WO2021258644A1 - Procédé et système de régulation de degré sanitaire d'environnement intérieur, fondés sur la vision industrielle - Google Patents

Procédé et système de régulation de degré sanitaire d'environnement intérieur, fondés sur la vision industrielle Download PDF

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Publication number
WO2021258644A1
WO2021258644A1 PCT/CN2020/132843 CN2020132843W WO2021258644A1 WO 2021258644 A1 WO2021258644 A1 WO 2021258644A1 CN 2020132843 W CN2020132843 W CN 2020132843W WO 2021258644 A1 WO2021258644 A1 WO 2021258644A1
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WO
WIPO (PCT)
Prior art keywords
data
heart rate
interval
health
environment
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PCT/CN2020/132843
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English (en)
Chinese (zh)
Inventor
李成栋
张桂青
彭伟
邓晓平
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山东建筑大学
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Publication of WO2021258644A1 publication Critical patent/WO2021258644A1/fr
Priority to ZA2022/05483A priority Critical patent/ZA202205483B/en

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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • F24F11/63—Electronic processing
    • F24F11/64—Electronic processing using pre-stored data
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2120/00—Control inputs relating to users or occupants
    • F24F2120/20—Feedback from users

Definitions

  • the invention relates to a method for judging the health of an indoor environment, in particular to an adjusting method for judging the health of an indoor environment based on machine vision.
  • the method relates to the field of smart home technology.
  • the present invention proposes a method and system for adjusting the health of the indoor environment based on machine vision.
  • the present invention provides a method for adjusting indoor environment health based on machine vision, which includes the following steps:
  • the steps of the step (1) are as follows:
  • the remote photoplethysmography heart rate monitoring method based on the joint blind source separation algorithm is adopted, and the independent vector is used for joint analysis to obtain the human heart rate data.
  • the specific steps of the step (1) are as follows:
  • step (2) the specific steps of the step (2) are as follows:
  • Step 1 Convert monitoring data to interval data
  • sample mean mi and sample standard deviation ⁇ i are first calculated, which are expressed as:
  • n i represents the total amount of data collected on the i day, and data i, j represents the jth data collected on the i day;
  • k represents the constraint coefficient, generally k is 2; after this processing, the data in the i-th day will be left n′′ i (n′′ i ⁇ n i ) Piece;
  • the maximum and minimum values are selected to form a daily interval, and the interval on the i-th day is expressed as:
  • i 1, ..., n, it represents the number of days after the data preprocessing stage above left, c i, and D i respectively represent the left end of the i-th day intervals day and right points;
  • Step 2 Interval data preprocessing
  • Q (.25) is called the lower four-digit score, which means that a quarter of the data value of all observations is smaller than it
  • Q (.75) is called the upper four-digit score , which means that a quarter of the data values in all observations are larger than it
  • IQR is called the interquartile range, which is the difference between the upper four scores and the lower four scores
  • ⁇ * ⁇ (m c ′( ⁇ ′ d ) 2 -m d ′( ⁇ ′ c ) 2 ) ⁇ c ′ ⁇ d ′[(m c ′-m d ′) 2 +2(( ⁇ ′ c ) 2 -( ⁇ ′ d ) 2 )ln( ⁇ c ′/ ⁇ d ′)] 1/2 ⁇ /(( ⁇ ′ c ) 2 -( ⁇ ′ d ) 2 ) (9)
  • Step 3 Build a language word model for environmental health
  • T q L t l [n′*q] +rem(n′*q,1)(t l [n′*q+1] -t l [n′*q] ) (10)
  • the left and right representative intervals of the environmental health language word model are Construct a language word model for environmental health.
  • the facial data in the actual environment is collected, and then the heart rate recognition module is called, and the super-sensing heart rate monitoring method based on the joint blind source separation algorithm is applied to jointly analyze the facial data to obtain the Heart rate data, and then compare the data with the environmental health language word model to determine whether the environmental temperature is high or low, so that the air conditioner can act accordingly.
  • step (3) the specific judgment rules in step (3) are as follows:
  • the monitored heart rate value of the actual environment is x
  • the present invention also provides an indoor environment health adjustment system based on machine vision, which is used to execute the steps of the above-mentioned machine vision-based indoor environment health adjustment method, including:
  • a heart rate recognition module which is used to perform the method of step (1);
  • Environmental health discrimination and adjustment module which is used to execute the method of step (3).
  • the heart rate monitoring adopts the super-sensing method, and the data collection is faster and more convenient, which improves the intelligence of the home.
  • the remote photoplethysmography heart rate monitoring method based on the combined blind source separation algorithm analyzes multiple sub-regions of the face, which can overcome the influence of light changes and exercise and improve the accuracy of the heart rate monitoring value.
  • Figure 1 is a diagram of the environmental health language word model of the present invention
  • Fig. 2 is a flow chart of judging and adjusting the indoor environment health of the present invention.
  • the high-definition camera is used to collect facial data of people living in a healthy environment for a period of time, and the heart rate data is analyzed from the facial data using the remote photoplethysmography heart rate monitoring method based on the joint blind source separation algorithm, and then the data is analyzed Preprocessing, and then convert the collected data into interval data, and then process the interval data, and build an environmental health language word model on this basis. Then, collect the human heart rate data in the actual environment and compare the data with the data in the environmental health language word model to determine whether the environmental temperature is high or low, and then give an adjustment strategy.
  • the invention is composed of a heart rate recognition module, an environmental health degree modeling module, and an environmental health degree discrimination and adjustment module.
  • the heart rate recognition module mainly uses a high-definition camera to collect human facial data, and uses independent vector analysis to analyze periodic signals from the facial data to detect the heart rate.
  • the environmental health modeling module is to call the heart rate recognition module to identify the heart rate in a healthy environment, and then preprocess the heart rate data to construct an environmental health language word model.
  • the environmental health discrimination and adjustment module collects facial data in the actual environment, calls the heart rate recognition module to obtain the heart rate data, compares it with the data in the constructed environmental health model, determines whether the environment is healthy, and gives appropriate adjustment strategies .
  • the function of this module is to analyze the person's heart rate data from the person's facial data.
  • the remote photoplethysmography heart rate monitoring method based on the joint blind source separation algorithm is adopted, and the independent vector is used for joint analysis to obtain the human heart rate data.
  • the skin area for data collection select the skin area for data collection; then calculate the spatial mean of the color RGB from the collected skin data; second, apply the signal processing method to the calculated spatial mean to obtain the components of each skin area containing the heart rate information; third, Use independent vector analysis to extract the common signal components of different mixed signal groups; finally, fast Fourier transform is applied to this component in order to estimate the corresponding frequency (or the number of peaks Ns during the processing duration T(s)).
  • the heart rate in beats per minute will be calculated as 60 ⁇ Fs (or Ns/T ⁇ 60).
  • the processed interval data is constructed into a language word model of environmental health by using the percentile method. details as follows:
  • sample mean mi and sample standard deviation ⁇ i are first calculated, which are expressed as:
  • n i represents the total amount of data collected on the i day, and data i, j represents the jth data collected on the i day;
  • k is the constraint coefficient, generally k is 2; after this processing, the data in the i-th day will be left n′′ i (n′′ i ⁇ n i ) Piece;
  • the maximum and minimum values are selected to form a daily interval, and the interval on the i-th day is expressed as:
  • i 1, ..., n, it represents the number of days after the data preprocessing stage above left, c i, and D i respectively represent the left end of the i-th day intervals day and right points;
  • Q (.25) is called the lower four-digit score, which means that a quarter of the data value of all observations is smaller than it
  • Q (.75) is called the upper four-digit score , which means that a quarter of the data values in all observations are larger than it
  • IQR is called the interquartile range, which is the difference between the upper four scores and the lower four scores
  • ⁇ * ⁇ (m c ′( ⁇ ′ d ) 2 -m d ′( ⁇ ′ c ) 2 ) ⁇ c ′ ⁇ d ′[(m c ′-m d ′) 2 +2(( ⁇ ′ c ) 2 -( ⁇ ′ d ) 2 )ln( ⁇ c ′/ ⁇ d ′)] 1/2 ⁇ /(( ⁇ ′ c ) 2 -( ⁇ ′ d ) 2 ) (9)
  • T q L t l [n′*q] +rem(n′*q,1)(t l [n′*q+1] -t l [n′*q] ) (10)
  • Collect the facial data in the actual environment then call the heart rate recognition module, apply the super-sensing heart rate monitoring method based on the joint blind source separation algorithm to jointly analyze the facial data to obtain the heart rate data in the environment, and then compare the data with the health of the environment
  • the degree language word model ( Figure 1) is compared to determine whether the ambient temperature is high or low, and the air conditioner can act accordingly.
  • the specific judgment rules are as follows: set the monitored heart rate value of the actual environment as x;

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  • Engineering & Computer Science (AREA)
  • Medical Informatics (AREA)
  • Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Primary Health Care (AREA)
  • Signal Processing (AREA)
  • Pathology (AREA)
  • Data Mining & Analysis (AREA)
  • Epidemiology (AREA)
  • General Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Databases & Information Systems (AREA)
  • Physics & Mathematics (AREA)
  • Fuzzy Systems (AREA)
  • Mathematical Physics (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)

Abstract

Procédé et système de régulation du degré sanitaire d'un environnement intérieur, fondés sur la vision industrielle. Le procédé comprend les étapes suivantes consistant : (1) à collecter des données faciales d'une personne, et à appliquer une analyse vectorielle indépendante afin d'analyser un signal périodique à partir des données faciales en vue de détecter la fréquence cardiaque; (2) à pré-traiter des données de fréquence cardiaque collectées dans un environnement sanitaire afin de construire un modèle de mot de langage du degré sanitaire de l'environnement; et (3) à comparer des données de fréquence cardiaque collectées dans un environnement réel avec des données dans le modèle construit de mot de langage du degré sanitaire de l'environnement, afin de déterminer si l'environnement est sain ou non.
PCT/CN2020/132843 2020-06-24 2020-11-30 Procédé et système de régulation de degré sanitaire d'environnement intérieur, fondés sur la vision industrielle WO2021258644A1 (fr)

Priority Applications (1)

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ZA2022/05483A ZA202205483B (en) 2020-06-24 2022-05-18 Indoor environment health degree regulating method and system based on machine vision

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CN202010590624.9A CN111637610B (zh) 2020-06-24 2020-06-24 基于机器视觉的室内环境健康度调节方法与系统

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