Indoor environment detection system for environmental art design

Abstract

The invention belongs to the technical field of environment detection, and provides an indoor environment detection system for environmental art design, comprising a data acquisition module which acquires indoor environment parameters in real time through various sensors as original data; the data processing module is used for receiving the original data, performing deep analysis, identifying an environment change trend and predicting a future state; the data storage module is used for storing the processed data; the intelligent execution module automatically generates optimization suggestions or directly controls indoor environment equipment based on data analysis results and user preferences; the real-time early warning module prompts a user to take corresponding measures in time according to a preset condition when the indoor environment parameters exceed a preset range; according to an intelligent adjusting mechanism, the environment factors are automatically adjusted according to the preference of the user and the environment data, the optimal living experience is provided, different atmospheres are provided for the user, and the artistic feeling of the living space is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of environmental detection, and specifically relates to an indoor environmental detection system for environmental art design. Background Art

[0002] In recent years, with the improvement of people's requirements for the quality of living environment, indoor environmental detection and automatic regulation technologies have developed rapidly. Traditional indoor environmental monitoring systems mainly focus on the monitoring of temperature and humidity, lacking comprehensive consideration of air quality, lighting, and other environmental factors. These systems usually adopt fixed control strategies and fail to make dynamic adjustments according to users' personalized needs and preferences, resulting in their inability to meet the increasingly diverse and personalized living needs of modern families.

[0003] Currently, most indoor environmental detection systems on the market can only achieve basic temperature and humidity monitoring and control, lacking the understanding and application of users' personalized preferences. Such systems are often static and unable to make automatic real-time adjustments according to users' living habits, preference changes, and environmental data. For example, traditional thermostats cannot automatically adjust the temperature according to time periods, time, or user behavior, but simply follow the set temperature for on-off control. This leads to a mediocre living experience and fails to create the specific atmosphere required by users. Moreover, existing systems mostly involve one-way information transmission, lacking interaction with users and unable to obtain user feedback in a timely manner. Users usually can only make passive adjustments and maintenance based on the observed environmental data, without a system that can respond to their environment and preferences in real time. This design lacking two-way communication limits users' ability to effectively utilize technology and also weakens users' enthusiasm for participating in the optimization of the living environment, resulting in an overall poor user experience. At the same time, existing environmental monitoring technologies often separate art design from intelligent technology, lacking effective collaboration and communication between traditional environmental designers and technical engineers. This makes it impossible for art design to be effectively integrated into technology implementation, leading to a contradiction between the artistic sense and functionality of living spaces and thus affecting users' living experience. That is to say, many smart home solutions ignore an important aspect, namely how to combine aesthetics and technology in the design so that the living environment is not only full of a sense of technology but also can convey warm and personalized emotions.

[0004] Therefore, those skilled in the art have proposed an indoor environmental detection system for environmental art design, aiming to real-time monitor multiple environmental parameters through an intelligent adjustment mechanism and combine them with users' personalized needs, so that the living space is not only comfortable and safe but also can exhibit a unique artistic style. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides an indoor environmental detection system for environmental art design to solve the problems raised in the background art.

[0006] An indoor environment detection system for environmental art design, comprising:

[0007] A data acquisition module, configured to collect indoor environment parameters in real time through various sensors as raw data;

[0008] A data processing module, configured to receive the raw data from the data acquisition module, perform preprocessing, and conduct in-depth analysis using an algorithm model to identify environmental change trends and predict future states;

[0009] A data storage module, configured to store the processed data, including all historical data and analysis results;

[0010] An intelligent execution module, configured to automatically generate optimization suggestions or directly control indoor environment devices based on the data analysis results and user preferences;

[0011] A real-time warning module, configured to timely remind the user to take corresponding measures when the indoor environment parameters exceed the preset range according to the preset conditions.

[0012] Preferably, the various sensors used in the data acquisition module include a temperature sensor, a humidity sensor, a light sensor, and an air quality sensor, which are used to collect temperature, humidity, light intensity, and air quality parameters in the indoor environment.

[0013] Preferably, the data processing module performs preprocessing on the received raw data, including denoising and filtering, and uses the ARIMA model for time series analysis to identify the change trend of environmental parameters, expressed as:

[0014] Y t = c + φY t-1 + θε t-1 + ε t

[0015] Wherein, Y t is the current parameter value, c is the constant term, φ and θ are the autoregressive coefficient and the moving average coefficient respectively, and ε t is white noise;

[0016] According to historical and actual data, trend extrapolation prediction is used to find the law of development and change over time, so as to infer its future situation. Its linear trend model is expressed as:

[0017] y = mx + b

[0018] Through least squares fitting, where b is the intercept and m is the slope;

[0019] And the original data is trained by the BP neural network prediction model, continuously correcting the network weights and thresholds to make the error function decrease along the negative gradient direction and approach the expected output. The expression of this model is:

[0020]

[0021] Among them, η is the learning rate, and E is the error function. represents the weight from the i-th node to the j-th node in the l-th layer. represents the partial derivative of the error function E with respect to the weight , that is, the degree of influence of the weight on the error; the activation function of this model is expressed as:

[0022]

[0023] Among them, σ(x) is the output of this activation function, which is used to map any output value to the interval (0, 1), and x represents the value input to this function.

[0024] Preferably, the data storage composition of the data storage module includes processed data, historical data, and analysis results. The processed data includes preprocessed and analyzed data, which are real-time readings and status information of temperature, humidity, light intensity, and air quality; the historical data includes long-term preserved original data, recording the change history of environmental parameters for time series analysis and backtracking; the analysis results are outputs generated through statistical analysis and machine learning models, including predicted values and trend analysis results.

[0025] Preferably, the intelligent execution module is based on the environmental data analysis results provided by the data processing module, including real-time data of temperature, humidity, light intensity, and air quality, future conditions predicted by the machine learning model, development trends, and historical data, and is also adjusted based on the user's settings and preferences.

[0026] The optimization goal is set by setting the objective function, and its comfort optimization is expressed as:

[0027] C = ω t ·(T ideal - T) + ω h ·(H ideal - H) + ω l ·(L ideal - L) + ω a ·(A ideal - A)

[0028] Among them, C represents the comprehensive comfort score, T, H, L, and A are the actually measured temperature, humidity, light, and air quality respectively, and T ideal , Hideal , L ideal , A ideal is the ideal value corresponding to the user's expectation, ω t , ω h , ω l , ω a are the weights of the corresponding indicators, used to reflect the user's preferences;

[0029] According to the optimization goal and real-time data, the intelligent execution module automatically adjusts the environment by controlling the instructions of the device. The instructions for controlling the device are represented by the linear adjustment formula as follows:

[0030] U T = k T ·(T ideal - T)

[0031] U H = k H ·(H ideal - H)

[0032] U L = k L ·(L ideal - L)

[0033] U A = k A ·(A ideal - A)

[0034] Among them, U T , U H , U L , U A are the adjustment instructions for temperature, humidity, light, and air quality respectively. k T , k H , k L , k A are the corresponding adjustment coefficients, which determine the rate and amplitude of the device adjustment;

[0035] After implementing the adjustment measures, the intelligent execution module monitors the adjusted environmental parameters in real time and compares them with the expected values to determine the adjustment effect, which is expressed as:

[0036] E = |C after - C before |

[0037] Among them, E represents the evaluation index of the adjustment effect, which is the absolute value of the difference between the adjusted environmental parameter C after and the environmental parameter C before before adjustment, reflecting the actual impact of the adjustment measures on the environmental parameters. When E reaches the preset threshold, it indicates that the optimization is successful; otherwise, adjust again.

[0038] Preferably, the environmental parameters monitored by the real-time warning module include temperature, humidity, light intensity, and air quality. Normal ranges and warning thresholds are set for each environmental parameter. That is, the preset range of temperature is [T min , T max , the preset range of humidity is [H min , H max , the preset range of light intensity is [L min , L max , the preset range of air quality is [A min , A max . The real-time warning module detects whether the current environmental parameters exceed the preset range through the over-limit condition. When it detects that any parameter exceeds the set range, the real-time warning module immediately generates an alarm and automatically sends a notification to the user. At the same time, different levels of alarms are set according to the degree of over-limit;

[0039] Evaluate the impact of the measures taken by the user on the environmental parameters. By defining the actual adjustment effect, it is expressed as:

[0040] E i = C i (T′, H′, L′, A′) - C i (T, H, L, A)

[0041] Wherein, E i is the actual change amplitude when adjusting a certain environmental parameter, that is, the change amount of the environmental parameter, which is used to evaluate the effectiveness of the adjustment measure. C i represents the new environmental conditions (T′, H′, L′, A′) obtained after the user takes adjustment measures under the current parameters (T, H, L, A).

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] 1. Through the intelligent adjustment mechanism, the present invention automatically adjusts environmental factors according to the user's preferences and environmental data, provides the best living experience, and can provide different atmospheres for the user through the automatic adjustment of environmental factors such as lighting and temperature, increasing the artistic sense of the living space. That is, it intelligently adjusts the environment according to the user's personal preferences and enhances the personalized experience of living.

[0044] 2. By real-time monitoring of air quality, temperature and humidity, the present invention encourages users to maintain a healthy lifestyle. Good environmental conditions can improve the user's psychological comfort, relieve stress and enhance the overall life satisfaction.

[0045] 3. By combining art design with intelligent technology, the present invention promotes the interaction and cooperation among designers, engineers, and users, provides advanced technical support for the design of smart homes, enhances the intelligent level of the living environment, and promotes the realization of innovative concepts. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a block diagram of an indoor environment detection system for environmental art design of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] The following further describes in detail the embodiments of the present invention with reference to the drawings and embodiments. The following embodiments are used to illustrate the present invention, but cannot be used to limit the scope of the present invention.

[0048] As shown in the Figure 1 drawing:

[0049] Embodiment: The present invention provides an indoor environment detection system for environmental art design, including:

[0050] A data acquisition module for real-time collecting indoor environment parameters through various sensors as raw data; the various sensors used include a temperature sensor, a humidity sensor, a light sensor, and an air quality sensor for collecting temperature, humidity, light intensity, and air quality parameters in the indoor environment.

[0051] A data processing module for receiving the raw data from the data acquisition module, performing preprocessing, and using an algorithm model for in-depth analysis to identify environmental change trends and predict future states;

[0052] A data storage module for storing the processed data, including all historical data and analysis results;

[0053] An intelligent execution module for automatically generating optimization suggestions or directly controlling indoor environment devices based on the data analysis results and user preferences;

[0054] A real-time warning module for timely reminding the user to take corresponding measures when the indoor environment parameters exceed the preset range according to the preset conditions.

[0055] The indoor environment detection system for environmental art design has a wide range of application prospects. It can provide scientific data support for environmental art design, help designers better understand and grasp the characteristics of the indoor environment, so as to design a more comfortable, beautiful, and practical indoor space. At the same time, the system can also provide an intelligent solution for indoor environment management, improve the comfort and energy-saving performance of the indoor environment, and meet the pursuit of modern people for a high-quality life.

[0056] The data processing module preprocesses the received raw data, including denoising and filtering, and uses the ARI MA model for time series analysis to identify the changing trends of environmental parameters, expressed as:

[0057] Y t = c + φY t-1 + θε t-1 + ε t

[0058] Where Y t is the current parameter value, c is the constant term, φ and θ are the autoregressive coefficient and the moving average coefficient respectively, and ε t is white noise;

[0059] According to historical and actual data, trend extrapolation prediction is used to find the laws of development and change over time, so as to infer its future situation. Its linear trend model is expressed as:

[0060] y = mx + b

[0061] Fitted by the least squares method, where b is the intercept and m is the slope;

[0062] And the original data is trained by the BP neural network prediction model, continuously correcting the network weights and thresholds to make the error function decrease along the negative gradient direction and approach the expected output. The expression of this model is:

[0063]

[0064] Where η is the learning rate, E is the error function, represents the weight from the i-th node to the j-th node in the l-th layer, represents the partial derivative of the error function E with respect to the weight , that is, the influence degree of the weight on the error; The activation function of this model is expressed as:

[0065]

[0066] Where σ(x) is the output of this activation function, used to map any output value to the interval (0,1), and x represents the value input to this function.

[0067] The data processing module deeply analyzes the temperature, humidity, light intensity and air quality parameters of the indoor environment by using a variety of algorithm models, so as to effectively identify and capture the changing trends of indoor environmental parameters and accurately predict the future state.

[0068] The data storage components of the data storage module include processed data, historical data, and analysis results. The processed data includes preprocessed and analyzed data, which are real-time readings and status information of temperature, humidity, light intensity, and air quality. Historical data includes long-term stored original data that records the change history of environmental parameters and is used for time series analysis and backtracking. The analysis results are outputs generated through statistical analysis and machine learning models, including predicted values and trend analysis results.

[0069] The intelligent execution module is adjusted based on the environmental data analysis results provided by the data processing module, including real-time data of temperature, humidity, light intensity, and air quality, future conditions predicted through machine learning models, development trends, and historical data, and also based on user settings and preferences.

[0070] The optimization goal is set by defining the objective function, and the comfort optimization is expressed as:

[0071] C = ω t ·(T ideal - T) + ω h ·(H ideal - H) + ω l ·(L ideal - L) + ω a ·(A ideal - A)

[0072] Where C represents the comprehensive comfort score, T, H, L, and A are the actually measured temperature, humidity, light, and air quality respectively, T ideal , H ideal , L ideal , A ideal are the corresponding ideal values expected by the user, and ω t , ω h , ω l , ω a are the weights of the corresponding indicators, used to reflect the user's preferences.

[0073] Based on the optimization goal and real-time data, the intelligent execution module automatically adjusts the environment through commands for controlling devices. The commands for controlling devices are expressed by the linear adjustment formula as:

[0074] U T = k T ·(T ideal - T)

[0075] U H = k H ·(H ideal - H)

[0076] U L = k L ·(Lideal -L)

[0077] U A = k A ·(A ideal -A)

[0078] where U T , U H , U L , U A are the adjustment instructions for temperature, humidity, light, and air quality respectively, and k T , k H , k L , k A are the corresponding adjustment coefficients, determining the rate and amplitude of equipment adjustment;

[0079] After implementing the adjustment measures, the intelligent execution module monitors the adjusted environmental parameters in real time and compares them with the expected values to determine the adjustment effect, expressed as:

[0080] E = |C after - C before |

[0081] where E represents the evaluation index of the adjustment effect, which is the absolute value of the difference between the adjusted environmental parameter C after and the environmental parameter C before before adjustment, reflecting the actual impact of the adjustment measures on the environmental parameters. When E reaches the preset threshold, it indicates successful optimization; otherwise, readjustment is performed.

[0082] The environmental parameters monitored by the real-time warning module include temperature, humidity, light intensity, and air quality. Normal ranges and warning thresholds are set for each environmental parameter, i.e., the preset range of temperature is [T min , T max , the preset range of humidity is [H min , H max , the preset range of light intensity is [L min , L max , and the preset range of air quality is [A min , A max . The real-time warning module detects whether the current environmental parameters exceed the preset ranges through over-limit conditions. When any parameter is detected to exceed the set range, the real-time warning module immediately generates an alarm and automatically sends a notification to the user, and sets different levels of alarms according to the degree of over-limit;

[0083] Evaluate the impact of the measures taken by the user on the environmental parameters. By defining the actual adjustment effect, it is expressed as:

[0084] E i = C i(T′, H′, L′, A′)-C i (T, H, L, A)

[0085] Among them, E i is the actual change amplitude during the adjustment of a certain environmental parameter, that is, the change amount of the environmental parameter, which is used to evaluate the effectiveness of the adjustment measure, and C i represents the new environmental conditions (T′, H′, L′, A′) obtained after the user takes the adjustment measure under the current parameters (T, H, L, A).

[0086] The main function of the real-time warning module is to timely remind the user to take corresponding measures when the indoor environmental parameters exceed the preset range. The module quickly responds by continuously monitoring the environmental parameters and comparing them with the preset thresholds to ensure that the user takes measures in a timely manner. By establishing an over-limit detection mechanism, an alarm system, and an evaluation formula for the impact of adjustments of the system, the module can effectively maintain the safety and comfort of the indoor environment.

[0087] As can be seen from the above, the indoor environmental detection system not only improves the comfort and safety of the space, but also provides comprehensive support for the quality of life of the occupants by optimizing resource use, promoting air quality improvement, and providing personalized aesthetic experiences. The introduction of such a system combines the artistic design of the indoor environment with intelligent technology, opening a more ideal new era of living.

[0088] It should be noted importantly that the structures and arrangements of the present application shown in multiple different exemplary embodiments are merely illustrative. Although only a few embodiments are described in detail in this disclosure, those who refer to this disclosure should easily understand that many modifications are possible on the premise of substantially not deviating from the novel teachings and advantages of the subject matter described in this application. Other substitutions, modifications, changes, and omissions can be made in the design, operating conditions, and arrangements of the exemplary embodiments without departing from the scope of the present invention. Therefore, the present invention is not limited to specific embodiments, but extends to various modifications that still fall within the scope of the appended claims.

[0089] In addition, in order to provide a concise description of the exemplary embodiments, all features of the actual embodiments may not be described (i.e., those features that are not relevant to the best mode of implementing the present invention currently considered, or those features that are not relevant to implementing the present invention).

[0090] It should be understood that in the development process of any actual implementation, in any engineering or design project, a large number of specific implementation decisions can be made. Such development efforts may be complex and time-consuming, but for those ordinary technicians who benefit from this disclosure, without excessive experimentation, the development efforts will be a routine work of design, manufacturing, and production.

[0091] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and all of them should be covered by the scope of the claims of the present invention.

Claims

1. An indoor environment detection system for environmental art design, characterized in that: include: The data acquisition module is used to collect indoor environmental parameters in real time through various sensors as raw data; The data processing module is used to receive the raw data from the data acquisition module, perform preprocessing, and use the algorithm model to perform in-depth analysis, identify environmental change trends, and predict future status; Data storage module, used to store processed data, including all historical data and analysis results; Intelligent execution module, which is used to automatically generate optimization suggestions or directly control indoor environmental equipment based on data analysis results and user preferences; The real-time warning module is used to promptly remind users to take corresponding measures when indoor environmental parameters exceed the preset range according to preset conditions.

2. The indoor environment detection system for environmental art design as claimed in claim 1, characterized in that: The various sensors used in the data acquisition module include temperature sensors, humidity sensors, light sensors, and air quality sensors, which are used to collect temperature, humidity, light intensity, and air quality parameters in the indoor environment.

3. The indoor environment detection system for environmental art design as claimed in claim 1, characterized in that: The data processing module preprocesses the received raw data, including denoising and filtering, and uses the ARIMA model to perform time series analysis to identify the changing trend of environmental parameters, which is expressed as: Y t =c+φY t-1 +god t-1 +e t Among them, Y t is the current parameter value, c is the constant term, φ and θ are the autoregressive coefficient and the moving average coefficient respectively, ε t is white noise; Based on historical and actual data, trend extrapolation forecasting is used to find the law of development and change over time, so as to infer its future status. Its linear trend model is expressed as: y=mx+b Fitted by the least squares method, where b is the intercept and m is the slope; The original data is trained through the BP neural network prediction model, and the network weights and thresholds are continuously corrected so that the error function decreases along the negative gradient direction and approaches the expected output. The model expression is: Among them, η is the learning rate, E is the error function, represents the weight from the i-th node to the j-th node in the l-th layer, Represents the error function E on the weight The partial derivative of The degree of influence on the error; the activation function of the model is expressed as: Among them, σ(x) is the output of the activation function, which is used to map any output value to the interval (0,1), and x represents the value input to the function.

4. The indoor environment detection system for environmental art design as claimed in claim 1, characterized in that: The data storage composition of the data storage module includes processed data, historical data and analysis results. The processed data includes pre-processed and analyzed data, which are real-time readings and status information of temperature, humidity, light intensity and air quality; the historical data includes long-term stored original data, which records the change history of environmental parameters and is used for time series analysis and backtracking; the analysis results are outputs generated by statistical analysis and machine learning models, including predicted values ​​and trend analysis results.

5. The indoor environment detection system for environmental art design as claimed in claim 1, characterized in that: The intelligent execution module is based on the environmental data analysis results provided by the data processing module, including real-time data of temperature, humidity, light intensity and air quality, future conditions predicted by machine learning models, development trends and historical data, and is also adjusted based on user settings and preferences; By setting the optimization goal through the objective function, the comfort optimization is expressed as: C=ω t ·(T ideal -T)+ω h ·(H ideal -H)+ω l ·(L ideal -L)+ω a ·(A ideal -A) Among them, C represents the comprehensive comfort score, T, H, L, and A are the actual measured temperature, humidity, light, and air quality, respectively. ideal , H ideal , L ideal , A ideal is the ideal value expected by the corresponding user, ω t ,ω h ,ω l ,ω a is the weight of each corresponding indicator, which is used to reflect the user's preference; According to the optimization target and real-time data, the intelligent execution module automatically adjusts the environment through the instructions of the control device. The instructions of the control device are expressed by the linear adjustment formula: U T =k T ·(T ideal -T) U H =k H ·(H ideal -H) And L =k L ·(L ideal -L) U A =k A ·(A ideal -A) Among them, U T , U H , U L , U A are the adjustment instructions for temperature, humidity, light and air quality, respectively, T , k H , k L , k A They are the corresponding adjustment coefficients, which determine the rate and amplitude of equipment adjustment; After implementing the adjustment measures, the intelligent execution module monitors the adjusted environmental parameters in real time and compares them with the expected values ​​to determine the adjustment effect, which is expressed as: E=|C after -C before | Among them, E represents the evaluation index of the adjustment effect, which is the environmental parameter C after adjustment. after Compared with the environmental parameters before adjustment C before The absolute value of the difference between them reflects the actual impact of the adjustment measures on the environmental parameters. When E reaches the preset threshold, it indicates that the optimization is successful, otherwise, it is adjusted again.

6. The indoor environment detection system for environmental art design as claimed in claim 1, characterized in that: The environmental parameters monitored by the real-time warning module include temperature, humidity, light intensity, and air quality. A normal range and a warning threshold are set for each environmental parameter, that is, the preset range of temperature is [T min ,T max ], the preset range of humidity is [H min ,H max ], the preset range of light intensity is [L min ,L max ], the preset range of air quality is [A min ,A max ], the real-time warning module detects whether the current environmental parameters exceed the preset range through the over-limit conditions. When any parameter is detected to be out of the set range, the real-time warning module immediately generates an alarm and automatically sends a notification to the user. At the same time, different levels of alarms are set according to the degree of over-limit; Evaluate the impact of the measures taken by the user on the environmental parameters by defining the actual adjustment effect, expressed as: E i =C i (T′,H′,L′,A′)-C i (T,H,L,A) Among them, E i The actual change in the adjustment of a certain environmental parameter, that is, the change in the environmental parameter, is used to evaluate the effectiveness of the adjustment measures. i It indicates the new environmental conditions (T′, H′, L′, A′) obtained after the user takes adjustment measures under the current parameters (T, H, L, A).

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