Building internal environment intelligent integration system based on multi-dimensional health factors

Abstract

The invention relates to the technical field of building environment management, in particular to a building internal environment intelligent integration system based on multi-dimensional health factors. The system comprises a building internal environment data monitoring module, an environment data cleaning processing module, a multi-dimensional health factor evaluation module and an environment intelligent integrated regulation and control module, and can monitor corresponding building internal harmful pollutant concentration data, building internal light environment data and building internal noise data in real time in a building internal space; meanwhile, transmitting to a data processing center; a data processing center is used for carrying out statistical cleaning preprocessing and multi-dimensional health factor evaluation on harmful pollutant concentration data in a building, illumination environment data in the building and noise data in the building, and meanwhile, environment intelligent integrated regulation and control design is carried out to generate an environment intelligent integrated regulation and control strategy in the building. And the corresponding intelligent regulation and control management work of the environment in the building is executed. According to the invention, intelligent monitoring, analysis and regulation of environment management in the building can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of building environment management, and particularly to an intelligent integrated system for indoor environment in buildings based on multi-dimensional health factors. Background Art

[0002] The indoor environment in buildings is not just a simple superposition of traditional factors such as temperature, humidity, and air quality. It also requires comprehensive consideration of multiple dimensions of health factors, including environmental parameters such as air circulation, noise control, lighting, temperature, humidity, air pollutant concentration, indoor plants, and furniture layout. In recent years, intelligent integration methods for indoor environment in buildings based on multi-dimensional health factors have gradually become a research hotspot. Such methods aim to achieve coordinated control and optimization of multiple factors in the building environment by integrating monitoring data of various health factors and combining machine learning, artificial intelligence algorithms, and Internet of Things technology. However, most of the existing indoor environment control systems in buildings focus on the regulation of single or a few health factors, such as temperature and humidity control, air quality monitoring, etc. Although they can improve the indoor environment in some aspects, they often neglect the synergistic effect of multi-dimensional health factors and lack overall and coordinated regulation of different environmental factors, thus making it difficult to achieve precise and real-time indoor environment regulation. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide an intelligent integrated system for indoor environment in buildings based on multi-dimensional health factors to solve at least one of the above technical problems.

[0004] To achieve the above object, an intelligent integrated system for indoor environment in buildings based on multi-dimensional health factors includes the following modules:

[0005] An indoor environment data monitoring module, which is used to deploy harmful pollutant sensors, lighting environment sensors, and noise sensors corresponding to different sensitivities and frequency response ranges in the indoor space of the building, and use the corresponding harmful pollutant sensors, lighting environment sensors, and noise sensors to monitor the concentration data of harmful pollutants in the building, the lighting environment data in the building, and the noise data in the building in real time, and transmit them to the data processing center at the same time;

[0006] An environmental data cleaning and processing module, which is used to use the data processing center to perform statistical cleaning and preprocessing on the concentration data of harmful pollutants in the building, the lighting environment data in the building, and the noise data in the building to obtain standard data of harmful pollutant concentration, standard data of lighting environment, and standard noise data;

[0007] A multi-dimensional health factor evaluation module, which is used to perform multi-dimensional health factor evaluation on the standard data of harmful pollutant concentration, the standard data of lighting environment, and the standard noise data to generate an air quality grade factor in the building, a lighting health evaluation factor, and a noise impact evaluation factor;

[0008] The ambient intelligence integrated control module is used to perform ambient intelligence integrated control design on the spaces within a building based on the air quality level factor, lighting health assessment factor, and noise impact assessment factor within the building, generate an ambient intelligence integrated control strategy for the building's environment, and execute corresponding ambient intelligent control management work for the building.

[0009] Furthermore, the building's ambient data monitoring module includes the following functions:

[0010] By installing and deploying harmful pollutant sensors, lighting environment sensors, and noise sensors corresponding to different sensitivities and frequency response ranges in the spaces within the building;

[0011] Using the toxic pollutant sensor to conduct real-time monitoring of harmful pollution in the spaces within the building, in order to monitor in real-time the concentrations of corresponding harmful pollutants such as formaldehyde, benzene, PM2.5, and microbial content, and obtain the harmful pollutant concentration data within the building;

[0012] Using the lighting environment sensor to conduct real-time monitoring of the lighting environment in the spaces within the building, in order to monitor in real-time the corresponding lighting intensity, color temperature, and spectral distribution, and obtain the lighting environment data within the building;

[0013] Using the noise sensors corresponding to different sensitivities and frequency response ranges to conduct real-time monitoring of the noise in the spaces within the building, in order to monitor in real-time the indoor noise in the building corresponding to different frequency response ranges, and obtain the noise data within the building;

[0014] Transmitting the harmful pollutant concentration data, lighting environment data, and noise data within the building to the data processing center through wireless transmission technology.

[0015] Furthermore, the ambient data cleaning and processing module includes the following functions:

[0016] Using the data processing center to identify and remove data anomalies from the harmful pollutant concentration data, lighting environment data, and noise data within the building, in order to statistically calculate the mean and standard deviation corresponding to different data, and removing the corresponding outliers according to the mean and standard deviation using the 3σ criterion, to obtain the filtered harmful pollutant concentration data, lighting environment data, and noise data;

[0017] Performing interpolation and supplementation on the filtered harmful pollutant concentration data, lighting environment data, and noise data, in order to use linear interpolation to supplement the missing values corresponding to each data, and obtain the harmful pollutant concentration data, lighting environment data, and noise data after missing value interpolation compensation;

[0018] Standardize the harmful pollutant concentration data, light environment data, and noise data after missing interpolation compensation to obtain the standard harmful pollutant concentration data, standard light environment data, and standard noise data.

[0019] Furthermore, the multi-dimensional health factor evaluation module includes the following functions:

[0020] Evaluate the air grade factor for the standard harmful pollutant concentration data to generate the indoor air quality grade factor;

[0021] Evaluate the light health factor for the standard light environment data to generate the light health evaluation factor;

[0022] Evaluate the noise impact factor for the standard noise data to generate the noise impact evaluation factor.

[0023] Furthermore, the evaluation of the air grade factor for the standard harmful pollutant concentration data includes:

[0024] Analyze the concentration distribution changes of formaldehyde, benzene, PM2.5, and microbial content corresponding in the standard harmful pollutant concentration data to obtain the corresponding formaldehyde concentration change rate, benzene concentration change rate, particle concentration change rate, and microbial concentration change rate at each spatial position;

[0025] Obtain the building material characteristics corresponding to the indoor space, and perform harmful pollution adsorption calculation on the standard harmful pollutant concentration data based on the building material characteristics to obtain the adsorption efficiency of the building material for the corresponding harmful pollutants;

[0026] Obtain the ventilation capacity corresponding to each spatial position in the indoor space, and perform pollutant diffusion analysis on the standard harmful pollutant concentration data based on the ventilation capacity corresponding to each spatial position in the indoor space to obtain the corresponding harmful pollutant ventilation diffusion rate at each spatial position;

[0027] Based on the adsorption efficiency of the building material for the corresponding harmful pollutants and the corresponding harmful pollutant ventilation diffusion rate at each spatial position, use the air quality grade calculation formula to evaluate the air grade factor for the corresponding formaldehyde concentration change rate, benzene concentration change rate, particle concentration change rate, and microbial concentration change rate at each spatial position to generate the indoor air quality grade factor.

[0028] Furthermore, the specific air quality grade calculation formula is:

[0029]

[0030] In the formula, A Qis the air quality grade factor in the building, Ω is the spatial range in the building, n is the total number of spatial positions, and x i is the parameter of the i-th spatial position, and Q(x i ) is the formaldehyde concentration change rate corresponding to the i-th spatial position, and B(x i ) is the benzene concentration change rate corresponding to the i-th spatial position, and K(x i ) is the particle concentration change rate corresponding to the i-th spatial position, and W(x i ) is the microorganism concentration change rate corresponding to the i-th spatial position, S is the contact area between the surface of the spatial area in the building and the air for harmful pollutants, exp is the exponential function, ε is the adsorption efficiency of building materials for corresponding harmful pollutants, and κ(x i ) is the ventilation diffusion rate of harmful pollutants corresponding to the i-th spatial position.

[0031] Further, the evaluation of the light health factor for the light environment standard data includes:

[0032] Obtain the building occlusion and terrain undulation factors corresponding to the spatial area in the building;

[0033] Based on the building occlusion and terrain undulation factors, correct the light intensity, color temperature, and spectral distribution in the light environment standard data for light deviation to obtain the corrected data of the light environment distribution deviation;

[0034] Obtain the circadian rhythm corresponding to the spatial area in the building, and based on the circadian rhythm, conduct an analysis of the correlation between the light intensity, color temperature, and spectral distribution in the light environment standard data to obtain the influence correlation relationship between the changes in light intensity, color temperature, and spectral distribution over time and the circadian rhythm;

[0035] Based on the influence correlation relationship between the changes in light intensity, color temperature, and spectral distribution over time and the circadian rhythm, evaluate the light health factor for the corrected data of the light environment distribution deviation to evaluate and analyze the corresponding health impact degree of the light environment on the circadian rhythm, so as to generate the light health evaluation factor.

[0036] Further, the evaluation of the noise impact factor for the noise standard data includes:

[0037] Obtain the characteristics of the noise measurement environment corresponding to the spatial area in the model, including the sound insulation effect of the building and the influence of the building terrain on noise propagation;

[0038] Based on the characteristics of the noise measurement environment, analyze the noise propagation to analyze the propagation path and attenuation law of noise under different topographies and landforms, and obtain the noise propagation attenuation characteristics in the building;

[0039] Based on the noise propagation attenuation characteristics in the building and combined with time series analysis, perform noise time-domain feature analysis on the noise standard data to consider the influence of the propagation duration and attenuation law corresponding to the noise in the building on the time-domain change of the noise, extract the time-domain change characteristics corresponding to the noise, and obtain the time-domain change characteristics of the noise in the building;

[0040] Perform frequency-domain conversion and frequency-domain feature analysis on the noise standard data to obtain the frequency-domain change characteristics of the noise in the building;

[0041] Evaluate the noise impact factors according to the time-domain change characteristics of the noise in the building and the frequency-domain change characteristics of the noise in the building, to evaluate and analyze the influence degree of the time-domain and frequency-domain characteristics corresponding to the noise in the building on human health, and generate noise impact assessment factors.

[0042] Furthermore, the environmental intelligent integrated control module includes the following functions:

[0043] Develop a multi-dimensional decision engine for the space in the building based on the air quality level factor, light health assessment factor, and noise impact assessment factor in the building to generate a multi-dimensional decision engine for the space in the building;

[0044] Design the environmental intelligent integrated control for the space in the building based on the multi-dimensional decision engine for the space in the building, generate an environmental intelligent integrated control strategy for the space in the building, and execute the corresponding environmental intelligent control management work for the space in the building.

[0045] Furthermore, the environmental intelligent integrated control strategy for the space in the building is specifically as follows: when the harmful gas concentration corresponding to the air quality level displayed by the multi-dimensional decision engine for the space in the building exceeds level 2, automatically start the air purification equipment corresponding to the space in the building and adjust the operating parameters of the ventilation system to increase the exhaust air volume; when the light evaluation result displayed by the multi-dimensional decision engine for the space in the building shows that the light intensity is insufficient or the color temperature is inappropriate, automatically start the control lighting system corresponding to the space in the building to operate the brightness and color temperature adjustment; when the multi-dimensional decision engine for the space in the building shows that the noise exceeds the standard, automatically control the operating time and operating power of the sound insulation equipment corresponding to the space in the building to reduce the noise generation.

[0046] The beneficial effects of the present invention:

[0047] The intelligent integrated system for the indoor environment of buildings based on multi-dimensional health factors proposed by the present invention is generally composed of an indoor environment data monitoring module, an environmental data cleaning and processing module, a multi-dimensional health factor evaluation module, and an environmental intelligent integrated regulation module. Compared with the prior art, the beneficial effects of this application are that by deploying harmful pollutant sensors, lighting environment sensors, and noise sensors inside the building, key data in the building environment can be obtained in real time, thereby providing accurate basic data for subsequent environmental optimization and intelligent regulation. First of all, the harmful pollutant sensors can accurately detect the concentration of toxic gases in the air, such as harmful pollutants such as formaldehyde, benzene, PM2.5, and microbial content, and timely discover air pollution problems; the lighting environment sensors can monitor the indoor lighting intensity and quality to ensure that the indoor lighting meets the human health needs. By adjusting the lighting environment, not only can the living comfort be improved, but also support for energy conservation can be provided; the role of the noise sensors is to monitor the environmental noise level. Especially in different frequency and sensitivity ranges, it can identify the impacts of different noise sources and reduce the health hazards of noise pollution to the occupants. Through this comprehensive sensor system, data collection is more accurate and real-time, and the change trends of various environmental factors can be obtained. The real-time transmission of the data to the data processing center provides support for subsequent analysis, processing, and decision-making, ensuring that the indoor environment of the building can always be in a state of being monitored, and thus providing a timely feedback mechanism for further environmental optimization. Secondly, by using the data processing center to perform statistical cleaning and preprocessing on the large amount of original environmental data collected, the accuracy and reliability of the data are ensured. The sensors in the building continuously collect data, but the original data often has certain noise, such as sensor errors, short-term fluctuations, etc. Through statistical cleaning, these invalid data can be removed, making the final analysis results more accurate and avoiding misjudgment caused by noise data. In addition, data preprocessing includes the standardization and normalization of different environmental factors, enabling them to be compared and analyzed under the same standard. This process not only improves the comparability of the data, but also ensures that various data can be used for subsequent health assessment and environmental regulation according to a unified standard. This process lays a solid foundation for the subsequent multi-dimensional health factor evaluation.Then, the various environmental data after data preprocessing can generate various environmental health assessment factors through a multi-dimensional health factor assessment method. The importance of this assessment step lies in that it converts various environmental data into health indicators with practical significance, directly affecting the comfort and health level of the occupants. The air quality grade factor comprehensively assesses the air quality in the building based on the detection results of harmful pollutant concentrations to ensure that the pollutant concentration does not exceed the healthy standard range; the lighting health assessment factor judges whether the indoor lighting meets the physiological needs of the human body by analyzing the standardized data of indicators such as lighting intensity and color temperature, reducing eye diseases or physical discomforts caused by inappropriate lighting; the noise impact assessment factor evaluates the impact of noise pollution in the environment on the occupants based on the standardized results of noise data, especially the psychological and physical health problems caused by long-term exposure to high-noise environments. Through the generation of these assessment factors, the health status of the indoor environment in the building can be comprehensively understood, which provides the basic data guarantee for realizing the overall and coordinated control of different environmental factors in the subsequent process. Finally, through the aforementioned air quality grade factor, lighting health assessment factor and noise impact assessment factor, the environmental intelligent integrated control design can achieve automatic building environment control, maximizing the comfort and health level of the occupants. At this stage, the system will dynamically adjust the environmental settings in the building according to the changes of different factors, such as adjusting the working state of the air conditioning system, optimizing the indoor air circulation, adjusting the indoor lighting intensity and color temperature, and even automatically adjusting the intervention measures of noise sources. Through the intelligent integrated control strategy, the comfort and health of the building environment can be effectively improved, and at the same time, more efficient management can be achieved in energy use. In this way, through the implementation of the intelligent control strategy, the indoor space in the building can not only ensure healthy environmental conditions, but also achieve energy conservation and resource optimization, so as to achieve precise and real-time indoor environment control. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Other features, objects and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments read in conjunction with the accompanying drawings:

[0049] Figure 1 It is a schematic diagram of the modules of the intelligent integrated system for the indoor environment of a building based on multi-dimensional health factors according to the present invention;

[0050] Figure 2 is Figure 1 a schematic diagram of the functional flow of the indoor environmental data monitoring module in

[0051] Figure 3 is Figure 1 a schematic diagram of the functional flow of the environmental data cleaning and processing module in DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] The technical system of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0053] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor systems and / or microcontroller systems.

[0054] It should be understood that although terms such as "first" and "second" may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0055] To achieve the above object, please refer to Figures 1 to 3 , the present invention provides an intelligent integrated system for the indoor environment of a building based on multi-dimensional health factors, and the system includes the following modules:

[0056] An indoor environment data monitoring module, configured to deploy harmful pollutant sensors, light environment sensors, and noise sensors corresponding to different sensitivities and frequency response ranges in the indoor space of the building, and use the corresponding harmful pollutant sensors, light environment sensors, and noise sensors to monitor the corresponding indoor harmful pollutant concentration data, indoor light environment data, and indoor noise data in real time, and transmit them to the data processing center at the same time;

[0057] An environment data cleaning and processing module, configured to use the data processing center to perform statistical cleaning and preprocessing on the indoor harmful pollutant concentration data, indoor light environment data, and indoor noise data to obtain harmful pollutant concentration standard data, light environment standard data, and noise standard data;

[0058] The multi - dimensional health factor evaluation module is used to conduct multi - dimensional health factor evaluation on the concentration standard data of harmful pollutants, the lighting environment standard data, and the noise standard data, so as to generate the air quality grade factor in the building, the lighting health evaluation factor, and the noise impact evaluation factor;

[0059] The environmental intelligent integrated control module is used to conduct environmental intelligent integrated control design on the space in the building based on the air quality grade factor in the building, the lighting health evaluation factor, and the noise impact evaluation factor, generate the environmental intelligent integrated control strategy in the building, so as to execute the corresponding environmental intelligent control management work in the building.

[0060] In the embodiment of the present invention, please refer to Figure 1 As shown, it is a schematic diagram of the modules of the environmental intelligent integrated system in the building based on multi - dimensional health factors of the present invention. In this example, the environmental intelligent integrated system in the building based on multi - dimensional health factors includes the following modules:

[0061] S1: The environmental data monitoring module in the building is used to deploy harmful pollutant sensors, lighting environment sensors, and noise sensors corresponding to different sensitivities and frequency response ranges in the space of the building, and use the corresponding harmful pollutant sensors, lighting environment sensors, and noise sensors to monitor the harmful pollutant concentration data, the lighting environment data, and the noise data in the building in real - time, and transmit them to the data processing center at the same time;

[0062] In the embodiment of the present invention, sensors are accurately deployed in each functional area of the building according to its characteristics. High - sensitivity harmful pollutant sensors are installed in crowded office areas and rest areas to monitor formaldehyde, benzene, PM2.5, etc.; Lighting environment sensors are arranged in key lighting areas such as exhibition halls and classrooms to monitor lighting intensity, color temperature, etc.; For different noise sources, such as installing noise sensors with a wide frequency response range near the elevator machine room and narrow - frequency response - range sensors in quiet areas. The sensors collect data at intervals of minutes, encrypt the data through the ZigBee wireless communication protocol, and transmit it to the server of the data processing center in real - time to ensure that the data arrives accurately and in a timely manner.

[0063] S2: The environmental data cleaning and processing module is used to use the data processing center to conduct statistical cleaning and pre - processing on the harmful pollutant concentration data, the lighting environment data, and the noise data in the building, so as to obtain the concentration standard data of harmful pollutants, the lighting environment standard data, and the noise standard data;

[0064] In the embodiment of the present invention, after the server of the data processing center receives data, it first classifies and stores three types of data. For abnormal data, the mean-standard deviation method is used to identify outliers. If the formaldehyde concentration at a certain moment far exceeds the normal fluctuation range, it is determined as abnormal and eliminated; for missing data, the linear interpolation method is used to supplement the missing values; for data noise, the moving average filter is used to remove interference, and then, in accordance with relevant national standards and industry specifications, the data is normalized to unify data in different ranges to the [0, 1] interval, and finally, the standard data of harmful pollutant concentration, the standard data of lighting environment, and the standard data of noise are obtained.

[0065] S3: A multi-dimensional health factor evaluation module, which is used to perform multi-dimensional health factor evaluation on the standard data of harmful pollutant concentration, the standard data of lighting environment, and the standard data of noise, so as to generate an air quality grade factor in the building, a lighting health evaluation factor, and a noise impact evaluation factor;

[0066] In the embodiments of the present invention, by evenly arranging a plurality of high-precision harmful pollutant concentration sensors in a building, such as sensors for detecting formaldehyde, benzene, PM2.5 and microbial content, these sensors collect data at fixed time intervals, such as once every half hour, to form standard data on harmful pollutant concentrations. Using professional data analysis software, the concentration data of various pollutants are substituted into a specific air quality grade calculation formula, and combined with the corresponding adsorption efficiency of building materials and the diffusion rate of harmful pollutants to calculate the air quality grade factor of this area. And by installing light sensors in different functional areas of the building, such as offices, bedrooms, corridors, etc., to collect data on light intensity, color temperature and spectral distribution in real time, forming standard data on the lighting environment. Using lighting simulation software, combined with the research results related to the human biological clock rhythm, analyze the impact of lighting parameters at different times on human health. For example, during the morning working hours, the appropriate light intensity is 800 - 1000 lux and the color temperature is 5000 - 6000 K. If the light intensity in a certain office at this time is only 600 lux, the software calculates the negative impact degree on the work efficiency and emotional state of employees according to the preset algorithm, compares the lighting parameters in each time period and each area with the health impact standard, quantitatively calculates the lighting health impact value, and through weighted average, such as the weight of the working area is 0.6 and the weight of the rest area is 0.4, to obtain the lighting health assessment factor, so as to reflect the comprehensive impact of the lighting environment in the building on human health. Then, by deploying noise monitoring equipment at different positions in the building, such as near the outer wall, equipment room, areas with frequent personnel activities, etc., continuously record the noise decibel value to form standard noise data. With the help of acoustic analysis software, based on the national noise control standard and the threshold of the impact of noise on human health in medical research, analyze the noise data. For example, if a certain area is near the elevator machine room and the noise remains at 65 dB(A) for a long time, exceeding the appropriate noise standard for the office area (55 dB(A)), the software calculates the impact degree of this noise on aspects such as human hearing, sleep quality, and mental state according to the relationship model between noise exposure time, intensity and health impact, conducts a similar assessment on the noise data at each monitoring point in the building, sets weights according to the importance of different areas, such as the weight of the office area is 0.5, the weight of the rest area is 0.3, and the weight of the public activity area is 0.2, and generates a noise impact assessment factor through weighted calculation, which is used to measure the overall impact of the noise in the building on human health, and finally generates a noise impact assessment factor.

[0067] S4: The environmental intelligent integrated control module is used to perform environmental intelligent integrated control design on the space in the building based on the air quality grade factor, lighting health assessment factor and noise impact assessment factor in the building, generate an environmental intelligent integrated control strategy for the building, so as to execute the corresponding environmental intelligent control management work in the building.

[0068] In the embodiments of the present invention, based on the comprehensive results of the air quality level factor, the lighting health assessment factor, and the noise impact assessment factor within the building, an intelligent integrated control design for the building environment is carried out. During this design process, the system will dynamically adjust the environmental control strategies within the building according to the calculation results of different assessment factors. For example, when the air quality level factor shows that the concentration of harmful gases is too high, the system can activate the fresh air system or air purifier and adjust the air circulation rate to optimize the indoor air quality; when the lighting health factor indicates insufficient indoor lighting, the system will automatically adjust the brightness of artificial lighting equipment or introduce more natural light sources to ensure that the indoor environment meets the requirements of healthy lighting; when the noise impact assessment factor shows that the noise exceeds the standard, the system can reduce the noise pollution by increasing sound insulation materials or adjusting the distribution of noise sources. All these adjustment operations will be executed in real time by the building environment management system and form an intelligent control management strategy to automatically complete the environmental optimization task, thereby improving the health level of the indoor living or working environment.

[0069] Furthermore, the building indoor environment data monitoring module includes the following functions:

[0070] By installing and deploying harmful pollutant sensors, lighting environment sensors, and noise sensors corresponding to different sensitivities and frequency response ranges in the building space;

[0071] Using the toxic pollutant sensor to conduct real-time monitoring of harmful pollution in the building space to real-time monitor the concentrations of corresponding harmful pollutants such as formaldehyde, benzene, PM2.5, and microbial content, and obtain the harmful pollutant concentration data in the building;

[0072] Using the lighting environment sensor to conduct real-time monitoring of the lighting environment in the building space to real-time monitor the corresponding lighting intensity, color temperature, and spectral distribution, and obtain the lighting environment data in the building;

[0073] Using the noise sensors corresponding to different sensitivities and frequency response ranges to conduct real-time monitoring of the noise in the building space to real-time monitor the indoor noise in the building corresponding to different frequency response ranges, and obtain the noise data in the building;

[0074] Transmitting the harmful pollutant concentration data, lighting environment data, and noise data in the building to the data processing center through wireless transmission technology.

[0075] As an embodiment of the present invention, referring to Figure 2 shown, it is Figure 1 the functional flow diagram of the building indoor environment data monitoring module in

[0076] S11: Install and deploy harmful pollutant sensors, light environment sensors, and noise sensors with different sensitivities and frequency response ranges in the interior space of the building;

[0077] In the embodiment of the present invention, according to the layout and functional zoning of the interior space of the building, various sensors are accurately installed in different areas. Harmful pollutant sensors are installed in densely populated office areas, rest areas, and kitchens and bathrooms where pollution is likely to occur to ensure comprehensive monitoring of the content of formaldehyde, benzene, PM2.5, and microorganisms. Light environment sensors are reasonably arranged in areas with high lighting requirements such as offices, classrooms, and exhibition halls to accurately obtain data on light intensity, color temperature, and spectral distribution. For the sources of noise with different frequencies, such as near elevator machine rooms and air conditioning equipment, noise sensors with high sensitivity and wide frequency response ranges are installed, while in relatively quiet areas, noise sensors with low sensitivity and narrow frequency response ranges are installed. Professional installation tools and fixing methods are used to ensure that the sensors are firmly installed and in appropriate positions to effectively collect the required data.

[0078] S12: Use the toxic pollutant sensor to conduct real-time monitoring of harmful pollution in the interior space of the building to monitor the concentration of harmful pollutants corresponding to formaldehyde, benzene, PM2.5, and microorganisms in real time, and obtain the harmful pollutant concentration data in the building;

[0079] In the embodiment of the present invention, through the harmful pollutant sensor, advanced chemical sensing technology and optical sensing technology are used to continuously sample and analyze the air in the interior space of the building. The sensitive elements inside the sensor react chemically or optically with harmful gases such as formaldehyde and benzene to convert their concentrations into electrical signals. For PM2.5, the principle of laser scattering is used to determine its concentration by detecting the intensity of the scattered light of the laser by the particulate matter. For the content of microorganisms, microbial culture and counting technology are used. The sensor regularly collects air samples, cultivates and counts microorganisms in the internal culture environment. The sensor records and stores the concentration data of various harmful pollutants detected at set time intervals, such as once a minute, to form the harmful pollutant concentration data in the building.

[0080] S13: Use the light environment sensor to conduct real-time monitoring of the light environment in the interior space of the building to monitor the corresponding light intensity, color temperature, and spectral distribution, and obtain the light environment data in the building;

[0081] In an embodiment of the present invention, by using the principle of the photoelectric effect through a light environment sensor, the photodetector inside it converts the received light into an electrical signal. For the monitoring of light intensity, the strength of the light is determined by measuring the intensity of the electrical signal, with the unit being lux. When measuring the color temperature, the sensor calculates the corresponding color temperature value according to the proportional relationship of light with different wavelengths, with the unit being Kelvin (K). For the monitoring of spectral distribution, a spectral splitting technique is adopted to decompose the light into components with different wavelengths and measure the relative intensities of the components with each wavelength. The sensor measures and records the light intensity, color temperature, and spectral distribution at fixed time intervals, such as once every 5 minutes, and integrates these data to form the light environment data inside the building.

[0082] S14: Use noise sensors with different sensitivities and corresponding frequency response ranges to perform real-time noise monitoring on the spaces inside the building, so as to monitor the indoor noise in the building corresponding to different frequency response ranges in real time and obtain the noise data inside the building.

[0083] In an embodiment of the present invention, noise sensors with different sensitivities and frequency response ranges are divided for monitoring according to the noise characteristics of different areas inside the building. Noise sensors with high sensitivity and wide frequency response ranges mainly target areas where high-frequency and high-intensity noise are generated, such as equipment machine rooms and near elevators. When noise is generated, the microphone inside the sensor converts the sound signal into an electrical signal, and the signal processing circuit separates and amplifies signals with different frequencies to measure the noise intensity of each frequency component. Noise sensors with low sensitivity and narrow frequency response ranges are used to monitor the low-frequency noise in relatively quiet areas. The sensors measure and record the noise in different frequency response ranges at set time intervals, such as once every 3 minutes, and summarize these data to obtain the noise data inside the building.

[0084] S15: Transmit the data of the concentration of harmful pollutants inside the building, the light environment data inside the building, and the noise data inside the building to the data processing center through wireless transmission technology.

[0085] In an embodiment of the present invention, by equipping each sensor with a wireless communication module, such as a Wi-Fi module or a Bluetooth module, to enable it to have the ability of wireless data transmission, the sensor encapsulates the data of the concentration of harmful pollutants inside the building, the light environment data, and the noise data collected according to a specific data format, and adds information such as data identification and timestamp. When the set transmission time interval is reached, such as once every 10 minutes, the sensor sends the encapsulated data to the nearby wireless access point through the wireless communication module. The wireless access point performs preliminary processing and integration on the received data, and then transmits the data to the data processing center through a wired network or a wireless transmission device with higher power, such as a 4G / 5G router. The data processing center receives and stores these data to provide a basis for subsequent data analysis and processing.

[0086] Furthermore, the environmental data cleaning and processing module includes the following functions:

[0087] Using the data processing center to identify and remove data anomalies from the harmful pollutant concentration data, indoor lighting environment data, and indoor noise data in the building, so as to statistically calculate the mean and standard deviation corresponding to different data, and according to the mean and standard deviation, use the 3σ criterion to remove the corresponding outliers, obtaining the filtered harmful pollutant concentration data, lighting environment data, and noise data;

[0088] Interpolate and supplement the filtered harmful pollutant concentration data, lighting environment data, and noise data to use linear interpolation to supplement the missing values corresponding to each data, obtaining the harmful pollutant concentration data, lighting environment data, and noise data after missing value interpolation compensation;

[0089] Perform standardization processing on the harmful pollutant concentration data, lighting environment data, and noise data after missing value interpolation compensation to obtain the standard harmful pollutant concentration data, standard lighting environment data, and standard noise data.

[0090] As an embodiment of the present invention, refer to Figure 3 shown, for Figure 1 the functional flowchart of the environmental data cleaning and processing module in

[0091] S21: Using the data processing center to identify and remove data anomalies from the harmful pollutant concentration data, indoor lighting environment data, and indoor noise data in the building, so as to statistically calculate the mean and standard deviation corresponding to different data, and according to the mean and standard deviation, use the 3σ criterion to remove the corresponding outliers, obtaining the filtered harmful pollutant concentration data, lighting environment data, and noise data;

[0092] In the embodiment of the present invention, by installing harmful pollutant concentration sensors, lighting sensors, and noise sensors at each key position in the building, continuously collecting the harmful pollutant concentration data, indoor lighting environment data, and indoor noise data in the building, and transmitting these data to the data processing center, the data processing center classifies, stores, and manages these data. For each type of data, calculate its mean and standard deviation respectively. Taking the harmful pollutant concentration data as an example, add up all the data and divide by the number of data to get the mean, then calculate the sum of the squared deviations of each data from the mean, take the average value and take the square root to get the standard deviation. According to the 3σ criterion, if the deviation of a certain data from the mean exceeds 3 times the standard deviation, then determine that this data is an outlier. For example, if the mean of the harmful pollutant concentration data is 10 mg / m 3 , and the standard deviation is 2 mg / m 3, then the data exceeding (10 - 3×2) mg / m 3 to (10 + 3×2) mg / m 3 is an outlier, which is removed from the dataset to obtain the filtered harmful pollutant concentration data, light environment data, and noise data.

[0093] S22: Interpolate and supplement the filtered harmful pollutant concentration data, light environment data, and noise data to fill in the missing values corresponding to each data using linear interpolation, obtaining the harmful pollutant concentration data, light environment data, and noise data after missing value interpolation compensation;

[0094] In the embodiment of the present invention, the data processing center checks the three types of filtered data to find the data points with missing values. For the harmful pollutant concentration data, if the data at two consecutive time points are 12 mg / m 3 and 16 mg / m 3 , and the data at the middle time point is missing, the linear interpolation method is used for supplementation. The principle of linear interpolation is to assume that the data changes linearly between these two known points, and calculate the data of the missing point based on the time interval and the difference of the known data. Let the time interval between the two known data points be t, and the time of the missing point from the first known point be t1, then the data of the missing point = the first known data + (the second known data - the first known data) × (t1 / t). According to this method, the missing values in the light environment data and noise data are linearly interpolated and supplemented in the same way, and finally the harmful pollutant concentration data, light environment data, and noise data after missing value interpolation compensation are obtained.

[0095] S23: Standardize the harmful pollutant concentration data, light environment data, and noise data after missing value interpolation compensation to obtain the standard harmful pollutant concentration data, standard light environment data, and standard noise data.

[0096] In the embodiment of the present invention, the data processing center standardizes the three types of data after missing value interpolation compensation using the Z-score standardization method. For the harmful pollutant concentration data, first calculate its mean μ and standard deviation σ. For each data point x, the standardized data z = (x - μ) / σ. For example, if the mean of the harmful pollutant concentration data is 15 mg / m 3 , the standard deviation is 3 mg / m 3 , and a certain data point is 18 mg / m 3Then the standardized data is (18 - 15) / 3 = 1. In the same way, the lighting environment data and noise data are standardized, and the standardized data is stored as a new dataset to obtain the harmful pollutant concentration standard data, lighting environment standard data, and noise standard data respectively, for subsequent intelligent integrated analysis of the indoor environment based on multi-dimensional health factors.

[0097] Furthermore, the multi-dimensional health factor evaluation module includes the following functions:

[0098] Evaluate the air quality grade factor for the harmful pollutant concentration standard data to generate the indoor air quality grade factor in the building;

[0099] In the embodiment of the present invention, by uniformly arranging a plurality of high-precision harmful pollutant concentration sensors in the building, such as sensors for detecting formaldehyde, benzene, PM2.5, and microbial content, these sensors collect data at fixed time intervals, such as once every half hour, to form the harmful pollutant concentration standard data. Using professional data analysis software, substitute the concentration data of various pollutants into a specific air quality grade calculation formula, and calculate the air quality grade factor of this area in combination with the corresponding adsorption efficiency of building materials and the diffusion rate of harmful pollutants. Similar processing is performed on the data of all collection points in the building, and the overall indoor air quality grade factor in the building is generated by summarization.

[0100] Preferably, evaluate the lighting health factor for the lighting environment standard data to generate the lighting health evaluation factor;

[0101] In the embodiment of the present invention, by installing lighting sensors in different functional areas of the building, such as offices, bedrooms, corridors, etc., to collect real-time data on light intensity, color temperature, and spectral distribution, which constitutes the lighting environment standard data. Using lighting simulation software, combined with the research results related to the human biological clock rhythm, analyze the impact of lighting parameters at different times on human health. For example, during the morning working hours, the appropriate light intensity is 800 - 1000 lux, and the color temperature is 5000 - 6000 K. If the light intensity in a certain office at this time is only 600 lux, the software calculates the negative impact degree on the work efficiency and emotional state of employees according to the preset algorithm. Compare the lighting parameters at different times and in different areas with the health impact standard, quantitatively calculate the lighting health impact value, and through weighted average, such as the weight of the working area is 0.6 and the weight of the rest area is 0.4, to obtain the lighting health evaluation factor, which reflects the comprehensive impact of the lighting environment in the building on human health, and finally generates the lighting health evaluation factor.

[0102] Preferably, evaluate the noise impact factor for the noise standard data to generate the noise impact evaluation factor.

[0103] In the embodiments of the present invention, by deploying noise monitoring devices at different locations within a building, such as near exterior walls, equipment rooms, areas with frequent human activities, etc., continuously record the noise decibel values to form noise standard data. With the aid of acoustic analysis software, based on the national noise control standards and the thresholds of the impact of noise on human health in medical research, analyze the noise data. For example, if a certain area is close to an elevator machine room and the noise remains at 65 dB(A) for a long time, exceeding the appropriate noise standard for the office area (55 dB(A)), the software calculates the degree of impact of this noise on aspects such as human hearing, sleep quality, and mental state according to the relationship model between noise exposure time, intensity, and health effects, and conducts similar evaluations on the noise data at each monitoring point within the building. Set weights according to the importance of different areas, such as a weight of 0.5 for the office area, a weight of 0.3 for the rest area, and a weight of 0.2 for the public activity area, and calculate the weighted noise impact assessment factor to measure the overall impact of the noise within the building on human health, and finally generate the noise impact assessment factor.

[0104] Further, the air grade factor assessment of the harmful pollutant concentration standard data includes:

[0105] Conduct an analysis of the concentration distribution changes of the corresponding formaldehyde, benzene, PM2.5, and microorganism content in the harmful pollutant concentration standard data to obtain the corresponding formaldehyde concentration change rate, benzene concentration change rate, particle concentration change rate, and microorganism concentration change rate at each spatial position;

[0106] In the embodiments of the present invention, by arranging high-precision harmful pollutant concentration sensors at different spatial positions within a building, such as at the four corners, the center, near doors and windows of a room, etc., the sensors detect and record the data of formaldehyde, benzene, PM2.5, and microorganism content in real time at a set time interval, such as every 15 minutes. Using data analysis software, such as Excel or professional statistical analysis tool SPSS, compare the detection data at the same position at different time points. Taking formaldehyde as an example, assume that at a certain spatial position, the initial formaldehyde concentration is 0.05 mg / m 3 and it becomes 0.06 mg / m after 1 hour 3 . Calculate the formaldehyde concentration change rate at this position as (0.06 - 0.05) ÷ 0.05 × 100% = 20% through the formula (current concentration - initial concentration) ÷ initial concentration × 100%. Similarly, process the data of benzene, PM2.5, and microorganism content at each spatial position, and finally obtain the corresponding formaldehyde concentration change rate, benzene concentration change rate, particle concentration change rate, and microorganism concentration change rate at each spatial position.

[0107] Preferably, obtain the building material characteristics corresponding to the space within the building, and perform harmful pollution adsorption calculations on the harmful pollutant concentration standard data based on the building material characteristics to obtain the adsorption efficiency of the building material for the corresponding harmful pollutants.

[0108] In the embodiments of the present invention, by referring to materials such as product manuals and test reports of building materials, obtain the characteristic data of various materials within the building, such as the composition and porosity of wall coatings, floor materials, furniture boards, etc. For different harmful pollutants, use the adsorption kinetic model for calculation. For example, for formaldehyde, assume that a certain wall coating contains a specific activated carbon component. According to the pore structure of the activated carbon and the size of formaldehyde molecules, combined with adsorption theoretical formulas such as the Langmuir adsorption isotherm, calculate the adsorption capacity of the coating for formaldehyde. By simulating the adsorption process of building materials for harmful pollutants at different times, calculate the proportion of the amount of harmful pollutants adsorbed by the material per unit time to the total initial amount of harmful pollutants, that is, obtain the adsorption efficiency of the building material for the corresponding harmful pollutants. For example, the adsorption efficiency of a certain floor for benzene is 15% within 24 hours, which means that after 24 hours, the amount of benzene adsorbed by the floor accounts for 15% of the total initial benzene amount, and finally obtain the adsorption efficiency of the building material for the corresponding harmful pollutants.

[0109] Preferably, obtain the ventilation capacity corresponding to each spatial position within the building, and perform pollutant diffusion analysis on the harmful pollutant concentration standard data based on the ventilation capacity corresponding to each spatial position within the building to obtain the harmful pollutant ventilation diffusion rate corresponding to each spatial position.

[0110] In the embodiments of the present invention, by using equipment such as anemometers and air flow meters, measure the wind speed and ventilation volume at different spatial positions within the building. For example, at a position near the window, the wind speed is 2 m / s and the ventilation volume is 50 m 3 / h. Using the principles of fluid mechanics and diffusion models such as Fick's diffusion law, combined with the harmful pollutant concentration standard data, analyze the diffusion situation of pollutants under the action of ventilation. Assume that the initial formaldehyde concentration at a certain spatial position is relatively high. Under the action of ventilation, as time goes by, according to parameters such as wind speed, ventilation volume, and space volume, calculate the reduction rate of formaldehyde concentration. Through the formula (initial concentration - concentration after ventilation) ÷ initial concentration × 100%, obtain the harmful pollutant ventilation diffusion rate at this position. For example, the initial PM2.5 concentration at a corner of a certain room is 50 μg / m 3 , and it becomes 30 μg / m 3 after 1 hour of ventilation. Then the ventilation diffusion rate of PM2.5 at this position is (50 - 30) ÷ 50 × 100% = 40%, and finally obtain the harmful pollutant ventilation diffusion rate corresponding to each spatial position.

[0111] Preferably, based on the adsorption efficiency of building materials for corresponding harmful pollutants and the corresponding ventilation diffusion rate of harmful pollutants at each spatial position, the air quality grade calculation formula is used to evaluate the air grade factors of the change rates of formaldehyde concentration, benzene concentration, particle concentration, and microorganism concentration at each spatial position, so as to generate the air quality grade factors inside the building.

[0112] In the embodiment of the present invention, by obtaining the contact area between the corresponding harmful pollutants and air in the building space, and combining the change rates of formaldehyde concentration, benzene concentration, particle concentration, microorganism concentration, ventilation diffusion rate of harmful pollutants, adsorption efficiency of building materials for corresponding harmful pollutants, and related parameters, a suitable air quality grade calculation formula is formed for quantitative calculation to determine the air quality grade corresponding to the spatial position, and finally the air quality grade factors inside the building are obtained. In addition, this air quality grade calculation formula can also use any air quality grade detection algorithm in the field to replace the process of air grade factor evaluation, and is not limited to this air quality grade calculation formula.

[0113] Further, the air quality grade calculation formula is specifically:

[0114]

[0115] In the formula, A Q is the air quality grade factor inside the building, Ω is the spatial range inside the building, n is the total number of spatial positions, x i is the parameter of the i-th spatial position, Q(x i ) is the change rate of formaldehyde concentration corresponding to the i-th spatial position, B(x i ) is the change rate of benzene concentration corresponding to the i-th spatial position, K(x i ) is the change rate of particle concentration corresponding to the i-th spatial position, W(x i ) is the change rate of microorganism concentration corresponding to the i-th spatial position, S is the contact area between the surface of the harmful pollutants in the building space and air, exp is the exponential function, ε is the adsorption efficiency of building materials for corresponding harmful pollutants, and κ(x i ) is the ventilation diffusion rate of harmful pollutants corresponding to the i-th spatial position.

[0116] The present invention obtains an air quality grade calculation formula through the use of a specific mathematical model and verification, which is used to evaluate the air grade factors of the change rates of formaldehyde concentration, benzene concentration, particle concentration, and microorganism concentration at each spatial position. This formula fully considers the air quality grade factor A inside the building. Q, the spatial range Ω within the building, the total number n of spatial positions, and the i-th spatial position parameter x i , the formaldehyde concentration change rate Q(x i ) corresponding to the i-th spatial position, the benzene concentration change rate B(x i ) corresponding to the i-th spatial position, the particulate concentration change rate K(x i ) corresponding to the i-th spatial position, the microbial concentration change rate W(x i ) corresponding to the i-th spatial position, the contact area S between the surface of the harmful pollutants in the building space and the air, the exponential function exp, the adsorption efficiency ε of the building materials corresponding to the harmful pollutants, the ventilation diffusion rate κ(x i ) of the harmful pollutants corresponding to the i-th spatial position, according to the air quality grade factor A Q and the mutual correlation relationships among the above parameters constitute a functional relationship This formula can achieve the air quality grade factor evaluation process for the change rates of formaldehyde concentration, benzene concentration, particulate matter concentration, and microorganism concentration corresponding to each spatial position. At the same time, this air quality grade calculation formula takes into account the change rates of various harmful pollutants (formaldehyde, benzene, particulate matter, microorganisms), and combines factors such as the adsorption effect of building materials and the spatial ventilation diffusion rate, enabling a comprehensive and accurate evaluation of the air quality level inside the building. By separately evaluating the air quality at different spatial positions, this formula can reflect the differences in air quality in different areas of the building. For example, the area near the window may have better ventilation, while pollutants may accumulate in the enclosed internal space. This formula can generate a more refined air quality evaluation based on these differences. This formula not only focuses on pollutant concentrations but also considers the adsorption capacity of building materials and the diffusion capacity of ventilation. These two factors have a crucial impact on air quality. The adsorption efficiency of building materials can reduce harmful substances in the air, and good ventilation capacity can help with the diffusion and dilution of pollutants. Through the change rates in the formula (such as the change rate of formaldehyde concentration, benzene concentration, etc.), it is possible to dynamically track the changes in pollutant concentrations over time and space, which is crucial for real-time monitoring and optimization of indoor air quality. In particular, it can evaluate the changing trends of air quality at different time periods or under different usage conditions. The calculated air quality grade factor provides a comprehensive quantitative indicator for the indoor air quality. This factor not only depends on the change in pollutant concentration but is also closely related to the comprehensive performance of building materials and ventilation systems. Through the evaluation of the air quality factor, it can provide a scientific basis for building design, ventilation system improvement, and material selection. Through mathematical formulas, the evaluation of air quality is no longer a vague qualitative description but a quantitative result based on actual data, which helps to formulate more precise building air quality standards and provides data support during the building design, decoration, and maintenance processes.

[0117] Furthermore, the evaluation of the lighting health factor for the lighting environment standard data includes:

[0118] Obtaining the building occlusion and terrain undulation factors corresponding to the space inside the building;

[0119] In an embodiment of the present invention, by using Geographic Information System (GIS) technology and combining high-precision satellite images and topographic survey data, detailed topographic information of the area where the building is located is obtained, including parameters such as the height and slope of the terrain undulation. Through on-site investigation and building drawing analysis, the location, height, orientation of the building, and the distribution of surrounding buildings are determined, so as to determine the building occlusion situation. For example, for a building near a valley, it can be known from GIS data that the height difference of the surrounding terrain undulation reaches 50 meters. At the same time, through on-site measurement and drawing inspection, it is determined that there is a building 20 meters higher than it on the east side of the building, which will cause occlusion during a specific time period. These data are recorded in detail to provide a basis for subsequent analysis.

[0120] Preferably, based on the building occlusion and terrain undulation factors, light deviation correction is performed on the corresponding light intensity, color temperature, and spectral distribution in the standard light environment data to obtain light environment distribution deviation correction data;

[0121] In an embodiment of the present invention, by using professional light simulation software, such as Daysim, the previously obtained building occlusion and terrain undulation data are input into the software, and combined with the preset standard light environment data, including the theoretical values of light intensity, color temperature, and spectral distribution at different time periods. For building occlusion, the software simulates the change in light intensity after the light is blocked according to the position and shape of the occluder. For example, in a certain area, due to building occlusion, the light intensity drops from the standard 1000 lux to 300 lux at noon. Considering the terrain undulation, when the light shines on the undulating terrain, reflection and scattering will occur, affecting the color temperature and spectral distribution of the light. The software corrects the standard data by calculating these effects. For example, at the bottom of the valley, due to terrain reflection, the blue light component increases, and the software adjusts the spectral distribution data accordingly, and finally generates light environment distribution deviation correction data.

[0122] Preferably, the circadian rhythm corresponding to the space inside the building is obtained, and light rhythm correlation analysis is performed on the corresponding light intensity, color temperature, and spectral distribution in the standard light environment data to obtain the influence correlation relationship between the change of light intensity, color temperature, and spectral distribution over time and the circadian rhythm;

[0123] In the embodiments of the present invention, by means of questionnaire surveys and wearable device monitoring, the rest time data of the people in the building is collected to determine the circadian rhythm. For example, most people get up at 7:00 in the morning and go to bed at 10:00 in the evening. Using data analysis methods, the standard data of the lighting environment is corresponded to the circadian rhythm according to the time series, and the effects of the light intensity, color temperature and spectral distribution in different time periods on the physiological and psychological states of people are analyzed. For example, it is found through research that after getting up in the morning, a higher light intensity (800 - 1000 lux) and a relatively cold color temperature (5000 - 6000 K) can effectively improve people's wakefulness, while before going to bed at night, a lower light intensity (100 - 200 lux) and a relatively warm color temperature (2700 - 3000 K) help to relax. Through a large amount of data statistics and analysis, an impact correlation model between the changes of light intensity, color temperature and spectral distribution over time and the circadian rhythm is established, and finally the impact correlation between the changes of light intensity, color temperature and spectral distribution over time and the circadian rhythm is obtained.

[0124] Preferably, based on the impact correlation between the changes of light intensity, color temperature and spectral distribution over time and the circadian rhythm, a lighting health factor assessment is carried out on the lighting environment distribution deviation correction data to evaluate and analyze the corresponding health impact degree of the lighting environment on the circadian rhythm, so as to generate a lighting health assessment factor.

[0125] In the embodiments of the present invention, through evaluation by combining the lighting environment distribution deviation correction data based on the previously established impact correlation model, for the lighting parameters at each time point in the deviation correction data, the impact degree on the circadian rhythm is judged by referring to the impact correlation model. For example, during the night rest time, if the light intensity still remains at 500 lux (far exceeding the appropriate 100 - 200 lux), according to the model, this will interfere with people's sleep and have a greater negative impact on the circadian rhythm. By setting quantization indexes for different impact degrees, such as 1 point for slight impact, 3 points for moderate impact, and 5 points for severe impact, the lighting deviation conditions in each time period are scored, and the scores of all time periods are calculated by weighted average to obtain a value as the lighting health assessment factor, which quantifies the health impact degree of the lighting environment on the circadian rhythm, and finally the lighting health assessment factor is generated through evaluation.

[0126] Furthermore, the noise impact factor assessment on the noise standard data includes:

[0127] Obtaining the characteristics of the noise measurement environment corresponding to the space within the model, including the sound insulation effect of the building and the impact of the building terrain on noise propagation;

[0128] In the embodiments of the present invention, the characteristics of the noise measurement environment are obtained through on-site investigation and professional measurement means. By using a noise tester, the noise values are measured at different positions of the building, such as near the outer wall and in the internal core area, etc. The difference in noise intensity with and without building obstruction is compared to evaluate the sound insulation effect of the building. At the same time, with the help of Geographic Information System (GIS) technology, the building terrain data is obtained, and it is analyzed whether the terrain where the building is located is an open plain, a valley, or a city block with high-rise buildings, etc. For example, if the building is located in a valley, factors such as the terrain trend and slope of the valley will affect the noise propagation path. By combining GIS data with acoustic principles, the influence of the building terrain on noise propagation is clarified, and finally the corresponding characteristics of the noise measurement environment are obtained.

[0129] Preferably, based on the characteristics of the noise measurement environment, noise propagation analysis is performed on the noise standard data to analyze the corresponding propagation paths and attenuation laws of noise under different topographies and landforms, and the noise propagation attenuation characteristics inside the building are obtained;

[0130] In the embodiments of the present invention, by using acoustic simulation software, such as Odeon, the previously obtained data on the characteristics of the noise measurement environment is input into it. For different topographies and landforms, such as in the plain area, after setting the noise source position, the software, based on the acoustic propagation principle, simulates the noise spreading around in the form of a spherical wave, considering factors such as air absorption and ground reflection, and analyzes the propagation path. For the valley terrain, considering the effects of terrain obstruction and reflection, the multiple reflections and propagation of noise in the valley are simulated. By combining the sound insulation effect data of the building, the attenuation situation of the noise after entering the building interior is analyzed. For example, after the noise passes through a thick sound insulation wall, according to the sound absorption coefficient of the wall material, the noise attenuation amount is calculated. Finally, the noise propagation attenuation characteristics inside the building are obtained, and it is clarified how the noise propagates and attenuates in different environments.

[0131] Preferably, based on the noise propagation attenuation characteristics inside the building and combined with time series analysis, noise time-domain feature analysis is performed on the noise standard data to extract the corresponding time-domain change features of the noise by considering the influence of the propagation duration and attenuation law of the noise inside the building on the time-domain change of the noise, and the noise time-domain change characteristics inside the building are obtained;

[0132] In the embodiments of the present invention, by using time series analysis methods, such as autoregressive integrated moving average model (ARIMA), to process the noise standard data. First, the noise propagation attenuation characteristics are incorporated into the data processing process. For example, according to the intensity change of the noise caused by propagation attenuation in different time periods, a time series model is constructed. Assuming that the noise source emits sound continuously, as time goes by, due to propagation attenuation, the noise intensity gradually decreases. By analyzing the change curve of the noise intensity over time, the noise duration information and the attenuation rate in different time periods are extracted. For example, in the initial stage of noise propagation, the attenuation rate is slow, and in the later stage, due to the increase in distance and environmental factors, the attenuation rate accelerates. These changes are fitted by the ARIMA model, and time domain change characteristics such as the time when the noise intensity peak appears and the attenuation turning point are extracted, and finally, the time domain change characteristic data of the noise in the building are formed.

[0133] Preferably, perform frequency domain conversion and frequency domain feature analysis on the noise standard data to obtain the frequency domain change characteristics of the noise in the building;

[0134] In the embodiments of the present invention, by using the Fourier transform algorithm and implementing the frequency domain conversion of the noise standard data with the help of the NumPy library in Python, the time domain noise data is converted into frequency domain data through Fourier transform, and the distribution of the noise signal at different frequencies is obtained. By analyzing the frequency domain data, the main frequency components of the noise are determined. For example, if the noise in a certain area is mainly composed of traffic noise, it will show peaks in a specific frequency range in the frequency domain. By calculating the energy distribution, frequency bandwidth and other characteristics of the noise at different frequencies, the frequency domain change characteristics of the noise in the building are obtained. For example, high-frequency noise causes more direct damage to the human ear auditory system. By analyzing the frequency domain characteristics, the proportion and energy size of high-frequency noise can be clarified, providing a basis for subsequent evaluation.

[0135] Preferably, evaluate the noise impact factors according to the time domain change characteristics of the noise in the building and the frequency domain change characteristics of the noise in the building, so as to evaluate and analyze the influence degree of the corresponding time domain and frequency domain characteristics of the noise in the building on human health, and generate noise impact evaluation factors.

[0136] In the embodiments of the present invention, by referring to the standards regarding the impact of noise on human health in medical research and combining the time-domain and frequency-domain change characteristics of the noise in the building for evaluation. For the time-domain characteristics, such as excessive noise duration leading to human fatigue, sleep disorders, etc., different impact levels are set according to different duration ranges. For the frequency-domain characteristics, high-frequency noise is prone to damaging hearing, and the degree of its impact on hearing is determined based on the proportion of high-frequency noise energy and the frequency range. An evaluation model is established, taking the time-domain and frequency-domain characteristics as input parameters, and through methods such as weighted calculation, the degree of impact of the noise on human health is comprehensively evaluated. For example, the impact weight of the noise duration is set to 0.4, and the impact weight of the proportion of high-frequency noise energy is set to 0.6, and a value is calculated as the noise impact evaluation factor, which quantifies the degree of impact of the noise in the building on human health, and finally the noise impact evaluation factor is obtained.

[0137] Furthermore, the environmental intelligent integrated control module includes the following functions:

[0138] Develop a multi-dimensional decision-making engine for the space in the building based on the air quality level factor, the light health evaluation factor, and the noise impact evaluation factor in the building to generate a multi-dimensional decision-making engine in the building;

[0139] In the embodiments of the present invention, a multi-dimensional decision-making engine is developed by using the programming language Python combined with relevant development frameworks (such as Django). First, the concentration data of harmful gases (such as formaldehyde, PM2.5, etc.) in the air in the building is collected in real time through an air quality sensor, and according to the pre-set air quality level standard, the concentration data is converted into an air quality level factor. The light intensity and color temperature data are obtained by using a light sensor, and a light health evaluation factor is generated with reference to the healthy lighting standard. The noise decibel data is collected by means of a noise sensor, and according to the noise impact standard, a noise impact evaluation factor is obtained. These factors are used as input parameters to construct a decision tree model. For example, in the decision tree, the air quality level factor is an important branch node. When the concentration of harmful gases exceeds a certain threshold, a corresponding decision path is followed. By continuously training and optimizing the decision tree model, it can make accurate decisions according to different factor combinations, and finally generate a multi-dimensional decision-making engine in the building and deploy it on the intelligent control server of the building.

[0140] Preferably, based on the multi-dimensional decision-making engine in the building, an environmental intelligent integrated control design for the space in the building is carried out to generate an environmental intelligent integrated control strategy in the building to perform the corresponding environmental intelligent control management work in the building.

[0141] In an embodiment of the present invention, by connecting a multi-dimensional decision-making engine in a building to various environmental control devices in the building, such as air purification devices, ventilation systems, lighting systems, and sound insulation devices, data interaction is realized through an intelligent control system. When the multi-dimensional decision-making engine determines that the concentration of harmful gases corresponding to the air quality level exceeds level 2 based on the real-time collected data, it immediately sends start-up and parameter adjustment instructions to the air purification device and the ventilation system. For example, it sends a start signal to the air purification device, and at the same time sends an instruction to the controller of the ventilation system to increase the fan speed by 20% to increase the exhaust air volume. If the decision-making engine shows that the light intensity is insufficient or the color temperature is inappropriate, it sends a signal to the intelligent controller of the lighting system to adjust the current of the lighting fixtures, increase the brightness by 30%, and adjust the color temperature to the appropriate range by adjusting the driving circuit of the fixtures. When it detects that the noise exceeds the standard, the decision-making engine sends an instruction to the control unit of the sound insulation device to extend the operation time of the sound insulation device by 30 minutes and increase its operation power by 15%, thereby reducing noise generation, and thus realizing the intelligent integrated regulation and management of the building environment.

[0142] Further, the intelligent integrated regulation strategy for the building environment is specifically as follows: when the multi-dimensional decision-making engine in the building shows that the concentration of harmful gases corresponding to the air quality level exceeds level 2, it automatically starts the air purification device corresponding to the space in the building and adjusts the operation parameters corresponding to the ventilation system to increase the exhaust air volume; when the multi-dimensional decision-making engine in the building shows that the light evaluation result indicates that the light intensity is insufficient or the color temperature is inappropriate, it automatically starts the control lighting system corresponding to the space in the building to operate the corresponding brightness and color temperature adjustment; when the multi-dimensional decision-making engine in the building shows that the noise exceeds the standard, it automatically controls the operation time and operation power of the sound insulation device corresponding to the space in the building to reduce noise generation.

[0143] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to cover all changes falling within the meaning and scope of the equivalent elements of the application documents within the present invention.

[0144] The above description is only the specific implementation manners of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An intelligent integrated system for the indoor environment in buildings based on multi-dimensional health factors, characterized in that, It includes the following modules: The indoor environmental data monitoring module in the building is used to deploy harmful pollutant sensors, light environment sensors, and noise sensors corresponding to different sensitivities and frequency response ranges in the indoor space of the building, and use the corresponding harmful pollutant sensors, light environment sensors, and noise sensors to monitor the indoor harmful pollutant concentration data, indoor light environment data, and indoor noise data in real time, and transmit them to the data processing center at the same time; The environmental data cleaning and processing module is used to use the data processing center to perform statistical cleaning and preprocessing on the indoor harmful pollutant concentration data, indoor light environment data, and indoor noise data in the building to obtain harmful pollutant concentration standard data, light environment standard data, and noise standard data; The multi-dimensional health factor evaluation module is used to perform multi-dimensional health factor evaluation on the harmful pollutant concentration standard data, light environment standard data, and noise standard data to generate the indoor air quality grade factor, light health evaluation factor, and noise impact evaluation factor; The environmental intelligent integrated control module is used to perform environmental intelligent integrated control design on the indoor space of the building based on the indoor air quality grade factor, light health evaluation factor, and noise impact evaluation factor, generate the environmental intelligent integrated control strategy for the indoor environment of the building, and execute the corresponding indoor environmental intelligent control management work.

2. The intelligent integrated system for the indoor environment in a building based on multi-dimensional health factors according to claim 1, characterized in that The indoor environmental data monitoring module in the building includes the following functions: Install and deploy harmful pollutant sensors, light environment sensors, and noise sensors corresponding to different sensitivities and frequency response ranges in the indoor space of the building; Use the toxic pollutant sensor to perform real-time monitoring of harmful pollution in the indoor space of the building to monitor the concentrations of corresponding harmful pollutants such as formaldehyde, benzene, PM2.5, and microorganisms in real time, and obtain the indoor harmful pollutant concentration data; Use the light environment sensor to perform real-time monitoring of the light environment in the indoor space of the building to monitor the corresponding light intensity, color temperature, and spectral distribution in real time, and obtain the indoor light environment data; Use the noise sensors corresponding to different sensitivities and frequency response ranges to perform real-time monitoring of the noise in the indoor space of the building to monitor the indoor noise in the corresponding different frequency response ranges in real time, and obtain the indoor noise data; Transmit the indoor harmful pollutant concentration data, indoor light environment data, and indoor noise data to the data processing center through wireless transmission technology.

3. The intelligent integrated system for the indoor environment in a building based on multi-dimensional health factors according to claim 1, characterized in that, The environmental data cleaning and processing module includes the following functions: Use the data processing center to identify and remove data anomalies from the indoor harmful pollutant concentration data, indoor light environment data, and indoor noise data in the building, calculate the mean and standard deviation corresponding to different data statistically, and remove the corresponding outliers according to the mean and standard deviation using the 3σ criterion to obtain the filtered harmful pollutant concentration data, light environment data, and noise data; Interpolate and supplement the harmful pollutant concentration data, light environment data, and noise data after filtering out anomalies to utilize linear interpolation to supplement the missing values corresponding to each data, obtaining the harmful pollutant concentration data, light environment data, and noise data after missing value interpolation compensation; Perform standardization processing on the harmful pollutant concentration data, light environment data, and noise data after missing value interpolation compensation to obtain the harmful pollutant concentration standard data, light environment standard data, and noise standard data.

4. The intelligent integrated system for the indoor environment in a building based on multi-dimensional health factors according to claim 1, characterized in that, The multi-dimensional health factor evaluation module includes the following functions: Evaluate the air quality grade factor for the harmful pollutant concentration standard data to generate the indoor air quality grade factor in the building; Evaluate the light health factor for the light environment standard data to generate the light health evaluation factor; Evaluate the noise impact factor for the noise standard data to generate the noise impact evaluation factor.

5. The intelligent integrated system for the indoor environment in a building based on multi-dimensional health factors according to claim 4, wherein The evaluation of the air quality grade factor for the harmful pollutant concentration standard data includes: Analyze the concentration distribution changes of formaldehyde, benzene, PM2.5, and microbial content corresponding in the harmful pollutant concentration standard data to obtain the corresponding formaldehyde concentration change rate, benzene concentration change rate, particulate concentration change rate, and microbial concentration change rate at each spatial position; Obtain the building material characteristics corresponding to the indoor space in the building, and perform harmful pollution adsorption calculation on the harmful pollutant concentration standard data based on the building material characteristics to obtain the adsorption efficiency of the building materials for the corresponding harmful pollutants; Obtain the ventilation capacity corresponding to each spatial position in the indoor space in the building, and perform pollutant diffusion analysis on the harmful pollutant concentration standard data based on the ventilation capacity corresponding to each spatial position in the indoor space in the building to obtain the corresponding harmful pollutant ventilation diffusion rate at each spatial position; Based on the adsorption efficiency of the building materials for the corresponding harmful pollutants and the corresponding harmful pollutant ventilation diffusion rate at each spatial position, use the air quality grade calculation formula to evaluate the air quality grade factor for the corresponding formaldehyde concentration change rate, benzene concentration change rate, particulate concentration change rate, and microbial concentration change rate at each spatial position to generate the indoor air quality grade factor in the building.

6. The intelligent integrated system for the indoor environment in a building based on multi-dimensional health factors according to claim 5, characterized in that The specific air quality grade calculation formula is: Wherein, A Q is the air quality grade factor in the building, Ω is the spatial range in the building, n is the total number of spatial positions, x i is the i-th spatial position parameter, Q(x i ) is the formaldehyde concentration change rate corresponding to the i-th spatial position, B(x i ) is the benzene concentration change rate corresponding to the i-th spatial position, K(x i ) is the particulate concentration change rate corresponding to the i-th spatial position, W(x i ) is the microorganism concentration change rate corresponding to the i-th spatial position, S is the contact area between the surface of the harmful pollutant in the building space and the air, exp is the exponential function, ε is the adsorption efficiency of the building material to the corresponding harmful pollutant, κ(x i ) is the ventilation diffusion rate of the harmful pollutant corresponding to the i-th spatial position.

7. The intelligent integrated system for the indoor environment in a building based on multi-dimensional health factors according to claim 4, wherein The evaluation of the light health factor for the light environment standard data includes: Obtain the building occlusion and terrain undulation factors corresponding to the indoor space in the building; Based on the building occlusion and terrain undulation factors, perform light deviation correction on the corresponding light intensity, color temperature, and spectral distribution in the light environment standard data to obtain the light environment distribution deviation correction data; Obtain the circadian rhythm corresponding to the indoor space in the building, and perform light rhythm correlation analysis on the corresponding light intensity, color temperature, and spectral distribution in the light environment standard data based on the circadian rhythm to obtain the influence correlation relationship between the changes of light intensity, color temperature, and spectral distribution over time and the circadian rhythm; Based on the impact correlation between the changes of light intensity, color temperature, and spectral distribution over time and the circadian rhythm, the light health factor assessment is carried out on the corrected data of the light environment distribution deviation, so as to evaluate and analyze the corresponding health impact degree of the light environment on the circadian rhythm, and generate the light health assessment factor.

8. The intelligent integrated system for the indoor environment in a building based on multi-dimensional health factors according to claim 4, characterized in that The noise impact factor assessment for the noise standard data includes: Obtain the characteristics of the noise measurement environment corresponding to the space within the model, including the sound insulation effect of the building and the impact of the building terrain on noise propagation; Based on the characteristics of the noise measurement environment, perform noise propagation analysis on the noise standard data to analyze the corresponding propagation paths and attenuation laws of noise under different topographies and landforms, and obtain the noise propagation attenuation characteristics within the building; Based on the noise propagation attenuation characteristics within the building and combined with time series analysis, perform noise time-domain feature analysis on the noise standard data to extract the corresponding time-domain change characteristics of the noise by considering the impact of the propagation duration and attenuation law of the noise within the building on the time-domain change of the noise, and obtain the noise time-domain change characteristics within the building; Perform frequency-domain conversion and frequency-domain feature analysis on the noise standard data to obtain the frequency-domain change characteristics of the noise within the building; Based on the noise time-domain change characteristics within the building and the noise frequency-domain change characteristics within the building, perform noise impact factor assessment to evaluate and analyze the impact degree of the corresponding time-domain and frequency-domain characteristics of the noise within the building on human health, and generate the noise impact assessment factor.

9. The intelligent integrated system for the indoor environment in a building based on multi-dimensional health factors according to claim 1, wherein The environmental intelligent integrated control module includes the following functions: Based on the air quality level factor, light health assessment factor, and noise impact assessment factor within the building, develop a multi-dimensional decision-making engine for the space within the building to generate a multi-dimensional decision-making engine within the building; Based on the multi-dimensional decision-making engine within the building, carry out environmental intelligent integrated control design for the space within the building, generate an environmental intelligent integrated control strategy within the building, and execute the corresponding environmental intelligent control management work within the building.

10. The intelligent integrated system for the indoor environment in a building based on multi-dimensional health factors according to claim 9, wherein The environmental intelligent integrated control strategy within the building is specifically as follows: when the multi-dimensional decision-making engine within the building shows that the concentration of harmful gases corresponding to the air quality level exceeds level 2, the air purification equipment corresponding to the space within the building is automatically started and the operating parameters of the ventilation system are adjusted to increase the exhaust air volume; when the multi-dimensional decision-making engine within the building shows that the light evaluation result indicates insufficient light intensity or inappropriate color temperature, the brightness and color temperature adjustment of the corresponding control lighting system within the building is automatically started; when the multi-dimensional decision-making engine within the building shows that the noise exceeds the standard, the operating time and operating power of the sound insulation equipment corresponding to the space within the building are automatically controlled to reduce noise generation.

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