Intelligent furniture integration system with human body characteristic self-adaptive adjustment function

Abstract

The invention belongs to the technical field of intelligent furniture integration systems, particularly relates to an intelligent furniture integration system with a human body characteristic self-adaptive adjustment function, and aims to solve the problems that an existing intelligent furniture integration system is mostly automatically adjusted through a control instruction in the use process, self-adaptive adjustment is inconvenient to carry out according to human body characteristics and environment states, and the use cost is high. According to the technical scheme, the system comprises a user monitoring module, a home monitoring module and an environment monitoring module; the data acquisition module is connected with the user monitoring module, the home monitoring module and the environment monitoring module; according to the invention, adaptive adjustment can be carried out according to the human body characteristics and the environment state, the comfort level and the use experience of the user are remarkably improved, the adjustment strategy can be continuously optimized according to the preference and habit of the user, and more intelligent and personalized furniture use experience is provided for the user.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent furniture integration systems, and in particular to an intelligent furniture integration system with self - adaptive adjustment according to human body characteristics. Background Art

[0002] An intelligent furniture integration system is a system that combines traditional furniture with intelligent devices through advanced technical means to achieve intelligent management and control of the home environment. It takes a residence as a platform and uses automatic control technology to interconnect and centrally manage various devices and systems in the home.

[0003] In the prior art, during the use of intelligent furniture integration systems, most of them are automatically adjusted through control instructions, which are not convenient for self - adaptive adjustment according to human body characteristics and environmental conditions, and cannot adapt to the personalized needs of users in real time. Therefore, we propose an intelligent furniture integration system with self - adaptive adjustment according to human body characteristics to solve the above problems. Summary of the Invention

[0004] The purpose of the present invention is to solve the disadvantages in the prior art that during the use of intelligent furniture integration systems, most of them are automatically adjusted through control instructions, which are not convenient for self - adaptive adjustment according to human body characteristics and environmental conditions, and cannot adapt to the personalized needs of users in real time, and to propose an intelligent furniture integration system with self - adaptive adjustment according to human body characteristics.

[0005] The intelligent furniture integration system with self - adaptive adjustment according to human body characteristics provided by this application adopts the following technical solutions:

[0006] An intelligent furniture integration system with self - adaptive adjustment according to human body characteristics, comprising:

[0007] A user monitoring module, a home monitoring module and an environment monitoring module;

[0008] A data acquisition module, which is connected to the user monitoring module, the home monitoring module and the environment monitoring module;

[0009] An intelligent adjustment module, the intelligent adjustment module is connected to an analysis module, the analysis module is connected to a data processing module, and the data processing module is connected to the data acquisition module;

[0010] An interaction module, the interaction module is connected to the intelligent adjustment module, the interaction module is connected to a learning module, the learning module is connected to a control module, the control module is connected to an anomaly monitoring module, and the anomaly monitoring module is connected to an alarm module;

[0011] A feedback module, the feedback module is connected to the learning module, the feedback module is connected to a recording module and a model generation module, the model generation module is connected to a model library module, the model library module is connected to a matching module, and the matching module is connected to an intelligent adjustment module.

[0012] Further, the user monitoring module includes a physiological perception unit and a behavior perception unit, and the physiological perception unit is connected to the behavior perception unit.

[0013] Further, the interaction module includes a voice recognition unit, a touch operation unit, and a setting unit, the voice recognition unit is connected to the touch operation unit, and the touch operation unit is connected to the setting unit.

[0014] Further, the data acquisition module is used to collect the monitoring data of the furniture monitoring module, the user monitoring module, and the environment monitoring module, and transmit it to the data processing module.

[0015] Further, the intelligent adjustment module automatically adjusts the mechanical state of the furniture according to the analysis result of the data processing module, and the interaction module is used to set user preferences through the voice recognition unit, the touch operation unit, and the setting unit.

[0016] Further, the learning module is used to learn according to the user's usage habits and feedback, as well as historical adjustment records, and generate an intelligent adjustment model to optimize the adjustment strategy.

[0017] Further, the data processing module is used to process the collected data, extract the user's body posture characteristics, and the analysis module is used to analyze the processed data.

[0018] Further, the furniture monitoring module is used to monitor the usage status of the furniture in real time, the user monitoring module is used to monitor the user's physical characteristics in real time, and the environment monitoring module is used to monitor the indoor environment in real time.

[0019] Further, the data processing module includes a data acquisition unit, a filtering processing unit, a normalization processing unit, and a physical sign extraction unit. The data acquisition unit is connected to the filtering processing unit, the filtering processing unit is connected to the normalization processing unit, and the normalization processing unit is connected to the feature extraction unit.

[0020] Further, the environment monitoring module includes a temperature monitoring unit, a humidity monitoring unit, and an air quality monitoring unit. The temperature monitoring unit is connected to the humidity monitoring unit, and the humidity monitoring unit is connected to the air quality monitoring unit.

[0021] In summary, the present application includes at least one of the following beneficial technical effects:

[0022] 1. This solution collects users' physical characteristic data, home appliance status data, and environmental data through a user monitoring module, a home monitoring module, and an environmental monitoring module. The data acquisition module collects the data monitored by the user monitoring module, the home monitoring module, and the environmental monitoring module.

[0023] 2. After the data in this solution is converted into digital signals, it is transmitted to the data processing module. The data processing module preprocesses the collected data, including operations such as filtering and normalization, and extracts the user's physical characteristics, such as weight distribution, sitting posture, heart rate, breathing frequency, etc. The learning module analyzes the user's comfort level and determines whether it is necessary to adjust the furniture state. The intelligent adjustment module automatically adjusts the mechanical state of the furniture according to the analysis results of the data processing module and the analysis module.

[0024] 3. The learning module in this solution adjusts the adjustment strategy according to the data of the feedback module and the recording module, and generates an automatic adjustment model. The recording module can record each adjustment operation of the user and optimize the adjustment strategy through the reinforcement learning algorithm. The abnormal monitoring module can monitor the indoor abnormal situation in real time. When an abnormal situation is detected, the alarm module issues a warning.

[0025] The present invention can perform adaptive adjustment according to human characteristics and environmental conditions, significantly improving the user's comfort and usage experience. At the same time, it can continuously optimize the adjustment strategy according to the user's preferences and habits, providing a more intelligent and personalized furniture usage experience for the user. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a structural block diagram of an intelligent furniture integration system for adaptive adjustment based on human characteristics proposed by the present invention;

[0027] Figure 2 is a structural block diagram of the environmental monitoring module of an intelligent furniture integration system for adaptive adjustment based on human characteristics proposed by the present invention;

[0028] Figure 3 is a structural block diagram of the user monitoring module of an intelligent furniture integration system for adaptive adjustment based on human characteristics proposed by the present invention;

[0029] Figure 4 is a structural block diagram of the data processing module of an intelligent furniture integration system for adaptive adjustment based on human characteristics proposed by the present invention;

[0030] Figure 5 is a structural block diagram of the interaction module of an intelligent furniture integration system for adaptive adjustment based on human characteristics proposed by the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0031] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0032] Embodiment 1

[0033] Referring to Figures 1 - 5 , an intelligent furniture integration system with adaptive adjustment of human body characteristics includes:

[0034] A user monitoring module, a home monitoring module, and an environment monitoring module;

[0035] A data acquisition module, which is connected to the user monitoring module, the home monitoring module, and the environment monitoring module;

[0036] An intelligent adjustment module, the intelligent adjustment module is connected to an analysis module, the analysis module is connected to a data processing module, and the data processing module is connected to the data acquisition module;

[0037] An interaction module, the interaction module is connected to the intelligent adjustment module, the interaction module is connected to a learning module, the learning module is connected to a control module, the control module is connected to an anomaly monitoring module, and the anomaly monitoring module is connected to an alarm module;

[0038] A feedback module, the feedback module is connected to the learning module, the feedback module is connected to a recording module and a model generation module, the model generation module is connected to a model library module, the model library module is connected to a matching module, and the matching module is connected to the intelligent adjustment module. The furniture monitoring module is used to monitor the usage status of furniture in real time, the user monitoring module is used to monitor the physical characteristics of users in real time, the environment monitoring module is used to monitor the indoor environment in real time, the data acquisition module is used to collect the monitoring data of the furniture monitoring module, the user monitoring module, and the environment monitoring module, and transmit it to the data processing module. The data processing module is used to process the collected data, extract the body postures of users (such as weight distribution, sitting posture, heart rate, breathing rate, etc.). The analysis module is used to analyze the processed data. The intelligent adjustment module automatically adjusts the mechanical state of the furniture according to the analysis results of the data processing module. The interaction module is used to set user preferences through a voice recognition unit, a touch operation unit, and a setting unit. The learning module is used to learn according to the user's usage habits and feedback, as well as historical adjustment records, and generate an intelligent adjustment model to optimize the adjustment strategy. The learning module adopts machine learning algorithms, and the machine learning algorithms include: Linear Regression, Logistic Regression, Support Vector Machine (SVM), and K-means clustering;

[0039] 1. Linear Regression (LinearRegression)

[0040] Linear regression is used to establish a linear relationship between the independent variable X and the dependent variable y. The goal is to minimize the error (such as mean square error) between the predicted value and the true value.

[0041] formula:

[0042] y=β0+β1X+∈

[0043] Where: β0 is the intercept; β1 is the regression coefficient; ∈ is the error term, which is usually assumed to follow a normal distribution with a mean of 0;

[0044] 2. Logistic Regression

[0045] Logistic regression is used for binary classification problems. The Sigmoid function maps the input to the (0,1) interval, indicating the probability that the sample belongs to a certain category.

[0046] formula:

[0047]

[0048] 3. Support Vector Machine (SVM)

[0049] SVM is a classification algorithm that maximizes the interval between categories by finding the optimal hyperplane;

[0050] formula:

[0051] f(x)=sign(β0+β1X) where: β0 is the bias term, β1 is the normal vector of the hyperplane;

[0052] 4. K-means clustering

[0053] K-means is an unsupervised learning algorithm that continuously updates the cluster center to minimize the sum of the squared distances from each data point to the center of its cluster.

[0054] formula:

[0055]

[0056] Where: C i is the i-th cluster, μ i is the cluster center.

[0057] Reference Figure 2 The environmental monitoring module includes a temperature monitoring unit, a humidity monitoring unit and an air quality monitoring unit. The temperature monitoring unit is connected to the humidity monitoring unit, and the humidity monitoring unit is connected to the air quality monitoring unit.

[0058] Reference Figure 3, the user monitoring module includes a physiological perception unit and a behavior perception unit. The physiological perception unit is connected to the behavior perception unit. The physiological perception unit and the behavior perception unit adopt pressure sensors, attitude sensors, millimeter-wave radars, bioelectric sensors, etc.

[0059] Refer to Figure 4 , the data processing module includes a data acquisition unit, a filtering processing unit, a normalization processing unit, and a feature extraction unit. The data acquisition unit is connected to the filtering processing unit, the filtering processing unit is connected to the normalization processing unit, and the normalization processing unit is connected to the feature extraction unit.

[0060] Refer to Figure 5 , the interaction module includes a voice recognition unit, a touch operation unit, and a setting unit. The voice recognition unit is connected to the touch operation unit, and the touch operation unit is connected to the setting unit.

[0061] The implementation principle in this embodiment is as follows: When in use, the user monitoring module, the home monitoring module, and the environment monitoring module collect the user's physical characteristic data, the status data of home appliances, and the environmental data. The data acquisition module collects the data monitored by the user monitoring module, the home monitoring module, and the environment monitoring module. After the data is converted into a digital signal, it is transmitted to the data processing module. The data processing module preprocesses the collected data, including operations such as filtering and normalization, and extracts the user's body posture characteristics, such as weight distribution, sitting posture, heart rate, breathing rate, etc. The comfort state of the user is analyzed through the learning module to determine whether it is necessary to adjust the state of the furniture. The intelligent adjustment module automatically adjusts the mechanical state of the furniture according to the analysis results of the data processing module and the analysis module. The user can adjust the furniture state through voice commands and touchscreens through the interaction module. At the same time, the user can input preference settings through the setting unit, and the system will optimize the adjustment strategy according to these settings. The user can feedback the comfort level, and the feedback information is entered into the feedback module. The learning module adjusts the adjustment strategy according to the data of the feedback module and the recording module, and generates an automatic adjustment model. The recording module can record each adjustment operation of the user and optimize the adjustment strategy through the reinforcement learning algorithm. The abnormal monitoring module can monitor the indoor abnormal situation in real time. When an abnormal situation is detected, the alarm module issues a warning.

[0062] Embodiment Two

[0063] The difference between this embodiment and Embodiment One is that the data acquisition module is connected to an encryption module. The encryption module adopts advanced encryption technology and strict data management policies to ensure the security of user data, protect user privacy, and prevent data leakage.

[0064] Embodiment Three

[0065] The difference between this embodiment and the first embodiment is that: the control module is connected to a local control module, which is used to provide local control functions, reduce the dependence on the Internet, and ensure the operation of basic functions even in case of network failures.

[0066] Embodiment Four

[0067] The difference between this embodiment and the first embodiment is that: the intelligent adjustment module is connected to an optimization module, which is used to optimize the device search and connection algorithms, reduce resource conflicts, and improve the concurrent processing ability of the system.

[0068] Embodiment Five

[0069] The difference between this embodiment and the first embodiment is that: the data acquisition module is connected to a protocol unification module, which is used to formulate unified communication protocols and technical standards, reduce compatibility problems between devices of different brands, lower the user's usage cost, and enhance the overall experience.

[0070] Experimental Example

[0071] Through the intelligent furniture integration solutions proposed in Embodiment One to Embodiment Five, compared with the conventional furniture integration solutions, the experimental data are as follows in the table:

[0072] Example 1 Example 2 Example 3 Example 4 Example 5 Improved comfort Improved safety Improved stability Improved stability Improved compatibility 45％ 30％ 15％ 20％ 12％

[0073] As mentioned above, the above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solutions and inventive concepts of the present invention, makes equivalent substitutions or changes, and all should be covered within the protection scope of the present invention.

Claims

1. An intelligent furniture integrated system with adaptive adjustment of human body characteristics, characterized by: include: User monitoring module, home monitoring module and environment monitoring module; A data acquisition module, wherein the data acquisition module is connected to the user monitoring module, the home monitoring module and the environment monitoring module; An intelligent adjustment module, wherein the intelligent adjustment module is connected to an analysis module, wherein the analysis module is connected to a data processing module, and wherein the data processing module is connected to a data acquisition module; An interactive module, wherein the interactive module is connected to the intelligent adjustment module, the interactive module is connected to a learning module, the learning module is connected to a control module, the control module is connected to an abnormality monitoring module, and the abnormality monitoring module is connected to an alarm module; A feedback module is connected to the learning module, the feedback module is connected to the recording module and the model generation module, the model generation module is connected to the model library module, the model library module is connected to the matching module, and the matching module is connected to the intelligent adjustment module.

2. According to claim 1, the intelligent furniture integrated system with adaptive adjustment of human characteristics is characterized by: The environment monitoring module includes a temperature monitoring unit, a humidity monitoring unit and an air quality monitoring unit. The temperature monitoring unit is connected to the humidity monitoring unit, and the humidity monitoring unit is connected to the air quality monitoring unit.

3. The intelligent furniture integrated system with adaptive adjustment of human characteristics according to claim 2 is characterized in that: The user monitoring module includes a physiological sensing unit and a behavioral sensing unit, and the physiological sensing unit is connected to the behavioral sensing unit.

4. The intelligent furniture integrated system with adaptive adjustment of human characteristics according to claim 3 is characterized by: The data processing module includes a data acquisition unit, a filtering processing unit, a normalization processing unit and a vital sign extraction unit. The data acquisition unit is connected to the filtering processing unit, the filtering processing unit is connected to the normalization processing unit, and the normalization processing unit is connected to the feature extraction unit.

5. The intelligent furniture integrated system with adaptive adjustment of human characteristics according to claim 4 is characterized in that: The interaction module includes a voice recognition unit, a touch operation unit and a setting unit. The voice recognition unit is connected to the touch operation unit, and the touch operation unit is connected to the setting unit.

6. The intelligent furniture integrated system with adaptive adjustment of human characteristics according to claim 5 is characterized by: The furniture monitoring module is used to monitor the furniture usage status in real time, the user monitoring module is used to monitor the user's physical characteristics in real time, and the environment monitoring module is used to monitor the indoor environment in real time.

7. The intelligent furniture integrated system with adaptive adjustment of human characteristics according to claim 6 is characterized by: The data acquisition module is used to collect monitoring data from the furniture monitoring module, the user monitoring module and the environment monitoring module, and transmit the data to the data processing module.

8. The intelligent furniture integrated system with adaptive adjustment of human characteristics according to claim 7 is characterized by: The data processing module is used to process the collected data and extract the user's body characteristics, and the analysis module is used to analyze the processed data.

9. The intelligent furniture integrated system with adaptive adjustment of human characteristics according to claim 8, characterized in that: The intelligent adjustment module automatically adjusts the mechanical state of the furniture according to the analysis results of the data processing module, and the interactive module is used to set user preferences through the voice recognition unit, the touch operation unit and the setting unit.

10. The intelligent furniture integrated system with adaptive adjustment of human characteristics according to claim 9, characterized in that: The learning module is used to learn according to the user's usage habits and feedback, as well as historical adjustment records, and generate an intelligent adjustment model to optimize the adjustment strategy.

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