WO2020056812A1 - Procédé et système de détermination de poids de paramètres environnementaux pour évaluer une qualité environnementale intérieure - Google Patents

Procédé et système de détermination de poids de paramètres environnementaux pour évaluer une qualité environnementale intérieure Download PDF

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WO2020056812A1
WO2020056812A1 PCT/CN2018/109858 CN2018109858W WO2020056812A1 WO 2020056812 A1 WO2020056812 A1 WO 2020056812A1 CN 2018109858 W CN2018109858 W CN 2018109858W WO 2020056812 A1 WO2020056812 A1 WO 2020056812A1
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weight
data
environmental
environmental parameter
model
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PCT/CN2018/109858
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English (en)
Chinese (zh)
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卞春
孙宝石
曹石
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苏州数言信息技术有限公司
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Publication of WO2020056812A1 publication Critical patent/WO2020056812A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00—Measuring or testing not otherwise provided for
    • G01D21/02—Measuring two or more variables by means not covered by a single other subclass

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  • the invention belongs to the technical field of indoor environmental quality monitoring, and particularly relates to a method for determining environmental parameter weights for evaluating indoor environmental quality, and a weight determination system.
  • the invention provides a method for determining environmental parameter weights for evaluating indoor environmental quality, which fills a gap in the current industry, automatically generates environmental parameter weights for evaluating indoor environmental quality, and meets requirements for operation stability and accuracy.
  • the present invention provides an environmental parameter weight determination method for evaluating indoor environmental quality, including:
  • the environmental parameters include, but are not limited to, temperature, humidity, illuminance, color temperature, PM2.5, PM10, formaldehyde, TVOC, carbon dioxide, and noise;
  • the method further includes obtaining the weight model through model training.
  • the training process is:
  • the weighted model is obtained by training the characterized data samples based on a related algorithm.
  • the training process that further includes the weight model further includes:
  • the optimal weight model is used to perform weight analysis on the characteristics of the environmental parameters to obtain the weight of the environmental parameters.
  • the matching degree of the optimal weight model is not less than 95%.
  • further related algorithms for training to obtain weight models include, but are not limited to, machine learning algorithms, convolutional neural network algorithms, recurrent neural network algorithms, decision trees, and Bayesian decision theory-based Classification algorithms and deep learning algorithms.
  • the data sample in the current test data group is merged into the training data group, and the new data sample enters the test data group.
  • the present invention also provides an environmental parameter weight determination system for evaluating indoor environmental quality, including:
  • a data acquisition module for collecting indoor environmental parameter data includes, but are not limited to, temperature, humidity, illumination, color temperature, PM2.5, PM10, formaldehyde, TVOC, carbon dioxide, and noise;
  • Feature extraction module which is used to extract the environmental parameter data in combination with the use scenario of the indoor environment to obtain the environmental parameter characteristics
  • the weight analysis module uses a weight model to perform weight analysis on the characteristics of the environmental parameters to obtain the weight of the environmental parameters.
  • a weight model training module that trains environmental parameter data samples to obtain the weight model, including:
  • a data sample collection unit configured to collect data samples of indoor environmental parameters, including, but not limited to, temperature, humidity, illumination, color temperature, PM2.5, PM10, formaldehyde, TVOC, carbon dioxide, and noise;
  • a feature extraction unit configured to perform feature extraction on a sample of environmental parameter data to obtain a characterized data sample
  • a model training unit is used to train a characterized data sample using a related algorithm to obtain a weight model.
  • the weight model training module further includes a data grouping unit, which randomly groups the characteristic data samples into a training data group and a test data group; the model training unit The data samples in the training data set are trained to obtain a weight model; the data samples in the test data are used to test and verify the matching degree of the weight model.
  • the model training unit further uses several related algorithms to separately train the data samples in the training test group to obtain several weight models;
  • the model training unit uses the data samples in the test data set to test and verify the performance of the several weight models, and selects the one with the highest matching degree among the several weight models as the optimal weight model;
  • the weight analysis module uses the optimal weight model to perform weight analysis on the characteristics of the environmental parameters to obtain the weight of the environmental parameters.
  • the method for determining the weight of environmental parameters for evaluating the quality of an indoor environment of the present invention obtains indoor environment parameter data and performs feature extraction, and characterizes the extracted environmental parameters, and uses a weight model to perform weight analysis on the characteristic environmental parameter data to obtain an environment.
  • the weight of the parameter Both operational stability and accuracy meet requirements, filling gaps in the current industry.
  • FIG. 1 is a structural block diagram of a weight determination system in a preferred embodiment of the present invention.
  • FIG. 3 is a flowchart of training a weight model.
  • this embodiment discloses an environment parameter weight determination system for evaluating indoor environmental quality, particularly an environment parameter weight determination system for evaluating indoor environmental quality, a data acquisition module, and feature extraction.
  • Module weight analysis module and weight training module.
  • the weight training module trains the environmental parameter data samples used as samples to obtain the weight module.
  • the weight model obtained through training is used to perform weight analysis on the actual environmental parameter data, and directly output the weight corresponding to the environmental parameter.
  • the weight model training module includes a data sample collection unit, a feature extraction unit, a data grouping unit, and a model training unit.
  • a data sample acquisition unit is used to collect indoor environmental data samples.
  • Environmental parameters include, but are not limited to, temperature, humidity, illuminance, color temperature, PM2.5, PM10, formaldehyde, TVOC, carbon dioxide, and noise.
  • Collect various environmental parameter data in the current state through various sensors distributed in the environment, for example, the temperature sensor collects the current temperature of 28 ° C, the humidity sensor collects 47% of humidity, etc. Data samples of various environmental parameters in the current state.
  • a feature extraction unit is used for feature extraction of environmental parameter data samples in combination with an indoor environment usage scenario to obtain a characterized data sample.
  • abnormal data samples and non-working time data samples are excluded.
  • the collected data is abnormal (far greater than or far less than the normal value), or "-" is output when the power is off, and this abnormal data sample is eliminated by the feature extraction unit.
  • the time of an indoor environment is normally from 9:00 to 17:00, and data samples other than this working time are eliminated by a feature extraction unit.
  • a data grouping unit that randomly divides the characterized data samples into a training data group and a test data group. Specifically, 80% of all the data samples after the characterization are classified as a training data group, and 20% are classified as a test data group.
  • a model training unit is configured to use a related algorithm to train data samples in the training array to obtain a weight model.
  • related algorithms include, but are not limited to, machine learning algorithms, convolutional neural network algorithms, recurrent neural network algorithms, decision trees, classification algorithms based on Bayesian decision theory, and deep learning algorithms.
  • the model training unit uses the above several related algorithms to separately train data samples to obtain several weight models.
  • the data samples in the test data set are used to test and verify the performance of several weight models.
  • the one with the highest matching degree is selected as the optimal weight model.
  • the data samples in the test data set are imported into each weight model to see whether the weights output by each weight model match the actual situation, and the weight model with the highest matching degree is selected as the optimal weight model.
  • the matching degree of the optimal weight model is not less than 95%. After testing and verification, if the matching degree of all weight models is less than 95%, the weighting model with the highest matching degree is iteratively optimized until the matching degree is not less than 95%.
  • the optimal weight model After obtaining the optimal weight model through training and screening, use the optimal weight model to perform weight analysis on the environmental parameters in the current state, and obtain the weight of each environmental parameter in the current state, which includes the data acquisition module, feature extraction module, and weight analysis. Module.
  • a data acquisition module is used to collect indoor environmental data; environmental parameters include, but are not limited to, temperature, humidity, illumination, color temperature, PM2.5, PM10, formaldehyde, TVOC, carbon dioxide, and noise. Collect various environmental parameter data in the current state through various sensors distributed in the environment, for example, the temperature sensor collects the current temperature of 28 ° C, the humidity sensor collects 47% of humidity, etc. Data of various environmental parameters in the current state.
  • a feature extraction module is used to extract environmental parameter data in combination with an indoor environment usage scenario to obtain environmental parameter characteristics. Specifically, in combination with an indoor environment usage scenario, the normal environmental parameter data and the non-working time environmental parameter data are excluded. For example, when the sensor is in a fault or power failure state, the collected data is abnormal (much larger than or far less than the normal value), or "-" is output when the sensor is powered off, and this abnormal environmental parameter data is eliminated by the feature extraction module. In another case, for example, the indoor environment time is 9: 00-17: 00 under normal circumstances, and the environmental parameter data other than this working time is eliminated by the feature extraction module.
  • a weight analysis module which uses the optimal weight model obtained through training and screening to perform weight analysis on the characteristics of the environmental parameters to obtain the weight of the environmental parameters. Specifically, the characteristic environmental parameter data is imported into the optimal weight model, and the optimal weight model performs weight analysis on the environmental parameter data to directly output the weight of each environmental parameter.
  • this embodiment discloses a method for determining an environmental parameter weight for evaluating indoor environmental quality, including:
  • environmental parameter data include, but are not limited to, temperature, humidity, illuminance, color temperature, PM2.5, PM10, formaldehyde, TVOC, carbon dioxide, and noise.
  • environmental parameter data include, but are not limited to, temperature, humidity, illuminance, color temperature, PM2.5, PM10, formaldehyde, TVOC, carbon dioxide, and noise.
  • Collect various environmental parameter data in the current state through various sensors distributed in the environment, for example, the temperature sensor collects the current temperature of 28 ° C, the humidity sensor collects 47% of humidity, etc. Data of various environmental parameters in the current state.
  • the normal environmental parameter data and the non-working time environmental parameter data are excluded.
  • the collected data is abnormal (much larger than or far less than the normal value), or "-" is output when the sensor is powered off, and this abnormal environmental parameter data is eliminated by the feature extraction module.
  • the indoor environment time is 9: 00-17: 00 under normal circumstances, and the environmental parameter data other than this working time is eliminated by the feature extraction module.
  • the foregoing weight model is obtained through training.
  • the training process is as follows:
  • Environmental parameters include, but are not limited to, temperature, humidity, illumination, color temperature, PM2.5, PM10, formaldehyde, TVOC, carbon dioxide and noise.
  • Collect various environmental parameter data in the current state through various sensors distributed in the environment, for example, the temperature sensor collects the current temperature of 28 ° C, the humidity sensor collects 47% of humidity, etc. Data samples of various environmental parameters in the current state.
  • the feature parameters of the environmental parameter data samples are extracted in combination with the use scenarios of the indoor environment to obtain the characterized data samples. Specifically, in combination with the use scenario of the indoor environment, except for normal data samples and non-working time data samples. For example, when the sensor is in a fault or power failure state, the collected data is abnormal (far greater than or far less than the normal value), or "-" is output when the power is off, and this abnormal data sample is eliminated by the feature extraction unit. In another case, for example, the time of an indoor environment is normally from 9:00 to 17:00, and data samples other than this working time are eliminated by a feature extraction unit.
  • the characteristic data samples are randomly divided into training data groups and test data groups. Specifically, 80% of all characteristic data samples are classified as training data groups and 20% are classified as test data groups.
  • the characteristic data samples are trained based on the correlation algorithm to obtain a weight model.
  • related algorithms include, but are not limited to, machine learning algorithms, convolutional neural network algorithms, recurrent neural network algorithms, decision trees, classification algorithms based on Bayesian decision theory, and deep learning algorithms.
  • the model training unit uses the above several related algorithms to separately train data samples to obtain several weight models.
  • the data samples in the test data set are used to test and verify the performance of several weight models. Among the several weight models, the one with the highest matching degree is selected as the optimal weight model. For example, the data samples in the test data set are imported into each weight model to see whether the weights output by each weight model match the actual situation, and the weight model with the highest matching degree is selected as the optimal weight model.
  • the matching degree of the optimal weight model is not less than 95%. After testing and verification, if the matching degree of all weight models is less than 95%, the weighting model with the highest matching degree is iteratively optimized until the matching degree is not less than 95%.

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  • General Physics & Mathematics (AREA)
  • Testing Resistance To Weather, Investigating Materials By Mechanical Methods (AREA)

Abstract

La présente invention concerne un procédé de détermination de poids de paramètres environnementaux pour évaluer une qualité environnementale intérieure, comprenant les étapes consistant à : obtenir des données de paramètres environnementaux, les paramètres environnementaux comprenant, mais sans y être limités, la température, l'humidité, l'éclairage, la température de couleur, le PM2.5, le PM10, le méthyl aldéhyde, le TVOC, le dioxyde de carbone et le bruit ; réaliser une extraction de caractéristique sur les données de paramètres environnementaux par combinaison d'un scénario d'utilisation d'un environnement intérieur pour obtenir des caractéristiques de paramètres environnementaux ; et réaliser une analyse de poids sur les caractéristiques de paramètres environnementaux à l'aide d'un modèle de poids obtenu par apprentissage pour obtenir le poids des paramètres environnementaux. La stabilité et la précision de fonctionnement du procédé atteignent des exigences plus élevées.
PCT/CN2018/109858 2018-09-21 2018-10-11 Procédé et système de détermination de poids de paramètres environnementaux pour évaluer une qualité environnementale intérieure WO2020056812A1 (fr)

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CN110675006A (zh) * 2019-11-07 2020-01-10 桂林理工大学 一种室内空气质量实时监测与甲醛衰减预测系统
CN112561244B (zh) * 2020-11-26 2021-08-03 清华大学 一种结合室内人员信息的建筑环境评价方法及系统
CN112903028A (zh) * 2021-03-03 2021-06-04 中国建筑股份有限公司 一种室内环境人员满意度评价方法

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