returned driving target value group and outputs to the distributed driving subsystem control module;

The multi-variable optimization solving step: generating a driving strategy for the current user mode category, parsing, optimizing and correcting the user environment adaptation target value group according to the driving strategy, and outputting the driving target value group to the user environment adaptation decision module;

The distributed driving subsystem control step: receiving the driving target value group output by the user environment adaptation decision module, generating a corresponding driving value group and/or task command, outputting to the corresponding driving subsystem module for cooperative driving execution, and completing the target task, And returning an operation value, the operation value is saved as a device driver record in the data access module;

Distributed multiple drive subsystem steps: Perform drive tasks to support surface adjustments.

Compared with the prior art, the present invention has the following beneficial effects:

1. The intelligent adaptive surface multi-variable optimization driving control system and method provided by the invention are particularly suitable for optimal control of multi-target tasks of multiple driving systems, and can cooperatively perform complex surface motion changes under the control of the global optimization strategy. Good user experience. And it can meet the user's personalized customized priority strategy and calculation method.

2. The invention changes from a simple target value drive to a multi-variable optimization drive that considers optimization strategies such as user customization, multiple environment adaptation and drive resources, and improves the user experience by data-driven and artificial intelligence.

3. The distributed modular multiple drive subsystems of the present invention are capable of cooperatively performing complex surface change actions under a global optimization strategy control system.

DRAWINGS

Other features, objects, and advantages of the present invention will become apparent from the Detailed Description of Description

1 is a schematic diagram of a multi-variable optimized drive control system for an intelligent adaptive surface provided in the present invention.

detailed description

The invention will now be described in detail in connection with specific embodiments. The following examples are intended to further understand the invention, but are not intended to limit the invention in any way. It should be noted that a number of changes and modifications may be made by those skilled in the art without departing from the inventive concept. These are all within the scope of protection of the present invention.

The data access module includes: a data temporary storage module and a database, wherein: the data temporary storage module stores a current user mode category value, and the database stores the duration, real-time behavior, and status tag data of the contact object.

The contact object includes a partial or total body area where the user lies, sits, and is in contact with the support surface.

The multivariate optimization solution module includes: a user customized optimization strategy module, and a user environment adaptation optimization strategy module Block, drive resource optimization strategy module, target task solution decision module, specifically:

Specifically, different user personalization strategies include:

1), sleep quality priority strategy:

2), special posture to maintain priority strategy:

A gesture priority strategy is adopted for all sleep stages;

When the user is in a non-target posture, an uncomfortable strategy is adopted;

4), physiological indicators monitoring priority strategy:

Monitor the contact priority strategy during all sleep stages;

The user environment adaptation optimization policy module is configured to set different adjustment strategies, and a priority relationship of target value adjustment between driving variables under the policy;

Attitude priority strategy, a strategy of adjusting the attitude (height deformation) variable to the target value priority;

Comfort priority strategy, a strategy that prioritizes pressure variables to target values;

A global stress equalization strategy that balances the pressure variables of various parts of the body to the target value;

Focus on the partial pressure equalization strategy, and adjust the pressure variable of the key parts to the target value priority strategy;

Monitor the contact priority strategy and keep the area of the monitoring sensor module in a reasonable contact state;

Uncomfortable strategy.

Instant adaptation of priority strategies;

Non-perceived adaptation priority strategy.

The distributed drive subsystem control module includes: an instruction accepting and communication module, and a driver execution module,

The driver execution module is configured to accept the driving target value group output by the multivariate optimization solving module, generate a driving value group and drive the task command to the instruction accepting and communication module, and return the operation value to be saved in the data access module.

It should be noted that the steps in the multi-variable optimization driving control method of the intelligent adaptive surface provided by the present invention may be implemented by using corresponding modules, devices, units, etc. in the multi-variable optimization driving control system of the intelligent adaptive surface. The steps of the method can be implemented by a person skilled in the art with reference to the technical solution of the system. That is, the embodiment in the system can be understood as a preferred example of implementing the method, and details are not described herein.

The technical solution in the present invention will be described in more detail below with reference to specific embodiments.

Example 1

The method of the present invention is applied to a support surface control in which the distributed drive adapts to the user's sleep behavior and state. For the deformable support surface of the adaptive human body lying, in addition to accurately calculating the surface target value that adapts to the current state of the user, the process of completing the deformation drive of the support surface is also very important for the user experience. In particular, for different user preference requirements, the sleep state stage requirements of the user, and the surface adaptation requirements of different body parts of the user, a set of optimization strategies are needed to control and manage the driving process. For example, for different weight body parts, how to naturally complete the adaptation action at the same time is very important for the user experience.

When the human body lies on the support surface, the database module is invoked to obtain the duration and immediate behavior and status tag data of the contact object, and the user behavior and state mode evaluation module and the user environment adaptation mode evaluation module are input.

The user behavior and state pattern recognition module compares the pre-entered pattern category features according to the acquired instant and diachronic contact object behavior and state tag data, and determines the current user mode category according to the sleep cycle and phase characteristics, the body motion behavior characteristics, and the posture. The state feature and the like perform pattern recognition and classification marking, and write the current user's sleep cycle/stage and posture state mode category values into the data access module-data temporary storage module.

The user environment adaptation decision module obtains the current user's sleep cycle/stage, posture state mode category value from the data access module-data temporary storage module, and calls the user environment adaptation target value group corresponding to the mode category from the database module. Then, the user customization optimization strategy module, the user environment adaptation optimization strategy module, and the driving resource optimization strategy module in the multivariate optimization solution module are invoked to evaluate the current user mode and the environmental state, for example, user comfort priority is adopted during the sleep phase. The strategy adopts the user attitude priority strategy in the deep sleep stage; according to the driving resource optimization strategy, the global driver is optimized to obtain a natural coordinated driving implementation process suitable for the user experience. Then, the target task solving decision module is called to optimize and correct the target value group. The drive target value is output to the drive subsystem control module according to the corrected drive target value. .

The driving subsystem control module generates a driving value group and a driving task command according to the received driving target value group, performs cooperative driving execution on the distributed multiple driving subsystem modules, completes the target task, and returns the operation value to the driving subsystem control. The module is written by the module as a device driver record to the data storage module - the database module.

The specific embodiments of the present invention have been described above. It is to be understood that the invention is not limited to the specific embodiments described above, and various changes or modifications may be made by those skilled in the art without departing from the scope of the invention. The features of the embodiments and the embodiments of the present application may be combined with each other arbitrarily without conflict.

Claims

An intelligent adaptive surface control multi-variable optimization driving control system, comprising: a user environment adaptation decision module and a multivariate optimization solving module;

The user environment adapts the decision module output target value group to the multivariate optimization solution module, obtains the returned drive target value group, and outputs the output to the distributed drive subsystem control module;

The multivariate optimization solving module outputs the adjusted target value group to the user environment adaptation decision module.
The intelligent adaptive surface multivariable optimization driving control system according to claim 1, further comprising: a data calling module, a data access module, a user behavior and state pattern identifying module, and a distributed driving subsystem control module. And distributed multiple drive subsystem modules, where:

The data invoking module is configured to transmit the contact object duration and real-time behavior and status tag data retrieved from the data access module to the user behavior and state pattern recognition module and the user environment adaptation decision module;

a data access module for accessing the diachronic and real-time behavior and status tag data of the contact object;

The user behavior and state pattern recognition module is configured to compare the acquired real-time and chronological contact object behavior and state tag data with the pattern category features in the database, and perform pattern recognition and classification marking on the current user mode category, and the current user mode The category value is written into the data access module;

The user environment adaptation decision module is configured to obtain a current user mode category from the data access module, and retrieve a user environment adaptation target value group corresponding to the current user mode category from the data access module, and output the target value group to at most The variable optimization solving module obtains the returned driving target value group and outputs to the distributed driving subsystem control module;

The multi-variable optimization solving module is configured to generate a driving strategy for the current user mode category, parse, optimize and correct the user environment adaptive target value group according to the driving strategy, and output the driving target value group to the user environment adaptive decision module;

The distributed driving subsystem control module is configured to receive a driving target value group that is adapted by the user environment to the decision module output, generate a corresponding driving value group and/or a task command, and output to the corresponding driving subsystem module for cooperative driving execution to complete the target. Task, and returning an operation value, the operation value is saved as a device driver record in the data access module;

A plurality of distributed drive subsystem modules for performing drive tasks for supporting surface adjustment.
The intelligent adaptive surface multi-variable optimal drive control system according to claim 2, wherein the data access module comprises: a data temporary storage module and a database, wherein: the current temporary user mode is stored in the data temporary storage module. The value, the database stores the duration, real-time behavior, and status tag data of the contact object.
The intelligently adapted surface multivariable optimal drive control system according to claim 2, wherein The contact object includes a partial or total body area where the user lies, sits, and is in contact with the support surface.
The intelligent adaptive surface multi-variable optimization driving control system according to claim 1, wherein the multi-variable optimization solving module comprises: a user customized optimization strategy module, a user environment adaptation optimization strategy module, and a driving resource optimization strategy module. The target task solving decision module, specifically:

The user customization optimization policy module is configured to set a user environment adaptation optimization strategy and a driving resource optimization strategy scheme corresponding to the current user mode category under different user personalized requirement conditions;

The user environment adaptation optimization policy module is configured to set different adjustment strategies, and a priority relationship of target value adjustment between driving variables under the policy;

The driving resource optimization policy module is configured to set a calling scheme of different driving resources under a driving resource optimization strategy with different requirements;

The target task solving decision module is configured to accept a user environment adaptation target value group output by the user environment adaptation decision module, and generate the condition constraint constraint of the user customization optimization strategy module, the user environment adaptation optimization strategy module, and the driving resource optimization strategy module. The driving target value group adapted to the driving operation is output to the driving subsystem control module.
The intelligent adaptive surface multi-variable optimization drive control system according to claim 1 or 2, wherein the distributed drive subsystem control module comprises: an instruction acceptance and communication module, and a drive execution module.

The instruction accepting and communicating module is configured to accept a driving value group and a driving task command output by the driving execution module, and output a driving value group and/or a task command to the corresponding driving subsystem module;

The driver execution module is configured to accept the driving target value group output by the multivariate optimization solving module, generate a driving value group and drive the task command to the instruction accepting and communication module, and return the operation value to be saved in the data access module.
A multi-variable optimal driving control method for intelligently adapting surfaces, comprising the following steps:

Data invocation step: transferring the contact object duration and real-time behavior and status tag data retrieved from the data access module to the user behavior and state pattern recognition module and the user environment adaptation decision module;

Data access step: accessing the duration and real-time behavior and status tag data of the contact object;

User behavior and status pattern recognition step: compare the acquired real-time and duration contact object behavior and status tag data with the pattern category features in the database, and perform pattern recognition and classification marking on the current user mode category, and the current user mode category value Write to the data access module;

The user environment adapts to the decision step: obtaining the current user mode category from the data access module, and calling the user environment adaptation target value group corresponding to the current user mode category from the data access module, and outputting the target value group to multivariate optimization The solving module obtains the returned driving target value group and outputs to the distributed driving subsystem control module;

Multivariate optimization solving step: generating a driving strategy for the current user mode category, and a user ring according to the driving strategy The target adaptation target value group is parsed, optimized and corrected, and the output drive target value group is output to the user environment adaptation decision module;

The distributed driving subsystem control step: receiving the driving target value group output by the user environment adaptation decision module, generating a corresponding driving value group and/or task command, outputting to the corresponding driving subsystem module for cooperative driving execution, and completing the target task, And returning an operation value, the operation value is saved as a device driver record in the data access module;

Distributed multiple drive subsystem steps: Perform drive tasks to support surface adjustments.