WO2020056812A1 - Environmental parameter weight determining method and system for evaluating indoor environmental quality - Google Patents

Environmental parameter weight determining method and system for evaluating indoor environmental quality 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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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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  • 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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Abstract

An environmental parameter weight determining method for evaluating indoor environmental quality, comprising: obtaining environmental parameter data, the environmental parameters comprising, but not limited to, temperature, humidity, illuminance, color temperature, PM2.5, PM10, methyl aldehyde, TVOC, carbon dioxide, and noise; performing feature extraction on the environmental parameter data by combining a usage scenario of an indoor environment to obtain environmental parameter features; and performing weight analysis on the environmental parameter features by using a weight model obtained through training to obtain the weight of the environmental parameters. The operation stability and accuracy of the method reach higher requirements.

Description

用于评价室内环境质量的环境参数权重确定方法及系统Method and system for determining environmental parameter weights for evaluating indoor environmental quality 技术领域Technical field
本发明属于室内环境质量监测技术领域，具体涉及一种用于评价室内环境质量的环境参数权重确定方法，及权重确定系统。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.
背景技术Background technique
测量室内环境质量的客观物理指标有很多，包括照度、色温、温度、湿度、PM2.5浓度、二氧化碳浓度等等。目前可以通过各种传感器测量这些物理指标，然后将各项指标的数值报告给相关信息使用者。这样做的问题在于相关信息使用者通常无法直观地理解这些物理指标与身心健康的关系。目前的室内环境质量指标缺少对环境质量的综合量化评价，缺少易于理解的指标。为了解决技术问题，业内正在尝试开发既能定性又能定量的评估方案来评估室内环境质量，将包括照度、色温、温度、湿度、PM2.5浓度、二氧化碳浓度等等的环境参数均囊括进来，每个环境参数对环境质量的影响程度不一样，为此需要对每个环境参数引入权重，如何确定环境参数的权重成为目前需要进一步突破的技术难题。There are many objective physical indicators for measuring indoor environmental quality, including illumination, color temperature, temperature, humidity, PM2.5 concentration, carbon dioxide concentration, and so on. At present, these physical indicators can be measured by various sensors, and then the values of each indicator are reported to relevant information users. The problem with this is that users of related information often cannot intuitively understand the relationship between these physical indicators and physical and mental health. The current indoor environmental quality indicators lack a comprehensive quantitative assessment of environmental quality and lack easy-to-understand indicators. In order to solve technical problems, the industry is trying to develop both qualitative and quantitative assessment solutions to evaluate indoor environmental quality, including environmental parameters including illumination, color temperature, temperature, humidity, PM2.5 concentration, carbon dioxide concentration, etc. The impact of each environmental parameter on environmental quality is different. To this end, weights need to be introduced for each environmental parameter. How to determine the weight of environmental parameters has become a technical problem that requires further breakthroughs.
发明内容Summary of the Invention
本发明提供一种用于评价室内环境质量的环境参数权重确定方法，填补目前行业内的空白，自动生成用于评价室内环境质量的环境参数权重，运行稳定性和准确性均达到要求。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.
为了解决上述技术问题，本发明提供了一种用于评价室内环境质量的环境参数权重确定方法，包括，In order to solve the above technical problems, the present invention provides an environmental parameter weight determination method for evaluating indoor environmental quality, including:
获取环境参数数据；所述环境参数包括但不限于温度、湿度、照度、色温、PM2.5、PM10、甲醛、TVOC、二氧化碳和噪音；Acquiring environmental parameter data; the environmental parameters include, but are not limited to, temperature, humidity, illuminance, color temperature, PM2.5, PM10, formaldehyde, TVOC, carbon dioxide, and noise;
结合室内环境的使用场景对所述环境参数数据进行特征提取，获得环境参数特征；Performing feature extraction on the environmental parameter data in combination with an indoor environment use scenario to obtain environmental parameter characteristics;
使用经训练获得的权重模型对所述环境参数特征做权重分析，获得环境参数的权重。Perform weight analysis on the characteristics of the environmental parameters using the trained weight model to obtain the weight of the environmental parameters.
本发明一个较佳实施例中，进一步包括通过模型训练获得所述权重模型，其训练过程为，In a preferred embodiment of the present invention, the method further includes obtaining the weight model through model training. The training process is:
获取用作样本的环境参数数据样本；Obtain a sample of environmental parameter data used as a sample;
结合室内环境的使用场景对环境参数数据样本进行特征提取，获得特征化后的数据样本；Feature extraction of environmental parameter data samples in combination with indoor environment usage scenarios to obtain characterized data samples;
基于相关算法训练特征化后的数据样本，获得所述权重模型。The weighted model is obtained by training the characterized data samples based on a related algorithm.
本发明一个较佳实施例中，进一步包括所述权重模型的训练过程还包括，In a preferred embodiment of the present invention, the training process that further includes the weight model further includes:
对特征后的数据样本进行随机分组，分为训练数据组和测试数据组，使用训练数据组中的数据样本进行模型训练获得所述权重模型；Randomly grouping the characteristic data samples into training data groups and test data groups, and using the data samples in the training data group to perform model training to obtain the weight model;
使用若干个相关算法分别训练所述训练数据组中的数据样本，获得若干个权重模型；Using several related algorithms to separately train data samples in the training data set to obtain several weight models;
使用所述测试数据组中的数据样本分别测试验证所述若干个权重模型的性能，在所述若干个权重模型中选取匹配度最高的一个为最优权重模型；Use the data samples in the test data set to test and verify the performance of the several weight models, and select the one with the highest matching degree among the several weight models as the optimal weight model;
使用最优权重模型对环境参数特征做权重分析，获得环境参数的权重。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.
本发明一个较佳实施例中，进一步包括所述最优权重模型的匹配度不低于95％。In a preferred embodiment of the present invention, the matching degree of the optimal weight model is not less than 95%.
本发明一个较佳实施例中，进一步包括用于训练获得权重模型的相关算法包括但不局限于机器学习算法、卷积神经网络算法、循环神经网络算法、决策树、基于贝叶斯决策理论的分类算法和深度学习算法。In a preferred embodiment of the present invention, 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.
本发明一个较佳实施例中，进一步包括在有新的数据样本导入时，当前测试数据组内的数据样本合并至训练数据组中，新的数据样本进入测试数据组。In a preferred embodiment of the present invention, when a new data sample is imported, 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.
为了解决上述技术问题，本发明还提供了一种用于评价室内环境质量的环境参数权重确定系统，包括，In order to solve the above technical problems, the present invention also provides an environmental parameter weight determination system for evaluating indoor environmental quality, including:
数据采集模块，用于采集室内的环境参数数据；所述环境参数包括但不限于温度、湿度、照度、色温、PM2.5、PM10、甲醛、TVOC、二氧化碳和噪音；A data acquisition module for collecting indoor environmental parameter data; the environmental parameters include, but are not limited to, temperature, humidity, illumination, color temperature, PM2.5, PM10, formaldehyde, TVOC, carbon dioxide, and noise;
特征提取模块，用于结合室内环境的使用场景对环境参数数据进行提取，获得环境参数特征；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;
权重分析模块，其使用权重模型对环境参数特征做权重分析，获得环境参数的权重。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.
本发明一个较佳实施例中，进一步包括其还包括权重模型训练模块，所述权重模型训练模块对环境参数数据样本进行训练，获得所述权重模型，其包括，In a preferred embodiment of the present invention, it further includes a weight model training module that trains environmental parameter data samples to obtain the weight model, including:
数据样本采集单元，用于采集室内的环境参数数据样本，所述环境参数包括但不限于温度、湿度、照度、色温、PM2.5、PM10、甲醛、TVOC、二氧化碳和噪音；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;
特征提取单元，用于对环境参数数据样本进行特征提取，获得特征化后的数据样本；A feature extraction unit, configured to perform feature extraction on a sample of environmental parameter data to obtain a characterized data sample;
模型训练单元，用于使用相关算法训练特征化后的数据样本，获得权重模型。A model training unit is used to train a characterized data sample using a related algorithm to obtain a weight model.
本发明一个较佳实施例中，进一步包括所述权重模型训练模块还包括数据分组单元，所述数据分组单元将特征后的数据样本随机分组为训练数据组和测试数据组；所述模型训练单元训练训练数据组中的数据样本获得权重模型；所述测试数据中的数据样本用于测试验证所述权重模型的匹配度。In a preferred embodiment of the present invention, 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.
本发明一个较佳实施例中，进一步包括所述模型训练单元使用若干个相关算法分别训练所述训练测试组中的数据样本，获得若干个权重模型；In a preferred embodiment of the present invention, 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;
所述模型训练单元使用所述测试数据组中的数据样本分别测试验证所述若干个权重模型的性能，在所述若干个权重模型中选取匹配度最高的一个为最优权重模型；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;
权重分析模块使用最优权重模型对环境参数特征做权重分析，获得环境参数的权重。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.
本发明用于评价室内环境质量的环境参数权重确定方法，获取室内环境参数数据后进行特征提取，并对提取的环境参数特征化，使用权重模型对特征化后的环境参数数据做权重分析获得环境参数的权重。运行稳定性和准确性均达到要求，填补目前行业内的空白。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.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1是本发明优选实施例中权重确定系统的结构框图；1 is a structural block diagram of a weight determination system in a preferred embodiment of the present invention;
图2是本发明优选实施例中权重确定方法的流程图；2 is a flowchart of a weight determining method in a preferred embodiment of the present invention;
图3是训练获得权重模型的流程图。FIG. 3 is a flowchart of training a weight model.
具体实施方式detailed description
下面结合附图和具体实施例对本发明作进一步说明，以使本领域的技术人员可以更好地理解本发明并能予以实施，但所举实施例不作为对本发明的限定。The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand the present invention and implement it, but the embodiments are not intended to limit the present invention.
实施例一Example one
如图1所示，本实施例公开了一种用于评价室内环境质量的环境参数权重确定系统，尤其是一种用于评价教室室内环境质量的环境参数权重确定系统，数据采集模块、特征提取模块、权重分析模块和权重训练模块。权重训练模块对用作样本的环境参数数据样本进行训练，获得权重模块。使用经训练获得的权重模型对实际的环境参数数据做权重分析，直接输出环境参数对应的权重。As shown in FIG. 1, 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.
具体的，通过模型训练获得权重模型的过程如下：Specifically, the process of obtaining a weight model through model training is as follows:
如图3所示，权重模型训练模块包括数据样本采集单元、特征提取单元、数据分组单元和模型训练单元。As shown in FIG. 3, the weight model training module includes a data sample collection unit, a feature extraction unit, a data grouping unit, and a model training unit.
数据样本采集单元，用于采集室内的环境参数数据样本，环境参数包括但不限于温度、湿度、照度、色温、PM2.5、PM10、甲醛、TVOC、二氧化碳和噪音。通过分布在环境中的各种传感器采集当前状态下的各个环境参数数据，比如，温度传感器采集当前状态下的温度为28℃、湿度传感器采集的湿度为47％等等，以此来获取环境中当前状态下各个环境参数的数据样本。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.
特征提取单元，用于结合室内环境的使用场景对环境参数数据样本进行特征提取，获得特征化后的数据样本。具体的，结合室内环境的使用场景，剔除 非正常的数据样本和非工作时间的数据样本。比如，传感器处于故障或者断电状态时，采集的数据异常(远大于或者远小于正常值)、或者断电状态时输出“--”，通过特征提取单元剔除这种非正常的数据样本。另一种情况下，比如，正常情况下室内环境的时间是9:00-17:00，通过特征提取单元剔除这一工作时间以外的数据样本。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. Specifically, according to the indoor environment usage scenario, abnormal data samples and non-working time data samples are excluded. 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.
数据分组单元，其将特征化后的数据样本随机分为训练数据组和测试数据组。具体的，将特征化后的所有数据样本中的80％归为训练数据组，20％归为测试数据组。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.
模型训练单元，用于使用相关算法训练上述训练数组中的数据样本，获得权重模型。具体的，相关算法包括但不局限于机器学习算法、卷积神经网络算法、循环神经网络算法、决策树、基于贝叶斯决策理论的分类算法和深度学习算法。模型训练单元使用以上的若干个相关算法分别训练数据样本，获得若干个权重模型。并使用测试数据组中的数据样本分别测试验证若干个权重模型的性能，在若干个权重模型中选取匹配度最高的一个为最优权重模型。比如，将测试数据组中的数据样本导入各个权重模型中，看各个权重模型输出的权重是否与实际情况相匹配，选取匹配度最高的的权重模型为最优权重模型，本实施例技术方案中，限定最优权重模型的匹配度不低于95％。测试验证后，如果所有的权重模型的匹配度都低于95％，则对匹配度最高的权重模型进行迭代优化，直至其匹配度不低于95％。A model training unit is configured to use a related algorithm to train data samples in the training array to obtain a weight model. Specifically, 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. In the technical solution of this embodiment, 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%.
需要注意的是，在有新的数据样本导入时，当前测试数据组内的数据样本合并至训练数据组中，新的数据样本进入测试数据组，以此不断优化权重模型。It should be noted that when new data samples are imported, the data samples in the current test data group are merged into the training data group, and the new data samples enter the test data group to continuously optimize the weight model.
通过训练、筛选获得最优权重模型后，使用最优权重模型来对当前状态下 的环境参数做权重分析，获得当前状态下各个环境参数的权重，其包括数据采集模块、特征提取模块、权重分析模块。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.
数据采集模块，用于采集室内的环境参数数据；环境参数包括但不限于温度、湿度、照度、色温、PM2.5、PM10、甲醛、TVOC、二氧化碳和噪音。通过分布在环境中的各种传感器采集当前状态下的各个环境参数数据，比如，温度传感器采集当前状态下的温度为28℃、湿度传感器采集的湿度为47％等等，以此来获取环境中当前状态下各个环境参数的数据。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.
特征提取模块，用于结合室内环境的使用场景对环境参数数据进行提取，获得环境参数特征。具体的，结合室内环境的使用场景，剔除非正常的环境参数数据和非工作时间的环境参数数据。比如，传感器处于故障或者断电状态时，采集的数据异常(远大于或者远小于正常值)、或者断电状态时输出“--”，通过特征提取模块剔除这种非正常的环境参数数据。另一种情况下，比如，正常情况下室内环境的时间是9:00-17:00，通过特征提取模块剔除这一工作时间以外的环境参数数据。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.
权重分析模块，其使用经训练和筛选获得的最优权重模型对环境参数特征做权重分析，获得环境参数的权重。具体的，将特征化后的环境参数数据导入最优权重模型中，最优权重模型对环境参数数据做权重分析直接输出各个环境参数的权重。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.
实施例二Example two
如图2所示，本实施例公开了一种用于评价室内环境质量的环境参数权重确定方法，包括，As shown in FIG. 2, this embodiment discloses a method for determining an environmental parameter weight for evaluating indoor environmental quality, including:
(1)获取环境参数数据；环境参数包括但不限于温度、湿度、照度、色温、 PM2.5、PM10、甲醛、TVOC、二氧化碳和噪音。通过分布在环境中的各种传感器采集当前状态下的各个环境参数数据，比如，温度传感器采集当前状态下的温度为28℃、湿度传感器采集的湿度为47％等等，以此来获取环境中当前状态下各个环境参数的数据。(1) Obtain environmental parameter data; 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 of various environmental parameters in the current state.
(2)结合室内环境的使用场景对环境参数数据进行特征提取，获得环境参数特征。具体的，结合室内环境的使用场景，剔除非正常的环境参数数据和非工作时间的环境参数数据。比如，传感器处于故障或者断电状态时，采集的数据异常(远大于或者远小于正常值)、或者断电状态时输出“--”，通过特征提取模块剔除这种非正常的环境参数数据。另一种情况下，比如，正常情况下室内环境的时间是9:00-17:00，通过特征提取模块剔除这一工作时间以外的环境参数数据。(2) Feature extraction of environmental parameter data in combination with the use scenario of the indoor environment 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.
(3)使用经训练获得的权重模型对环境参数特征做权重分析，获得环境参数的权重。(3) Use the trained weight model to perform weight analysis on the characteristics of the environmental parameters to obtain the weight of the environmental parameters.
本实施例技术方案中，上述权重模型通过训练获得，如图3所示，其训练过程为：In the technical solution of this embodiment, the foregoing weight model is obtained through training. As shown in FIG. 3, the training process is as follows:
(3.1)获取用作样本的环境参数数据样本。环境参数包括但不限于温度、湿度、照度、色温、PM2.5、PM10、甲醛、TVOC、二氧化碳和噪音。通过分布在环境中的各种传感器采集当前状态下的各个环境参数数据，比如，温度传感器采集当前状态下的温度为28℃、湿度传感器采集的湿度为47％等等，以此来获取环境中当前状态下各个环境参数的数据样本。(3.1) Acquire a sample of environmental parameter data used as a sample. 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.
(3.2)结合室内环境的使用场景对环境参数数据样本进行特征提取，获得特征化后的数据样本。具体的，结合室内环境的使用场景，剔除非正常的数据 样本和非工作时间的数据样本。比如，传感器处于故障或者断电状态时，采集的数据异常(远大于或者远小于正常值)、或者断电状态时输出“--”，通过特征提取单元剔除这种非正常的数据样本。另一种情况下，比如，正常情况下室内环境的时间是9:00-17:00，通过特征提取单元剔除这一工作时间以外的数据样本。(3.2) 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.
(3.3将特征化后的数据样本随机分为训练数据组和测试数据组。具体的，将特征化后的所有数据样本中的80％归为训练数据组，20％归为测试数据组。(3.3 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.
(3.4)基于相关算法训练特征化后的数据样本，获得权重模型。具体的，相关算法包括但不局限于机器学习算法、卷积神经网络算法、循环神经网络算法、决策树、基于贝叶斯决策理论的分类算法和深度学习算法。模型训练单元使用以上的若干个相关算法分别训练数据样本，获得若干个权重模型。并使用测试数据组中的数据样本分别测试验证若干个权重模型的性能，在若干个权重模型中选取匹配度最高的一个为最优权重模型。比如，将测试数据组中的数据样本导入各个权重模型中，看各个权重模型输出的权重是否与实际情况相匹配，选取匹配度最高的的权重模型为最优权重模型，本实施例技术方案中，限定最优权重模型的匹配度不低于95％。测试验证后，如果所有的权重模型的匹配度都低于95％，则对匹配度最高的权重模型进行迭代优化，直至其匹配度不低于95％。(3.4) The characteristic data samples are trained based on the correlation algorithm to obtain a weight model. Specifically, 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. In the technical solution of this embodiment, 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%.
需要注意的是，在有新的数据样本导入时，当前测试数据组内的数据样本合并至训练数据组中，新的数据样本进入测试数据组，以此不断优化权重模型。It should be noted that when new data samples are imported, the data samples in the current test data group are merged into the training data group, and the new data samples enter the test data group to continuously optimize the weight model.
(4)使用经训练、筛选获得的最优权重模型对环境参数特征做权重分析，获得环境参数的权重。具体的，将特征化后的环境参数数据导入最优权重模型 中，最优权重模型对环境参数数据做权重分析直接输出各个环境参数的权重。(4) Use the optimal weight model obtained by 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.
以上所述实施例仅是为充分说明本发明而所举的较佳的实施例，本发明的保护范围不限于此。本技术领域的技术人员在本发明基础上所作的等同替代或变换，均在本发明的保护范围之内。本发明的保护范围以权利要求书为准。The embodiments described above are merely preferred embodiments for fully explaining the present invention, and the protection scope of the present invention is not limited thereto. Equivalent substitutions or changes made by those skilled in the art on the basis of the present invention are all within the protection scope of the present invention. The protection scope of the present invention is subject to the claims.

Claims (10)

  1. 一种用于评价室内环境质量的环境参数权重确定方法，其特征在于：包括，An environmental parameter weight determination method for evaluating indoor environmental quality is characterized in that it includes:
    获取环境参数数据；所述环境参数包括但不限于温度、湿度、照度、色温、PM2.5、PM10、甲醛、TVOC、二氧化碳和噪音；Acquiring environmental parameter data; the environmental parameters include, but are not limited to, temperature, humidity, illuminance, color temperature, PM2.5, PM10, formaldehyde, TVOC, carbon dioxide, and noise;
    结合室内环境的使用场景对所述环境参数数据进行特征提取，获得环境参数特征；Performing feature extraction on the environmental parameter data in combination with an indoor environment use scenario to obtain environmental parameter characteristics;
    使用经训练获得的权重模型对所述环境参数特征做权重分析，获得环境参数的权重。Perform weight analysis on the characteristics of the environmental parameters using the trained weight model to obtain the weight of the environmental parameters.
  2. 如权利要求1所述的用于评价室内环境质量的环境参数权重确定方法，其特征在于：通过模型训练获得所述权重模型，其训练过程为，The method for determining the weight of an environmental parameter for evaluating the quality of an indoor environment according to claim 1, wherein the weight model is obtained through model training, and the training process is:
    获取用作样本的环境参数数据样本；Obtain a sample of environmental parameter data used as a sample;
    结合室内环境的使用场景对环境参数数据样本进行特征提取，获得特征化后的数据样本；Feature extraction of environmental parameter data samples in combination with indoor environment usage scenarios to obtain characterized data samples;
    基于相关算法训练特征化后的数据样本，获得所述权重模型。The weighted model is obtained by training the characterized data samples based on a related algorithm.
  3. 如权利要求2所述的用于评价室内环境质量的环境参数权重确定方法，其特征在于：所述权重模型的训练过程还包括，The method for determining the weight of an environmental parameter for evaluating the quality of an indoor environment according to claim 2, wherein the training process of the weight model further comprises:
    对特征后的数据样本进行随机分组，分为训练数据组和测试数据组，使用训练数据组中的数据样本进行模型训练获得所述权重模型；Randomly grouping the characteristic data samples into training data groups and test data groups, and using the data samples in the training data group to perform model training to obtain the weight model;
    使用若干个相关算法分别训练所述训练数据组中的数据样本，获得若干个权重模型；Using several related algorithms to separately train data samples in the training data set to obtain several weight models;
    使用所述测试数据组中的数据样本分别测试验证所述若干个权重模型的性能，在所述若干个权重模型中选取匹配度最高的一个为最优权重模型；Use the data samples in the test data set to test and verify the performance of the several weight models, and select the one with the highest matching degree among the several weight models as the optimal weight model;
    使用最优权重模型对环境参数特征做权重分析，获得环境参数的权重。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.
  4. 如权利要求3所述的用于评价室内环境质量的环境参数权重确定方法，其特征在于：所述最优权重模型的匹配度不低于95％。The method for determining an environmental parameter weight for evaluating indoor environmental quality according to claim 3, wherein the matching degree of the optimal weight model is not less than 95%.
  5. 如权利要求3所述的用于评价室内环境质量的环境参数权重确定方法，其特征在于：用于训练获得权重模型的相关算法包括但不局限于机器学习算法、卷积神经网络算法、循环神经网络算法、决策树、基于贝叶斯决策理论的分类算法和深度学习算法。The method for determining environmental parameter weights for evaluating indoor environmental quality according to claim 3, characterized in that the relevant algorithms used for training to obtain the weight model include, but are not limited to, machine learning algorithms, convolutional neural network algorithms, and recurrent neural networks Network algorithms, decision trees, classification algorithms based on Bayesian decision theory, and deep learning algorithms.
  6. 如权利要求3所述的用于评价室内环境质量的环境参数权重确定方法，其特征在于：在有新的数据样本导入时，当前测试数据组内的数据样本合并至训练数据组中，新的数据样本进入测试数据组。The method for determining environmental parameter weights for evaluating indoor environmental quality according to claim 3, characterized in that: when a new data sample is imported, the data samples in the current test data group are merged into the training data group, and the new The data samples enter the test data set.
  7. 一种用于评价室内环境质量的环境参数权重确定系统，其特征在于：包括，An environmental parameter weight determination system for evaluating the quality of an indoor environment is characterized in that it includes:
    数据采集模块，用于采集室内的环境参数数据；所述环境参数包括但不限于温度、湿度、照度、色温、PM2.5、PM10、甲醛、TVOC、二氧化碳和噪音；A data acquisition module for collecting indoor environmental parameter data; the environmental parameters include, but are not limited to, temperature, humidity, illumination, color temperature, PM2.5, PM10, formaldehyde, TVOC, carbon dioxide, and noise;
    特征提取模块，用于结合室内环境的使用场景对环境参数数据进行提取，获得环境参数特征；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;
    权重分析模块，其使用权重模型对环境参数特征做权重分析，获得环境参数的权重。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.
  8. 如权利要求7所述的用于评价室内环境质量的环境参数权重确定系统， 其特征在于：其还包括权重模型训练模块，所述权重模型训练模块对环境参数数据样本进行训练，获得所述权重模型，其包括，The environmental parameter weight determination system for evaluating indoor environment quality according to claim 7, further comprising a weight model training module, wherein the weight model training module trains environmental parameter data samples to obtain the weights Model, which includes,
    数据样本采集单元，用于采集室内的环境参数数据样本，所述环境参数包括但不限于温度、湿度、照度、色温、PM2.5、PM10、甲醛、TVOC、二氧化碳和噪音；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;
    特征提取单元，用于对环境参数数据样本进行特征提取，获得特征化后的数据样本；A feature extraction unit, configured to perform feature extraction on a sample of environmental parameter data to obtain a characterized data sample;
    模型训练单元，用于使用相关算法训练特征化后的数据样本，获得权重模型。A model training unit is used to train a characterized data sample using a related algorithm to obtain a weight model.
  9. 如权利要求8所述的用于评价室内环境质量的环境参数权重确定系统，其特征在于：所述权重模型训练模块还包括数据分组单元，所述数据分组单元将特征后的数据样本随机分组为训练数据组和测试数据组；所述模型训练单元训练训练数据组中的数据样本获得权重模型；所述测试数据中的数据样本用于测试验证所述权重模型的匹配度。The environmental parameter weight determination system for evaluating indoor environment quality according to claim 8, wherein the weight model training module further comprises a data grouping unit, and the data grouping unit randomly groups the data samples after the feature into A training data group and a test data group; the model training unit trains data samples in the training data group 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.
  10. 如权利要求9所述的用于评价室内环境质量的环境参数权重确定系统，其特征在于：所述模型训练单元使用若干个相关算法分别训练所述训练测试组中的数据样本，获得若干个权重模型；The environmental parameter weight determination system for evaluating indoor environment quality according to claim 9, characterized in that the model training unit uses several related algorithms to separately train data samples in the training test group to obtain several weights model;
    所述模型训练单元使用所述测试数据组中的数据样本分别测试验证所述若干个权重模型的性能，在所述若干个权重模型中选取匹配度最高的一个为最优权重模型；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;
    权重分析模块使用最优权重模型对环境参数特征做权重分析，获得环境参数的权重。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.
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